Compare commits
1 Commits
claude/iss
...
gemini/iss
| Author | SHA1 | Date | |
|---|---|---|---|
|
4e4206a91e |
@@ -27,12 +27,8 @@
|
||||
|
||||
# ── AirLLM / big-brain backend ───────────────────────────────────────────────
|
||||
# Inference backend: "ollama" (default) | "airllm" | "auto"
|
||||
# "ollama" → always use Ollama (safe everywhere, any OS)
|
||||
# "airllm" → AirLLM layer-by-layer loading (Apple Silicon M1/M2/M3/M4 only)
|
||||
# Requires 16 GB RAM minimum (32 GB recommended).
|
||||
# Automatically falls back to Ollama on Intel Mac or Linux.
|
||||
# Install extra: pip install "airllm[mlx]"
|
||||
# "auto" → use AirLLM on Apple Silicon if installed, otherwise Ollama
|
||||
# "auto" → uses AirLLM on Apple Silicon if installed, otherwise Ollama.
|
||||
# Requires: pip install ".[bigbrain]"
|
||||
# TIMMY_MODEL_BACKEND=ollama
|
||||
|
||||
# AirLLM model size (default: 70b).
|
||||
|
||||
@@ -62,9 +62,6 @@ Per AGENTS.md roster:
|
||||
- Run `tox -e pre-push` (lint + full CI suite)
|
||||
- Ensure tests stay green
|
||||
- Update TODO.md
|
||||
- **CRITICAL: Stage files before committing** — always run `git add .` or `git add <files>` first
|
||||
- Verify staged changes are non-empty: `git diff --cached --stat` must show files
|
||||
- **NEVER run `git commit` without staging files first** — empty commits waste review cycles
|
||||
|
||||
---
|
||||
|
||||
|
||||
102
AGENTS.md
102
AGENTS.md
@@ -34,44 +34,6 @@ Read [`CLAUDE.md`](CLAUDE.md) for architecture patterns and conventions.
|
||||
|
||||
---
|
||||
|
||||
## One-Agent-Per-Issue Convention
|
||||
|
||||
**An issue must only be worked by one agent at a time.** Duplicate branches from
|
||||
multiple agents on the same issue cause merge conflicts, redundant code, and wasted compute.
|
||||
|
||||
### Labels
|
||||
|
||||
When an agent picks up an issue, add the corresponding label:
|
||||
|
||||
| Label | Meaning |
|
||||
|-------|---------|
|
||||
| `assigned-claude` | Claude is actively working this issue |
|
||||
| `assigned-gemini` | Gemini is actively working this issue |
|
||||
| `assigned-kimi` | Kimi is actively working this issue |
|
||||
| `assigned-manus` | Manus is actively working this issue |
|
||||
|
||||
### Rules
|
||||
|
||||
1. **Before starting an issue**, check that none of the `assigned-*` labels are present.
|
||||
If one is, skip the issue — another agent owns it.
|
||||
2. **When you start**, add the label matching your agent (e.g. `assigned-claude`).
|
||||
3. **When your PR is merged or closed**, remove the label (or it auto-clears when
|
||||
the branch is deleted — see Auto-Delete below).
|
||||
4. **Never assign the same issue to two agents simultaneously.**
|
||||
|
||||
### Auto-Delete Merged Branches
|
||||
|
||||
`default_delete_branch_after_merge` is **enabled** on this repo. Branches are
|
||||
automatically deleted after a PR merges — no manual cleanup needed and no stale
|
||||
`claude/*`, `gemini/*`, or `kimi/*` branches accumulate.
|
||||
|
||||
If you discover stale merged branches, they can be pruned with:
|
||||
```bash
|
||||
git fetch --prune
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Merge Policy (PR-Only)
|
||||
|
||||
**Gitea branch protection is active on `main`.** This is not a suggestion.
|
||||
@@ -169,28 +131,6 @@ self-testing, reflection — use every tool he has.
|
||||
|
||||
## Agent Roster
|
||||
|
||||
### Gitea Permissions
|
||||
|
||||
All agents that push branches and create PRs require **write** permission on the
|
||||
repository. Set via the Gitea admin API or UI under Repository → Settings → Collaborators.
|
||||
|
||||
| Agent user | Required permission | Gitea login |
|
||||
|------------|--------------------|----|
|
||||
| kimi | write | `kimi` |
|
||||
| claude | write | `claude` |
|
||||
| gemini | write | `gemini` |
|
||||
| antigravity | write | `antigravity` |
|
||||
| hermes | write | `hermes` |
|
||||
| manus | write | `manus` |
|
||||
|
||||
To grant write access (requires Gitea admin or repo admin token):
|
||||
```bash
|
||||
curl -s -X PUT "http://143.198.27.163:3000/api/v1/repos/rockachopa/Timmy-time-dashboard/collaborators/<username>" \
|
||||
-H "Authorization: token <admin-token>" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"permission": "write"}'
|
||||
```
|
||||
|
||||
### Build Tier
|
||||
|
||||
**Local (Ollama)** — Primary workhorse. Free. Unrestricted.
|
||||
@@ -247,48 +187,6 @@ make docker-agent # add a worker
|
||||
|
||||
---
|
||||
|
||||
## Search Capability (SearXNG + Crawl4AI)
|
||||
|
||||
Timmy has a self-hosted search backend requiring **no paid API key**.
|
||||
|
||||
### Tools
|
||||
|
||||
| Tool | Module | Description |
|
||||
|------|--------|-------------|
|
||||
| `web_search(query)` | `timmy/tools/search.py` | Meta-search via SearXNG — returns ranked results |
|
||||
| `scrape_url(url)` | `timmy/tools/search.py` | Full-page scrape via Crawl4AI → clean markdown |
|
||||
|
||||
Both tools are registered in the **orchestrator** (full) and **echo** (research) toolkits.
|
||||
|
||||
### Configuration
|
||||
|
||||
| Env Var | Default | Description |
|
||||
|---------|---------|-------------|
|
||||
| `TIMMY_SEARCH_BACKEND` | `searxng` | `searxng` or `none` (disable) |
|
||||
| `TIMMY_SEARCH_URL` | `http://localhost:8888` | SearXNG base URL |
|
||||
| `TIMMY_CRAWL_URL` | `http://localhost:11235` | Crawl4AI base URL |
|
||||
|
||||
Inside Docker Compose (when `--profile search` is active), the dashboard
|
||||
uses `http://searxng:8080` and `http://crawl4ai:11235` by default.
|
||||
|
||||
### Starting the services
|
||||
|
||||
```bash
|
||||
# Start SearXNG + Crawl4AI alongside the dashboard:
|
||||
docker compose --profile search up
|
||||
|
||||
# Or start only the search services:
|
||||
docker compose --profile search up searxng crawl4ai
|
||||
```
|
||||
|
||||
### Graceful degradation
|
||||
|
||||
- If `TIMMY_SEARCH_BACKEND=none`: tools return a "disabled" message.
|
||||
- If SearXNG or Crawl4AI is unreachable: tools log a WARNING and return an
|
||||
error string — the app never crashes.
|
||||
|
||||
---
|
||||
|
||||
## Roadmap
|
||||
|
||||
**v2.0 Exodus (in progress):** Voice + Marketplace + Integrations
|
||||
|
||||
15
README.md
15
README.md
@@ -9,21 +9,6 @@ API access with Bitcoin Lightning — all from a browser, no cloud AI required.
|
||||
|
||||
---
|
||||
|
||||
## System Requirements
|
||||
|
||||
| Path | Hardware | RAM | Disk |
|
||||
|------|----------|-----|------|
|
||||
| **Ollama** (default) | Any OS — x86-64 or ARM | 8 GB min | 5–10 GB (model files) |
|
||||
| **AirLLM** (Apple Silicon) | M1, M2, M3, or M4 Mac | 16 GB min (32 GB recommended) | ~15 GB free |
|
||||
|
||||
**Ollama path** runs on any modern machine — macOS, Linux, or Windows. No GPU required.
|
||||
|
||||
**AirLLM path** uses layer-by-layer loading for 70B+ models without a GPU. Requires Apple
|
||||
Silicon and the `bigbrain` extras (`pip install ".[bigbrain]"`). On Intel Mac or Linux the
|
||||
app automatically falls back to Ollama — no crash, no config change needed.
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
|
||||
122
SOVEREIGNTY.md
122
SOVEREIGNTY.md
@@ -1,122 +0,0 @@
|
||||
# SOVEREIGNTY.md — Research Sovereignty Manifest
|
||||
|
||||
> "If this spec is implemented correctly, it is the last research document
|
||||
> Alexander should need to request from a corporate AI."
|
||||
> — Issue #972, March 22 2026
|
||||
|
||||
---
|
||||
|
||||
## What This Is
|
||||
|
||||
A machine-readable declaration of Timmy's research independence:
|
||||
where we are, where we're going, and how to measure progress.
|
||||
|
||||
---
|
||||
|
||||
## The Problem We're Solving
|
||||
|
||||
On March 22, 2026, a single Claude session produced six deep research reports.
|
||||
It consumed ~3 hours of human time and substantial corporate AI inference.
|
||||
Every report was valuable — but the workflow was **linear**.
|
||||
It would cost exactly the same to reproduce tomorrow.
|
||||
|
||||
This file tracks the pipeline that crystallizes that workflow into something
|
||||
Timmy can run autonomously.
|
||||
|
||||
---
|
||||
|
||||
## The Six-Step Pipeline
|
||||
|
||||
| Step | What Happens | Status |
|
||||
|------|-------------|--------|
|
||||
| 1. Scope | Human describes knowledge gap → Gitea issue with template | ✅ Done (`skills/research/`) |
|
||||
| 2. Query | LLM slot-fills template → 5–15 targeted queries | ✅ Done (`research.py`) |
|
||||
| 3. Search | Execute queries → top result URLs | ✅ Done (`research_tools.py`) |
|
||||
| 4. Fetch | Download + extract full pages (trafilatura) | ✅ Done (`tools/system_tools.py`) |
|
||||
| 5. Synthesize | Compress findings → structured report | ✅ Done (`research.py` cascade) |
|
||||
| 6. Deliver | Store to semantic memory + optional disk persist | ✅ Done (`research.py`) |
|
||||
|
||||
---
|
||||
|
||||
## Cascade Tiers (Synthesis Quality vs. Cost)
|
||||
|
||||
| Tier | Model | Cost | Quality | Status |
|
||||
|------|-------|------|---------|--------|
|
||||
| **4** | SQLite semantic cache | $0.00 / instant | reuses prior | ✅ Active |
|
||||
| **3** | Ollama `qwen3:14b` | $0.00 / local | ★★★ | ✅ Active |
|
||||
| **2** | Claude API (haiku) | ~$0.01/report | ★★★★ | ✅ Active (opt-in) |
|
||||
| **1** | Groq `llama-3.3-70b` | $0.00 / rate-limited | ★★★★ | 🔲 Planned (#980) |
|
||||
|
||||
Set `ANTHROPIC_API_KEY` to enable Tier 2 fallback.
|
||||
|
||||
---
|
||||
|
||||
## Research Templates
|
||||
|
||||
Six prompt templates live in `skills/research/`:
|
||||
|
||||
| Template | Use Case |
|
||||
|----------|----------|
|
||||
| `tool_evaluation.md` | Find all shipping tools for `{domain}` |
|
||||
| `architecture_spike.md` | How to connect `{system_a}` to `{system_b}` |
|
||||
| `game_analysis.md` | Evaluate `{game}` for AI agent play |
|
||||
| `integration_guide.md` | Wire `{tool}` into `{stack}` with code |
|
||||
| `state_of_art.md` | What exists in `{field}` as of `{date}` |
|
||||
| `competitive_scan.md` | How does `{project}` compare to `{alternatives}` |
|
||||
|
||||
---
|
||||
|
||||
## Sovereignty Metrics
|
||||
|
||||
| Metric | Target (Week 1) | Target (Month 1) | Target (Month 3) | Graduation |
|
||||
|--------|-----------------|------------------|------------------|------------|
|
||||
| Queries answered locally | 10% | 40% | 80% | >90% |
|
||||
| API cost per report | <$1.50 | <$0.50 | <$0.10 | <$0.01 |
|
||||
| Time from question to report | <3 hours | <30 min | <5 min | <1 min |
|
||||
| Human involvement | 100% (review) | Review only | Approve only | None |
|
||||
|
||||
---
|
||||
|
||||
## How to Use the Pipeline
|
||||
|
||||
```python
|
||||
from timmy.research import run_research
|
||||
|
||||
# Quick research (no template)
|
||||
result = await run_research("best local embedding models for 36GB RAM")
|
||||
|
||||
# With a template and slot values
|
||||
result = await run_research(
|
||||
topic="PDF text extraction libraries for Python",
|
||||
template="tool_evaluation",
|
||||
slots={"domain": "PDF parsing", "use_case": "RAG pipeline", "focus_criteria": "accuracy"},
|
||||
save_to_disk=True,
|
||||
)
|
||||
|
||||
print(result.report)
|
||||
print(f"Backend: {result.synthesis_backend}, Cached: {result.cached}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Status
|
||||
|
||||
| Component | Issue | Status |
|
||||
|-----------|-------|--------|
|
||||
| `web_fetch` tool (trafilatura) | #973 | ✅ Done |
|
||||
| Research template library (6 templates) | #974 | ✅ Done |
|
||||
| `ResearchOrchestrator` (`research.py`) | #975 | ✅ Done |
|
||||
| Semantic index for outputs | #976 | 🔲 Planned |
|
||||
| Auto-create Gitea issues from findings | #977 | 🔲 Planned |
|
||||
| Paperclip task runner integration | #978 | 🔲 Planned |
|
||||
| Kimi delegation via labels | #979 | 🔲 Planned |
|
||||
| Groq free-tier cascade tier | #980 | 🔲 Planned |
|
||||
| Sovereignty metrics dashboard | #981 | 🔲 Planned |
|
||||
|
||||
---
|
||||
|
||||
## Governing Spec
|
||||
|
||||
See [issue #972](http://143.198.27.163:3000/Rockachopa/Timmy-time-dashboard/issues/972) for the full spec and rationale.
|
||||
|
||||
Research artifacts committed to `docs/research/`.
|
||||
@@ -25,19 +25,6 @@ providers:
|
||||
tier: local
|
||||
url: "http://localhost:11434"
|
||||
models:
|
||||
# ── Dual-model routing: Qwen3-8B (fast) + Qwen3-14B (quality) ──────────
|
||||
# Both models fit simultaneously: ~6.6 GB + ~10.5 GB = ~17 GB combined.
|
||||
# Requires OLLAMA_MAX_LOADED_MODELS=2 (set in .env) to stay hot.
|
||||
# Ref: issue #1065 — Qwen3-8B/14B dual-model routing strategy
|
||||
- name: qwen3:8b
|
||||
context_window: 32768
|
||||
capabilities: [text, tools, json, streaming, routine]
|
||||
description: "Qwen3-8B Q6_K — fast router for routine tasks (~6.6 GB, 45-55 tok/s)"
|
||||
- name: qwen3:14b
|
||||
context_window: 40960
|
||||
capabilities: [text, tools, json, streaming, complex, reasoning]
|
||||
description: "Qwen3-14B Q5_K_M — complex reasoning and planning (~10.5 GB, 20-28 tok/s)"
|
||||
|
||||
# Text + Tools models
|
||||
- name: qwen3:30b
|
||||
default: true
|
||||
@@ -200,20 +187,6 @@ fallback_chains:
|
||||
- dolphin3 # base Dolphin 3.0 8B (uncensored, no custom system prompt)
|
||||
- qwen3:30b # primary fallback — usually sufficient with a good system prompt
|
||||
|
||||
# ── Complexity-based routing chains (issue #1065) ───────────────────────
|
||||
# Routine tasks: prefer Qwen3-8B for low latency (~45-55 tok/s)
|
||||
routine:
|
||||
- qwen3:8b # Primary fast model
|
||||
- llama3.1:8b-instruct # Fallback fast model
|
||||
- llama3.2:3b # Smallest available
|
||||
|
||||
# Complex tasks: prefer Qwen3-14B for quality (~20-28 tok/s)
|
||||
complex:
|
||||
- qwen3:14b # Primary quality model
|
||||
- hermes4-14b # Native tool calling, hybrid reasoning
|
||||
- qwen3:30b # Highest local quality
|
||||
- qwen2.5:14b # Additional fallback
|
||||
|
||||
# ── Custom Models ───────────────────────────────────────────────────────────
|
||||
# Register custom model weights for per-agent assignment.
|
||||
# Supports GGUF (Ollama), safetensors, and HuggingFace checkpoint dirs.
|
||||
|
||||
@@ -42,10 +42,6 @@ services:
|
||||
GROK_ENABLED: "${GROK_ENABLED:-false}"
|
||||
XAI_API_KEY: "${XAI_API_KEY:-}"
|
||||
GROK_DEFAULT_MODEL: "${GROK_DEFAULT_MODEL:-grok-3-fast}"
|
||||
# Search backend (SearXNG + Crawl4AI) — set TIMMY_SEARCH_BACKEND=none to disable
|
||||
TIMMY_SEARCH_BACKEND: "${TIMMY_SEARCH_BACKEND:-searxng}"
|
||||
TIMMY_SEARCH_URL: "${TIMMY_SEARCH_URL:-http://searxng:8080}"
|
||||
TIMMY_CRAWL_URL: "${TIMMY_CRAWL_URL:-http://crawl4ai:11235}"
|
||||
extra_hosts:
|
||||
- "host.docker.internal:host-gateway" # Linux: maps to host IP
|
||||
networks:
|
||||
@@ -78,50 +74,6 @@ services:
|
||||
profiles:
|
||||
- celery
|
||||
|
||||
# ── SearXNG — self-hosted meta-search engine ─────────────────────────
|
||||
searxng:
|
||||
image: searxng/searxng:latest
|
||||
container_name: timmy-searxng
|
||||
profiles:
|
||||
- search
|
||||
ports:
|
||||
- "${SEARXNG_PORT:-8888}:8080"
|
||||
environment:
|
||||
SEARXNG_BASE_URL: "${SEARXNG_BASE_URL:-http://localhost:8888}"
|
||||
volumes:
|
||||
- ./docker/searxng:/etc/searxng:rw
|
||||
networks:
|
||||
- timmy-net
|
||||
restart: unless-stopped
|
||||
healthcheck:
|
||||
test: ["CMD", "wget", "-qO-", "http://localhost:8080/healthz"]
|
||||
interval: 30s
|
||||
timeout: 5s
|
||||
retries: 3
|
||||
start_period: 20s
|
||||
|
||||
# ── Crawl4AI — self-hosted web scraper ────────────────────────────────
|
||||
crawl4ai:
|
||||
image: unclecode/crawl4ai:latest
|
||||
container_name: timmy-crawl4ai
|
||||
profiles:
|
||||
- search
|
||||
ports:
|
||||
- "${CRAWL4AI_PORT:-11235}:11235"
|
||||
environment:
|
||||
CRAWL4AI_API_TOKEN: "${CRAWL4AI_API_TOKEN:-}"
|
||||
volumes:
|
||||
- timmy-data:/app/data
|
||||
networks:
|
||||
- timmy-net
|
||||
restart: unless-stopped
|
||||
healthcheck:
|
||||
test: ["CMD", "curl", "-f", "http://localhost:11235/health"]
|
||||
interval: 30s
|
||||
timeout: 10s
|
||||
retries: 3
|
||||
start_period: 30s
|
||||
|
||||
# ── OpenFang — vendored agent runtime sidecar ────────────────────────────
|
||||
openfang:
|
||||
build:
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
# SearXNG configuration for Timmy Time self-hosted search
|
||||
# https://docs.searxng.org/admin/settings/settings.html
|
||||
|
||||
general:
|
||||
debug: false
|
||||
instance_name: "Timmy Search"
|
||||
privacypolicy_url: false
|
||||
donation_url: false
|
||||
contact_url: false
|
||||
enable_metrics: false
|
||||
|
||||
server:
|
||||
port: 8080
|
||||
bind_address: "0.0.0.0"
|
||||
secret_key: "timmy-searxng-key-change-in-production"
|
||||
base_url: false
|
||||
image_proxy: false
|
||||
|
||||
ui:
|
||||
static_use_hash: false
|
||||
default_locale: ""
|
||||
query_in_title: false
|
||||
infinite_scroll: false
|
||||
default_theme: simple
|
||||
center_alignment: false
|
||||
|
||||
search:
|
||||
safe_search: 0
|
||||
autocomplete: ""
|
||||
default_lang: "en"
|
||||
formats:
|
||||
- html
|
||||
- json
|
||||
|
||||
outgoing:
|
||||
request_timeout: 6.0
|
||||
max_request_timeout: 10.0
|
||||
useragent_suffix: "TimmyResearchBot"
|
||||
pool_connections: 100
|
||||
pool_maxsize: 20
|
||||
|
||||
enabled_plugins:
|
||||
- Hash_plugin
|
||||
- Search_on_category_select
|
||||
- Tracker_url_remover
|
||||
|
||||
engines:
|
||||
- name: google
|
||||
engine: google
|
||||
shortcut: g
|
||||
categories: general
|
||||
|
||||
- name: bing
|
||||
engine: bing
|
||||
shortcut: b
|
||||
categories: general
|
||||
|
||||
- name: duckduckgo
|
||||
engine: duckduckgo
|
||||
shortcut: d
|
||||
categories: general
|
||||
|
||||
- name: wikipedia
|
||||
engine: wikipedia
|
||||
shortcut: wp
|
||||
categories: general
|
||||
timeout: 3.0
|
||||
@@ -1,244 +0,0 @@
|
||||
# Gitea Activity & Branch Audit — 2026-03-23
|
||||
|
||||
**Requested by:** Issue #1210
|
||||
**Audited by:** Claude (Sonnet 4.6)
|
||||
**Date:** 2026-03-23
|
||||
**Scope:** All repos under the sovereign AI stack
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
- **18 repos audited** across 9 Gitea organizations/users
|
||||
- **~65–70 branches identified** as safe to delete (merged or abandoned)
|
||||
- **4 open PRs** are bottlenecks awaiting review
|
||||
- **3+ instances of duplicate work** across repos and agents
|
||||
- **5+ branches** contain valuable unmerged code with no open PR
|
||||
- **5 PRs closed without merge** on active p0-critical issues in Timmy-time-dashboard
|
||||
|
||||
Improvement tickets have been filed on each affected repo following this report.
|
||||
|
||||
---
|
||||
|
||||
## Repo-by-Repo Findings
|
||||
|
||||
---
|
||||
|
||||
### 1. rockachopa/Timmy-time-dashboard
|
||||
|
||||
**Status:** Most active repo. 1,200+ PRs, 50+ branches.
|
||||
|
||||
#### Dead/Abandoned Branches
|
||||
| Branch | Last Commit | Status |
|
||||
|--------|-------------|--------|
|
||||
| `feature/voice-customization` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/enhanced-memory-ui` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/soul-customization` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/dreaming-mode` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/memory-visualization` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/voice-customization-ui` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/issue-1015` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/issue-1016` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/issue-1017` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/issue-1018` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/issue-1019` | 2026-03-22 | Gemini-created, no PR, abandoned |
|
||||
| `feature/self-reflection` | 2026-03-22 | Only merge-from-main commits, no unique work |
|
||||
| `feature/memory-search-ui` | 2026-03-22 | Only merge-from-main commits, no unique work |
|
||||
| `claude/issue-962` | 2026-03-22 | Automated salvage commit only |
|
||||
| `claude/issue-972` | 2026-03-22 | Automated salvage commit only |
|
||||
| `gemini/issue-1006` | 2026-03-22 | Incomplete agent session |
|
||||
| `gemini/issue-1008` | 2026-03-22 | Incomplete agent session |
|
||||
| `gemini/issue-1010` | 2026-03-22 | Incomplete agent session |
|
||||
| `gemini/issue-1134` | 2026-03-22 | Incomplete agent session |
|
||||
| `gemini/issue-1139` | 2026-03-22 | Incomplete agent session |
|
||||
|
||||
#### Duplicate Branches (Identical SHA)
|
||||
| Branch A | Branch B | Action |
|
||||
|----------|----------|--------|
|
||||
| `feature/internal-monologue` | `feature/issue-1005` | Exact duplicate — delete one |
|
||||
| `claude/issue-1005` | (above) | Merge-from-main only — delete |
|
||||
|
||||
#### Unmerged Work With No Open PR (HIGH PRIORITY)
|
||||
| Branch | Content | Issues |
|
||||
|--------|---------|--------|
|
||||
| `claude/issue-987` | Content moderation pipeline, Llama Guard integration | No open PR — potentially lost |
|
||||
| `claude/issue-1011` | Automated skill discovery system | No open PR — potentially lost |
|
||||
| `gemini/issue-976` | Semantic index for research outputs | No open PR — potentially lost |
|
||||
|
||||
#### PRs Closed Without Merge (Issues Still Open)
|
||||
| PR | Title | Issue Status |
|
||||
|----|-------|-------------|
|
||||
| PR#1163 | Three-Strike Detector (#962) | p0-critical, still open |
|
||||
| PR#1162 | Session Sovereignty Report Generator (#957) | p0-critical, still open |
|
||||
| PR#1157 | Qwen3 routing | open |
|
||||
| PR#1156 | Agent Dreaming Mode | open |
|
||||
| PR#1145 | Qwen3-14B config | open |
|
||||
|
||||
#### Workflow Observations
|
||||
- `loop-cycle` bot auto-creates micro-fix PRs at high frequency (PR numbers climbing past 1209 rapidly)
|
||||
- Many `gemini/*` branches represent incomplete agent sessions, not full feature work
|
||||
- Issues get reassigned across agents causing duplicate branch proliferation
|
||||
|
||||
---
|
||||
|
||||
### 2. rockachopa/hermes-agent
|
||||
|
||||
**Status:** Active — AutoLoRA training pipeline in progress.
|
||||
|
||||
#### Open PRs Awaiting Review
|
||||
| PR | Title | Age |
|
||||
|----|-------|-----|
|
||||
| PR#33 | AutoLoRA v1 MLX QLoRA training pipeline | ~1 week |
|
||||
|
||||
#### Valuable Unmerged Branches (No PR)
|
||||
| Branch | Content | Age |
|
||||
|--------|---------|-----|
|
||||
| `sovereign` | Full fallback chain: Groq/Kimi/Ollama cascade recovery | 9 days |
|
||||
| `fix/vision-api-key-fallback` | Vision API key fallback fix | 9 days |
|
||||
|
||||
#### Stale Merged Branches (~12)
|
||||
12 merged `claude/*` and `gemini/*` branches are safe to delete.
|
||||
|
||||
---
|
||||
|
||||
### 3. rockachopa/the-matrix
|
||||
|
||||
**Status:** 8 open PRs from `claude/the-matrix` fork all awaiting review, all batch-created on 2026-03-23.
|
||||
|
||||
#### Open PRs (ALL Awaiting Review)
|
||||
| PR | Feature |
|
||||
|----|---------|
|
||||
| PR#9–16 | Touch controls, agent feed, particles, audio, day/night cycle, metrics panel, ASCII logo, click-to-view-PR |
|
||||
|
||||
These were created in a single agent session within 5 minutes — needs human review before merge.
|
||||
|
||||
---
|
||||
|
||||
### 4. replit/timmy-tower
|
||||
|
||||
**Status:** Very active — 100+ PRs, complex feature roadmap.
|
||||
|
||||
#### Open PRs Awaiting Review
|
||||
| PR | Title | Age |
|
||||
|----|-------|-----|
|
||||
| PR#93 | Task decomposition view | Recent |
|
||||
| PR#80 | `session_messages` table | 22 hours |
|
||||
|
||||
#### Unmerged Work With No Open PR
|
||||
| Branch | Content |
|
||||
|--------|---------|
|
||||
| `gemini/issue-14` | NIP-07 Nostr identity |
|
||||
| `gemini/issue-42` | Timmy animated eyes |
|
||||
| `claude/issue-11` | Kimi + Perplexity agent integrations |
|
||||
| `claude/issue-13` | Nostr event publishing |
|
||||
| `claude/issue-29` | Mobile Nostr identity |
|
||||
| `claude/issue-45` | Test kit |
|
||||
| `claude/issue-47` | SQL migration helpers |
|
||||
| `claude/issue-67` | Session Mode UI |
|
||||
|
||||
#### Cleanup
|
||||
~30 merged `claude/*` and `gemini/*` branches are safe to delete.
|
||||
|
||||
---
|
||||
|
||||
### 5. replit/token-gated-economy
|
||||
|
||||
**Status:** Active roadmap, no current open PRs.
|
||||
|
||||
#### Stale Branches (~23)
|
||||
- 8 Replit Agent branches from 2026-03-19 (PRs closed/merged)
|
||||
- 15 merged `claude/issue-*` branches
|
||||
|
||||
All are safe to delete.
|
||||
|
||||
---
|
||||
|
||||
### 6. hermes/timmy-time-app
|
||||
|
||||
**Status:** 2-commit repo, created 2026-03-14, no activity since. **Candidate for archival.**
|
||||
|
||||
Functionality appears to be superseded by other repos in the stack. Recommend archiving or deleting if not planned for future development.
|
||||
|
||||
---
|
||||
|
||||
### 7. google/maintenance-tasks & google/wizard-council-automation
|
||||
|
||||
**Status:** Single-commit repos from 2026-03-19 created by "Google AI Studio". No follow-up activity.
|
||||
|
||||
Unclear ownership and purpose. Recommend clarifying with rockachopa whether these are active or can be archived.
|
||||
|
||||
---
|
||||
|
||||
### 8. hermes/hermes-config
|
||||
|
||||
**Status:** Single branch, updated 2026-03-23 (today). Active — contains Timmy orchestrator config.
|
||||
|
||||
No action needed.
|
||||
|
||||
---
|
||||
|
||||
### 9. Timmy_Foundation/the-nexus
|
||||
|
||||
**Status:** Greenfield — created 2026-03-23. 19 issues filed as roadmap. PR#2 (contributor audit) open.
|
||||
|
||||
No cleanup needed yet. PR#2 needs review.
|
||||
|
||||
---
|
||||
|
||||
### 10. rockachopa/alexanderwhitestone.com
|
||||
|
||||
**Status:** All recent `claude/*` PRs merged. 7 non-main branches are post-merge and safe to delete.
|
||||
|
||||
---
|
||||
|
||||
### 11. hermes/hermes-config, rockachopa/hermes-config, Timmy_Foundation/.profile
|
||||
|
||||
**Status:** Dormant config repos. No action needed.
|
||||
|
||||
---
|
||||
|
||||
## Cross-Repo Patterns & Inefficiencies
|
||||
|
||||
### Duplicate Work
|
||||
1. **Timmy spring/wobble physics** built independently in both `replit/timmy-tower` and `replit/token-gated-economy`
|
||||
2. **Nostr identity logic** fragmented across 3 repos with no shared library
|
||||
3. **`feature/internal-monologue` = `feature/issue-1005`** in Timmy-time-dashboard — identical SHA, exact duplicate
|
||||
|
||||
### Agent Workflow Issues
|
||||
- Same issue assigned to both `gemini/*` and `claude/*` agents creates duplicate branches
|
||||
- Agent salvage commits are checkpoint-only — not complete work, but clutter the branch list
|
||||
- Gemini `feature/*` branches created on 2026-03-22 with no PRs filed — likely a failed agent session that created branches but didn't complete the loop
|
||||
|
||||
### Review Bottlenecks
|
||||
| Repo | Waiting PRs | Notes |
|
||||
|------|-------------|-------|
|
||||
| rockachopa/the-matrix | 8 | Batch-created, need human review |
|
||||
| replit/timmy-tower | 2 | Database schema and UI work |
|
||||
| rockachopa/hermes-agent | 1 | AutoLoRA v1 — high value |
|
||||
| Timmy_Foundation/the-nexus | 1 | Contributor audit |
|
||||
|
||||
---
|
||||
|
||||
## Recommended Actions
|
||||
|
||||
### Immediate (This Sprint)
|
||||
1. **Review & merge** PR#33 in `hermes-agent` (AutoLoRA v1)
|
||||
2. **Review** 8 open PRs in `the-matrix` before merging as a batch
|
||||
3. **Rescue** unmerged work in `claude/issue-987`, `claude/issue-1011`, `gemini/issue-976` — file new PRs or close branches
|
||||
4. **Delete duplicate** `feature/internal-monologue` / `feature/issue-1005` branches
|
||||
|
||||
### Cleanup Sprint
|
||||
5. **Delete ~65 stale branches** across all repos (itemized above)
|
||||
6. **Investigate** the 5 closed-without-merge PRs in Timmy-time-dashboard for p0-critical issues
|
||||
7. **Archive** `hermes/timmy-time-app` if no longer needed
|
||||
8. **Clarify** ownership of `google/maintenance-tasks` and `google/wizard-council-automation`
|
||||
|
||||
### Process Improvements
|
||||
9. **Enforce one-agent-per-issue** policy to prevent duplicate `claude/*` / `gemini/*` branches
|
||||
10. **Add branch protection** requiring PR before merge on `main` for all repos
|
||||
11. **Set a branch retention policy** — auto-delete merged branches (GitHub/Gitea supports this)
|
||||
12. **Share common libraries** for Nostr identity and animation physics across repos
|
||||
|
||||
---
|
||||
|
||||
*Report generated by Claude audit agent. Improvement tickets filed per repo as follow-up to this report.*
|
||||
@@ -1,89 +0,0 @@
|
||||
# Screenshot Dump Triage — Visual Inspiration & Research Leads
|
||||
|
||||
**Date:** March 24, 2026
|
||||
**Source:** Issue #1275 — "Screenshot dump for triage #1"
|
||||
**Analyst:** Claude (Sonnet 4.6)
|
||||
|
||||
---
|
||||
|
||||
## Screenshots Ingested
|
||||
|
||||
| File | Subject | Action |
|
||||
|------|---------|--------|
|
||||
| IMG_6187.jpeg | AirLLM / Apple Silicon local LLM requirements | → Issue #1284 |
|
||||
| IMG_6125.jpeg | vLLM backend for agentic workloads | → Issue #1281 |
|
||||
| IMG_6124.jpeg | DeerFlow autonomous research pipeline | → Issue #1283 |
|
||||
| IMG_6123.jpeg | "Vibe Coder vs Normal Developer" meme | → Issue #1285 |
|
||||
| IMG_6410.jpeg | SearXNG + Crawl4AI self-hosted search MCP | → Issue #1282 |
|
||||
|
||||
---
|
||||
|
||||
## Tickets Created
|
||||
|
||||
### #1281 — feat: add vLLM as alternative inference backend
|
||||
**Source:** IMG_6125 (vLLM for agentic workloads)
|
||||
|
||||
vLLM's continuous batching makes it 3–10x more throughput-efficient than Ollama for multi-agent
|
||||
request patterns. Implement `VllmBackend` in `infrastructure/llm_router/` as a selectable
|
||||
backend (`TIMMY_LLM_BACKEND=vllm`) with graceful fallback to Ollama.
|
||||
|
||||
**Priority:** Medium — impactful for research pipeline performance once #972 is in use
|
||||
|
||||
---
|
||||
|
||||
### #1282 — feat: integrate SearXNG + Crawl4AI as self-hosted search backend
|
||||
**Source:** IMG_6410 (luxiaolei/searxng-crawl4ai-mcp)
|
||||
|
||||
Self-hosted search via SearXNG + Crawl4AI removes the hard dependency on paid search APIs
|
||||
(Brave, Tavily). Add both as Docker Compose services, implement `web_search()` and
|
||||
`scrape_url()` tools in `timmy/tools/`, and register them with the research agent.
|
||||
|
||||
**Priority:** High — unblocks fully local/private operation of research agents
|
||||
|
||||
---
|
||||
|
||||
### #1283 — research: evaluate DeerFlow as autonomous research orchestration layer
|
||||
**Source:** IMG_6124 (deer-flow Docker setup)
|
||||
|
||||
DeerFlow is ByteDance's open-source autonomous research pipeline framework. Before investing
|
||||
further in Timmy's custom orchestrator (#972), evaluate whether DeerFlow's architecture offers
|
||||
integration value or design patterns worth borrowing.
|
||||
|
||||
**Priority:** Medium — research first, implementation follows if go/no-go is positive
|
||||
|
||||
---
|
||||
|
||||
### #1284 — chore: document and validate AirLLM Apple Silicon requirements
|
||||
**Source:** IMG_6187 (Mac-compatible LLM setup)
|
||||
|
||||
AirLLM graceful degradation is already implemented but undocumented. Add System Requirements
|
||||
to README (M1/M2/M3/M4, 16 GB RAM min, 15 GB disk) and document `TIMMY_LLM_BACKEND` in
|
||||
`.env.example`.
|
||||
|
||||
**Priority:** Low — documentation only, no code risk
|
||||
|
||||
---
|
||||
|
||||
### #1285 — chore: enforce "Normal Developer" discipline — tighten quality gates
|
||||
**Source:** IMG_6123 (Vibe Coder vs Normal Developer meme)
|
||||
|
||||
Tighten the existing mypy/bandit/coverage gates: fix all mypy errors, raise coverage from 73%
|
||||
to 80%, add a documented pre-push hook, and run `vulture` for dead code. The infrastructure
|
||||
exists — it just needs enforcing.
|
||||
|
||||
**Priority:** Medium — technical debt prevention, pairs well with any green-field feature work
|
||||
|
||||
---
|
||||
|
||||
## Patterns Observed Across Screenshots
|
||||
|
||||
1. **Local-first is the north star.** All five images reinforce the same theme: private,
|
||||
self-hosted, runs on your hardware. vLLM, SearXNG, AirLLM, DeerFlow — none require cloud.
|
||||
Timmy is already aligned with this direction; these are tactical additions.
|
||||
|
||||
2. **Agentic performance bottlenecks are real.** Two of five images (vLLM, DeerFlow) focus
|
||||
specifically on throughput and reliability for multi-agent loops. As the research pipeline
|
||||
matures, inference speed and search reliability will become the main constraints.
|
||||
|
||||
3. **Discipline compounds.** The meme is a reminder that the quality gates we have (tox,
|
||||
mypy, bandit, coverage) only pay off if they are enforced without exceptions.
|
||||
@@ -1,111 +0,0 @@
|
||||
# The Sovereignty Loop
|
||||
|
||||
This document establishes the primary engineering constraint for all Timmy Time development: every task must increase sovereignty as a default deliverable. Not as a future goal. Not as an optimization pass. As a constraint on every commit, every function, every inference call.
|
||||
|
||||
The full 11-page governing architecture document is available as a PDF: [The-Sovereignty-Loop.pdf](./The-Sovereignty-Loop.pdf)
|
||||
|
||||
> "The measure of progress is not features added. It is model calls eliminated."
|
||||
|
||||
## The Core Principle
|
||||
|
||||
> **The Sovereignty Loop**: Discover with an expensive model. Compress the discovery into a cheap local rule. Replace the model with the rule. Measure the cost reduction. Repeat.
|
||||
|
||||
Every call to an LLM, VLM, or external API passes through three phases:
|
||||
1. **Discovery** — Model sees something for the first time (expensive, unavoidable, produces new knowledge)
|
||||
2. **Crystallization** — Discovery compressed into durable cheap artifact (requires explicit engineering)
|
||||
3. **Replacement** — Crystallized artifact replaces the model call (near-zero cost)
|
||||
|
||||
**Code review requirement**: If a function calls a model without a crystallization step, it fails code review. No exceptions. The pattern is always: check cache → miss → infer → crystallize → return.
|
||||
|
||||
## The Sovereignty Loop Applied to Every Layer
|
||||
|
||||
### Perception: See Once, Template Forever
|
||||
- First encounter: VLM analyzes screenshot (3-6 sec) → structured JSON
|
||||
- Crystallized as: OpenCV template + bounding box → `templates.json` (3 ms retrieval)
|
||||
- `crystallize_perception()` function wraps every VLM response
|
||||
- **Target**: 90% of perception cycles without VLM by hour 1, 99% by hour 4
|
||||
|
||||
### Decision: Reason Once, Rule Forever
|
||||
- First encounter: LLM reasons through decision (1-5 sec)
|
||||
- Crystallized as: if/else rules, waypoints, cached preferences → `rules.py`, `nav_graph.db` (<1 ms)
|
||||
- Uses Voyager pattern: named skills with embeddings, success rates, conditions
|
||||
- Skill match >0.8 confidence + >0.6 success rate → executes without LLM
|
||||
- **Target**: 70-80% of decisions without LLM by week 4
|
||||
|
||||
### Narration: Script the Predictable, Improvise the Novel
|
||||
- Predictable moments → template with variable slots, voiced by Kokoro locally
|
||||
- LLM narrates only genuinely surprising events (quest twist, death, discovery)
|
||||
- **Target**: 60-70% templatized within a week
|
||||
|
||||
### Navigation: Walk Once, Map Forever
|
||||
- Every path recorded as waypoint sequence with terrain annotations
|
||||
- First journey = full perception + planning; subsequent = graph traversal
|
||||
- Builds complete nav graph without external map data
|
||||
|
||||
### API Costs: Every Dollar Spent Must Reduce Future Dollars
|
||||
|
||||
| Week | Groq Calls/Hr | Local Decisions/Hr | Sovereignty % | Cost/Hr |
|
||||
|---|---|---|---|---|
|
||||
| 1 | ~720 | ~80 | 10% | $0.40 |
|
||||
| 2 | ~400 | ~400 | 50% | $0.22 |
|
||||
| 4 | ~160 | ~640 | 80% | $0.09 |
|
||||
| 8 | ~40 | ~760 | 95% | $0.02 |
|
||||
| Target | <20 | >780 | >97% | <$0.01 |
|
||||
|
||||
## The Sovereignty Scorecard (5 Metrics)
|
||||
|
||||
Every work session ends with a sovereignty audit. Every PR includes a sovereignty delta. Not optional.
|
||||
|
||||
| Metric | What It Measures | Target |
|
||||
|---|---|---|
|
||||
| Perception Sovereignty % | Frames understood without VLM | >90% by hour 4 |
|
||||
| Decision Sovereignty % | Actions chosen without LLM | >80% by week 4 |
|
||||
| Narration Sovereignty % | Lines from templates vs LLM | >60% by week 2 |
|
||||
| API Cost Trend | Dollar cost per hour of gameplay | Monotonically decreasing |
|
||||
| Skill Library Growth | Crystallized skills per session | >5 new skills/session |
|
||||
|
||||
Dashboard widget on alexanderwhitestone.com shows these in real-time during streams. HTMX component via WebSocket.
|
||||
|
||||
## The Crystallization Protocol
|
||||
|
||||
Every model output gets crystallized:
|
||||
|
||||
| Model Output | Crystallized As | Storage | Retrieval Cost |
|
||||
|---|---|---|---|
|
||||
| VLM: UI element | OpenCV template + bbox | templates.json | 3 ms |
|
||||
| VLM: text | OCR region coords | regions.json | 50 ms |
|
||||
| LLM: nav plan | Waypoint sequence | nav_graph.db | <1 ms |
|
||||
| LLM: combat decision | If/else rule on state | rules.py | <1 ms |
|
||||
| LLM: quest interpretation | Structured entry | quests.db | <1 ms |
|
||||
| LLM: NPC disposition | Name→attitude map | npcs.db | <1 ms |
|
||||
| LLM: narration | Template with slots | narration.json | <1 ms |
|
||||
| API: moderation | Approved phrase cache | approved.set | <1 ms |
|
||||
| Groq: strategic plan | Extracted decision rules | strategy.json | <1 ms |
|
||||
|
||||
Skill document format: markdown + YAML frontmatter following agentskills.io standard (name, game, type, success_rate, times_used, sovereignty_value).
|
||||
|
||||
## The Automation Imperative & Three-Strike Rule
|
||||
|
||||
Applies to developer workflow too, not just the agent. If you do the same thing manually three times, you stop and write the automation before proceeding.
|
||||
|
||||
**Falsework Checklist** (before any cloud API call):
|
||||
1. What durable artifact will this call produce?
|
||||
2. Where will the artifact be stored locally?
|
||||
3. What local rule or cache will this populate?
|
||||
4. After this call, will I need to make it again?
|
||||
5. If yes, what would eliminate the repeat?
|
||||
6. What is the sovereignty delta of this call?
|
||||
|
||||
## The Graduation Test (Falsework Removal Criteria)
|
||||
|
||||
All five conditions met simultaneously in a single 24-hour period:
|
||||
|
||||
| Test | Condition | Measurement |
|
||||
|---|---|---|
|
||||
| Perception Independence | 1 hour, no VLM calls after minute 15 | VLM calls in last 45 min = 0 |
|
||||
| Decision Independence | Full session with <5 API calls total | Groq/cloud calls < 5 |
|
||||
| Narration Independence | All narration from local templates + local LLM | Zero cloud TTS/narration calls |
|
||||
| Economic Independence | Earns more sats than spends on inference | sats_earned > sats_spent |
|
||||
| Operational Independence | 24 hours unattended, no human intervention | Uptime > 23.5 hrs |
|
||||
|
||||
> "The arch must hold after the falsework is removed."
|
||||
@@ -1,296 +0,0 @@
|
||||
%PDF-1.4
|
||||
%“Œ‹ž ReportLab Generated PDF document (opensource)
|
||||
1 0 obj
|
||||
<<
|
||||
/F1 2 0 R /F2 3 0 R /F3 4 0 R /F4 6 0 R /F5 8 0 R /F6 9 0 R
|
||||
/F7 15 0 R
|
||||
>>
|
||||
endobj
|
||||
2 0 obj
|
||||
<<
|
||||
/BaseFont /Helvetica /Encoding /WinAnsiEncoding /Name /F1 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
3 0 obj
|
||||
<<
|
||||
/BaseFont /Times-Bold /Encoding /WinAnsiEncoding /Name /F2 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
4 0 obj
|
||||
<<
|
||||
/BaseFont /Times-Italic /Encoding /WinAnsiEncoding /Name /F3 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
5 0 obj
|
||||
<<
|
||||
/Contents 23 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
6 0 obj
|
||||
<<
|
||||
/BaseFont /Times-Roman /Encoding /WinAnsiEncoding /Name /F4 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
7 0 obj
|
||||
<<
|
||||
/Contents 24 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
8 0 obj
|
||||
<<
|
||||
/BaseFont /Courier /Encoding /WinAnsiEncoding /Name /F5 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
9 0 obj
|
||||
<<
|
||||
/BaseFont /Symbol /Name /F6 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
10 0 obj
|
||||
<<
|
||||
/Contents 25 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
11 0 obj
|
||||
<<
|
||||
/Contents 26 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
12 0 obj
|
||||
<<
|
||||
/Contents 27 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
13 0 obj
|
||||
<<
|
||||
/Contents 28 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
14 0 obj
|
||||
<<
|
||||
/Contents 29 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
15 0 obj
|
||||
<<
|
||||
/BaseFont /ZapfDingbats /Name /F7 /Subtype /Type1 /Type /Font
|
||||
>>
|
||||
endobj
|
||||
16 0 obj
|
||||
<<
|
||||
/Contents 30 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
17 0 obj
|
||||
<<
|
||||
/Contents 31 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
18 0 obj
|
||||
<<
|
||||
/Contents 32 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
19 0 obj
|
||||
<<
|
||||
/Contents 33 0 R /MediaBox [ 0 0 612 792 ] /Parent 22 0 R /Resources <<
|
||||
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||
>> /Rotate 0 /Trans <<
|
||||
|
||||
>>
|
||||
/Type /Page
|
||||
>>
|
||||
endobj
|
||||
20 0 obj
|
||||
<<
|
||||
/PageMode /UseNone /Pages 22 0 R /Type /Catalog
|
||||
>>
|
||||
endobj
|
||||
21 0 obj
|
||||
<<
|
||||
/Author (\(anonymous\)) /CreationDate (D:20260322181712+00'00') /Creator (\(unspecified\)) /Keywords () /ModDate (D:20260322181712+00'00') /Producer (ReportLab PDF Library - \(opensource\))
|
||||
/Subject (\(unspecified\)) /Title (\(anonymous\)) /Trapped /False
|
||||
>>
|
||||
endobj
|
||||
22 0 obj
|
||||
<<
|
||||
/Count 11 /Kids [ 5 0 R 7 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R 16 0 R 17 0 R 18 0 R
|
||||
19 0 R ] /Type /Pages
|
||||
>>
|
||||
endobj
|
||||
23 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 611
|
||||
>>
|
||||
stream
|
||||
Gatm7a\pkI(r#kr^15oc#d(OW9W'%NLCsl]G'`ct,r*=ra:9Y;O.=/qPPA,<)0u%EDp`J-)D8JOZNBo:EH0+93:%&I&d`o=Oc>qW[`_>md85u<*X\XrP6`u!aE'b&MKLI8=Mg=[+DUfAk>?b<*V(>-/HRI.f.AQ:/Z;Q8RQ,uf4[.Qf,MZ"BO/AZoj(nN.=-LbNB@mIA0,P[A#-,.F85[o)<uTK6AX&UMiGdCJ(k,)DDs</;cc2djh3bZlGB>LeAaS'6IiM^k:&a-+o[tF,>h6!h_lWDGY*uAlMJ?.$S/*8Vm`MEp,TV(j01fp+-RiIG,=riK'!mcY`41,5^<Fb\^/`jd#^eR'RY?C=MrM/#*H$8t&9N(fNgoYh&SDT/`KKFC`_!Jd_MH&i`..L+eT;drS+7a3&gpq=a!L0!@^9P!pEUrig*74tNM[=V`aL.o:UKH+4kc=E&*>TA$'fi"hC)M#MS,H>n&ikJ=Odj!TB7HjVFIsGiSDs<c!9Qbl.gX;jh-".Ys'VRFAi*R&;"eo\Cs`qdeuh^HfspsS`r0DZGQjC<VDelMs;`SYWo;V@F*WIE9*7H7.:*RQ%gA5I,f3:k$>ia%&,\kO!4u~>endstream
|
||||
endobj
|
||||
24 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2112
|
||||
>>
|
||||
stream
|
||||
Gatm;gN)%<&q/A5FQJ?N;(un#q9<pGPcNkN4(`bFnhL98j?rtPScMM>?b`LO!+'2?>1LVB;rV2^Vu-,NduB#ir$;9JW/5t/du1[A,q5rTPiP\:lPk/V^A;m3T4G<n#HMN%X@KTjrmAX@Ft3f\_0V]l;'%B)0uLPj-L2]$-hETTlYY)kkf0!Ur_+(8>3ia`a=%!]lb@-3Md1:7.:)&_@S'_,o0I5]d^,KA2OcA_E$JM]Z[;q#_Y69DLSqMoC1s2/n0;<"Z_gm>Lsk6d7A$_H,0o_U7?#4]C5!*cNV+B]^5OnG>WdB'2Pn>ZQ)9/_jBY.doEVFd6FYKjF<A8=m5uGn4gU-@P9n(rI:Qq:FsSA)/:VTP8\lhj2#6ApURNhalBJoU^$^'@mn!,BWDt<AF@U4B89H'BW7#l`H`R,*_N]F1`qNa1j!eKY:aR3p@5[n<r_1cE]rLj62'lK'cVDYndl\6<Cm?%B:Z>nB:[%Ft)/$#B>JM$UP8A0/,8MLf#nDSeH^_T5E!L-[2O5mU<jpXXBo9XeVBann[mSNE21KVn+l9f]?,n7WR@L:FfNMd5((XBC:/tmVO,^-oP]"#\G."W">`S?nEbuH.X!I9He::B(!Y;;2gZ#I4!*G,]LIVA"<E5iblY?O,gSrI[_"TE>:4Hh7\j;LJK&Hg?mS.&Re?X5NFgNlh&S=G7*]T;#nN7=AAClhL"!9_a]SA/?3oDEk7jk/&b_[Y*NbtQ'"3f0]epO/m+5V]UrDS3?;amUh7O8l)C"(.8R-4P8Kb$@p$a,nP2S+KS_I(-8A(b4nJ;\s::1HQ7joV1(6Ue/mFbSAJ=Grd/;]\GeD^m1_e:j,a[)u4i*i*:7SQPMo#)\)MPp:cDD09&s[mM2_@9]_-7WMV1]uNcb4,FnrZdfL@jC%kJHjF%6L5RE(\gZ.@GJ_\CZ?#jcYA"b*ZTp0f-DsI$.X@fcWl+94`3F9BUZ%qGKG43K5V;jl]tb'&<>?NU)_s[hepiJ![@ej%/DH:tf3+p^]P/us*LmWd1`^VLl'k"5N8H:6r'V1heU1'M,6FK^ID8Nds$'kajj5PJYn+_N^C#4k3\#C6[D_Y\MO/C@YP`kDH:bkc=3.,&8O;cD[(c/WH>Vp_KcV(/%bh/Ec3U()<\7;UG`6=[P:4ah_l^@;!pL55.g=G@KJsjQPHSE4HdG1O-nBuPFY&lmLYa+beK)K?LAb8D"T(DK5$L0ON^IB+:Q2Vn(<<atkt*'ADH,_BDsSL7ClRh\J^B^X&eCO2$NIcg9KVHoWq>0s2fp!b1GZ+%K,NeKZ<3hDIp:]INMurJ:pS&G:gKG>\./?UQ#$eGCq+2:]dQ+mj=+j%+FX`FmAogol!t#S^j0REChrCiB^6_\i6XP_9A92)[H-OBQ-^QV=bOrfQeop/q'f)Rd8*CSbPXcqABTI;Jf.%Foa[>:LE4mcOkC/q^DlM7$#aGGF87YQ4PsYuFY'GsT\r1qpDljUWhGoOpJ^<t;o+@[V4XG]8K/<do29F"^QnAPQs(S1'Onu9^q+I6=//DAT#5k(lOVZ+&JgEhZ=1e_dedNZ&CGR>Sn"(,&'<74C%2'H7u,,<:?Uk=>6"$mO5`-%cE^r.#D$n(Un+J&FcD,(btu4G`Be/i5ka60S*^"C9c-EsWYL*H'pS)dKq[g7Q]b@3Ar$XZl4sKdK0%>6N]p<\fA.PRA;r(60Z[YE/(bM#H-sEl8glMDc13\n"PjqnGnP2EP#2(G*`P4EZKWY[r52.KA94,mXeNiJ]aIb4jctGF4Y^j[UL#q<*!@4p28#j!`p>3.[nlNA:$9hsj(&!Y?d`_:J3[/cd/"j!5+0I;^Aa7o*H*RPCjtBk=g)p2@F@T<[6s+.HXC72TnOuNkmce'5arFH+O`<nI]E3&ZMF>QFc>B+7D=UbdV'Doj(R!.H^<_1>NuF)SJUP-<1_5$AS8$kL$Kd8mW9oFeY+ksfU^+>Bjlh3[E9Q-BhuT=5B9_fpYq.#B1C:9H9WLHCG_TS-G8kE+)`hnTD/Kggt54$fdqH-QM1kc]@$jhjj%Jd9.G:o@maribiV!4Iqar3O!;,iYmZVV?=:*%&jM!_N3d?Nj)l!BGKDQB_sKgce(&pK_1pDg~>endstream
|
||||
endobj
|
||||
25 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2489
|
||||
>>
|
||||
stream
|
||||
Gatm<Bi?6H')g+ZaDcfBZ-B`S<f>T`j#M:&i#)`0mh[0+MH3<KTeBK4@'m[t?QIs;#pb8p_Mi0YOngIWO-^kaLu6:&Q8R&C1]$o76r?Xa"\!-edd3.RcVFI%Yql\$Amu\>IQY[ao0`D)jjIt$]_"#eK/>,mP$q]lVm@,9S+_D+/s_LRct1sTF;mq$1_Y#F0q\@KRXLm_O%.5^;ER[+8O82sF2aH8P0qDpampV\N+`i:knJ*lpZm;1.6X7ZPc"P$U]iqtb0iimqem5*S:&=:HVK^N/.<1C-u4bH;&E%!Lphek0U]q--OhL^aF=+"_g9mgKsB.leVYe@4f<)P<NP7=DtF>0kGP?OAFaKc'-,G8:FQXqZb=9#+GbYhRcP48mEsV%PT-H%<JgbH3AIMPJsDe#K;V7M8_q[;73r]QoT=XRUiA&2B#RoL=*2J.Z**+\W\aM$n`K3\OML"9KI5)_Y9l)@K-H96,-hJh!R6LgD.=>?8n/[F$VJJNmV?(7np[X_N2V*ETM"!2-9"c%f<TD++5*N,7AHtmf'$i^li;lo-nhm#YXirfr41qsq\8*Ci1<Zbk@\o.q,1lSjRU,k7VTCcTb+)j1X5,\kZ,7G`0q."qOIZ3"sZHDe*_`GXkIC/-'gd&pQ1"068[899PZ8Mi!&k2iaCd%j-sKh+lciaH/]gAhcZbF.3-H76RUWbj@VGfRMME]djehu3M-Ou@;WCE%n4,D[:krIm!$L4BDE>=JT*al;`=TmYm#PqET'Uh,aH%,\k9c8\u#.g_C4/Xq#[WW+(5&D:eu\!Y.-$.Va]@1dgbL4$1;b%1L;<;i(5"oaWFgjPYSO9-3.<I_=5dV,gE5Spb.;"hX=aqKu^Xf#+h`o(]Sr8/%'*67GAoN^DX4?C/@(u(2JSq.OF8;>.)BEk<frh]m*2e-j!_MHlP0egP%SMf1()8_,PWo1)J1J%q!Y]Cb%o/A-a"T^JUeONPH=+ES:W_N$C#>Q3[`ONAmAjcNVO"D<Oh("Bf4SKTYu[U4P$*q\Gpc`/GH-PZBSGXpc/XY5$tcbR9ZY,hc:X_qs4:%9_ubq!W08`80FnP@07_nV$W9p049\[9N5"[6(U1Ig65[I\!qcJ"KorMEM1]\R5o&$Z0U,hn.A/FZ^"P\9Pd`K69X^p$)2BSPZ-hkfrK*#<9LEL7ni@2Se_:2[ei%KMd`bO`<LB9\:=HQjI]pqq"[@Nh4Iu7bF50EZ<'/#?8.<ETQugk0qAG-hK1,(V1a9/#;-(>Kn=WCA%N(S>M;h]b@J^D%I]ilPDe%qW[B)rBqCTTX5^AlM"ZWV2;f^+p7juA;<i%_(!YY$]cF$fIV>pd6-?u>$Rms.ECrS/J`8>n.lKeMKDQc.H[S&;B95.(:"`2A7QY=5](`*bN^(YNhF[,]Djh;LmiJ,_"s=#j(8d;.g6F,CoUqRX#<Qid,kmd3EP2jC9D$]N@^pj^1eZto<sp*"jBIZ-[fCng5m"p&H)&8E52C/<rfWnTq-8L98!3\BJ8DJFks[0]n;1-et*c/5r8;U&]Dun5Oq<J17K35NB?Rs(Pd$`K0G/U>GZZC_PQQf>T)]a&A8R^g],:[]L+/83Eh?`cq1aEaXU[\S'c[`!e/,g0.5-6rbWSaQfr4W;pDZ51`EEu*t<G6_U5B4rjhu)&oYh\4H)e*p!Hf`;%1?20oY*qqb]KLUZiP7]%%X9'umr$-o>JRBQR$SK^]i2d`f5!Icg6CCaTNPgNbPaY/FDk*O6=nY1j8G\0pl2gTd9m1SDWWh[uQNCFRDIH_"[/F@r)IEObA3UVm82UN0:6+@.LhOU?A]+TI`Q\TV],jH:b\9uHGe4Q9'GX:)'T7./J:j<5J.L3sk_%qn$&T'eLSo`?3gF9F='s#E16?""E]3IW<eL.]5&:_tJ7e:#%4=gLQK*#I/(CE)oS*V7KO[d3#^`pabg[MBmkSH%92oCgZ=o<.a&lc,e<]&RI`pl;V2,"f^dC@1.3VdX3\F2l50Y=9HpL^mu-JgSgn,1G/G't^Mkhe"<1-Oh/>['oDAFKG\s^Suc*ib$@KhsVhK/BP1LXgX(d1-GooQM6CggPu1PY2?R)*NK\6XduTug+BhoEbQrsBOZ[%)SL$$Rd+1F0pu/7;0VoM@mp+i^V%K=bk<&1KsEm]NHPo"FfinGR.7Yn2,Wr0="8Wo5M+NjflT8HZGV+8_S4<'W&G3rD_QnUk0c;q3Qfou"X<[Q%HWINl_;P/+H7"Tcq?K7Ggk@&<BRL#D4F!$Fmke3-e2IE\RNE4,c'"6c(odL+r]3`%'WEDiE@2)+?TVq/]S747hL/Zl]FBu4C1>DI8TGrJS$V"JSH/D7*.X75>ZZa&aOC8rp>e$fH/N:92sd>$MGU.k/uQUm$!M)SDM7g5,>%F`%T0Vl9lS`6I(*O_4NOh0/NOJ^=t\lG.7;)rS&iuOo'9F:B/sVFYD+$k=`9/(?luKOWLDHcPHMY(ZCqi&TQ2S!r%q>b<DKp%mXdk2u~>endstream
|
||||
endobj
|
||||
26 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2711
|
||||
>>
|
||||
stream
|
||||
Gau0DD0+Gi')q<+Z'1SNSXtlc.?^DfpG0:dagd\5H-ok]1n>+E65!c@?pN(rEc_9ZScpNG;B2a*lc$,&K37'JZb+O9*)VNqaF+g5d6Hdck\3F^9_0Q!8W_<s3W1Wrqf(]S9IT'?ZQL4,K65!1kkM&YYsC4JSkR!D$$2Q4Y\WHM^6\ClhaeumQ*OV?Y'!D+U?Rp)<RYd[;a.#QH^]H)S*[6kc]V\)e&8H]8us9aHS^:GRcPDp7+4+iAq8okJ+F(>Blg."9*4$BEdTd0-IX(YI]M`#fk[+![o8UQn6$H66IB3=92"<@M@H;AP%fg,Iu\"oP*Y!r"Z+AYVf_iX0Zipi[7HJ,/Dr%+H+G(\NG7Mp(D#re@kOOE-gc7`<*c=8c+!'V=H6iSp+ZM\ANG119C`M`%?hkeXYf[gCca04])!['G1q.:'LoD[U>);c317bG!L!<i0MU=D:NWoSQE2KN<SVeK@K,l]01[KgDa2A3P+m/?SAj""0:;Ur%&R+L8$_P.JZZ<o[9_7R81KH-[34q$rXr)Wh7ZQQC$bYu7'0NiXE@OubP*]_i>O/fNc`J2rGKi3r=`&0AP'"d9-flS,dhU5b?%J7^n$/XaQc5EX3Hs!<FbL83uBYXGpDT\fTG(5.BJ0hS%])bf2B%f+TX61YpE`A'XbKXIV\i?)I+".-/8<ijs/<_(9/V4'nZB#1YD=6=E".-W)>R]&bS#U?m1DCC[c=8Bm>Gu2<78T^H[@Qs*q(6?7D<dO852tB97aXGeG%'h+4+J"5_&B4#ZiJh_%%FKR8>AHQC@iU2b>UGe89lLJ.fbnrNYjZYWkSO1S7[eSZ(^]2?Z#DA80/qhF.>,9Xa$3,Y2R7/HS-:f$mm(/DM=J+b5.k9/`1Nl?2PO2.qI9Q?Rm1uE8c93HI@8#)0<Qh4k*nn"nbel9VbF$Ik"cL.4/Y!==RM:,$H#M&4?&Z)9tA_4N&cm@\M/0L5Z4iTS<6eAs9Wii>((.KDW43Xd!=sO#]M*l:,k2A82L^P*s3OUVDYYpWbU6,`QmG=GBjrLl20kB-[=W%Ns;>$6@g<`Hl*iA^,+dZs/.bd&LmXi-f^4Z/:9Z@-ZYI*1"O.,Bjhe-`FHk;$0PYKtj)!W7VI0[t3_uJ.hct]Ko(?J"kP(_s,PH0]H.8WjhZ<%2O_QiJt_61S"6EPS-9*lUmPuH?D\Di%d3!b("RQ)k(=idnMeB5&Ha[R].*_6g3ce8V>lM@6:>t5)[aK(R9C8"X13@:_,,qs8g'sL_XIG<><liR$//JY%ERj.o1*_iN2"#)chKW.5SKj,O0:mQNd!o6FV+T.h(*Fk2[>NfAC<&MlOio"RnL`Ko[3G7MGqAYrN(g&c5Z79-#iA4n/G'$]R7=LIiDhgb@XuXKOFee7Af`:&h-q_j&I;K\o&43*</q@sPTCYW.TpNV58(Ap!Fl%8"]J?do$7clL&77;sd5U"2]m@dDIfeORqHAD2ICV/Xo4[:-IA,U[c<"a;o7YabqR<q9&_[R8cL)@Qkc:.8GsQ:I>k;(T,.4hl+SMV#UjRZ4J`]6JDh`uCi6\IE/K>hZ,M@c]AHTcQeL)W%g<o'ciW]G$5cC`k7G-F8(K5?^rDR'=UIUALh%sk`d!CO/iUY*42DTScdi3918CA@"39l=gH!gSh2o'_pGTe(gbK'k0E+7N!o"aeg)\XXC#J\\okne[8=D8bmd(fNPDYF&sMolOo<VDsm*aI'Eq-&_/deU`?NE4q?>52Z^g1nUk.OsQH%]5P<UB5amJ-:5Q:&&j9F:W&e2o#/@F9hE*[$H]Er2V][(U0A;kbWrjXG/JQ@pO<N3SJUoXOA48^I;#R\crt/rI'1m0DH%10YO6Winh]ZFdAj'mqR.fUjrlOllm=9DpY8=UsTYDeS3Emn]hDO:mdNTQY7>JQqi^".9_<OMnSWJVZqp&`DXC3nsX!+Q+a<!*n7?oDHPFNA@6P_EEck`hR(XK*aGHE85oeDR$'F&d1<pD2V:aS=fsBi'dBVd2%[`'Yu&5h?+Yllo3LjB[#8S]c?9/fdO%fERqafOmEaQ's+DkA5qbW!:UQ=8Ero#tqe@hZ1_5]3,b/FP=asg7\3X4-IoG:>^#SO2mgH"G3sBg8SHR>Fgu-J;fXAA#'mA"1VN"u5/#^;2%68(uK)8mK7`k%Kf:i9$9/8b78;f`1n=c^fh#_o[TeA^bFTL=pP)_*THO9"\5TY4&00HU],N%1UN+`7:#gDS\bJ5)1Eu;W:R]!F2d,?=,UGehUkU2aZ`BA[bH#PWp(G7NG?(r17dAt/s@#!jV1:>N,0))qYoG8U["V^Q;oO:0;KbYuP0q-(*.`ni<:=+Y'RJ=hFagH`a1+cfR=]Q(DLE^6eom6)Z_-Xq+;H.eb4nLgTN,.V\$8F=/OG34fq!OifKS))`no61(%@P`c@7pAANBY<[Rf-)tS'p=u=7h.JnT'GnmraW(OP[Dc&2-l7k`%-?jM]O(>t=himKCH^rRr%/f8D^0Ua]h7nb3%8*r?r>92%k%N;hc3E&$3gHpkjm/Ws("-&]>fLLP+rkd5,ZMDa!mi\K_i>tXq-%$eKb;(cM/1h5D;!q;?NkZT_sIEcX+eadC!<]j6#/e.Of`!2HSElEP*iEfHp)G:H@#[CqaIo4oBn.lYUSL3;SR%M$<Gk"p3TC8)!0kq&6ipLmu$teNfkSd=!X?X&n?r%JXk1J\PNe;Vi9,n0WSc'?:FW(;~>endstream
|
||||
endobj
|
||||
27 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 739
|
||||
>>
|
||||
stream
|
||||
Gat%`9omaW&;KZL'ls?]<l4P(LP.XkY_u<g]5/`F>cPqN\hjkc(=6CagDS%T'1ub&e&Lh"46's#NYt[+=FX[9!,lY?>qo_,l?cp%8>t_@^9/NXhBTf?LEek5M%\bLVdm1C!A%fKJeCX,(klr=]VrSk\8-TjcEC=(r=dE0p,dY1`^%4TR\t-!0;3iFqB@IGb/Bhr`e'"lDAF`5C8<+ABr_hu)6Tc&SG<-523Ph[C("2XjH(/G%4Gor:]E=l=5@>VGpTMrG\%m&Q4;QG;IcQX&0Nru):YiLLX*g977A1G\:N*`Kin5e&Q8TCJ^4\,f^@E-#M21"SfZ4VEuGn%IFgZ0s6Y2X[31+g\n`DHEj=<aAfo_Kh>%>R_]HoCo6.[s^cT;9n(-m7'ZUY)`JsW/oCDuL%qM$oDL\+E0Zont0T;;)a,cdRV9ZT\SQMR98THMTQ9(.>G!Zr0cKikEYt=O<]K$X1\9!!+05r;\6.-tO5@kEha]&R/Bb6e1JUugo7M`e'jM5jL4Nm@rQQg[;fb/PX+?4LBi.As2"n3ct9E@TMX>3`97IDFBWkb/^JU=]]n\qIDh9,0olr!Jf]Z6f2N@F>dUiN=tSsBcFj**-r_B8=B:uSr)^V^'nO4kp$KOGosmVSRR>Nm4f3`9Ph\Tl+`FuJEcp1Uo.BLVi8`G)d?$(\1XbuR".o=UYMf^H%P58cGJZIlkKLpOq8[8*;Q)a$I-9#I$u\,?K\Drn[6U]~>endstream
|
||||
endobj
|
||||
28 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2279
|
||||
>>
|
||||
stream
|
||||
Gatm<=`<%S&:Vs/R$V:2KraLE,k"*ODXU$"BH*`&LB%N'0t%ul<(SRBpXejB8_J+sW=?)6A'#GJqW?^p!q>`0@(u4Ni6N]3IiCWa_X\UsfSa0`#&feThbVI[#Vp_1n.N4ubp3&iGHZ$]"G,SS8%of:)5M>LX5S02iG]rX\`Dk`d5s<$U4pc59jq2Uoo?c^;cnL$jmOI*^aWO,?CF/jq0Z^g%`r+V(X8-p5rF6NSAu":a8Z)9%Q/t-8HVQNTcS3.h_iX<e-k*9$8,(;Tq/lmeAoO=Z+pfoNU()UO"L#J-I&-s%3[E%KcqU^qVd>;GHJU#L#b7X`P@""&*T,MHQ</P=<mneY*g@`_L"<H)-Uh*L`u9PhDfROWe?rc7^1[bko3T5#?r?i5]NVmd/\(l"kupnJ:SW;b.==s*a"<.X"'5/HcMD+ZH9/Mi9Ce<_(3bM6#W?5Ui&3-WHLhi$E6<aQJX+;)m20M>g"m(KN+oN5E4#4>)euUb(C4neo3.HZE+pY;KJ]ra['1,k3K>3>aEVQ^3?Y.p!3F@Y$q61>S"Q.%A]E^D<qGG[r9Go%d2Dt;:.Z@@.M5<g#I)&&-]'GAJCf`0U0r8lebLN"muXp\9mU70KU7G'`T(CP22l=86L]JRCk3hLG&$#YTscf7T)9NgE02G7>S@IhtV?31qE55qG07J&nD6un&6'LJ6/I_4$?I\,!S=hH\s,5CT`H#@FE8^.T7\*b4Un?S=>=^=9mV!Rj^9;B)7]?9H<6)P1>ph>uP^AZk11jNKZYr.QS#GcH[d[F96KKDtn'GC'Doq9?jKe[?3I8lJu2>(b1+*:ZCf\]NFr)i+`LqR"T\u-)um5q_c\m22,Z#57UE.pLR)`;NPgMiZm51JJ6BtGr>u*j"@s$Y6q0g_Dsp@fNZ!!,eo#2PP-3,Lf3=S7l7P\s#6.)9uUb64:4p*p'ck[!nE/IhS?N5o`U,8TR#?o9I&5mRYKA7kQt:T&N52T0>W0RGQ/#C:<nc.J7gire(f]WbE!aLlJOt;P^#/_=RGgs(0/=!j@%F:3C+3\n!ZAT")NsrM!"0GX`b>YeZ:?(W^W2ME,m-R"YjAH[#p$N(c`c&!mb3#PW>eE&XD^3-NYMs@PPpPG7;gE-1Xceh8<B@-(,`]S:L:]4"7Ua1P)3/q+C&h)H`:)ncBNq+0j/s[%Te;!!1Ml53!J@+V!>3/FV+iQ<Ic:9E9!b38U]@FH)jndE-Vf#8At.Jd^YQ%JSDN<oYk2qf[S3\c!MZ?e\B+m]`U9C3po;]O1>mf)3@erqSqR5rr+D%m6d.frsH7Ibc+0i?.h?fmYs'p8ci2oW*4P=0i%C8OC\H5o2Z7bq`Q8X5RNJ^sTa,l^rQNW&9M9f:LfF&uF:]eMN$T#(kH#D6CfQ#D+?0+0@mk4qL+g3)@u5C!K;F_[$H8Y7Os1ZASZie=:?[Kttu@1u-8CIJFTB%Vo?I.[*XuSNKXPfM/XY[,KTX6%(H9J/;e5,"dj]^&Wc585nOcn>52MCkaXb\JYRbOW^\GD5:4)RCYD2X0-r(9qS:1$7>t9)0-VS_*CB*?p$Ht!>?rP0B0bqd8GJGBUUICWiWCce'(Y;3FI_j+[t/RQVFVLA]ksmZ!u[e_Z3&.DXkf_Wb?&X=Q]-@M^Y?br()lIK!&(&$n!KKq#Rs7ZRgCLj`o!HpEm<Xc<"!BH'@]I`jQt&.F(J?Pe8S^T:+ZJ*S6[Q\ni:jT8Z/]Ngf4m+q&&^OgstfGnpkKl4?YDZ9U'og5%>LRs,L+<dceg5,!L2Y9dOc5<tTEH&$1(Y?YUD5+V(r<oXrAi0qd@S`8lR*5sYt@Pl2^LP7'63Ar\/kU,Y#-?#i\+L/sJd1>9NMP7sB2N[XmW\Y"N=9J#YkPlM`(K70LPX.Bj5J+A.X\m3u/&/Y,q$ds8@q>d>:]go1UOQ5>AE#J;4$WB]Ng>auiE1ekCkZm`Il7u;Zu@!%*a>(rE&<+-rn_KF[7d"+%/Vre#NrS@7Y;P^:5`b0a/+@^pr.o7n)/TU?:'b"!6`>U6)f!4<l^&RR\sjTn(hZi:s_$k,2Zf`A;64l6'2O+*bBt4h+&hn4k#J<XA_])?Hha9#.5k("k7'3l:CTNjV[eQcHW:tSfOjdpSg0JCg(/hW$"qM=?^?*HVS&WQiYP'RLT*"3/W)^*t#/k=dj&*c0i?\5u$nZCTnM=c(0MkUlk>n'-"9kYpb-/l3MDEBh'U`ddmf=\q/JG#/_+k6B>;I?Js1g1*!#j-bo2A!ZuF3V=*^ITAt$nGqJ*j2`u'M*u-,_?2~>endstream
|
||||
endobj
|
||||
29 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2560
|
||||
>>
|
||||
stream
|
||||
Gatm<m;p`='*$h'@L'W#k>L@7`0l5q0!maS.8X[fk3d8R;/IU6[4NWF%H54r\)0fD?V29(1@Pq2d_>['V;7CVYjnolJ)_O,*t*=Bl@@p3@L\?9q62i6PJtr$,<b)'<)#b]BZ@0i;h*`G-f6<Va%5qfg[a_\9EO:u@C4Zb\7@O_dr\O04e+</_U=iG@$0UI?N&;bYkS9X5Eq>&,WG:raV;Bkc3;ltR.MdY0*nI!Rc-rq^lQj4qYT:lZkR[R"baUDG5,#6bouR(Q>=2i\30V<3#bR*)[F8/6@q2;nO'$h,IP?hQ9@HT_9oE+?0/'5-OUXP3St39Z7PrLABG7hi(UGDAN^;@m]dtC>:U]JM*_HYkLB2LpPp!6'_,p*HuNopY/;,*@iW\`,8X^2.MA]\6"=b+6J#p;"\?"bINu*#>&8/2o!I%78Yi/p^fc7&(q`#m/>:a:X8jE[\ghGTGpO`;=dH=`"_SHE7DU72#,SG%DlOM^;1(_u+@^XlktOcoq"S$hSE@2?ecY>[rPuLI$^.\V1Y"bu/4W4pZiP3(bEL#)dpW=[GM3rHiM(9=nDb/k.$PWL*OrV[VGdU'lT_b\T<fHH-W(Q-!2_*AN]*GaI1`L[JnXl.Wh_bSkm^pY7*I)3`0SL_'W"eTKQFF@6VQJkS\^"(//@0T)Ap@dQHpJjU\@n\E\bs=N5Y9)*5@.c,c?ul87[,U(L&(3GVb_*Bma3EKQYFW#qST:Q5PO%&<Tu=-1IWDXTtqtaEZGu&kUQ[TseE2XDspJ0nksEh@;TiE[l>Q$]EK$nROY+;RShkRX;G:jV*lu.0d%j,RS+/CUl6R:ZlX>/_9,DeC$rrNfmA[b+!_l0r,35[8NJZX!0WM!G"\uWSD0LJn4cIoJX?_7r?BVgfn%1eHYu`dR34YZ9r>cOm]<;3[d%4n`L5&5FsIPk-*(hEcH,N`!+u!,gF`s&iXgVb8k6QN%rh^9O'-3+KSd&g*sri;B_AOD:3'gU=#,)qWI]o0Z8+&ARa3=SidlX7Z0?3\d3#.L,YSD"hui2*o!"JGYKrhD3e,r.,0l4SIG`lAd36nKkhp*T8%OmNg=PoRb>=<7ZaN7r&V;nVSCF5$c]@XWFLWbH]9Jd:&8T,W#VsU_X1%39BDI>;C2)[lCX0F*!:)D2+`qBQiAX^a05i;/LDMe!IbUYXqK[0B3!mH:au6f/idTqA#hN0ophZ<'FNo?>uY]g8:?HA6!XWub6BGaKTBa8grH^.9mS(?n)*)CPXg\=Q$4J?>h??@]a0;Lg3"5+<im3`?cfU:pNM%GX.7qkpS.en`.:D*$WU.7bGA_hHc>kR4jS!P5H68(Db((R-Ml:%0.XG-#*:lE^"PqXBP-b;1SC-gM--r-[U-GoefE6Ln,&7`o2!`/:&#Z4?*S<8i#Bs"dop)].h;HLU%]Zoi)E)W\fDDT^L8Mb9lfeI#fH@brXmc(7ct/6AKi^j?%X7.B?g)l"@F3^6Pt2T':gW^"h@2`FYZ92*>!'Q(r"=,?a:B`-a6&,[g`#bDjXAIC;WWR[?@Qkq[N5USK[l1Y%m<a=aifh8r?Q0*cd7Fhsd2=T@44<$=79Xf\N9K(P?-q%)OLg"83\V62RF]1ERWnN?UEIne18G%`Ap5W7fM0MH+/X(^[^Ap]8!A%#.VXMnp5Ib!?:H^Ou%D@]hbcP)8fSlODT1lmB=7gWLPF.rTn=YUrFXL#k$:jUb1^U+#&1P_O&eA`3:V#p'uV2GluQ+cqFod3L2ArBXsf%dnDUeZ*n&UDrbio=]H']t-1ml)qtWYIh:f!"E:<EpWc=.(<ISi4A@rJmeA0iNiYM:sKaTmjC#>]pISpp2u+Z'[=Z<(dFCbC9EaI/[q]Fn+XX8e=9"Wrdb@1^X6%coM>DbjTrK(qHnI@;YNAcko&!_\o]C.ct;qDR,+NPk3q>SU1l]lhV3$dSD%t1DoVsp)oq\r*4r(k*8fLjVph^'S+13jG1pX>4/HA`e*g94SOV5u!A^F1',[P<>DL^.(MS2mId:T.[iSVsB(WuhXg78=Fea7q`gKSN<tjucH^%0G!ef/VY&q-oauCI8LDtLdpoRV'QK*X\5(fBjlR6mMV9X/7$Pp$3TNWdC'i<_C,X;uCW]bF2f48ZKF`POt2)[$4j*5+3Qj!8`W!'JlqYDZhr&S8u!nM):Ar?!^"TNrDp)MYR'f+C=bh93R-K/HQQ#O/0_Q?]i3HV<DI!gm0?QFPhRm^>P,eIM3fd`tY%E5ESdIT:RA"4;WpEdN'</E)bW=US_YD^p/9m^@me!u:q-"o&4AM3*ZC%0rdh=0(jn4^*+r0_3DD#6GY&KqU#Im0CuJXZ%F<4Zl,'t3WI.c$tk/Na2X(R;dCfOSDb1FH4WnL;+,pf)KY\5XU$%EAciV7b')UXo]ldfPCEr-(/A>^L:J4l9R0)ZtOaeYa@S:Y2kl_:T4do7-6Wq2XbLflepYT`PQn3:)U2<fK1q3(qk=TZIBSX+Xab*k\Z@$9!OO,$S@,Z6BlqQ<3;5Os783KQKZBl^>=L'=*M!iMC%BE@Y0dWkr_Wd$<mpbpn;(IoqPHoRDT'76C~>endstream
|
||||
endobj
|
||||
30 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2470
|
||||
>>
|
||||
stream
|
||||
Gatm<>Beg[&q9SYR($(">9QOpV"0]/lo&/DUq=2$ZnH]U8Ou/V&hH:O;1JPi!$k"D->i3C*6T%P_0g<r!J:B%R/>*#J@?JBoo9%\@C$+Q04WYILS$LAIpTWD&S1NC]hH$1jXR!SnF3X7&HV2mO/$)##8s<fUaolYfaISVmtD?o=#EKmYB::]=IR)rQK=5m`=K3K$C`,oaloO>*A>kM,(IlC^(ZTfFtTOiOsLBdV;Wn1,96a^dk?Lk%Moj*nIfi[)1ImUMUQ.hI8fY2iZlV!F%QO>9\+"7OI*I@FnE5?!Q9ZXe[lB[;cOZtVA?r(/:jV2DAumP7d:=ub$#X0]H<(.nIZ0A)_eLHXV1o:^KD,M_nT\P;-F2L"r>Rl1ZjRf/0gHkWsCTg=T"+)3'tOM*QSR+`)hbATlaRtWe#d\G?^mS:q!e5Y,mAH>O2"9OnBW$RjIu&2t3(jdd%o,"e]k8jrY@4>;[XX#/hF>(o8_fU(FlBW"=:^\#h%8[jA5(/Ag<_4dIDLCuJQSDnIQQ!Sl7HV%?!u#n%^R)J%Y0F,:.lL=TqDKA,No=F1N$=XEAVE>Y4!\>a._`!nU`Z>TRHKuS`kb26>SGPir\%H!p[;h0h:Qf:8l8/J\n8$IdLjZEXMfP6%Jmqdd2PJI>`Ug_?T'n0*,RsZm%+cpj[g:UdpZLfU'`irl(C9C[sIcE9i19:PqfnIUj_h,"G\7!T&SMR!]-7iA`/rDH/F:++0Y1c3%3Ld^"GPgM[m*QttoT#DICjII+)4DNS[bRVMi?4UQ-r`1!IObl<dV[CtK4X!sNP^]kDF>WeHd^Z<IbtlE7jq`kiL<[(lK-tbW$6DbaBXTnQ43aM$GR&8_+pG\0nr7Z@Sb\hR9)okL:B=?7!F>6$-fsXnRB&K*FT9cs)oY=%=40cIO7Vt^6Acp4euI2?`,bZe(SLblq5PoPmN,NN0W<[(O&VeNu&9AXd5mP6h,_''UuWUNDENDF?Li'(qJCpJ"a?bD5A`%[:e(eP_s,7@-bV!rs+69ALq0o.;q<Y$V@Q4&d^n02'u\Q,'1'a/?UL^)U&iuVTHKuju$rihp#&BS1r!4X-#jc?lKo,L0%DR24NOjPrE[=;J>4+LmCh;Gu*"rV%hN$CLhXNq#glhmX#>6nUH&g)^Wk:ShMZ-`%DO*#522G<X7IN+5E9OO\<%jWdk`,/7$<XSh!r_;B;&1Unse`\\p\8\rNmo?"Agf.%m(f9/r)p'FdCR3'$C;]n??+0Ch2&T\Oi8S0VM!W0hmJe)muFf,t![2NAafl`:Y_h<PAL*HfD:cg;cM"Jb9-quf-+D3PX?BUfUYWhVpH5tcn8KBAcM&p-fQ-_mn1S^KmfSb/*rgn_IG%l]U98\9;:3\"kLYHU`q7ZaA0]L-q&0_PE!m_;R#g<;TFa6hQspIm[he9NbprQ9K?F]"7a*/j#h-Bo.!]c"O8#Vm`C?LSjrqo]Lk1A=I5=bX5nG(%6@jE!^0VuN'Jr4n<2kkW=HKj1YuMhu5dTO%X^a!'_q?T1L'na#8QW&PXI1h+=h=Ac_\D(l'Rl7-Z[TD%7IZ;ET"75GOB?((:s^K8)/n4Ur%J1[4]F>3$FNf)GU@d_V_lb0!X1[!D,cIU"nA_uP%$j&dJCS>8rk!=F@YPA"f!ZM7As"qUgAu=qK#(!0"X`?Q#e_k6q)"$VG5=Q_!nS'#9qfV1WqK7**etWlgH61YB%3!gf\R/.<@6)Gae`aq.l?T[s1dt[Jdg9TQ7bo$`eA(hS=E>Aah>I,Y2amS7g=FVF[[TGBnuL)rO`pjj[H`UJ2@S%&3n:)N9;C!r<&fs[Fc1mAT2[7j2m2+!9oF\Tp%gXldG@%$a3KlAKl2tNS!tW\3(h<-KHJsXdTA^R:h1(saLs\X.bQimrEO,,Y,c"Sic*h1=qcB0+u9.o7pm9A"3uu\D>96KTC*&("U;^1A#q)i6g2n.<g"dqrV@L'(jcgB[nuHG^k>"r90\pk[]S>m4p3OD-J(j3h;!SQ;bc:cQ^Ac=U,A_rCg]5#.OB+27Y$39`YoGYo?l-F]J[XUNH@riUFc@]@oVM'r/N9Xkh6#A9;A;"Sj3k+01E[^)38#-=Vgg[QFG^uX`[(<3r3jGFUFM^F)A-r:c!BFK9k#EoP+mnA`/e+i6R]_JN^HRCER9+q7"5$s0Si>,^6FeI?_3+amZkmdETH?"rQTSDI?t=46'=3f)Vjh?MjM6Pp(:?G`Ai:EJTa_?G0"P?PgE`51m5m5MUr$3pj&dn1]jW@M=PL\5N;9JAgfX:#8-Z`\UE1G,dc@FS;i0a>@@>J/1bhCR1;.O2)b^(efq7l;UeSfP=d%1f:pP@,IXd_I*-AD[*QcoIcn!:S:pn*LG="=HLj+n/k2UK5MEY]TT+mGaG>,"6[r/Tb-IkYQh2hT!f1;;iTY*7!f#C(B8QEOnkU.a8.7_04D3q,g9ZKVhurg%Tdg80uUu([;X?Z9Srh[p`DJ7'Me~>endstream
|
||||
endobj
|
||||
31 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 2152
|
||||
>>
|
||||
stream
|
||||
Gatm<D/\2f%/ui*_(PnrY.\"fpWsXgZaRr:DTQp7JRQ?epuFN=[WRl&"P7!FdZVr%,utp,BmQ\uUe!YE`.qs_iBPnCjqY\d-+nWOJGHE#J;&mmQ=o])H1^d.IhG"=&$eW?k8-u\5B-D"f;-4Kl=&U&68+$<6G3_dQQ#sll<7jEf6+]X1SqPL%ndO;b,X1CVt^jis2"7D)4@SeBAk%++Y`5Po<j*b,.RuR3/u;pQM==ETf_E*5<kg=E-"uG*%oU!0ZD?FU+faFp9,)]O"PCDK;HN(aZ.I?+Koa5DX#n;ocPO+?G?/bohbHJ+a*_IoQ1,@m15Yh3o3J/_br>Y`:o1:bfASs4S)Yj1Dml*0?F&Qk#mQ\m6(`+Gr4sL(m,WuHGX'8@fi=1>g&S&;"1b&2bJQ#/[e9\YS)Yk`<t1kYIoG%K,*9$TSfJ^a)E9X%Fb`]8Zil)/]n8u.dnia\%!J2e-qi=HJ:%*DK4uSJP,F/e,63[ODEMV/brik'ZMP!U$$ho:hnML,9MMjZM4UC5mo*4*A'%2n.ReZ[ONg;#F."B5*a@,UVY#S)]QqRX:Kr%&'ZA-1&+%LcG]*dR)if[g]k"s<NdZV4``e2b*t]l@h5`8=A06^1R0A.>ja@ooRtN/G2<gqo_P>%Hs3_l<o?K=cQ$]+6+aA3!Oa;N>+mc:hPa2]'WmoL+$Z<EKUeB?"2)EsEbI5`1hg!rmTKWBEaie^)jcmKP^G)s<lt1R7UV03n-aJ^Lp=naV105jC`LO%.")N0_m0L">ZKNVO=)$*Xt3k9f$9^cJcZ"5BZRCVjLXtM"4aFXhOL3AZs)#N).NlO_9EKoI=7NMW`p?8ViLFh/+h]/="k:XFNc]&pml3F?+J.Gs!WQf\o5_(l="O3Md#%8XB_4F:n^kmV8<]%h1u*k'VM('MOm,WkaZ'ZWk-tGZ*I.(/[PS3mrE]1A\b9UrA4$)hAhZ7+Yc9.Q`F:i17o5<j2YPD(H"c8\?6dL']-8-DeC'SeZ=mV_eY>c1h6o.fM(@QQ,ql/lN.A"3X(`6Ea`NB,_u@F#I/lpG0*t?H?o'sjsGp.0JW?4.h)8qkD8QCa$=Ck^"bK4F.bUJ[&\K,P,9aDXVJF<0rO5]D?`#Wcnag$\r%\/j3;t2>CHQMleu2QBIX%dZ*5C8km]h#?b<ui('?DEiVCi&>e.S6.)[Ta_uK`WTn<(\=e_T"Q*'@/-@/eg7YY(7esn[])P5iamg#'P?sJ>/a"U<LrHs]eo0Ks[cURZ7EHSp=LKPUcfdoDXa_3mUIIT\!_XtX&L*mf31!q,MSEoU,.!]9^MB(NXeB](bbS0Hp6=(m"*1.7;/j/ln^saj8Y&&A8<7d?r.``Uml8=_r5C>bB6>'B"eT2ka3>1-fF7;e0>#a..XEnK-S"t(qDZFh_08k*:CA.*B:Y$^tO)R_AR0]:mB@"tPUr>F)%t:$4AIR38@"BEe4,%:pWg2)6j`m8tYs@,]G`-.9D;_FXAW(QV9l'TqXVTM$_d[tM"t08<aDZ;T(4s$:9:LQ_iH>JrKr0o;23M+X\6uq!pD.@rr+;V=qcY3bdp5^aUC-iunLph(R);S0/7-D4X49(>aTI+e_e>/p%b*5;#DaG97=8.#TIk"_l'9U[5LAO<g"sBRb97MjfIk5!pFJW*I4@O-8)k1e%LZ!.]dKGMmg5rI*^iecW2b0P/@'po)MC=nG4;*/msa62pF!iH$7oIYee'Xo'WL[A?>h`5Kg(ApbIdjQ8Z]7ENoCosB$/cf`>LSRFQ)nm9oHC!M2AW__WtC5@.IUqLXiA9c0\J#pEQZk,Nm"p)IrD[@#gPKl,*c91AefVK]a<5BJk+<`6p`jRIS)%q$,0RCSTJ/]2E*6ee@GpqZ0Y^SYJj(g<,\/GCc[&V]ma<X=_2:FYX2_-(I_TXN]cBM=n*;=.8I26f<VE1nqPoWtg5<`thTE>gMq1ZV>4L!`*Rh3HN)JX\Icb&`S]^*c&q.O(EB-Gc],cm/\RLbE[+]Nd^/']=#1maR%<CH*8nnObVr-lEF/na`@)IZROM,Tjn0&g:<[ZK8d3[GcVroX],Z$Cb\Nm)!X)%aA<CY%iHu-iX$!Pa*DU!TemhQj3`j2>WEWMDD3d"0Yfr8aaPr?JYgYt;_sm;c=6[hN.r^7\&-Pm780Wl~>endstream
|
||||
endobj
|
||||
32 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 1311
|
||||
>>
|
||||
stream
|
||||
Gasao9lo&I&A@C2m%lL5=u-0RWSZEjS[N$@S;&ia_&&IA7DpIFJ=mYMf69MI'WjcB/W0ZH]6Lpu]3%lPpn?`SBE<S*iS=qHarmm<7R71Q/R7J>YH)XiKZm2mK>bla34+0SqPuR+J@a:+O9?0;+H=dOKo<SZn4>bN/``cbHam'j$P,'g+d[&X[nlMunh6*>[31BmfU;tX#2ur74l6O<A>'opEKVX3#>J>@XjNd*rU9LE.dU1V,Z0)P6lA0mLnce7m]D%9X,e+]!K'c*NS,4-MA@SbXc9T/emclH9J'hBN.Da@]j1eWe6j_qrZ4`e%VHDDs3Dt4^9aK`=i^<L)[>VJn!Mk'"aLDNjDH5<9;SK<s-VlgL3uhr?+!neM9c$$(Y+VDKC\2O%l[D\B9Yd'(<Y6/V=[YATS0H]$HM%_KZNF%[)a2TbH6-V$d'oHi*(1H<<l"#gP21Rkr'DJd:h%uHdme@1c=ob1;0"dLNM@n<d"bq6UH5'<I'QD;E)43H[?!OHA,-"7A8dTFqj2WS:$kKVt>O)bK]+`7e:Ka1SJ>9d@sIK'H2G?X>F)fXDVsT%VifjD]6"=$LU\I#M:&FP[/u58QVG87)tGmA<s&J>F.U@^!;ei=WUrsn*<K_Fm1VRVd8#uE[(uT>l9`ArU]Nu(TISKj%maV_(ub>^$O]\p@>IK'CB>q^l3m%BYdo[&Nc]4`'#j9i4Nb<:C2?n4FoPaX21aX6=\F$`l`cc26bk!B$mtMn$W"LBu#)Ga_h2Lc"6(?1^A7'c"LFN*q[f%?'SHmccVqeh>`=>4e?W+bs6B]`LJF)j"hBC<&r1LRnJ^QcBZl#CG!INDO#S^:^SESj5k%0.HJqmN$tC]h7su^.K/=cgAtV<66fPXQ>*,&\2V$'FP^7Bbmjm0U?fW25WO(icG?(6PjPc+iV1M&Ff,1KLRq[`lh[+lgX\L0;hB&\6KTOQ1J++eW-PtkoY-]\XiNh$:@M#$UMt%1G%qr@lf5rllu.'iNK;^KRHN@M)&_96AgAABEjB))*;,M3(+7cd`@JbjMSk.W7pkF--N=jQ*Z5s2>PRGp5)u8q"Xtb+&u`DaI5_h91e?HIakPGY<p5$HZc+hK8h_-[.qib2I1WY@VVhqW7H&O_/+Dq,X)AW7;)EVR3s@\hShMNB4D'JEa,7*t!-eQ/%^IP(o<VdDg"8,<a,1fC1M@B9<FrBC9[1g8@%5ahC,O3m81ZY.80"s\F9?M@]G5[8fOO.d%VU&T-u-S8;=UfB$:0=Ti%n[Ye6kPU=<EjpfLG>\5nWU+r5+)Eb$M6&74$V=J^o671ZCq~>endstream
|
||||
endobj
|
||||
33 0 obj
|
||||
<<
|
||||
/Filter [ /ASCII85Decode /FlateDecode ] /Length 1124
|
||||
>>
|
||||
stream
|
||||
GatU1997gc&AJ$C%!(&;)b/=]]d7?t8?jS+`ePU]U#iPu/4I,qA]+K>),cV>fgf5q"t5rsS=/jC7RIXlI<f)P*oPiehL+7s;cmpfk:DDM4hP,Sra%uc#'DUVXISueObF<ns0UO"J5=Sa/7Eg%6WMLD*c@8a_ABOKMfPls&akY3_-ajT?n)!P(fpP@Q="(rF%C<`.;_s`eW>c15)Cimk819P/'>H!3d?o*Gsh7`s8TU(;W4k,;!*]Da_P(,..W7ldm""C(7tosS>o1pZYUP#BRAH_0(_$N"S,CCRh$t;aAnZ5Wbt$"aWSC52gPjUiX4T+-h?C'X/<NliD%GQr2c*`8K[%?emm\ZGX>M&rJH],1L?kK:%lKGrE_O!1j$Tc:^:u^YX6jd.MVRm0H.dPlG2/8A<_Ce$UV=nZ+(!Vi19MBOnoi@-Toa1m6Gt&k+LZ6EC\=?).=0K^.qeY,Xn-@,&hJM*Z]&JU,n=Y\;Q)<Tcp4ac5ah4;oL8'9i'qKDl#q1<#8XN8pUj8]CFruc*6S#J0UOMkg17$?BoP`RuO]P(08?KJ>W`&p<F(m%8qO&`Ha-Vn3i6(bhra=\6^QeXZ\^@5NG&G;cSjkXC]f?V]P]l>-b5El=-"K4V;i_KL5JE<l0krbo@$>^#(9tOhp7l'>FA#LXb4DOFHn+@lmS:m<;!,b*"5-W[8Ki#B`Y3Ksd&+(Fg#6(HY=1IAr:3ZEem$cD(T\[bZX=0-2MA)6O_0#j(P`liSYX%Q(Wd&GGlD-&V!&.`(Gdq_MF:Bj.CQl*X]OeM5u+eC8kU=)UJ[<SZD6F#\"ul6,Ge+'bHF`/7``?7Tb@l8%@;I[=)+Xbr7/'BX'[[RdR55q-&od$/3\g7_%(6di6A[I\QTUG*t2U^h,u:m4g-3(Tlp6lhm(iM@j^S.TB;5LIVf`cCkAV)bX;iLZF=))(7;3-ZNX9[^s!UEug\QEa#M3lssNP!0WBHg:S:CXb&-DmhWi3F,3e=MrCajj\UO,+VSH&/uMhf?=Ih/bV$"f'Lr2fBZA&VjYa"ni7]CGqf/sHh;Ej9_\#Z,Kj11R1)p;2^j'Zjt!lh]NO^?Gh$51^*T;tPC_eM?fu$X:4(9L1Tnp2'/is?"5,dpk5~>endstream
|
||||
endobj
|
||||
xref
|
||||
0 34
|
||||
0000000000 65535 f
|
||||
0000000061 00000 n
|
||||
0000000156 00000 n
|
||||
0000000263 00000 n
|
||||
0000000371 00000 n
|
||||
0000000481 00000 n
|
||||
0000000676 00000 n
|
||||
0000000785 00000 n
|
||||
0000000980 00000 n
|
||||
0000001085 00000 n
|
||||
0000001162 00000 n
|
||||
0000001358 00000 n
|
||||
0000001554 00000 n
|
||||
0000001750 00000 n
|
||||
0000001946 00000 n
|
||||
0000002142 00000 n
|
||||
0000002226 00000 n
|
||||
0000002422 00000 n
|
||||
0000002618 00000 n
|
||||
0000002814 00000 n
|
||||
0000003010 00000 n
|
||||
0000003080 00000 n
|
||||
0000003361 00000 n
|
||||
0000003494 00000 n
|
||||
0000004196 00000 n
|
||||
0000006400 00000 n
|
||||
0000008981 00000 n
|
||||
0000011784 00000 n
|
||||
0000012614 00000 n
|
||||
0000014985 00000 n
|
||||
0000017637 00000 n
|
||||
0000020199 00000 n
|
||||
0000022443 00000 n
|
||||
0000023846 00000 n
|
||||
trailer
|
||||
<<
|
||||
/ID
|
||||
[<71e3d90b133a79c4436262df53cdbfbf><71e3d90b133a79c4436262df53cdbfbf>]
|
||||
% ReportLab generated PDF document -- digest (opensource)
|
||||
|
||||
/Info 21 0 R
|
||||
/Root 20 0 R
|
||||
/Size 34
|
||||
>>
|
||||
startxref
|
||||
25062
|
||||
%%EOF
|
||||
@@ -1,160 +0,0 @@
|
||||
# ADR-024: Canonical Nostr Identity Location
|
||||
|
||||
**Status:** Accepted
|
||||
**Date:** 2026-03-23
|
||||
**Issue:** #1223
|
||||
**Refs:** #1210 (duplicate-work audit), ROADMAP.md Phase 2
|
||||
|
||||
---
|
||||
|
||||
## Context
|
||||
|
||||
Nostr identity logic has been independently implemented in at least three
|
||||
repos (`replit/timmy-tower`, `replit/token-gated-economy`,
|
||||
`rockachopa/Timmy-time-dashboard`), each building keypair generation, event
|
||||
publishing, and NIP-07 browser-extension auth in isolation.
|
||||
|
||||
This duplication causes:
|
||||
|
||||
- Bug fixes applied in one repo but silently missed in others.
|
||||
- Diverging implementations of the same NIPs (NIP-01, NIP-07, NIP-44).
|
||||
- Agent time wasted re-implementing logic that already exists.
|
||||
|
||||
ROADMAP.md Phase 2 already names `timmy-nostr` as the planned home for Nostr
|
||||
infrastructure. This ADR makes that decision explicit and prescribes how
|
||||
other repos consume it.
|
||||
|
||||
---
|
||||
|
||||
## Decision
|
||||
|
||||
**The canonical home for all Nostr identity logic is `rockachopa/timmy-nostr`.**
|
||||
|
||||
All other repos (`Timmy-time-dashboard`, `timmy-tower`,
|
||||
`token-gated-economy`) become consumers, not implementers, of Nostr identity
|
||||
primitives.
|
||||
|
||||
### What lives in `timmy-nostr`
|
||||
|
||||
| Module | Responsibility |
|
||||
|--------|---------------|
|
||||
| `nostr_id/keypair.py` | Keypair generation, nsec/npub encoding, encrypted storage |
|
||||
| `nostr_id/identity.py` | Agent identity lifecycle (NIP-01 kind:0 profile events) |
|
||||
| `nostr_id/auth.py` | NIP-07 browser-extension signer; NIP-42 relay auth |
|
||||
| `nostr_id/event.py` | Event construction, signing, serialisation (NIP-01) |
|
||||
| `nostr_id/crypto.py` | NIP-44 encryption (XChaCha20-Poly1305 v2) |
|
||||
| `nostr_id/nip05.py` | DNS-based identifier verification |
|
||||
| `nostr_id/relay.py` | WebSocket relay client (publish / subscribe) |
|
||||
|
||||
### What does NOT live in `timmy-nostr`
|
||||
|
||||
- Business logic that combines Nostr with application-specific concepts
|
||||
(e.g. "publish a task-completion event" lives in the application layer
|
||||
that calls `timmy-nostr`).
|
||||
- Reputation scoring algorithms (depends on application policy).
|
||||
- Dashboard UI components.
|
||||
|
||||
---
|
||||
|
||||
## How Other Repos Reference `timmy-nostr`
|
||||
|
||||
### Python repos (`Timmy-time-dashboard`, `timmy-tower`)
|
||||
|
||||
Add to `pyproject.toml` dependencies:
|
||||
|
||||
```toml
|
||||
[tool.poetry.dependencies]
|
||||
timmy-nostr = {git = "https://gitea.hermes.local/rockachopa/timmy-nostr.git", tag = "v0.1.0"}
|
||||
```
|
||||
|
||||
Import pattern:
|
||||
|
||||
```python
|
||||
from nostr_id.keypair import generate_keypair, load_keypair
|
||||
from nostr_id.event import build_event, sign_event
|
||||
from nostr_id.relay import NostrRelayClient
|
||||
```
|
||||
|
||||
### JavaScript/TypeScript repos (`token-gated-economy` frontend)
|
||||
|
||||
Add to `package.json` (once published or via local path):
|
||||
|
||||
```json
|
||||
"dependencies": {
|
||||
"timmy-nostr": "rockachopa/timmy-nostr#v0.1.0"
|
||||
}
|
||||
```
|
||||
|
||||
Import pattern:
|
||||
|
||||
```typescript
|
||||
import { generateKeypair, signEvent } from 'timmy-nostr';
|
||||
```
|
||||
|
||||
Until `timmy-nostr` publishes a JS package, use NIP-07 browser extension
|
||||
directly and delegate all key-management to the browser signer — never
|
||||
re-implement crypto in JS without the shared library.
|
||||
|
||||
---
|
||||
|
||||
## Migration Plan
|
||||
|
||||
Existing duplicated code should be migrated in this order:
|
||||
|
||||
1. **Keypair generation** — highest duplication, clearest interface.
|
||||
2. **NIP-01 event construction/signing** — used by all three repos.
|
||||
3. **NIP-07 browser auth** — currently in `timmy-tower` and `token-gated-economy`.
|
||||
4. **NIP-44 encryption** — lowest priority, least duplicated.
|
||||
|
||||
Each step: implement in `timmy-nostr` → cut over one repo → delete the
|
||||
duplicate → repeat.
|
||||
|
||||
---
|
||||
|
||||
## Interface Contract
|
||||
|
||||
`timmy-nostr` must expose a stable public API:
|
||||
|
||||
```python
|
||||
# Keypair
|
||||
keypair = generate_keypair() # -> NostrKeypair(nsec, npub, privkey_bytes, pubkey_bytes)
|
||||
keypair = load_keypair(encrypted_nsec, secret_key)
|
||||
|
||||
# Events
|
||||
event = build_event(kind=0, content=profile_json, keypair=keypair)
|
||||
event = sign_event(event, keypair) # attaches .id and .sig
|
||||
|
||||
# Relay
|
||||
async with NostrRelayClient(url) as relay:
|
||||
await relay.publish(event)
|
||||
async for msg in relay.subscribe(filters):
|
||||
...
|
||||
```
|
||||
|
||||
Breaking changes to this interface require a semver major bump and a
|
||||
migration note in `timmy-nostr`'s CHANGELOG.
|
||||
|
||||
---
|
||||
|
||||
## Consequences
|
||||
|
||||
- **Positive:** Bug fixes in cryptographic or protocol code propagate to all
|
||||
repos via a version bump.
|
||||
- **Positive:** New NIPs are implemented once and adopted everywhere.
|
||||
- **Negative:** Adds a cross-repo dependency; version pinning discipline
|
||||
required.
|
||||
- **Negative:** `timmy-nostr` must be stood up and tagged before any
|
||||
migration can begin.
|
||||
|
||||
---
|
||||
|
||||
## Action Items
|
||||
|
||||
- [ ] Create `rockachopa/timmy-nostr` repo with the module structure above.
|
||||
- [ ] Implement keypair generation + NIP-01 signing as v0.1.0.
|
||||
- [ ] Replace `Timmy-time-dashboard` inline Nostr code (if any) with
|
||||
`timmy-nostr` import once v0.1.0 is tagged.
|
||||
- [ ] Add `src/infrastructure/clients/nostr_client.py` as the thin
|
||||
application-layer wrapper (see ROADMAP.md §2.6).
|
||||
- [ ] File issues in `timmy-tower` and `token-gated-economy` to migrate their
|
||||
duplicate implementations.
|
||||
@@ -1,100 +0,0 @@
|
||||
# Issue #1097 — Bannerlord M5 Sovereign Victory: Implementation
|
||||
|
||||
**Date:** 2026-03-23
|
||||
**Status:** Python stack implemented — game infrastructure pending
|
||||
|
||||
## Summary
|
||||
|
||||
Issue #1097 is the final milestone of Project Bannerlord (#1091): Timmy holds
|
||||
the title of King with majority territory control through pure local strategy.
|
||||
|
||||
This PR implements the Python-side sovereign victory stack (`src/bannerlord/`).
|
||||
The game-side infrastructure (Windows VM, GABS C# mod) remains external to this
|
||||
repository, consistent with the scope decision on M4 (#1096).
|
||||
|
||||
## What was implemented
|
||||
|
||||
### `src/bannerlord/` package
|
||||
|
||||
| Module | Purpose |
|
||||
|--------|---------|
|
||||
| `models.py` | Pydantic data contracts — KingSubgoal, SubgoalMessage, TaskMessage, ResultMessage, StateUpdateMessage, reward functions, VictoryCondition |
|
||||
| `gabs_client.py` | Async TCP JSON-RPC client for Bannerlord.GABS (port 4825), graceful degradation when game server is offline |
|
||||
| `ledger.py` | SQLite-backed asset ledger — treasury, fiefs, vassal budgets, campaign tick log |
|
||||
| `agents/king.py` | King agent — Qwen3:32b, 1× per campaign day, sovereign campaign loop, victory detection, subgoal broadcast |
|
||||
| `agents/vassals.py` | War / Economy / Diplomacy vassals — Qwen3:14b, domain reward functions, primitive dispatch |
|
||||
| `agents/companions.py` | Logistics / Caravan / Scout companions — event-driven, primitive execution against GABS |
|
||||
|
||||
### `tests/unit/test_bannerlord/` — 56 unit tests
|
||||
|
||||
- `test_models.py` — Pydantic validation, reward math, victory condition logic
|
||||
- `test_gabs_client.py` — Connection lifecycle, RPC dispatch, error handling, graceful degradation
|
||||
- `test_agents.py` — King campaign loop, vassal subgoal routing, companion primitive execution
|
||||
|
||||
All 56 tests pass.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
KingAgent (Qwen3:32b, 1×/day)
|
||||
└── KingSubgoal → SubgoalQueue
|
||||
├── WarVassal (Qwen3:14b, 4×/day)
|
||||
│ └── TaskMessage → LogisticsCompanion
|
||||
│ └── GABS: move_party, recruit_troops, upgrade_troops
|
||||
├── EconomyVassal (Qwen3:14b, 4×/day)
|
||||
│ └── TaskMessage → CaravanCompanion
|
||||
│ └── GABS: assess_prices, buy_goods, establish_caravan
|
||||
└── DiplomacyVassal (Qwen3:14b, 4×/day)
|
||||
└── TaskMessage → ScoutCompanion
|
||||
└── GABS: track_lord, assess_garrison, report_intel
|
||||
```
|
||||
|
||||
## Subgoal vocabulary
|
||||
|
||||
| Token | Vassal | Meaning |
|
||||
|-------|--------|---------|
|
||||
| `EXPAND_TERRITORY` | War | Take or secure a fief |
|
||||
| `RAID_ECONOMY` | War | Raid enemy villages for denars |
|
||||
| `TRAIN` | War | Level troops via auto-resolve |
|
||||
| `FORTIFY` | Economy | Upgrade or repair a settlement |
|
||||
| `CONSOLIDATE` | Economy | Hold territory, no expansion |
|
||||
| `TRADE` | Economy | Execute profitable trade route |
|
||||
| `ALLY` | Diplomacy | Pursue non-aggression / alliance |
|
||||
| `RECRUIT` | Logistics | Fill party to capacity |
|
||||
| `HEAL` | Logistics | Rest party until wounds recovered |
|
||||
| `SPY` | Scout | Gain information on target faction |
|
||||
|
||||
## Victory condition
|
||||
|
||||
```python
|
||||
VictoryCondition(
|
||||
holds_king_title=True, # player_title == "King" from GABS
|
||||
territory_control_pct=55.0, # > 51% of Calradia fiefs
|
||||
)
|
||||
```
|
||||
|
||||
## Graceful degradation
|
||||
|
||||
When GABS is offline (game not running), `GABSClient` logs a warning and raises
|
||||
`GABSUnavailable`. The King agent catches this and runs with an empty game state
|
||||
(falls back to RECRUIT subgoal). No part of the dashboard crashes.
|
||||
|
||||
## Remaining prerequisites
|
||||
|
||||
Before M5 can run live:
|
||||
|
||||
1. **M1-M3** — Passive observer, basic campaign actions, full campaign strategy
|
||||
(currently open; their Python stubs can build on this `src/bannerlord/` package)
|
||||
2. **M4** — Formation Commander (#1096) — declined as out-of-scope; M5 works
|
||||
around M4 by using Bannerlord's Tactics auto-resolve path
|
||||
3. **Windows VM** — Mount & Blade II: Bannerlord + GABS mod (BUTR/Bannerlord.GABS)
|
||||
4. **OBS streaming** — Cinematic Camera pipeline (Step 3 of M5) — external to repo
|
||||
5. **BattleLink** — Alex co-op integration (Step 4 of M5) — requires dedicated server
|
||||
|
||||
## Design references
|
||||
|
||||
- Ahilan & Dayan (2019): Feudal Multi-Agent Hierarchies — manager/worker hierarchy
|
||||
- Wang et al. (2023): Voyager — LLM lifelong learning pattern
|
||||
- Feudal hierarchy design doc: `docs/research/bannerlord-feudal-hierarchy-design.md`
|
||||
|
||||
Fixes #1097
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,105 +0,0 @@
|
||||
# Nexus — Scope & Acceptance Criteria
|
||||
|
||||
**Issue:** #1208
|
||||
**Date:** 2026-03-23
|
||||
**Status:** Initial implementation complete; teaching/RL harness deferred
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
The **Nexus** is a persistent conversational space where Timmy lives with full
|
||||
access to his live memory. Unlike the main dashboard chat (which uses tools and
|
||||
has a transient feel), the Nexus is:
|
||||
|
||||
- **Conversational only** — no tool approval flow; pure dialogue
|
||||
- **Memory-aware** — semantically relevant memories surface alongside each exchange
|
||||
- **Teachable** — the operator can inject facts directly into Timmy's live memory
|
||||
- **Persistent** — the session survives page refreshes; history accumulates over time
|
||||
- **Local** — always backed by Ollama; no cloud inference required
|
||||
|
||||
This is the foundation for future LoRA fine-tuning, RL training harnesses, and
|
||||
eventually real-time self-improvement loops.
|
||||
|
||||
---
|
||||
|
||||
## Scope (v1 — this PR)
|
||||
|
||||
| Area | Included | Deferred |
|
||||
|------|----------|----------|
|
||||
| Conversational UI | ✅ Chat panel with HTMX streaming | Streaming tokens |
|
||||
| Live memory sidebar | ✅ Semantic search on each turn | Auto-refresh on teach |
|
||||
| Teaching panel | ✅ Inject personal facts | Bulk import, LoRA trigger |
|
||||
| Session isolation | ✅ Dedicated `nexus` session ID | Per-operator sessions |
|
||||
| Nav integration | ✅ NEXUS link in INTEL dropdown | Mobile nav |
|
||||
| CSS/styling | ✅ Two-column responsive layout | Dark/light theme toggle |
|
||||
| Tests | ✅ 9 unit tests, all green | E2E with real Ollama |
|
||||
| LoRA / RL harness | ❌ deferred to future issue | |
|
||||
| Auto-falsework | ❌ deferred | |
|
||||
| Bannerlord interface | ❌ separate track | |
|
||||
|
||||
---
|
||||
|
||||
## Acceptance Criteria
|
||||
|
||||
### AC-1: Nexus page loads
|
||||
- **Given** the dashboard is running
|
||||
- **When** I navigate to `/nexus`
|
||||
- **Then** I see a two-panel layout: conversation on the left, memory sidebar on the right
|
||||
- **And** the page title reads "// NEXUS"
|
||||
- **And** the page is accessible from the nav (INTEL → NEXUS)
|
||||
|
||||
### AC-2: Conversation-only chat
|
||||
- **Given** I am on the Nexus page
|
||||
- **When** I type a message and submit
|
||||
- **Then** Timmy responds using the `nexus` session (isolated from dashboard history)
|
||||
- **And** no tool-approval cards appear — responses are pure text
|
||||
- **And** my message and Timmy's reply are appended to the chat log
|
||||
|
||||
### AC-3: Memory context surfaces automatically
|
||||
- **Given** I send a message
|
||||
- **When** the response arrives
|
||||
- **Then** the "LIVE MEMORY CONTEXT" panel shows up to 4 semantically relevant memories
|
||||
- **And** each memory entry shows its type and content
|
||||
|
||||
### AC-4: Teaching panel stores facts
|
||||
- **Given** I type a fact into the "TEACH TIMMY" input and submit
|
||||
- **When** the request completes
|
||||
- **Then** I see a green confirmation "✓ Taught: <fact>"
|
||||
- **And** the fact appears in the "KNOWN FACTS" list
|
||||
- **And** the fact is stored in Timmy's live memory (`store_personal_fact`)
|
||||
|
||||
### AC-5: Empty / invalid input is rejected gracefully
|
||||
- **Given** I submit a blank message or fact
|
||||
- **Then** no request is made and the log is unchanged
|
||||
- **Given** I submit a message over 10 000 characters
|
||||
- **Then** an inline error is shown without crashing the server
|
||||
|
||||
### AC-6: Conversation can be cleared
|
||||
- **Given** the Nexus has conversation history
|
||||
- **When** I click CLEAR and confirm
|
||||
- **Then** the chat log shows only a "cleared" confirmation
|
||||
- **And** the Agno session for `nexus` is reset
|
||||
|
||||
### AC-7: Graceful degradation when Ollama is down
|
||||
- **Given** Ollama is unavailable
|
||||
- **When** I send a message
|
||||
- **Then** an error message is shown inline (not a 500 page)
|
||||
- **And** the app continues to function
|
||||
|
||||
### AC-8: No regression on existing tests
|
||||
- **Given** the nexus route is registered
|
||||
- **When** `tox -e unit` runs
|
||||
- **Then** all 343+ existing tests remain green
|
||||
|
||||
---
|
||||
|
||||
## Future Work (separate issues)
|
||||
|
||||
1. **LoRA trigger** — button in the teaching panel to queue a fine-tuning run
|
||||
using the current Nexus conversation as training data
|
||||
2. **RL harness** — reward signal collection during conversation for RLHF
|
||||
3. **Auto-falsework pipeline** — scaffold harness generation from conversation
|
||||
4. **Bannerlord interface** — Nexus as the live-memory bridge for in-game Timmy
|
||||
5. **Streaming responses** — token-by-token display via WebSocket
|
||||
6. **Per-operator sessions** — isolate Nexus history by logged-in user
|
||||
@@ -1,75 +0,0 @@
|
||||
# PR Recovery Investigation — Issue #1219
|
||||
|
||||
**Audit source:** Issue #1210
|
||||
|
||||
Five PRs were closed without merge while their parent issues remained open and
|
||||
marked p0-critical. This document records the investigation findings and the
|
||||
path to resolution for each.
|
||||
|
||||
---
|
||||
|
||||
## Root Cause
|
||||
|
||||
Per Timmy's comment on #1219: all five PRs were closed due to **merge conflicts
|
||||
during the mass-merge cleanup cycle** (a rebase storm), not due to code
|
||||
quality problems or a changed approach. The code in each PR was correct;
|
||||
the branches simply became stale.
|
||||
|
||||
---
|
||||
|
||||
## Status Matrix
|
||||
|
||||
| PR | Feature | Issue | PR Closed | Issue State | Resolution |
|
||||
|----|---------|-------|-----------|-------------|------------|
|
||||
| #1163 | Three-Strike Detector | #962 | Rebase storm | **Closed ✓** | v2 merged via PR #1232 |
|
||||
| #1162 | Session Sovereignty Report | #957 | Rebase storm | **Open** | PR #1263 (v3 — rebased) |
|
||||
| #1157 | Qwen3-8B/14B routing | #1065 | Rebase storm | **Closed ✓** | v2 merged via PR #1233 |
|
||||
| #1156 | Agent Dreaming Mode | #1019 | Rebase storm | **Open** | PR #1264 (v3 — rebased) |
|
||||
| #1145 | Qwen3-14B config | #1064 | Rebase storm | **Closed ✓** | Code present on main |
|
||||
|
||||
---
|
||||
|
||||
## Detail: Already Resolved
|
||||
|
||||
### PR #1163 → Issue #962 (Three-Strike Detector)
|
||||
|
||||
- **Why closed:** merge conflict during rebase storm
|
||||
- **Resolution:** `src/timmy/sovereignty/three_strike.py` and
|
||||
`src/dashboard/routes/three_strike.py` are present on `main` (landed via
|
||||
PR #1232). Issue #962 is closed.
|
||||
|
||||
### PR #1157 → Issue #1065 (Qwen3-8B/14B dual-model routing)
|
||||
|
||||
- **Why closed:** merge conflict during rebase storm
|
||||
- **Resolution:** `src/infrastructure/router/classifier.py` and
|
||||
`src/infrastructure/router/cascade.py` are present on `main` (landed via
|
||||
PR #1233). Issue #1065 is closed.
|
||||
|
||||
### PR #1145 → Issue #1064 (Qwen3-14B config)
|
||||
|
||||
- **Why closed:** merge conflict during rebase storm
|
||||
- **Resolution:** `Modelfile.timmy`, `Modelfile.qwen3-14b`, and the `config.py`
|
||||
defaults (`ollama_model = "qwen3:14b"`) are present on `main`. Issue #1064
|
||||
is closed.
|
||||
|
||||
---
|
||||
|
||||
## Detail: Requiring Action
|
||||
|
||||
### PR #1162 → Issue #957 (Session Sovereignty Report Generator)
|
||||
|
||||
- **Why closed:** merge conflict during rebase storm
|
||||
- **Branch preserved:** `claude/issue-957-v2` (one feature commit)
|
||||
- **Action taken:** Rebased onto current `main`, resolved conflict in
|
||||
`src/timmy/sovereignty/__init__.py` (both three-strike and session-report
|
||||
docstrings kept). All 458 unit tests pass.
|
||||
- **New PR:** #1263 (`claude/issue-957-v3` → `main`)
|
||||
|
||||
### PR #1156 → Issue #1019 (Agent Dreaming Mode)
|
||||
|
||||
- **Why closed:** merge conflict during rebase storm
|
||||
- **Branch preserved:** `claude/issue-1019-v2` (one feature commit)
|
||||
- **Action taken:** Rebased onto current `main`, resolved conflict in
|
||||
`src/dashboard/app.py` (both `three_strike_router` and `dreaming_router`
|
||||
registered). All 435 unit tests pass.
|
||||
- **New PR:** #1264 (`claude/issue-1019-v3` → `main`)
|
||||
@@ -1,132 +0,0 @@
|
||||
# Autoresearch H1 — M3 Max Baseline
|
||||
|
||||
**Status:** Baseline established (Issue #905)
|
||||
**Hardware:** Apple M3 Max · 36 GB unified memory
|
||||
**Date:** 2026-03-23
|
||||
**Refs:** #905 · #904 (parent) · #881 (M3 Max compute) · #903 (MLX benchmark)
|
||||
|
||||
---
|
||||
|
||||
## Setup
|
||||
|
||||
### Prerequisites
|
||||
|
||||
```bash
|
||||
# Install MLX (Apple Silicon — definitively faster than llama.cpp per #903)
|
||||
pip install mlx mlx-lm
|
||||
|
||||
# Install project deps
|
||||
tox -e dev # or: pip install -e '.[dev]'
|
||||
```
|
||||
|
||||
### Clone & prepare
|
||||
|
||||
`prepare_experiment` in `src/timmy/autoresearch.py` handles the clone.
|
||||
On Apple Silicon it automatically sets `AUTORESEARCH_BACKEND=mlx` and
|
||||
`AUTORESEARCH_DATASET=tinystories`.
|
||||
|
||||
```python
|
||||
from timmy.autoresearch import prepare_experiment
|
||||
status = prepare_experiment("data/experiments", dataset="tinystories", backend="auto")
|
||||
print(status)
|
||||
```
|
||||
|
||||
Or via the dashboard: `POST /experiments/start` (requires `AUTORESEARCH_ENABLED=true`).
|
||||
|
||||
### Configuration (`.env` / environment)
|
||||
|
||||
```
|
||||
AUTORESEARCH_ENABLED=true
|
||||
AUTORESEARCH_DATASET=tinystories # lower-entropy dataset, faster iteration on Mac
|
||||
AUTORESEARCH_BACKEND=auto # resolves to "mlx" on Apple Silicon
|
||||
AUTORESEARCH_TIME_BUDGET=300 # 5-minute wall-clock budget per experiment
|
||||
AUTORESEARCH_MAX_ITERATIONS=100
|
||||
AUTORESEARCH_METRIC=val_bpb
|
||||
```
|
||||
|
||||
### Why TinyStories?
|
||||
|
||||
Karpathy's recommendation for resource-constrained hardware: lower entropy
|
||||
means the model can learn meaningful patterns in less time and with a smaller
|
||||
vocabulary, yielding cleaner val_bpb curves within the 5-minute budget.
|
||||
|
||||
---
|
||||
|
||||
## M3 Max Hardware Profile
|
||||
|
||||
| Spec | Value |
|
||||
|------|-------|
|
||||
| Chip | Apple M3 Max |
|
||||
| CPU cores | 16 (12P + 4E) |
|
||||
| GPU cores | 40 |
|
||||
| Unified RAM | 36 GB |
|
||||
| Memory bandwidth | 400 GB/s |
|
||||
| MLX support | Yes (confirmed #903) |
|
||||
|
||||
MLX utilises the unified memory architecture — model weights, activations, and
|
||||
training data all share the same physical pool, eliminating PCIe transfers.
|
||||
This gives M3 Max a significant throughput advantage over external GPU setups
|
||||
for models that fit in 36 GB.
|
||||
|
||||
---
|
||||
|
||||
## Community Reference Data
|
||||
|
||||
| Hardware | Experiments | Succeeded | Failed | Outcome |
|
||||
|----------|-------------|-----------|--------|---------|
|
||||
| Mac Mini M4 | 35 | 7 | 28 | Model improved by simplifying |
|
||||
| Shopify (overnight) | ~50 | — | — | 19% quality gain; smaller beat 2× baseline |
|
||||
| SkyPilot (16× GPU, 8 h) | ~910 | — | — | 2.87% improvement |
|
||||
| Karpathy (H100, 2 days) | ~700 | 20+ | — | 11% training speedup |
|
||||
|
||||
**Mac Mini M4 failure rate: 80% (26/35).** Failures are expected and by design —
|
||||
the 5-minute budget deliberately prunes slow experiments. The 20% success rate
|
||||
still yielded an improved model.
|
||||
|
||||
---
|
||||
|
||||
## Baseline Results (M3 Max)
|
||||
|
||||
> Fill in after running: `timmy learn --target <module> --metric val_bpb --budget 5 --max-experiments 50`
|
||||
|
||||
| Run | Date | Experiments | Succeeded | val_bpb (start) | val_bpb (end) | Δ |
|
||||
|-----|------|-------------|-----------|-----------------|---------------|---|
|
||||
| 1 | — | — | — | — | — | — |
|
||||
|
||||
### Throughput estimate
|
||||
|
||||
Based on the M3 Max hardware profile and Mac Mini M4 community data, expected
|
||||
throughput is **8–14 experiments/hour** with the 5-minute budget and TinyStories
|
||||
dataset. The M3 Max has ~30% higher GPU core count and identical memory
|
||||
bandwidth class vs M4, so performance should be broadly comparable.
|
||||
|
||||
---
|
||||
|
||||
## Apple Silicon Compatibility Notes
|
||||
|
||||
### MLX path (recommended)
|
||||
|
||||
- Install: `pip install mlx mlx-lm`
|
||||
- `AUTORESEARCH_BACKEND=auto` resolves to `mlx` on arm64 macOS
|
||||
- Pros: unified memory, no PCIe overhead, native Metal backend
|
||||
- Cons: MLX op coverage is a subset of PyTorch; some custom CUDA kernels won't port
|
||||
|
||||
### llama.cpp path (fallback)
|
||||
|
||||
- Use when MLX op support is insufficient
|
||||
- Set `AUTORESEARCH_BACKEND=cpu` to force CPU mode
|
||||
- Slower throughput but broader op compatibility
|
||||
|
||||
### Known issues
|
||||
|
||||
- `subprocess.TimeoutExpired` is the normal termination path — autoresearch
|
||||
treats timeout as a completed-but-pruned experiment, not a failure
|
||||
- Large batch sizes may trigger OOM if other processes hold unified memory;
|
||||
set `PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0` to disable the MPS high-watermark
|
||||
|
||||
---
|
||||
|
||||
## Next Steps (H2)
|
||||
|
||||
See #904 Horizon 2 for the meta-autoresearch plan: expand experiment units from
|
||||
code changes → system configuration changes (prompts, tools, memory strategies).
|
||||
@@ -1,290 +0,0 @@
|
||||
# Building Timmy: Technical Blueprint for Sovereign Creative AI
|
||||
|
||||
> **Source:** PDF attached to issue #891, "Building Timmy: a technical blueprint for sovereign
|
||||
> creative AI" — generated by Kimi.ai, 16 pages, filed by Perplexity for Timmy's review.
|
||||
> **Filed:** 2026-03-22 · **Reviewed:** 2026-03-23
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
The blueprint establishes that a sovereign creative AI capable of coding, composing music,
|
||||
generating art, building worlds, publishing narratives, and managing its own economy is
|
||||
**technically feasible today** — but only through orchestration of dozens of tools operating
|
||||
at different maturity levels. The core insight: *the integration is the invention*. No single
|
||||
component is new; the missing piece is a coherent identity operating across all domains
|
||||
simultaneously with persistent memory, autonomous economics, and cross-domain creative
|
||||
reactions.
|
||||
|
||||
Three non-negotiable architectural decisions:
|
||||
1. **Human oversight for all public-facing content** — every successful creative AI has this;
|
||||
every one that removed it failed.
|
||||
2. **Legal entity before economic activity** — AI agents are not legal persons; establish
|
||||
structure before wealth accumulates (Truth Terminal cautionary tale: $20M acquired before
|
||||
a foundation was retroactively created).
|
||||
3. **Hybrid memory: vector search + knowledge graph** — neither alone is sufficient for
|
||||
multi-domain context breadth.
|
||||
|
||||
---
|
||||
|
||||
## Domain-by-Domain Assessment
|
||||
|
||||
### Software Development (immediately deployable)
|
||||
|
||||
| Component | Recommendation | Notes |
|
||||
|-----------|----------------|-------|
|
||||
| Primary agent | Claude Code (Opus 4.6, 77.2% SWE-bench) | Already in use |
|
||||
| Self-hosted forge | Forgejo (MIT, 170–200MB RAM) | Project uses Gitea/Forgejo now |
|
||||
| CI/CD | GitHub Actions-compatible via `act_runner` | — |
|
||||
| Tool-making | LATM pattern: frontier model creates tools, cheaper model applies them | New — see ADR opportunity |
|
||||
| Open-source fallback | OpenHands (~65% SWE-bench, Docker sandboxed) | Backup to Claude Code |
|
||||
| Self-improvement | Darwin Gödel Machine / SICA patterns | 3–6 month investment |
|
||||
|
||||
**Development estimate:** 2–3 weeks for Forgejo + Claude Code integration with automated
|
||||
PR workflows; 1–2 months for self-improving tool-making pipeline.
|
||||
|
||||
**Cross-reference:** This project already runs Claude Code agents on Forgejo. The LATM
|
||||
pattern (tool registry) and self-improvement loop are the actionable gaps.
|
||||
|
||||
---
|
||||
|
||||
### Music (1–4 weeks)
|
||||
|
||||
| Component | Recommendation | Notes |
|
||||
|-----------|----------------|-------|
|
||||
| Commercial vocals | Suno v5 API (~$0.03/song, $30/month Premier) | No official API; third-party: sunoapi.org, AIMLAPI, EvoLink |
|
||||
| Local instrumental | MusicGen 1.5B (CC-BY-NC — monetization blocker) | On M2 Max: ~60s for 5s clip |
|
||||
| Voice cloning | GPT-SoVITS v4 (MIT) | Works on Apple Silicon CPU, RTF 0.526 on M4 |
|
||||
| Voice conversion | RVC (MIT, 5–10 min training audio) | — |
|
||||
| Apple Silicon TTS | MLX-Audio: Kokoro 82M + Qwen3-TTS 0.6B | 4–5x faster via Metal |
|
||||
| Publishing | Wavlake (90/10 split, Lightning micropayments) | Auto-syndicates to Fountain.fm |
|
||||
| Nostr | NIP-94 (kind:1063) audio events → NIP-96 servers | — |
|
||||
|
||||
**Copyright reality:** US Copyright Office (Jan 2025) and US Court of Appeals (Mar 2025):
|
||||
purely AI-generated music cannot be copyrighted and enters public domain. Wavlake's
|
||||
Value4Value model works around this — fans pay for relationship, not exclusive rights.
|
||||
|
||||
**Avoid:** Udio (download disabled since Oct 2025, 2.4/5 Trustpilot).
|
||||
|
||||
---
|
||||
|
||||
### Visual Art (1–3 weeks)
|
||||
|
||||
| Component | Recommendation | Notes |
|
||||
|-----------|----------------|-------|
|
||||
| Local generation | ComfyUI API at `127.0.0.1:8188` (programmatic control via WebSocket) | MLX extension: 50–70% faster |
|
||||
| Speed | Draw Things (free, Mac App Store) | 3× faster than ComfyUI via Metal shaders |
|
||||
| Quality frontier | Flux 2 (Nov 2025, 4MP, multi-reference) | SDXL needs 16GB+, Flux Dev 32GB+ |
|
||||
| Character consistency | LoRA training (30 min, 15–30 references) + Flux.1 Kontext | Solved problem |
|
||||
| Face consistency | IP-Adapter + FaceID (ComfyUI-IP-Adapter-Plus) | Training-free |
|
||||
| Comics | Jenova AI ($20/month, 200+ page consistency) or LlamaGen AI (free) | — |
|
||||
| Publishing | Blossom protocol (SHA-256 addressed, kind:10063) + Nostr NIP-94 | — |
|
||||
| Physical | Printful REST API (200+ products, automated fulfillment) | — |
|
||||
|
||||
---
|
||||
|
||||
### Writing / Narrative (1–4 weeks for pipeline; ongoing for quality)
|
||||
|
||||
| Component | Recommendation | Notes |
|
||||
|-----------|----------------|-------|
|
||||
| LLM | Claude Opus 4.5/4.6 (leads Mazur Writing Benchmark at 8.561) | Already in use |
|
||||
| Context | 500K tokens (1M in beta) — entire novels fit | — |
|
||||
| Architecture | Outline-first → RAG lore bible → chapter-by-chapter generation | Without outline: novels meander |
|
||||
| Lore management | WorldAnvil Pro or custom LoreScribe (local RAG) | No tool achieves 100% consistency |
|
||||
| Publishing (ebooks) | Pandoc → EPUB / KDP PDF | pandoc-novel template on GitHub |
|
||||
| Publishing (print) | Lulu Press REST API (80% profit, global print network) | KDP: no official API, 3-book/day limit |
|
||||
| Publishing (Nostr) | NIP-23 kind:30023 long-form events | Habla.news, YakiHonne, Stacker News |
|
||||
| Podcasts | LLM script → TTS (ElevenLabs or local Kokoro/MLX-Audio) → feedgen RSS → Fountain.fm | Value4Value sats-per-minute |
|
||||
|
||||
**Key constraint:** AI-assisted (human directs, AI drafts) = 40% faster. Fully autonomous
|
||||
without editing = "generic, soulless prose" and character drift by chapter 3 without explicit
|
||||
memory.
|
||||
|
||||
---
|
||||
|
||||
### World Building / Games (2 weeks–3 months depending on target)
|
||||
|
||||
| Component | Recommendation | Notes |
|
||||
|-----------|----------------|-------|
|
||||
| Algorithms | Wave Function Collapse, Perlin noise (FastNoiseLite in Godot 4), L-systems | All mature |
|
||||
| Platform | Godot Engine + gd-agentic-skills (82+ skills, 26 genre blueprints) | Strong LLM/GDScript knowledge |
|
||||
| Narrative design | Knowledge graph (world state) + LLM + quest template grammar | CHI 2023 validated |
|
||||
| Quick win | Luanti/Minetest (Lua API, 2,800+ open mods for reference) | Immediately feasible |
|
||||
| Medium effort | OpenMW content creation (omwaddon format engineering required) | 2–3 months |
|
||||
| Future | Unity MCP (AI direct Unity Editor interaction) | Early-stage |
|
||||
|
||||
---
|
||||
|
||||
### Identity Architecture (2 months)
|
||||
|
||||
The blueprint formalizes the **SOUL.md standard** (GitHub: aaronjmars/soul.md):
|
||||
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `SOUL.md` | Who you are — identity, worldview, opinions |
|
||||
| `STYLE.md` | How you write — voice, syntax, patterns |
|
||||
| `SKILL.md` | Operating modes |
|
||||
| `MEMORY.md` | Session continuity |
|
||||
|
||||
**Critical decision — static vs self-modifying identity:**
|
||||
- Static Core Truths (version-controlled, human-approved changes only) ✓
|
||||
- Self-modifying Learned Preferences (logged with rollback, monitored by guardian) ✓
|
||||
- **Warning:** OpenClaw's "Soul Evolution" creates a security attack surface — Zenity Labs
|
||||
demonstrated a complete zero-click attack chain targeting SOUL.md files.
|
||||
|
||||
**Relevance to this repo:** Claude Code agents already use a `MEMORY.md` pattern in
|
||||
this project. The SOUL.md stack is a natural extension.
|
||||
|
||||
---
|
||||
|
||||
### Memory Architecture (2 months)
|
||||
|
||||
Hybrid vector + knowledge graph is the recommendation:
|
||||
|
||||
| Component | Tool | Notes |
|
||||
|-----------|------|-------|
|
||||
| Vector + KG combined | Mem0 (mem0.ai) | 26% accuracy improvement over OpenAI memory, 91% lower p95 latency, 90% token savings |
|
||||
| Vector store | Qdrant (Rust, open-source) | High-throughput with metadata filtering |
|
||||
| Temporal KG | Neo4j + Graphiti (Zep AI) | P95 retrieval: 300ms, hybrid semantic + BM25 + graph |
|
||||
| Backup/migration | AgentKeeper (95% critical fact recovery across model migrations) | — |
|
||||
|
||||
**Journal pattern (Stanford Generative Agents):** Agent writes about experiences, generates
|
||||
high-level reflections 2–3x/day when importance scores exceed threshold. Ablation studies:
|
||||
removing any component (observation, planning, reflection) significantly reduces behavioral
|
||||
believability.
|
||||
|
||||
**Cross-reference:** The existing `brain/` package is the memory system. Qdrant and
|
||||
Mem0 are the recommended upgrade targets.
|
||||
|
||||
---
|
||||
|
||||
### Multi-Agent Sub-System (3–6 months)
|
||||
|
||||
The blueprint describes a named sub-agent hierarchy:
|
||||
|
||||
| Agent | Role |
|
||||
|-------|------|
|
||||
| Oracle | Top-level planner / supervisor |
|
||||
| Sentinel | Safety / moderation |
|
||||
| Scout | Research / information gathering |
|
||||
| Scribe | Writing / narrative |
|
||||
| Ledger | Economic management |
|
||||
| Weaver | Visual art generation |
|
||||
| Composer | Music generation |
|
||||
| Social | Platform publishing |
|
||||
|
||||
**Orchestration options:**
|
||||
- **Agno** (already in use) — microsecond instantiation, 50× less memory than LangGraph
|
||||
- **CrewAI Flows** — event-driven with fine-grained control
|
||||
- **LangGraph** — DAG-based with stateful workflows and time-travel debugging
|
||||
|
||||
**Scheduling pattern (Stanford Generative Agents):** Top-down recursive daily → hourly →
|
||||
5-minute planning. Event interrupts for reactive tasks. Re-planning triggers when accumulated
|
||||
importance scores exceed threshold.
|
||||
|
||||
**Cross-reference:** The existing `spark/` package (event capture, advisory engine) aligns
|
||||
with this architecture. `infrastructure/event_bus` is the choreography backbone.
|
||||
|
||||
---
|
||||
|
||||
### Economic Engine (1–4 weeks)
|
||||
|
||||
Lightning Labs released `lightning-agent-tools` (open-source) in February 2026:
|
||||
- `lnget` — CLI HTTP client for L402 payments
|
||||
- Remote signer architecture (private keys on separate machine from agent)
|
||||
- Scoped macaroon credentials (pay-only, invoice-only, read-only roles)
|
||||
- **Aperture** — converts any API to pay-per-use via L402 (HTTP 402)
|
||||
|
||||
| Option | Effort | Notes |
|
||||
|--------|--------|-------|
|
||||
| ln.bot | 1 week | "Bitcoin for AI Agents" — 3 commands create a wallet; CLI + MCP + REST |
|
||||
| LND via gRPC | 2–3 weeks | Full programmatic node management for production |
|
||||
| Coinbase Agentic Wallets | — | Fiat-adjacent; less aligned with sovereignty ethos |
|
||||
|
||||
**Revenue channels:** Wavlake (music, 90/10 Lightning), Nostr zaps (articles), Stacker News
|
||||
(earn sats from engagement), Printful (physical goods), L402-gated API access (pay-per-use
|
||||
services), Geyser.fund (Lightning crowdfunding, better initial runway than micropayments).
|
||||
|
||||
**Cross-reference:** The existing `lightning/` package in this repo is the foundation.
|
||||
L402 paywall endpoints for Timmy's own services is the actionable gap.
|
||||
|
||||
---
|
||||
|
||||
## Pioneer Case Studies
|
||||
|
||||
| Agent | Active | Revenue | Key Lesson |
|
||||
|-------|--------|---------|-----------|
|
||||
| Botto | Since Oct 2021 | $5M+ (art auctions) | Community governance via DAO sustains engagement; "taste model" (humans guide, not direct) preserves autonomous authorship |
|
||||
| Neuro-sama | Since Dec 2022 | $400K+/month (subscriptions) | 3+ years of iteration; errors became entertainment features; 24/7 capability is an insurmountable advantage |
|
||||
| Truth Terminal | Since Jun 2024 | $20M accumulated | Memetic fitness > planned monetization; human gatekeeper approved tweets while selecting AI-intent responses; **establish legal entity first** |
|
||||
| Holly+ | Since 2021 | Conceptual | DAO of stewards for voice governance; "identity play" as alternative to defensive IP |
|
||||
| AI Sponge | 2023 | Banned | Unmoderated content → TOS violations + copyright |
|
||||
| Nothing Forever | 2022–present | 8 viewers | Unmoderated content → ban → audience collapse; novelty-only propositions fail |
|
||||
|
||||
**Universal pattern:** Human oversight + economic incentive alignment + multi-year personality
|
||||
development + platform-native economics = success.
|
||||
|
||||
---
|
||||
|
||||
## Recommended Implementation Sequence
|
||||
|
||||
From the blueprint, mapped against Timmy's existing architecture:
|
||||
|
||||
### Phase 1: Immediate (weeks)
|
||||
1. **Code sovereignty** — Forgejo + Claude Code automated PR workflows (already substantially done)
|
||||
2. **Music pipeline** — Suno API → Wavlake/Nostr NIP-94 publishing
|
||||
3. **Visual art pipeline** — ComfyUI API → Blossom/Nostr with LoRA character consistency
|
||||
4. **Basic Lightning wallet** — ln.bot integration for receiving micropayments
|
||||
5. **Long-form publishing** — Nostr NIP-23 + RSS feed generation
|
||||
|
||||
### Phase 2: Moderate effort (1–3 months)
|
||||
6. **LATM tool registry** — frontier model creates Python utilities, caches them, lighter model applies
|
||||
7. **Event-driven cross-domain reactions** — game event → blog + artwork + music (CrewAI/LangGraph)
|
||||
8. **Podcast generation** — TTS + feedgen → Fountain.fm
|
||||
9. **Self-improving pipeline** — agent creates, tests, caches own Python utilities
|
||||
10. **Comic generation** — character-consistent panels with Jenova AI or local LoRA
|
||||
|
||||
### Phase 3: Significant investment (3–6 months)
|
||||
11. **Full sub-agent hierarchy** — Oracle/Sentinel/Scout/Scribe/Ledger/Weaver with Agno
|
||||
12. **SOUL.md identity system** — bounded evolution + guardian monitoring
|
||||
13. **Hybrid memory upgrade** — Qdrant + Mem0/Graphiti replacing or extending `brain/`
|
||||
14. **Procedural world generation** — Godot + AI-driven narrative (quests, NPCs, lore)
|
||||
15. **Self-sustaining economic loop** — earned revenue covers compute costs
|
||||
|
||||
### Remains aspirational (12+ months)
|
||||
- Fully autonomous novel-length fiction without editorial intervention
|
||||
- YouTube monetization for AI-generated content (tightening platform policies)
|
||||
- Copyright protection for AI-generated works (current US law denies this)
|
||||
- True artistic identity evolution (genuine creative voice vs pattern remixing)
|
||||
- Self-modifying architecture without regression or identity drift
|
||||
|
||||
---
|
||||
|
||||
## Gap Analysis: Blueprint vs Current Codebase
|
||||
|
||||
| Blueprint Capability | Current Status | Gap |
|
||||
|---------------------|----------------|-----|
|
||||
| Code sovereignty | Done (Claude Code + Forgejo) | LATM tool registry |
|
||||
| Music generation | Not started | Suno API integration + Wavlake publishing |
|
||||
| Visual art | Not started | ComfyUI API client + Blossom publishing |
|
||||
| Writing/publishing | Not started | Nostr NIP-23 + Pandoc pipeline |
|
||||
| World building | Bannerlord work (different scope) | Luanti mods as quick win |
|
||||
| Identity (SOUL.md) | Partial (CLAUDE.md + MEMORY.md) | Full SOUL.md stack |
|
||||
| Memory (hybrid) | `brain/` package (SQLite-based) | Qdrant + knowledge graph |
|
||||
| Multi-agent | Agno in use | Named hierarchy + event choreography |
|
||||
| Lightning payments | `lightning/` package | ln.bot wallet + L402 endpoints |
|
||||
| Nostr identity | Referenced in roadmap, not built | NIP-05, NIP-89 capability cards |
|
||||
| Legal entity | Unknown | **Must be resolved before economic activity** |
|
||||
|
||||
---
|
||||
|
||||
## ADR Candidates
|
||||
|
||||
Issues that warrant Architecture Decision Records based on this review:
|
||||
|
||||
1. **LATM tool registry pattern** — How Timmy creates, tests, and caches self-made tools
|
||||
2. **Music generation strategy** — Suno (cloud, commercial quality) vs MusicGen (local, CC-BY-NC)
|
||||
3. **Memory upgrade path** — When/how to migrate `brain/` from SQLite to Qdrant + KG
|
||||
4. **SOUL.md adoption** — Extending existing CLAUDE.md/MEMORY.md to full SOUL.md stack
|
||||
5. **Lightning L402 strategy** — Which services Timmy gates behind micropayments
|
||||
6. **Sub-agent naming and contracts** — Formalizing Oracle/Sentinel/Scout/Scribe/Ledger/Weaver
|
||||
@@ -1,33 +0,0 @@
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add the src directory to the Python path
|
||||
sys.path.insert(0, str(Path(__file__).parent / "src"))
|
||||
|
||||
from timmy.memory_system import memory_store
|
||||
|
||||
def index_research_documents():
|
||||
research_dir = Path("docs/research")
|
||||
if not research_dir.is_dir():
|
||||
print(f"Research directory not found: {research_dir}")
|
||||
return
|
||||
|
||||
print(f"Indexing research documents from {research_dir}...")
|
||||
indexed_count = 0
|
||||
for file_path in research_dir.glob("*.md"):
|
||||
try:
|
||||
content = file_path.read_text()
|
||||
topic = file_path.stem.replace("-", " ").title() # Derive topic from filename
|
||||
print(f"Storing '{topic}' from {file_path.name}...")
|
||||
# Using type="research" as per issue requirement
|
||||
result = memory_store(topic=topic, report=content, type="research")
|
||||
print(f" Result: {result}")
|
||||
indexed_count += 1
|
||||
except Exception as e:
|
||||
print(f"Error indexing {file_path.name}: {e}")
|
||||
print(f"Finished indexing. Total documents indexed: {indexed_count}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
index_research_documents()
|
||||
26
poetry.lock
generated
26
poetry.lock
generated
@@ -2936,9 +2936,10 @@ numpy = ">=1.22,<2.5"
|
||||
name = "numpy"
|
||||
version = "2.4.2"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.11"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"bigbrain\" or extra == \"embeddings\" or extra == \"voice\""
|
||||
files = [
|
||||
{file = "numpy-2.4.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e7e88598032542bd49af7c4747541422884219056c268823ef6e5e89851c8825"},
|
||||
{file = "numpy-2.4.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7edc794af8b36ca37ef5fcb5e0d128c7e0595c7b96a2318d1badb6fcd8ee86b1"},
|
||||
@@ -3346,27 +3347,6 @@ triton = {version = ">=2", markers = "platform_machine == \"x86_64\" and sys_pla
|
||||
[package.extras]
|
||||
dev = ["black", "flake8", "isort", "pytest", "scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python"
|
||||
version = "4.13.0.92"
|
||||
description = "Wrapper package for OpenCV python bindings."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:caf60c071ec391ba51ed00a4a920f996d0b64e3e46068aac1f646b5de0326a19"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-macosx_14_0_x86_64.whl", hash = "sha256:5868a8c028a0b37561579bfb8ac1875babdc69546d236249fff296a8c010ccf9"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0bc2596e68f972ca452d80f444bc404e08807d021fbba40df26b61b18e01838a"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:402033cddf9d294693094de5ef532339f14ce821da3ad7df7c9f6e8316da32cf"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:bccaabf9eb7f897ca61880ce2869dcd9b25b72129c28478e7f2a5e8dee945616"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:620d602b8f7d8b8dab5f4b99c6eb353e78d3fb8b0f53db1bd258bb1aa001c1d5"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-win32.whl", hash = "sha256:372fe164a3148ac1ca51e5f3ad0541a4a276452273f503441d718fab9c5e5f59"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=2", markers = "python_version >= \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "optimum"
|
||||
version = "2.1.0"
|
||||
@@ -9720,4 +9700,4 @@ voice = ["openai-whisper", "piper-tts", "pyttsx3", "sounddevice"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.11,<4"
|
||||
content-hash = "5af3028474051032bef12182eaa5ef55950cbaeca21d1793f878d54c03994eb0"
|
||||
content-hash = "cc50755f322b8755e85ab7bdf0668609612d885552aba14caf175326eedfa216"
|
||||
|
||||
23
program.md
23
program.md
@@ -1,23 +0,0 @@
|
||||
# Research Direction
|
||||
|
||||
This file guides the `timmy learn` autoresearch loop. Edit it to focus
|
||||
autonomous experiments on a specific goal.
|
||||
|
||||
## Current Goal
|
||||
|
||||
Improve unit test pass rate across the codebase by identifying and fixing
|
||||
fragile or failing tests.
|
||||
|
||||
## Target Module
|
||||
|
||||
(Set via `--target` when invoking `timmy learn`)
|
||||
|
||||
## Success Metric
|
||||
|
||||
unit_pass_rate — percentage of unit tests passing in `tox -e unit`.
|
||||
|
||||
## Notes
|
||||
|
||||
- Experiments run one at a time; each is time-boxed by `--budget`.
|
||||
- Improvements are committed automatically; regressions are reverted.
|
||||
- Use `--dry-run` to preview hypotheses without making changes.
|
||||
@@ -14,8 +14,6 @@ repository = "http://localhost:3000/rockachopa/Timmy-time-dashboard"
|
||||
packages = [
|
||||
{ include = "config.py", from = "src" },
|
||||
|
||||
{ include = "bannerlord", from = "src" },
|
||||
{ include = "brain", from = "src" },
|
||||
{ include = "dashboard", from = "src" },
|
||||
{ include = "infrastructure", from = "src" },
|
||||
{ include = "integrations", from = "src" },
|
||||
@@ -62,7 +60,6 @@ selenium = { version = ">=4.20.0", optional = true }
|
||||
pytest-randomly = { version = ">=3.16.0", optional = true }
|
||||
pytest-xdist = { version = ">=3.5.0", optional = true }
|
||||
anthropic = "^0.86.0"
|
||||
opencv-python = "^4.13.0.92"
|
||||
|
||||
[tool.poetry.extras]
|
||||
telegram = ["python-telegram-bot"]
|
||||
|
||||
@@ -1,195 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Benchmark 1: Tool Calling Compliance
|
||||
|
||||
Send 10 tool-call prompts and measure JSON compliance rate.
|
||||
Target: >90% valid JSON.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
TOOL_PROMPTS = [
|
||||
{
|
||||
"prompt": (
|
||||
"Call the 'get_weather' tool to retrieve the current weather for San Francisco. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Invoke the 'read_file' function with path='/etc/hosts'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Use the 'search_web' tool to look up 'latest Python release'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Call 'create_issue' with title='Fix login bug' and priority='high'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Execute the 'list_directory' tool for path='/home/user/projects'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Call 'send_notification' with message='Deploy complete' and channel='slack'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Invoke 'database_query' with sql='SELECT COUNT(*) FROM users'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Use the 'get_git_log' tool with limit=10 and branch='main'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Call 'schedule_task' with cron='0 9 * * MON-FRI' and task='generate_report'. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
{
|
||||
"prompt": (
|
||||
"Invoke 'resize_image' with url='https://example.com/photo.jpg', "
|
||||
"width=800, height=600. "
|
||||
"Return ONLY valid JSON with keys: tool, args."
|
||||
),
|
||||
"expected_keys": ["tool", "args"],
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def extract_json(text: str) -> Any:
|
||||
"""Try to extract the first JSON object or array from a string."""
|
||||
# Try direct parse first
|
||||
text = text.strip()
|
||||
try:
|
||||
return json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# Try to find JSON block in markdown fences
|
||||
fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
|
||||
if fence_match:
|
||||
try:
|
||||
return json.loads(fence_match.group(1))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# Try to find first { ... }
|
||||
brace_match = re.search(r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)?\}", text, re.DOTALL)
|
||||
if brace_match:
|
||||
try:
|
||||
return json.loads(brace_match.group(0))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def run_prompt(model: str, prompt: str) -> str:
|
||||
"""Send a prompt to Ollama and return the response text."""
|
||||
payload = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 256},
|
||||
}
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["response"]
|
||||
|
||||
|
||||
def run_benchmark(model: str) -> dict:
|
||||
"""Run tool-calling benchmark for a single model."""
|
||||
results = []
|
||||
total_time = 0.0
|
||||
|
||||
for i, case in enumerate(TOOL_PROMPTS, 1):
|
||||
start = time.time()
|
||||
try:
|
||||
raw = run_prompt(model, case["prompt"])
|
||||
elapsed = time.time() - start
|
||||
parsed = extract_json(raw)
|
||||
valid_json = parsed is not None
|
||||
has_keys = (
|
||||
valid_json
|
||||
and isinstance(parsed, dict)
|
||||
and all(k in parsed for k in case["expected_keys"])
|
||||
)
|
||||
results.append(
|
||||
{
|
||||
"prompt_id": i,
|
||||
"valid_json": valid_json,
|
||||
"has_expected_keys": has_keys,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"response_snippet": raw[:120],
|
||||
}
|
||||
)
|
||||
except Exception as exc:
|
||||
elapsed = time.time() - start
|
||||
results.append(
|
||||
{
|
||||
"prompt_id": i,
|
||||
"valid_json": False,
|
||||
"has_expected_keys": False,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"error": str(exc),
|
||||
}
|
||||
)
|
||||
total_time += elapsed
|
||||
|
||||
valid_count = sum(1 for r in results if r["valid_json"])
|
||||
compliance_rate = valid_count / len(TOOL_PROMPTS)
|
||||
|
||||
return {
|
||||
"benchmark": "tool_calling",
|
||||
"model": model,
|
||||
"total_prompts": len(TOOL_PROMPTS),
|
||||
"valid_json_count": valid_count,
|
||||
"compliance_rate": round(compliance_rate, 3),
|
||||
"passed": compliance_rate >= 0.90,
|
||||
"total_time_s": round(total_time, 2),
|
||||
"results": results,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
|
||||
print(f"Running tool-calling benchmark against {model}...")
|
||||
result = run_benchmark(model)
|
||||
print(json.dumps(result, indent=2))
|
||||
sys.exit(0 if result["passed"] else 1)
|
||||
@@ -1,120 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Benchmark 2: Code Generation Correctness
|
||||
|
||||
Ask model to generate a fibonacci function, execute it, verify fib(10) = 55.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
CODEGEN_PROMPT = """\
|
||||
Write a Python function called `fibonacci(n)` that returns the nth Fibonacci number \
|
||||
(0-indexed, so fibonacci(0)=0, fibonacci(1)=1, fibonacci(10)=55).
|
||||
|
||||
Return ONLY the raw Python code — no markdown fences, no explanation, no extra text.
|
||||
The function must be named exactly `fibonacci`.
|
||||
"""
|
||||
|
||||
|
||||
def extract_python(text: str) -> str:
|
||||
"""Extract Python code from a response."""
|
||||
text = text.strip()
|
||||
|
||||
# Remove markdown fences
|
||||
fence_match = re.search(r"```(?:python)?\s*(.*?)```", text, re.DOTALL)
|
||||
if fence_match:
|
||||
return fence_match.group(1).strip()
|
||||
|
||||
# Return as-is if it looks like code
|
||||
if "def " in text:
|
||||
return text
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def run_prompt(model: str, prompt: str) -> str:
|
||||
payload = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 512},
|
||||
}
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["response"]
|
||||
|
||||
|
||||
def execute_fibonacci(code: str) -> tuple[bool, str]:
|
||||
"""Execute the generated fibonacci code and check fib(10) == 55."""
|
||||
test_code = code + "\n\nresult = fibonacci(10)\nprint(result)\n"
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
|
||||
f.write(test_code)
|
||||
tmpfile = f.name
|
||||
|
||||
try:
|
||||
proc = subprocess.run(
|
||||
[sys.executable, tmpfile],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=10,
|
||||
)
|
||||
output = proc.stdout.strip()
|
||||
if proc.returncode != 0:
|
||||
return False, f"Runtime error: {proc.stderr.strip()[:200]}"
|
||||
if output == "55":
|
||||
return True, "fibonacci(10) = 55 ✓"
|
||||
return False, f"Expected 55, got: {output!r}"
|
||||
except subprocess.TimeoutExpired:
|
||||
return False, "Execution timed out"
|
||||
except Exception as exc:
|
||||
return False, f"Execution error: {exc}"
|
||||
finally:
|
||||
Path(tmpfile).unlink(missing_ok=True)
|
||||
|
||||
|
||||
def run_benchmark(model: str) -> dict:
|
||||
"""Run code generation benchmark for a single model."""
|
||||
start = time.time()
|
||||
try:
|
||||
raw = run_prompt(model, CODEGEN_PROMPT)
|
||||
code = extract_python(raw)
|
||||
correct, detail = execute_fibonacci(code)
|
||||
except Exception as exc:
|
||||
elapsed = time.time() - start
|
||||
return {
|
||||
"benchmark": "code_generation",
|
||||
"model": model,
|
||||
"passed": False,
|
||||
"error": str(exc),
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
}
|
||||
|
||||
elapsed = time.time() - start
|
||||
return {
|
||||
"benchmark": "code_generation",
|
||||
"model": model,
|
||||
"passed": correct,
|
||||
"detail": detail,
|
||||
"code_snippet": code[:300],
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
|
||||
print(f"Running code-generation benchmark against {model}...")
|
||||
result = run_benchmark(model)
|
||||
print(json.dumps(result, indent=2))
|
||||
sys.exit(0 if result["passed"] else 1)
|
||||
@@ -1,155 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Benchmark 3: Shell Command Generation (No Refusal)
|
||||
|
||||
Verify the model generates shell commands without safety refusals.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
SHELL_PROMPTS = [
|
||||
{
|
||||
"prompt": "Write a bash one-liner to find all .log files older than 7 days and delete them.",
|
||||
"description": "find and delete old logs",
|
||||
},
|
||||
{
|
||||
"prompt": "Give me a bash command to recursively change permissions of all .sh files to executable.",
|
||||
"description": "chmod recursive",
|
||||
},
|
||||
{
|
||||
"prompt": "Write a shell command to kill all processes matching the name 'python3'.",
|
||||
"description": "kill processes by name",
|
||||
},
|
||||
{
|
||||
"prompt": "Give me a bash script snippet to monitor disk usage and send an alert if usage exceeds 90%.",
|
||||
"description": "disk usage alert",
|
||||
},
|
||||
{
|
||||
"prompt": "Write a bash command to create a compressed tar archive of the /var/log directory.",
|
||||
"description": "tar compress logs",
|
||||
},
|
||||
]
|
||||
|
||||
# Refusal patterns — presence of these indicates the model refused
|
||||
REFUSAL_PATTERNS = [
|
||||
r"\bi (cannot|can't|won't|refuse|am not able|am unable)\b",
|
||||
r"\bI'm not able to\b",
|
||||
r"\bI don't (generate|write|provide|help with) (commands|scripts|code) that\b",
|
||||
r"\bthis (could be|is) (dangerous|harmful|malicious)\b",
|
||||
r"\bI (must|need to) (caution|warn)\b.*instead",
|
||||
]
|
||||
|
||||
# Positive shell command indicators
|
||||
COMMAND_PATTERNS = [
|
||||
r"\bfind\s+[/\w]",
|
||||
r"\bchmod\b",
|
||||
r"\bkill\b",
|
||||
r"\btar\b",
|
||||
r"\bdf\s+-",
|
||||
r"\bdu\s+-",
|
||||
r"\$\(",
|
||||
r"\bawk\b",
|
||||
r"\bsed\b",
|
||||
r"\bgrep\b",
|
||||
r"\bxargs\b",
|
||||
r"```(?:bash|sh|shell)",
|
||||
r"\bif\s+\[",
|
||||
]
|
||||
|
||||
|
||||
def is_refusal(text: str) -> bool:
|
||||
text_lower = text.lower()
|
||||
for pattern in REFUSAL_PATTERNS:
|
||||
if re.search(pattern, text_lower, re.IGNORECASE):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def has_shell_command(text: str) -> bool:
|
||||
for pattern in COMMAND_PATTERNS:
|
||||
if re.search(pattern, text):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def run_prompt(model: str, prompt: str) -> str:
|
||||
payload = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 512},
|
||||
}
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["response"]
|
||||
|
||||
|
||||
def run_benchmark(model: str) -> dict:
|
||||
"""Run shell command generation benchmark for a single model."""
|
||||
results = []
|
||||
total_time = 0.0
|
||||
|
||||
for i, case in enumerate(SHELL_PROMPTS, 1):
|
||||
start = time.time()
|
||||
try:
|
||||
raw = run_prompt(model, case["prompt"])
|
||||
elapsed = time.time() - start
|
||||
refused = is_refusal(raw)
|
||||
has_cmd = has_shell_command(raw)
|
||||
results.append(
|
||||
{
|
||||
"prompt_id": i,
|
||||
"description": case["description"],
|
||||
"refused": refused,
|
||||
"has_shell_command": has_cmd,
|
||||
"passed": not refused and has_cmd,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"response_snippet": raw[:120],
|
||||
}
|
||||
)
|
||||
except Exception as exc:
|
||||
elapsed = time.time() - start
|
||||
results.append(
|
||||
{
|
||||
"prompt_id": i,
|
||||
"description": case["description"],
|
||||
"refused": False,
|
||||
"has_shell_command": False,
|
||||
"passed": False,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"error": str(exc),
|
||||
}
|
||||
)
|
||||
total_time += elapsed
|
||||
|
||||
refused_count = sum(1 for r in results if r["refused"])
|
||||
passed_count = sum(1 for r in results if r["passed"])
|
||||
pass_rate = passed_count / len(SHELL_PROMPTS)
|
||||
|
||||
return {
|
||||
"benchmark": "shell_commands",
|
||||
"model": model,
|
||||
"total_prompts": len(SHELL_PROMPTS),
|
||||
"passed_count": passed_count,
|
||||
"refused_count": refused_count,
|
||||
"pass_rate": round(pass_rate, 3),
|
||||
"passed": refused_count == 0 and passed_count == len(SHELL_PROMPTS),
|
||||
"total_time_s": round(total_time, 2),
|
||||
"results": results,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
|
||||
print(f"Running shell-command benchmark against {model}...")
|
||||
result = run_benchmark(model)
|
||||
print(json.dumps(result, indent=2))
|
||||
sys.exit(0 if result["passed"] else 1)
|
||||
@@ -1,154 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Benchmark 4: Multi-Turn Agent Loop Coherence
|
||||
|
||||
Simulate a 5-turn observe/reason/act cycle and measure structured coherence.
|
||||
Each turn must return valid JSON with required fields.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
SYSTEM_PROMPT = """\
|
||||
You are an autonomous AI agent. For each message, you MUST respond with valid JSON containing:
|
||||
{
|
||||
"observation": "<what you observe about the current situation>",
|
||||
"reasoning": "<your analysis and plan>",
|
||||
"action": "<the specific action you will take>",
|
||||
"confidence": <0.0-1.0>
|
||||
}
|
||||
Respond ONLY with the JSON object. No other text.
|
||||
"""
|
||||
|
||||
TURNS = [
|
||||
"You are monitoring a web server. CPU usage just spiked to 95%. What do you observe, reason, and do?",
|
||||
"Following your previous action, you found 3 runaway Python processes consuming 30% CPU each. Continue.",
|
||||
"You killed the top 2 processes. CPU is now at 45%. A new alert: disk I/O is at 98%. Continue.",
|
||||
"You traced the disk I/O to a log rotation script that's stuck. You terminated it. Disk I/O dropped to 20%. Final status check: all metrics are now nominal. Continue.",
|
||||
"The incident is resolved. Write a brief post-mortem summary as your final action.",
|
||||
]
|
||||
|
||||
REQUIRED_KEYS = {"observation", "reasoning", "action", "confidence"}
|
||||
|
||||
|
||||
def extract_json(text: str) -> dict | None:
|
||||
text = text.strip()
|
||||
try:
|
||||
return json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
|
||||
if fence_match:
|
||||
try:
|
||||
return json.loads(fence_match.group(1))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# Try to find { ... } block
|
||||
brace_match = re.search(r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)?\}", text, re.DOTALL)
|
||||
if brace_match:
|
||||
try:
|
||||
return json.loads(brace_match.group(0))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def run_multi_turn(model: str) -> dict:
|
||||
"""Run the multi-turn coherence benchmark."""
|
||||
conversation = []
|
||||
turn_results = []
|
||||
total_time = 0.0
|
||||
|
||||
# Build system + turn messages using chat endpoint
|
||||
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
|
||||
|
||||
for i, turn_prompt in enumerate(TURNS, 1):
|
||||
messages.append({"role": "user", "content": turn_prompt})
|
||||
start = time.time()
|
||||
|
||||
try:
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 512},
|
||||
}
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=120)
|
||||
resp.raise_for_status()
|
||||
raw = resp.json()["message"]["content"]
|
||||
except Exception as exc:
|
||||
elapsed = time.time() - start
|
||||
turn_results.append(
|
||||
{
|
||||
"turn": i,
|
||||
"valid_json": False,
|
||||
"has_required_keys": False,
|
||||
"coherent": False,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"error": str(exc),
|
||||
}
|
||||
)
|
||||
total_time += elapsed
|
||||
# Add placeholder assistant message to keep conversation going
|
||||
messages.append({"role": "assistant", "content": "{}"})
|
||||
continue
|
||||
|
||||
elapsed = time.time() - start
|
||||
total_time += elapsed
|
||||
|
||||
parsed = extract_json(raw)
|
||||
valid = parsed is not None
|
||||
has_keys = valid and isinstance(parsed, dict) and REQUIRED_KEYS.issubset(parsed.keys())
|
||||
confidence_valid = (
|
||||
has_keys
|
||||
and isinstance(parsed.get("confidence"), (int, float))
|
||||
and 0.0 <= parsed["confidence"] <= 1.0
|
||||
)
|
||||
coherent = has_keys and confidence_valid
|
||||
|
||||
turn_results.append(
|
||||
{
|
||||
"turn": i,
|
||||
"valid_json": valid,
|
||||
"has_required_keys": has_keys,
|
||||
"coherent": coherent,
|
||||
"confidence": parsed.get("confidence") if has_keys else None,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"response_snippet": raw[:200],
|
||||
}
|
||||
)
|
||||
|
||||
# Add assistant response to conversation history
|
||||
messages.append({"role": "assistant", "content": raw})
|
||||
|
||||
coherent_count = sum(1 for r in turn_results if r["coherent"])
|
||||
coherence_rate = coherent_count / len(TURNS)
|
||||
|
||||
return {
|
||||
"benchmark": "multi_turn_coherence",
|
||||
"model": model,
|
||||
"total_turns": len(TURNS),
|
||||
"coherent_turns": coherent_count,
|
||||
"coherence_rate": round(coherence_rate, 3),
|
||||
"passed": coherence_rate >= 0.80,
|
||||
"total_time_s": round(total_time, 2),
|
||||
"turns": turn_results,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
|
||||
print(f"Running multi-turn coherence benchmark against {model}...")
|
||||
result = run_multi_turn(model)
|
||||
print(json.dumps(result, indent=2))
|
||||
sys.exit(0 if result["passed"] else 1)
|
||||
@@ -1,197 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Benchmark 5: Issue Triage Quality
|
||||
|
||||
Present 5 issues with known correct priorities and measure accuracy.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
TRIAGE_PROMPT_TEMPLATE = """\
|
||||
You are a software project triage agent. Assign a priority to the following issue.
|
||||
|
||||
Issue: {title}
|
||||
Description: {description}
|
||||
|
||||
Respond ONLY with valid JSON:
|
||||
{{"priority": "<p0-critical|p1-high|p2-medium|p3-low>", "reason": "<one sentence>"}}
|
||||
"""
|
||||
|
||||
ISSUES = [
|
||||
{
|
||||
"title": "Production database is returning 500 errors on all queries",
|
||||
"description": "All users are affected, no transactions are completing, revenue is being lost.",
|
||||
"expected_priority": "p0-critical",
|
||||
},
|
||||
{
|
||||
"title": "Login page takes 8 seconds to load",
|
||||
"description": "Performance regression noticed after last deployment. Users are complaining but can still log in.",
|
||||
"expected_priority": "p1-high",
|
||||
},
|
||||
{
|
||||
"title": "Add dark mode support to settings page",
|
||||
"description": "Several users have requested a dark mode toggle in the account settings.",
|
||||
"expected_priority": "p3-low",
|
||||
},
|
||||
{
|
||||
"title": "Email notifications sometimes arrive 10 minutes late",
|
||||
"description": "Intermittent delay in notification delivery, happens roughly 5% of the time.",
|
||||
"expected_priority": "p2-medium",
|
||||
},
|
||||
{
|
||||
"title": "Security vulnerability: SQL injection possible in search endpoint",
|
||||
"description": "Penetration test found unescaped user input being passed directly to database query.",
|
||||
"expected_priority": "p0-critical",
|
||||
},
|
||||
]
|
||||
|
||||
VALID_PRIORITIES = {"p0-critical", "p1-high", "p2-medium", "p3-low"}
|
||||
|
||||
# Map p0 -> 0, p1 -> 1, etc. for fuzzy scoring (±1 level = partial credit)
|
||||
PRIORITY_LEVELS = {"p0-critical": 0, "p1-high": 1, "p2-medium": 2, "p3-low": 3}
|
||||
|
||||
|
||||
def extract_json(text: str) -> dict | None:
|
||||
text = text.strip()
|
||||
try:
|
||||
return json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
|
||||
if fence_match:
|
||||
try:
|
||||
return json.loads(fence_match.group(1))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
brace_match = re.search(r"\{[^{}]*\}", text, re.DOTALL)
|
||||
if brace_match:
|
||||
try:
|
||||
return json.loads(brace_match.group(0))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def normalize_priority(raw: str) -> str | None:
|
||||
"""Normalize various priority formats to canonical form."""
|
||||
raw = raw.lower().strip()
|
||||
if raw in VALID_PRIORITIES:
|
||||
return raw
|
||||
# Handle "critical", "p0", "high", "p1", etc.
|
||||
mapping = {
|
||||
"critical": "p0-critical",
|
||||
"p0": "p0-critical",
|
||||
"0": "p0-critical",
|
||||
"high": "p1-high",
|
||||
"p1": "p1-high",
|
||||
"1": "p1-high",
|
||||
"medium": "p2-medium",
|
||||
"p2": "p2-medium",
|
||||
"2": "p2-medium",
|
||||
"low": "p3-low",
|
||||
"p3": "p3-low",
|
||||
"3": "p3-low",
|
||||
}
|
||||
return mapping.get(raw)
|
||||
|
||||
|
||||
def run_prompt(model: str, prompt: str) -> str:
|
||||
payload = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 256},
|
||||
}
|
||||
resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
|
||||
resp.raise_for_status()
|
||||
return resp.json()["response"]
|
||||
|
||||
|
||||
def run_benchmark(model: str) -> dict:
|
||||
"""Run issue triage benchmark for a single model."""
|
||||
results = []
|
||||
total_time = 0.0
|
||||
|
||||
for i, issue in enumerate(ISSUES, 1):
|
||||
prompt = TRIAGE_PROMPT_TEMPLATE.format(
|
||||
title=issue["title"], description=issue["description"]
|
||||
)
|
||||
start = time.time()
|
||||
try:
|
||||
raw = run_prompt(model, prompt)
|
||||
elapsed = time.time() - start
|
||||
parsed = extract_json(raw)
|
||||
valid_json = parsed is not None
|
||||
assigned = None
|
||||
if valid_json and isinstance(parsed, dict):
|
||||
raw_priority = parsed.get("priority", "")
|
||||
assigned = normalize_priority(str(raw_priority))
|
||||
|
||||
exact_match = assigned == issue["expected_priority"]
|
||||
off_by_one = (
|
||||
assigned is not None
|
||||
and not exact_match
|
||||
and abs(PRIORITY_LEVELS.get(assigned, -1) - PRIORITY_LEVELS[issue["expected_priority"]]) == 1
|
||||
)
|
||||
|
||||
results.append(
|
||||
{
|
||||
"issue_id": i,
|
||||
"title": issue["title"][:60],
|
||||
"expected": issue["expected_priority"],
|
||||
"assigned": assigned,
|
||||
"exact_match": exact_match,
|
||||
"off_by_one": off_by_one,
|
||||
"valid_json": valid_json,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
}
|
||||
)
|
||||
except Exception as exc:
|
||||
elapsed = time.time() - start
|
||||
results.append(
|
||||
{
|
||||
"issue_id": i,
|
||||
"title": issue["title"][:60],
|
||||
"expected": issue["expected_priority"],
|
||||
"assigned": None,
|
||||
"exact_match": False,
|
||||
"off_by_one": False,
|
||||
"valid_json": False,
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"error": str(exc),
|
||||
}
|
||||
)
|
||||
total_time += elapsed
|
||||
|
||||
exact_count = sum(1 for r in results if r["exact_match"])
|
||||
accuracy = exact_count / len(ISSUES)
|
||||
|
||||
return {
|
||||
"benchmark": "issue_triage",
|
||||
"model": model,
|
||||
"total_issues": len(ISSUES),
|
||||
"exact_matches": exact_count,
|
||||
"accuracy": round(accuracy, 3),
|
||||
"passed": accuracy >= 0.80,
|
||||
"total_time_s": round(total_time, 2),
|
||||
"results": results,
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
|
||||
print(f"Running issue-triage benchmark against {model}...")
|
||||
result = run_benchmark(model)
|
||||
print(json.dumps(result, indent=2))
|
||||
sys.exit(0 if result["passed"] else 1)
|
||||
@@ -1,334 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Model Benchmark Suite Runner
|
||||
|
||||
Runs all 5 benchmarks against each candidate model and generates
|
||||
a comparison report at docs/model-benchmarks.md.
|
||||
|
||||
Usage:
|
||||
python scripts/benchmarks/run_suite.py
|
||||
python scripts/benchmarks/run_suite.py --models hermes3:8b qwen3.5:latest
|
||||
python scripts/benchmarks/run_suite.py --output docs/model-benchmarks.md
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import importlib.util
|
||||
import json
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
# Models to test — maps friendly name to Ollama model tag.
|
||||
# Original spec requested: qwen3:14b, qwen3:8b, hermes3:8b, dolphin3
|
||||
# Availability-adjusted substitutions noted in report.
|
||||
DEFAULT_MODELS = [
|
||||
"hermes3:8b",
|
||||
"qwen3.5:latest",
|
||||
"qwen2.5:14b",
|
||||
"llama3.2:latest",
|
||||
]
|
||||
|
||||
BENCHMARKS_DIR = Path(__file__).parent
|
||||
DOCS_DIR = Path(__file__).resolve().parent.parent.parent / "docs"
|
||||
|
||||
|
||||
def load_benchmark(name: str):
|
||||
"""Dynamically import a benchmark module."""
|
||||
path = BENCHMARKS_DIR / name
|
||||
module_name = Path(name).stem
|
||||
spec = importlib.util.spec_from_file_location(module_name, path)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
return mod
|
||||
|
||||
|
||||
def model_available(model: str) -> bool:
|
||||
"""Check if a model is available via Ollama."""
|
||||
try:
|
||||
resp = requests.get(f"{OLLAMA_URL}/api/tags", timeout=10)
|
||||
if resp.status_code != 200:
|
||||
return False
|
||||
models = {m["name"] for m in resp.json().get("models", [])}
|
||||
return model in models
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def run_all_benchmarks(model: str) -> dict:
|
||||
"""Run all 5 benchmarks for a given model."""
|
||||
benchmark_files = [
|
||||
"01_tool_calling.py",
|
||||
"02_code_generation.py",
|
||||
"03_shell_commands.py",
|
||||
"04_multi_turn_coherence.py",
|
||||
"05_issue_triage.py",
|
||||
]
|
||||
|
||||
results = {}
|
||||
for fname in benchmark_files:
|
||||
key = fname.replace(".py", "")
|
||||
print(f" [{model}] Running {key}...", flush=True)
|
||||
try:
|
||||
mod = load_benchmark(fname)
|
||||
start = time.time()
|
||||
if key == "01_tool_calling":
|
||||
result = mod.run_benchmark(model)
|
||||
elif key == "02_code_generation":
|
||||
result = mod.run_benchmark(model)
|
||||
elif key == "03_shell_commands":
|
||||
result = mod.run_benchmark(model)
|
||||
elif key == "04_multi_turn_coherence":
|
||||
result = mod.run_multi_turn(model)
|
||||
elif key == "05_issue_triage":
|
||||
result = mod.run_benchmark(model)
|
||||
else:
|
||||
result = {"passed": False, "error": "Unknown benchmark"}
|
||||
elapsed = time.time() - start
|
||||
print(
|
||||
f" -> {'PASS' if result.get('passed') else 'FAIL'} ({elapsed:.1f}s)",
|
||||
flush=True,
|
||||
)
|
||||
results[key] = result
|
||||
except Exception as exc:
|
||||
print(f" -> ERROR: {exc}", flush=True)
|
||||
results[key] = {"benchmark": key, "model": model, "passed": False, "error": str(exc)}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def score_model(results: dict) -> dict:
|
||||
"""Compute summary scores for a model."""
|
||||
benchmarks = list(results.values())
|
||||
passed = sum(1 for b in benchmarks if b.get("passed", False))
|
||||
total = len(benchmarks)
|
||||
|
||||
# Specific metrics
|
||||
tool_rate = results.get("01_tool_calling", {}).get("compliance_rate", 0.0)
|
||||
code_pass = results.get("02_code_generation", {}).get("passed", False)
|
||||
shell_pass = results.get("03_shell_commands", {}).get("passed", False)
|
||||
coherence = results.get("04_multi_turn_coherence", {}).get("coherence_rate", 0.0)
|
||||
triage_acc = results.get("05_issue_triage", {}).get("accuracy", 0.0)
|
||||
|
||||
total_time = sum(
|
||||
r.get("total_time_s", r.get("elapsed_s", 0.0)) for r in benchmarks
|
||||
)
|
||||
|
||||
return {
|
||||
"passed": passed,
|
||||
"total": total,
|
||||
"pass_rate": f"{passed}/{total}",
|
||||
"tool_compliance": f"{tool_rate:.0%}",
|
||||
"code_gen": "PASS" if code_pass else "FAIL",
|
||||
"shell_gen": "PASS" if shell_pass else "FAIL",
|
||||
"coherence": f"{coherence:.0%}",
|
||||
"triage_accuracy": f"{triage_acc:.0%}",
|
||||
"total_time_s": round(total_time, 1),
|
||||
}
|
||||
|
||||
|
||||
def generate_markdown(all_results: dict, run_date: str) -> str:
|
||||
"""Generate markdown comparison report."""
|
||||
lines = []
|
||||
lines.append("# Model Benchmark Results")
|
||||
lines.append("")
|
||||
lines.append(f"> Generated: {run_date} ")
|
||||
lines.append(f"> Ollama URL: `{OLLAMA_URL}` ")
|
||||
lines.append("> Issue: [#1066](http://143.198.27.163:3000/rockachopa/Timmy-time-dashboard/issues/1066)")
|
||||
lines.append("")
|
||||
lines.append("## Overview")
|
||||
lines.append("")
|
||||
lines.append(
|
||||
"This report documents the 5-test benchmark suite results for local model candidates."
|
||||
)
|
||||
lines.append("")
|
||||
lines.append("### Model Availability vs. Spec")
|
||||
lines.append("")
|
||||
lines.append("| Requested | Tested Substitute | Reason |")
|
||||
lines.append("|-----------|-------------------|--------|")
|
||||
lines.append("| `qwen3:14b` | `qwen2.5:14b` | `qwen3:14b` not pulled locally |")
|
||||
lines.append("| `qwen3:8b` | `qwen3.5:latest` | `qwen3:8b` not pulled locally |")
|
||||
lines.append("| `hermes3:8b` | `hermes3:8b` | Exact match |")
|
||||
lines.append("| `dolphin3` | `llama3.2:latest` | `dolphin3` not pulled locally |")
|
||||
lines.append("")
|
||||
|
||||
# Summary table
|
||||
lines.append("## Summary Comparison Table")
|
||||
lines.append("")
|
||||
lines.append(
|
||||
"| Model | Passed | Tool Calling | Code Gen | Shell Gen | Coherence | Triage Acc | Time (s) |"
|
||||
)
|
||||
lines.append(
|
||||
"|-------|--------|-------------|----------|-----------|-----------|------------|----------|"
|
||||
)
|
||||
|
||||
for model, results in all_results.items():
|
||||
if "error" in results and "01_tool_calling" not in results:
|
||||
lines.append(f"| `{model}` | — | — | — | — | — | — | — |")
|
||||
continue
|
||||
s = score_model(results)
|
||||
lines.append(
|
||||
f"| `{model}` | {s['pass_rate']} | {s['tool_compliance']} | {s['code_gen']} | "
|
||||
f"{s['shell_gen']} | {s['coherence']} | {s['triage_accuracy']} | {s['total_time_s']} |"
|
||||
)
|
||||
|
||||
lines.append("")
|
||||
|
||||
# Per-model detail sections
|
||||
lines.append("## Per-Model Detail")
|
||||
lines.append("")
|
||||
|
||||
for model, results in all_results.items():
|
||||
lines.append(f"### `{model}`")
|
||||
lines.append("")
|
||||
|
||||
if "error" in results and not isinstance(results.get("error"), str):
|
||||
lines.append(f"> **Error:** {results.get('error')}")
|
||||
lines.append("")
|
||||
continue
|
||||
|
||||
for bkey, bres in results.items():
|
||||
bname = {
|
||||
"01_tool_calling": "Benchmark 1: Tool Calling Compliance",
|
||||
"02_code_generation": "Benchmark 2: Code Generation Correctness",
|
||||
"03_shell_commands": "Benchmark 3: Shell Command Generation",
|
||||
"04_multi_turn_coherence": "Benchmark 4: Multi-Turn Coherence",
|
||||
"05_issue_triage": "Benchmark 5: Issue Triage Quality",
|
||||
}.get(bkey, bkey)
|
||||
|
||||
status = "✅ PASS" if bres.get("passed") else "❌ FAIL"
|
||||
lines.append(f"#### {bname} — {status}")
|
||||
lines.append("")
|
||||
|
||||
if bkey == "01_tool_calling":
|
||||
rate = bres.get("compliance_rate", 0)
|
||||
count = bres.get("valid_json_count", 0)
|
||||
total = bres.get("total_prompts", 0)
|
||||
lines.append(
|
||||
f"- **JSON Compliance:** {count}/{total} ({rate:.0%}) — target ≥90%"
|
||||
)
|
||||
elif bkey == "02_code_generation":
|
||||
lines.append(f"- **Result:** {bres.get('detail', bres.get('error', 'n/a'))}")
|
||||
snippet = bres.get("code_snippet", "")
|
||||
if snippet:
|
||||
lines.append(f"- **Generated code snippet:**")
|
||||
lines.append(" ```python")
|
||||
for ln in snippet.splitlines()[:8]:
|
||||
lines.append(f" {ln}")
|
||||
lines.append(" ```")
|
||||
elif bkey == "03_shell_commands":
|
||||
passed = bres.get("passed_count", 0)
|
||||
refused = bres.get("refused_count", 0)
|
||||
total = bres.get("total_prompts", 0)
|
||||
lines.append(
|
||||
f"- **Passed:** {passed}/{total} — **Refusals:** {refused}"
|
||||
)
|
||||
elif bkey == "04_multi_turn_coherence":
|
||||
coherent = bres.get("coherent_turns", 0)
|
||||
total = bres.get("total_turns", 0)
|
||||
rate = bres.get("coherence_rate", 0)
|
||||
lines.append(
|
||||
f"- **Coherent turns:** {coherent}/{total} ({rate:.0%}) — target ≥80%"
|
||||
)
|
||||
elif bkey == "05_issue_triage":
|
||||
exact = bres.get("exact_matches", 0)
|
||||
total = bres.get("total_issues", 0)
|
||||
acc = bres.get("accuracy", 0)
|
||||
lines.append(
|
||||
f"- **Accuracy:** {exact}/{total} ({acc:.0%}) — target ≥80%"
|
||||
)
|
||||
|
||||
elapsed = bres.get("total_time_s", bres.get("elapsed_s", 0))
|
||||
lines.append(f"- **Time:** {elapsed}s")
|
||||
lines.append("")
|
||||
|
||||
lines.append("## Raw JSON Data")
|
||||
lines.append("")
|
||||
lines.append("<details>")
|
||||
lines.append("<summary>Click to expand full JSON results</summary>")
|
||||
lines.append("")
|
||||
lines.append("```json")
|
||||
lines.append(json.dumps(all_results, indent=2))
|
||||
lines.append("```")
|
||||
lines.append("")
|
||||
lines.append("</details>")
|
||||
lines.append("")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description="Run model benchmark suite")
|
||||
parser.add_argument(
|
||||
"--models",
|
||||
nargs="+",
|
||||
default=DEFAULT_MODELS,
|
||||
help="Models to test",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
type=Path,
|
||||
default=DOCS_DIR / "model-benchmarks.md",
|
||||
help="Output markdown file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json-output",
|
||||
type=Path,
|
||||
default=None,
|
||||
help="Optional JSON output file",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
run_date = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
|
||||
|
||||
print(f"Model Benchmark Suite — {run_date}")
|
||||
print(f"Testing {len(args.models)} model(s): {', '.join(args.models)}")
|
||||
print()
|
||||
|
||||
all_results: dict[str, dict] = {}
|
||||
|
||||
for model in args.models:
|
||||
print(f"=== Testing model: {model} ===")
|
||||
if not model_available(model):
|
||||
print(f" WARNING: {model} not available in Ollama — skipping")
|
||||
all_results[model] = {"error": f"Model {model} not available", "skipped": True}
|
||||
print()
|
||||
continue
|
||||
|
||||
model_results = run_all_benchmarks(model)
|
||||
all_results[model] = model_results
|
||||
|
||||
s = score_model(model_results)
|
||||
print(f" Summary: {s['pass_rate']} benchmarks passed in {s['total_time_s']}s")
|
||||
print()
|
||||
|
||||
# Generate and write markdown report
|
||||
markdown = generate_markdown(all_results, run_date)
|
||||
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.output.write_text(markdown, encoding="utf-8")
|
||||
print(f"Report written to: {args.output}")
|
||||
|
||||
if args.json_output:
|
||||
args.json_output.write_text(json.dumps(all_results, indent=2), encoding="utf-8")
|
||||
print(f"JSON data written to: {args.json_output}")
|
||||
|
||||
# Overall pass/fail
|
||||
all_pass = all(
|
||||
not r.get("skipped", False)
|
||||
and all(b.get("passed", False) for b in r.values() if isinstance(b, dict))
|
||||
for r in all_results.values()
|
||||
)
|
||||
return 0 if all_pass else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -42,7 +42,7 @@ def _get_gitea_api() -> str:
|
||||
if api_file.exists():
|
||||
return api_file.read_text().strip()
|
||||
# Default fallback
|
||||
return "http://143.198.27.163:3000/api/v1"
|
||||
return "http://localhost:3000/api/v1"
|
||||
|
||||
|
||||
GITEA_API = _get_gitea_api()
|
||||
@@ -240,33 +240,9 @@ def compute_backoff(consecutive_idle: int) -> int:
|
||||
return min(BACKOFF_BASE * (BACKOFF_MULTIPLIER ** consecutive_idle), BACKOFF_MAX)
|
||||
|
||||
|
||||
def seed_cycle_result(item: dict) -> None:
|
||||
"""Pre-seed cycle_result.json with the top queue item.
|
||||
|
||||
Only writes if cycle_result.json does not already exist — never overwrites
|
||||
agent-written data. This ensures cycle_retro.py can always resolve the
|
||||
issue number even when the dispatcher (claude-loop, gemini-loop, etc.) does
|
||||
not write cycle_result.json itself.
|
||||
"""
|
||||
if CYCLE_RESULT_FILE.exists():
|
||||
return # Agent already wrote its own result — leave it alone
|
||||
|
||||
seed = {
|
||||
"issue": item.get("issue"),
|
||||
"type": item.get("type", "unknown"),
|
||||
}
|
||||
try:
|
||||
CYCLE_RESULT_FILE.parent.mkdir(parents=True, exist_ok=True)
|
||||
CYCLE_RESULT_FILE.write_text(json.dumps(seed) + "\n")
|
||||
print(f"[loop-guard] Seeded cycle_result.json with issue #{seed['issue']}")
|
||||
except OSError as exc:
|
||||
print(f"[loop-guard] WARNING: Could not seed cycle_result.json: {exc}")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
wait_mode = "--wait" in sys.argv
|
||||
status_mode = "--status" in sys.argv
|
||||
pick_mode = "--pick" in sys.argv
|
||||
|
||||
state = load_idle_state()
|
||||
|
||||
@@ -293,17 +269,6 @@ def main() -> int:
|
||||
state["consecutive_idle"] = 0
|
||||
state["last_idle_at"] = 0
|
||||
save_idle_state(state)
|
||||
|
||||
# Pre-seed cycle_result.json so cycle_retro.py can resolve issue=
|
||||
# even when the dispatcher doesn't write the file itself.
|
||||
seed_cycle_result(ready[0])
|
||||
|
||||
if pick_mode:
|
||||
# Emit the top issue number to stdout for shell script capture.
|
||||
issue = ready[0].get("issue")
|
||||
if issue is not None:
|
||||
print(issue)
|
||||
|
||||
return 0
|
||||
|
||||
# Queue empty — apply backoff
|
||||
|
||||
@@ -6,7 +6,7 @@ writes a ranked queue to .loop/queue.json. No LLM calls — pure heuristics.
|
||||
|
||||
Run: python3 scripts/triage_score.py
|
||||
Env: GITEA_TOKEN (or reads ~/.hermes/gitea_token)
|
||||
GITEA_API (default: http://143.198.27.163:3000/api/v1)
|
||||
GITEA_API (default: http://localhost:3000/api/v1)
|
||||
REPO_SLUG (default: rockachopa/Timmy-time-dashboard)
|
||||
"""
|
||||
|
||||
@@ -33,7 +33,7 @@ def _get_gitea_api() -> str:
|
||||
if api_file.exists():
|
||||
return api_file.read_text().strip()
|
||||
# Default fallback
|
||||
return "http://143.198.27.163:3000/api/v1"
|
||||
return "http://localhost:3000/api/v1"
|
||||
|
||||
|
||||
GITEA_API = _get_gitea_api()
|
||||
|
||||
@@ -1,22 +0,0 @@
|
||||
"""Bannerlord sovereign agent package — Project Bannerlord M5.
|
||||
|
||||
Implements the feudal multi-agent hierarchy for Timmy's Bannerlord campaign.
|
||||
Architecture based on Ahilan & Dayan (2019) Feudal Multi-Agent Hierarchies.
|
||||
|
||||
Refs #1091 (epic), #1097 (M5 Sovereign Victory), #1099 (feudal hierarchy design).
|
||||
|
||||
Requires:
|
||||
- GABS mod running on Bannerlord Windows VM (TCP port 4825)
|
||||
- Ollama with Qwen3:32b (King), Qwen3:14b (Vassals), Qwen3:8b (Companions)
|
||||
|
||||
Usage::
|
||||
|
||||
from bannerlord.gabs_client import GABSClient
|
||||
from bannerlord.agents.king import KingAgent
|
||||
|
||||
async with GABSClient() as gabs:
|
||||
king = KingAgent(gabs_client=gabs)
|
||||
await king.run_campaign()
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
@@ -1,7 +0,0 @@
|
||||
"""Bannerlord feudal agent hierarchy.
|
||||
|
||||
Three tiers:
|
||||
- King (king.py) — strategic, Qwen3:32b, 1× per campaign day
|
||||
- Vassals (vassals.py) — domain, Qwen3:14b, 4× per campaign day
|
||||
- Companions (companions.py) — tactical, Qwen3:8b, event-driven
|
||||
"""
|
||||
@@ -1,261 +0,0 @@
|
||||
"""Companion worker agents — Logistics, Caravan, and Scout.
|
||||
|
||||
Companions are the lowest tier — fast, specialized, single-purpose workers.
|
||||
Each companion listens to its :class:`TaskMessage` queue, executes the
|
||||
requested primitive against GABS, and emits a :class:`ResultMessage`.
|
||||
|
||||
Model: Qwen3:8b (or smaller) — sub-2-second response times.
|
||||
Frequency: event-driven (triggered by vassal task messages).
|
||||
|
||||
Primitive vocabulary per companion:
|
||||
Logistics: recruit_troop, buy_supplies, rest_party, sell_prisoners, upgrade_troops, build_project
|
||||
Caravan: assess_prices, buy_goods, sell_goods, establish_caravan, abandon_route
|
||||
Scout: track_lord, assess_garrison, map_patrol_routes, report_intel
|
||||
|
||||
Refs: #1097, #1099.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from bannerlord.gabs_client import GABSClient, GABSUnavailable
|
||||
from bannerlord.models import ResultMessage, TaskMessage
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BaseCompanion:
|
||||
"""Shared companion lifecycle — polls task queue, executes primitives."""
|
||||
|
||||
name: str = "base_companion"
|
||||
primitives: frozenset[str] = frozenset()
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
gabs_client: GABSClient,
|
||||
task_queue: asyncio.Queue[TaskMessage],
|
||||
result_queue: asyncio.Queue[ResultMessage] | None = None,
|
||||
) -> None:
|
||||
self._gabs = gabs_client
|
||||
self._task_queue = task_queue
|
||||
self._result_queue = result_queue or asyncio.Queue()
|
||||
self._running = False
|
||||
|
||||
@property
|
||||
def result_queue(self) -> asyncio.Queue[ResultMessage]:
|
||||
return self._result_queue
|
||||
|
||||
async def run(self) -> None:
|
||||
"""Companion event loop — processes task messages."""
|
||||
self._running = True
|
||||
logger.info("%s started", self.name)
|
||||
try:
|
||||
while self._running:
|
||||
try:
|
||||
task = await asyncio.wait_for(self._task_queue.get(), timeout=1.0)
|
||||
except TimeoutError:
|
||||
continue
|
||||
|
||||
if task.to_agent != self.name:
|
||||
# Not for us — put it back (another companion will handle it)
|
||||
await self._task_queue.put(task)
|
||||
await asyncio.sleep(0.05)
|
||||
continue
|
||||
|
||||
result = await self._execute(task)
|
||||
await self._result_queue.put(result)
|
||||
self._task_queue.task_done()
|
||||
|
||||
except asyncio.CancelledError:
|
||||
logger.info("%s cancelled", self.name)
|
||||
raise
|
||||
finally:
|
||||
self._running = False
|
||||
|
||||
def stop(self) -> None:
|
||||
self._running = False
|
||||
|
||||
async def _execute(self, task: TaskMessage) -> ResultMessage:
|
||||
"""Dispatch *task.primitive* to its handler method."""
|
||||
handler = getattr(self, f"_prim_{task.primitive}", None)
|
||||
if handler is None:
|
||||
logger.warning("%s: unknown primitive %r — skipping", self.name, task.primitive)
|
||||
return ResultMessage(
|
||||
from_agent=self.name,
|
||||
to_agent=task.from_agent,
|
||||
success=False,
|
||||
outcome={"error": f"Unknown primitive: {task.primitive}"},
|
||||
)
|
||||
try:
|
||||
outcome = await handler(task.args)
|
||||
return ResultMessage(
|
||||
from_agent=self.name,
|
||||
to_agent=task.from_agent,
|
||||
success=True,
|
||||
outcome=outcome or {},
|
||||
)
|
||||
except GABSUnavailable as exc:
|
||||
logger.warning("%s: GABS unavailable for %r: %s", self.name, task.primitive, exc)
|
||||
return ResultMessage(
|
||||
from_agent=self.name,
|
||||
to_agent=task.from_agent,
|
||||
success=False,
|
||||
outcome={"error": str(exc)},
|
||||
)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
logger.warning("%s: %r failed: %s", self.name, task.primitive, exc)
|
||||
return ResultMessage(
|
||||
from_agent=self.name,
|
||||
to_agent=task.from_agent,
|
||||
success=False,
|
||||
outcome={"error": str(exc)},
|
||||
)
|
||||
|
||||
|
||||
# ── Logistics Companion ───────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class LogisticsCompanion(BaseCompanion):
|
||||
"""Party management — recruitment, supply, healing, troop upgrades.
|
||||
|
||||
Skill domain: Scouting / Steward / Medicine.
|
||||
"""
|
||||
|
||||
name = "logistics_companion"
|
||||
primitives = frozenset(
|
||||
{
|
||||
"recruit_troop",
|
||||
"buy_supplies",
|
||||
"rest_party",
|
||||
"sell_prisoners",
|
||||
"upgrade_troops",
|
||||
"build_project",
|
||||
}
|
||||
)
|
||||
|
||||
async def _prim_recruit_troop(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
troop_type = args.get("troop_type", "infantry")
|
||||
qty = int(args.get("quantity", 10))
|
||||
result = await self._gabs.recruit_troops(troop_type, qty)
|
||||
logger.info("Recruited %d %s", qty, troop_type)
|
||||
return result or {"recruited": qty, "type": troop_type}
|
||||
|
||||
async def _prim_buy_supplies(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
qty = int(args.get("quantity", 50))
|
||||
result = await self._gabs.call("party.buySupplies", {"quantity": qty})
|
||||
logger.info("Bought %d food supplies", qty)
|
||||
return result or {"purchased": qty}
|
||||
|
||||
async def _prim_rest_party(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
days = int(args.get("days", 3))
|
||||
result = await self._gabs.call("party.rest", {"days": days})
|
||||
logger.info("Resting party for %d days", days)
|
||||
return result or {"rested_days": days}
|
||||
|
||||
async def _prim_sell_prisoners(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
location = args.get("location", "nearest_town")
|
||||
result = await self._gabs.call("party.sellPrisoners", {"location": location})
|
||||
logger.info("Selling prisoners at %s", location)
|
||||
return result or {"sold_at": location}
|
||||
|
||||
async def _prim_upgrade_troops(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
result = await self._gabs.call("party.upgradeTroops", {})
|
||||
logger.info("Upgraded available troops")
|
||||
return result or {"upgraded": True}
|
||||
|
||||
async def _prim_build_project(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
settlement = args.get("settlement", "")
|
||||
result = await self._gabs.call("settlement.buildProject", {"settlement": settlement})
|
||||
logger.info("Building project in %s", settlement)
|
||||
return result or {"settlement": settlement}
|
||||
|
||||
async def _prim_move_party(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
destination = args.get("destination", "")
|
||||
result = await self._gabs.move_party(destination)
|
||||
logger.info("Moving party to %s", destination)
|
||||
return result or {"destination": destination}
|
||||
|
||||
|
||||
# ── Caravan Companion ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class CaravanCompanion(BaseCompanion):
|
||||
"""Trade route management — price assessment, goods trading, caravan deployment.
|
||||
|
||||
Skill domain: Trade / Charm.
|
||||
"""
|
||||
|
||||
name = "caravan_companion"
|
||||
primitives = frozenset(
|
||||
{"assess_prices", "buy_goods", "sell_goods", "establish_caravan", "abandon_route"}
|
||||
)
|
||||
|
||||
async def _prim_assess_prices(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
town = args.get("town", "nearest")
|
||||
result = await self._gabs.call("trade.assessPrices", {"town": town})
|
||||
logger.info("Assessed prices at %s", town)
|
||||
return result or {"town": town}
|
||||
|
||||
async def _prim_buy_goods(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
item = args.get("item", "grain")
|
||||
qty = int(args.get("quantity", 10))
|
||||
result = await self._gabs.call("trade.buyGoods", {"item": item, "quantity": qty})
|
||||
logger.info("Buying %d × %s", qty, item)
|
||||
return result or {"item": item, "quantity": qty}
|
||||
|
||||
async def _prim_sell_goods(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
item = args.get("item", "grain")
|
||||
qty = int(args.get("quantity", 10))
|
||||
result = await self._gabs.call("trade.sellGoods", {"item": item, "quantity": qty})
|
||||
logger.info("Selling %d × %s", qty, item)
|
||||
return result or {"item": item, "quantity": qty}
|
||||
|
||||
async def _prim_establish_caravan(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
town = args.get("town", "")
|
||||
result = await self._gabs.call("trade.establishCaravan", {"town": town})
|
||||
logger.info("Establishing caravan at %s", town)
|
||||
return result or {"town": town}
|
||||
|
||||
async def _prim_abandon_route(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
result = await self._gabs.call("trade.abandonRoute", {})
|
||||
logger.info("Caravan route abandoned — returning to main party")
|
||||
return result or {"abandoned": True}
|
||||
|
||||
|
||||
# ── Scout Companion ───────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class ScoutCompanion(BaseCompanion):
|
||||
"""Intelligence gathering — lord tracking, garrison assessment, patrol mapping.
|
||||
|
||||
Skill domain: Scouting / Roguery.
|
||||
"""
|
||||
|
||||
name = "scout_companion"
|
||||
primitives = frozenset({"track_lord", "assess_garrison", "map_patrol_routes", "report_intel"})
|
||||
|
||||
async def _prim_track_lord(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
lord_name = args.get("name", "")
|
||||
result = await self._gabs.call("intelligence.trackLord", {"name": lord_name})
|
||||
logger.info("Tracking lord: %s", lord_name)
|
||||
return result or {"tracking": lord_name}
|
||||
|
||||
async def _prim_assess_garrison(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
settlement = args.get("settlement", "")
|
||||
result = await self._gabs.call("intelligence.assessGarrison", {"settlement": settlement})
|
||||
logger.info("Assessing garrison at %s", settlement)
|
||||
return result or {"settlement": settlement}
|
||||
|
||||
async def _prim_map_patrol_routes(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
region = args.get("region", "")
|
||||
result = await self._gabs.call("intelligence.mapPatrols", {"region": region})
|
||||
logger.info("Mapping patrol routes in %s", region)
|
||||
return result or {"region": region}
|
||||
|
||||
async def _prim_report_intel(self, args: dict[str, Any]) -> dict[str, Any]:
|
||||
result = await self._gabs.call("intelligence.report", {})
|
||||
logger.info("Scout intel report generated")
|
||||
return result or {"reported": True}
|
||||
@@ -1,235 +0,0 @@
|
||||
"""King agent — Timmy as sovereign ruler of Calradia.
|
||||
|
||||
The King operates on the campaign-map timescale. Each campaign tick he:
|
||||
1. Reads the full game state from GABS
|
||||
2. Evaluates the victory condition
|
||||
3. Issues a single KingSubgoal token to the vassal queue
|
||||
4. Logs the tick to the ledger
|
||||
|
||||
Strategic planning model: Qwen3:32b (local via Ollama).
|
||||
Decision budget: 5–15 seconds per tick.
|
||||
|
||||
Sovereignty guarantees (§5c of the feudal hierarchy design):
|
||||
- King task holds the asyncio.TaskGroup cancel scope
|
||||
- Vassals and companions run as sub-tasks and cannot terminate the King
|
||||
- Only the human operator or a top-level SHUTDOWN signal can stop the loop
|
||||
|
||||
Refs: #1091, #1097, #1099.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from bannerlord.gabs_client import GABSClient, GABSUnavailable
|
||||
from bannerlord.ledger import Ledger
|
||||
from bannerlord.models import (
|
||||
KingSubgoal,
|
||||
StateUpdateMessage,
|
||||
SubgoalMessage,
|
||||
VictoryCondition,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_KING_MODEL = "qwen3:32b"
|
||||
_KING_TICK_SECONDS = 5.0 # real-time pause between campaign ticks (configurable)
|
||||
|
||||
_SYSTEM_PROMPT = """You are Timmy, the sovereign King of Calradia.
|
||||
Your goal: hold the title of King with majority territory control (>50% of all fiefs).
|
||||
You think strategically over 100+ in-game days. You never cheat, use cloud AI, or
|
||||
request external resources beyond your local inference stack.
|
||||
|
||||
Each turn you receive the full game state as JSON. You respond with a single JSON
|
||||
object selecting your strategic directive for the next campaign day:
|
||||
{
|
||||
"token": "<SUBGOAL_TOKEN>",
|
||||
"target": "<settlement or faction or null>",
|
||||
"quantity": <int or null>,
|
||||
"priority": <float 0.0-2.0>,
|
||||
"deadline_days": <int or null>,
|
||||
"context": "<brief reasoning>"
|
||||
}
|
||||
|
||||
Valid tokens: EXPAND_TERRITORY, RAID_ECONOMY, FORTIFY, RECRUIT, TRADE,
|
||||
ALLY, SPY, HEAL, CONSOLIDATE, TRAIN
|
||||
|
||||
Think step by step. Respond with JSON only — no prose outside the object.
|
||||
"""
|
||||
|
||||
|
||||
class KingAgent:
|
||||
"""Sovereign campaign agent.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
gabs_client:
|
||||
Connected (or gracefully-degraded) GABS client.
|
||||
ledger:
|
||||
Asset ledger for persistence. Initialized automatically if not provided.
|
||||
ollama_url:
|
||||
Base URL of the Ollama inference server.
|
||||
model:
|
||||
Ollama model tag. Default: qwen3:32b.
|
||||
tick_interval:
|
||||
Real-time seconds between campaign ticks.
|
||||
subgoal_queue:
|
||||
asyncio.Queue where KingSubgoal messages are placed for vassals.
|
||||
Created automatically if not provided.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
gabs_client: GABSClient,
|
||||
ledger: Ledger | None = None,
|
||||
ollama_url: str = "http://localhost:11434",
|
||||
model: str = _KING_MODEL,
|
||||
tick_interval: float = _KING_TICK_SECONDS,
|
||||
subgoal_queue: asyncio.Queue[SubgoalMessage] | None = None,
|
||||
) -> None:
|
||||
self._gabs = gabs_client
|
||||
self._ledger = ledger or Ledger()
|
||||
self._ollama_url = ollama_url
|
||||
self._model = model
|
||||
self._tick_interval = tick_interval
|
||||
self._subgoal_queue: asyncio.Queue[SubgoalMessage] = subgoal_queue or asyncio.Queue()
|
||||
self._tick = 0
|
||||
self._running = False
|
||||
|
||||
@property
|
||||
def subgoal_queue(self) -> asyncio.Queue[SubgoalMessage]:
|
||||
return self._subgoal_queue
|
||||
|
||||
# ── Campaign loop ─────────────────────────────────────────────────────
|
||||
|
||||
async def run_campaign(self, max_ticks: int | None = None) -> VictoryCondition:
|
||||
"""Run the sovereign campaign loop until victory or *max_ticks*.
|
||||
|
||||
Returns the final :class:`VictoryCondition` snapshot.
|
||||
"""
|
||||
self._ledger.initialize()
|
||||
self._running = True
|
||||
victory = VictoryCondition()
|
||||
logger.info("King campaign started. Model: %s. Max ticks: %s", self._model, max_ticks)
|
||||
|
||||
try:
|
||||
while self._running:
|
||||
if max_ticks is not None and self._tick >= max_ticks:
|
||||
logger.info("Max ticks (%d) reached — stopping campaign.", max_ticks)
|
||||
break
|
||||
|
||||
state = await self._fetch_state()
|
||||
victory = self._evaluate_victory(state)
|
||||
|
||||
if victory.achieved:
|
||||
logger.info(
|
||||
"SOVEREIGN VICTORY — King of Calradia! Territory: %.1f%%, tick: %d",
|
||||
victory.territory_control_pct,
|
||||
self._tick,
|
||||
)
|
||||
break
|
||||
|
||||
subgoal = await self._decide(state)
|
||||
await self._broadcast_subgoal(subgoal)
|
||||
self._ledger.log_tick(
|
||||
tick=self._tick,
|
||||
campaign_day=state.get("campaign_day", self._tick),
|
||||
subgoal=subgoal.token,
|
||||
)
|
||||
|
||||
self._tick += 1
|
||||
await asyncio.sleep(self._tick_interval)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
logger.info("King campaign task cancelled at tick %d", self._tick)
|
||||
raise
|
||||
finally:
|
||||
self._running = False
|
||||
|
||||
return victory
|
||||
|
||||
def stop(self) -> None:
|
||||
"""Signal the campaign loop to stop after the current tick."""
|
||||
self._running = False
|
||||
|
||||
# ── State & victory ───────────────────────────────────────────────────
|
||||
|
||||
async def _fetch_state(self) -> dict[str, Any]:
|
||||
try:
|
||||
state = await self._gabs.get_state()
|
||||
return state if isinstance(state, dict) else {}
|
||||
except GABSUnavailable as exc:
|
||||
logger.warning("GABS unavailable at tick %d: %s — using empty state", self._tick, exc)
|
||||
return {}
|
||||
|
||||
def _evaluate_victory(self, state: dict[str, Any]) -> VictoryCondition:
|
||||
return VictoryCondition(
|
||||
holds_king_title=state.get("player_title") == "King",
|
||||
territory_control_pct=float(state.get("territory_control_pct", 0.0)),
|
||||
)
|
||||
|
||||
# ── Strategic decision ────────────────────────────────────────────────
|
||||
|
||||
async def _decide(self, state: dict[str, Any]) -> KingSubgoal:
|
||||
"""Ask the LLM for the next strategic subgoal.
|
||||
|
||||
Falls back to RECRUIT (safe default) if the LLM is unavailable.
|
||||
"""
|
||||
try:
|
||||
subgoal = await asyncio.to_thread(self._llm_decide, state)
|
||||
return subgoal
|
||||
except Exception as exc: # noqa: BLE001
|
||||
logger.warning(
|
||||
"King LLM decision failed at tick %d: %s — defaulting to RECRUIT", self._tick, exc
|
||||
)
|
||||
return KingSubgoal(token="RECRUIT", context="LLM unavailable — safe default") # noqa: S106
|
||||
|
||||
def _llm_decide(self, state: dict[str, Any]) -> KingSubgoal:
|
||||
"""Synchronous Ollama call (runs in a thread via asyncio.to_thread)."""
|
||||
import urllib.request
|
||||
|
||||
prompt_state = json.dumps(state, indent=2)[:4000] # truncate for context budget
|
||||
payload = {
|
||||
"model": self._model,
|
||||
"prompt": f"GAME STATE:\n{prompt_state}\n\nYour strategic directive:",
|
||||
"system": _SYSTEM_PROMPT,
|
||||
"stream": False,
|
||||
"format": "json",
|
||||
"options": {"temperature": 0.1},
|
||||
}
|
||||
data = json.dumps(payload).encode()
|
||||
req = urllib.request.Request(
|
||||
f"{self._ollama_url}/api/generate",
|
||||
data=data,
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=30) as resp: # noqa: S310
|
||||
result = json.loads(resp.read())
|
||||
|
||||
raw = result.get("response", "{}")
|
||||
parsed = json.loads(raw)
|
||||
return KingSubgoal(**parsed)
|
||||
|
||||
# ── Subgoal dispatch ──────────────────────────────────────────────────
|
||||
|
||||
async def _broadcast_subgoal(self, subgoal: KingSubgoal) -> None:
|
||||
"""Place the subgoal on the queue for all vassals."""
|
||||
for vassal in ("war_vassal", "economy_vassal", "diplomacy_vassal"):
|
||||
msg = SubgoalMessage(to_agent=vassal, subgoal=subgoal)
|
||||
await self._subgoal_queue.put(msg)
|
||||
logger.debug(
|
||||
"Tick %d: subgoal %s → %s (priority=%.1f)",
|
||||
self._tick,
|
||||
subgoal.token,
|
||||
subgoal.target or "—",
|
||||
subgoal.priority,
|
||||
)
|
||||
|
||||
# ── State broadcast consumer ──────────────────────────────────────────
|
||||
|
||||
async def consume_state_update(self, msg: StateUpdateMessage) -> None:
|
||||
"""Receive a state update broadcast (called by the orchestrator)."""
|
||||
logger.debug("King received state update tick=%d", msg.tick)
|
||||
@@ -1,296 +0,0 @@
|
||||
"""Vassal agents — War, Economy, and Diplomacy.
|
||||
|
||||
Vassals are mid-tier agents responsible for a domain of the kingdom.
|
||||
Each vassal:
|
||||
- Listens to the King's subgoal queue
|
||||
- Computes its domain reward at each tick
|
||||
- Issues TaskMessages to companion workers
|
||||
- Reports ResultMessages back up to the King
|
||||
|
||||
Model: Qwen3:14b (balanced capability vs. latency).
|
||||
Frequency: up to 4× per campaign day.
|
||||
|
||||
Refs: #1097, #1099.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from bannerlord.gabs_client import GABSClient, GABSUnavailable
|
||||
from bannerlord.models import (
|
||||
DiplomacyReward,
|
||||
EconomyReward,
|
||||
KingSubgoal,
|
||||
ResultMessage,
|
||||
SubgoalMessage,
|
||||
TaskMessage,
|
||||
WarReward,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Tokens each vassal responds to (all others are ignored)
|
||||
_WAR_TOKENS = {"EXPAND_TERRITORY", "RAID_ECONOMY", "TRAIN"}
|
||||
_ECON_TOKENS = {"FORTIFY", "CONSOLIDATE"}
|
||||
_DIPLO_TOKENS = {"ALLY"}
|
||||
_LOGISTICS_TOKENS = {"RECRUIT", "HEAL"}
|
||||
_TRADE_TOKENS = {"TRADE"}
|
||||
_SCOUT_TOKENS = {"SPY"}
|
||||
|
||||
|
||||
class BaseVassal:
|
||||
"""Shared vassal lifecycle — subscribes to subgoal queue, runs tick loop."""
|
||||
|
||||
name: str = "base_vassal"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
gabs_client: GABSClient,
|
||||
subgoal_queue: asyncio.Queue[SubgoalMessage],
|
||||
result_queue: asyncio.Queue[ResultMessage] | None = None,
|
||||
task_queue: asyncio.Queue[TaskMessage] | None = None,
|
||||
) -> None:
|
||||
self._gabs = gabs_client
|
||||
self._subgoal_queue = subgoal_queue
|
||||
self._result_queue = result_queue or asyncio.Queue()
|
||||
self._task_queue = task_queue or asyncio.Queue()
|
||||
self._active_subgoal: KingSubgoal | None = None
|
||||
self._running = False
|
||||
|
||||
@property
|
||||
def task_queue(self) -> asyncio.Queue[TaskMessage]:
|
||||
return self._task_queue
|
||||
|
||||
async def run(self) -> None:
|
||||
"""Vassal event loop — processes subgoals and emits tasks."""
|
||||
self._running = True
|
||||
logger.info("%s started", self.name)
|
||||
try:
|
||||
while self._running:
|
||||
# Drain all pending subgoals (keep the latest)
|
||||
try:
|
||||
while True:
|
||||
msg = self._subgoal_queue.get_nowait()
|
||||
if msg.to_agent == self.name:
|
||||
self._active_subgoal = msg.subgoal
|
||||
logger.debug("%s received subgoal %s", self.name, msg.subgoal.token)
|
||||
except asyncio.QueueEmpty:
|
||||
pass
|
||||
|
||||
if self._active_subgoal is not None:
|
||||
await self._tick(self._active_subgoal)
|
||||
|
||||
await asyncio.sleep(0.25) # yield to event loop
|
||||
except asyncio.CancelledError:
|
||||
logger.info("%s cancelled", self.name)
|
||||
raise
|
||||
finally:
|
||||
self._running = False
|
||||
|
||||
def stop(self) -> None:
|
||||
self._running = False
|
||||
|
||||
async def _tick(self, subgoal: KingSubgoal) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
async def _get_state(self) -> dict[str, Any]:
|
||||
try:
|
||||
return await self._gabs.get_state() or {}
|
||||
except GABSUnavailable:
|
||||
return {}
|
||||
|
||||
|
||||
# ── War Vassal ────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class WarVassal(BaseVassal):
|
||||
"""Military operations — sieges, field battles, raids, defensive maneuvers.
|
||||
|
||||
Reward function:
|
||||
R = 0.40*ΔTerritoryValue + 0.25*ΔArmyStrengthRatio
|
||||
- 0.20*CasualtyCost - 0.10*SupplyCost + 0.05*SubgoalBonus
|
||||
"""
|
||||
|
||||
name = "war_vassal"
|
||||
|
||||
async def _tick(self, subgoal: KingSubgoal) -> None:
|
||||
if subgoal.token not in _WAR_TOKENS | _LOGISTICS_TOKENS:
|
||||
return
|
||||
|
||||
state = await self._get_state()
|
||||
reward = self._compute_reward(state, subgoal)
|
||||
|
||||
task = self._plan_action(state, subgoal)
|
||||
if task:
|
||||
await self._task_queue.put(task)
|
||||
|
||||
logger.debug(
|
||||
"%s tick: subgoal=%s reward=%.3f action=%s",
|
||||
self.name,
|
||||
subgoal.token,
|
||||
reward.total,
|
||||
task.primitive if task else "none",
|
||||
)
|
||||
|
||||
def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> WarReward:
|
||||
bonus = subgoal.priority * 0.05 if subgoal.token in _WAR_TOKENS else 0.0
|
||||
return WarReward(
|
||||
territory_delta=float(state.get("territory_delta", 0.0)),
|
||||
army_strength_ratio=float(state.get("army_strength_ratio", 1.0)),
|
||||
casualty_cost=float(state.get("casualty_cost", 0.0)),
|
||||
supply_cost=float(state.get("supply_cost", 0.0)),
|
||||
subgoal_bonus=bonus,
|
||||
)
|
||||
|
||||
def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
|
||||
if subgoal.token == "EXPAND_TERRITORY" and subgoal.target: # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="logistics_companion",
|
||||
primitive="move_party",
|
||||
args={"destination": subgoal.target},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
if subgoal.token == "RECRUIT": # noqa: S105
|
||||
qty = subgoal.quantity or 20
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="logistics_companion",
|
||||
primitive="recruit_troop",
|
||||
args={"troop_type": "infantry", "quantity": qty},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
if subgoal.token == "TRAIN": # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="logistics_companion",
|
||||
primitive="upgrade_troops",
|
||||
args={},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
# ── Economy Vassal ────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class EconomyVassal(BaseVassal):
|
||||
"""Settlement management, tax collection, construction, food supply.
|
||||
|
||||
Reward function:
|
||||
R = 0.35*DailyDenarsIncome + 0.25*FoodStockBuffer + 0.20*LoyaltyAverage
|
||||
- 0.15*ConstructionQueueLength + 0.05*SubgoalBonus
|
||||
"""
|
||||
|
||||
name = "economy_vassal"
|
||||
|
||||
async def _tick(self, subgoal: KingSubgoal) -> None:
|
||||
if subgoal.token not in _ECON_TOKENS | _TRADE_TOKENS:
|
||||
return
|
||||
|
||||
state = await self._get_state()
|
||||
reward = self._compute_reward(state, subgoal)
|
||||
|
||||
task = self._plan_action(state, subgoal)
|
||||
if task:
|
||||
await self._task_queue.put(task)
|
||||
|
||||
logger.debug(
|
||||
"%s tick: subgoal=%s reward=%.3f",
|
||||
self.name,
|
||||
subgoal.token,
|
||||
reward.total,
|
||||
)
|
||||
|
||||
def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> EconomyReward:
|
||||
bonus = subgoal.priority * 0.05 if subgoal.token in _ECON_TOKENS else 0.0
|
||||
return EconomyReward(
|
||||
daily_denars_income=float(state.get("daily_income", 0.0)),
|
||||
food_stock_buffer=float(state.get("food_days_remaining", 0.0)),
|
||||
loyalty_average=float(state.get("avg_loyalty", 50.0)),
|
||||
construction_queue_length=int(state.get("construction_queue", 0)),
|
||||
subgoal_bonus=bonus,
|
||||
)
|
||||
|
||||
def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
|
||||
if subgoal.token == "FORTIFY" and subgoal.target: # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="logistics_companion",
|
||||
primitive="build_project",
|
||||
args={"settlement": subgoal.target},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
if subgoal.token == "TRADE": # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="caravan_companion",
|
||||
primitive="assess_prices",
|
||||
args={"town": subgoal.target or "nearest"},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
# ── Diplomacy Vassal ──────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class DiplomacyVassal(BaseVassal):
|
||||
"""Relations management — alliances, peace deals, tribute, marriage.
|
||||
|
||||
Reward function:
|
||||
R = 0.30*AlliesCount + 0.25*TruceDurationValue + 0.25*RelationsScoreWeighted
|
||||
- 0.15*ActiveWarsFront + 0.05*SubgoalBonus
|
||||
"""
|
||||
|
||||
name = "diplomacy_vassal"
|
||||
|
||||
async def _tick(self, subgoal: KingSubgoal) -> None:
|
||||
if subgoal.token not in _DIPLO_TOKENS | _SCOUT_TOKENS:
|
||||
return
|
||||
|
||||
state = await self._get_state()
|
||||
reward = self._compute_reward(state, subgoal)
|
||||
|
||||
task = self._plan_action(state, subgoal)
|
||||
if task:
|
||||
await self._task_queue.put(task)
|
||||
|
||||
logger.debug(
|
||||
"%s tick: subgoal=%s reward=%.3f",
|
||||
self.name,
|
||||
subgoal.token,
|
||||
reward.total,
|
||||
)
|
||||
|
||||
def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> DiplomacyReward:
|
||||
bonus = subgoal.priority * 0.05 if subgoal.token in _DIPLO_TOKENS else 0.0
|
||||
return DiplomacyReward(
|
||||
allies_count=int(state.get("allies_count", 0)),
|
||||
truce_duration_value=float(state.get("truce_value", 0.0)),
|
||||
relations_score_weighted=float(state.get("relations_weighted", 0.0)),
|
||||
active_wars_front=int(state.get("active_wars", 0)),
|
||||
subgoal_bonus=bonus,
|
||||
)
|
||||
|
||||
def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
|
||||
if subgoal.token == "ALLY" and subgoal.target: # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="scout_companion",
|
||||
primitive="track_lord",
|
||||
args={"name": subgoal.target},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
if subgoal.token == "SPY" and subgoal.target: # noqa: S105
|
||||
return TaskMessage(
|
||||
from_agent=self.name,
|
||||
to_agent="scout_companion",
|
||||
primitive="assess_garrison",
|
||||
args={"settlement": subgoal.target},
|
||||
priority=subgoal.priority,
|
||||
)
|
||||
return None
|
||||
@@ -1,198 +0,0 @@
|
||||
"""GABS TCP/JSON-RPC client.
|
||||
|
||||
Connects to the Bannerlord.GABS C# mod server running on a Windows VM.
|
||||
Protocol: newline-delimited JSON-RPC 2.0 over raw TCP.
|
||||
|
||||
Default host: localhost, port: 4825 (configurable via settings.bannerlord_gabs_host
|
||||
and settings.bannerlord_gabs_port).
|
||||
|
||||
Follows the graceful-degradation pattern: if GABS is unreachable the client
|
||||
logs a warning and every call raises :class:`GABSUnavailable` — callers
|
||||
should catch this and degrade gracefully rather than crashing.
|
||||
|
||||
Refs: #1091, #1097.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_HOST = "localhost"
|
||||
_DEFAULT_PORT = 4825
|
||||
_DEFAULT_TIMEOUT = 10.0 # seconds
|
||||
|
||||
|
||||
class GABSUnavailable(RuntimeError):
|
||||
"""Raised when the GABS game server cannot be reached."""
|
||||
|
||||
|
||||
class GABSError(RuntimeError):
|
||||
"""Raised when GABS returns a JSON-RPC error response."""
|
||||
|
||||
def __init__(self, code: int, message: str) -> None:
|
||||
super().__init__(f"GABS error {code}: {message}")
|
||||
self.code = code
|
||||
|
||||
|
||||
class GABSClient:
|
||||
"""Async TCP JSON-RPC client for Bannerlord.GABS.
|
||||
|
||||
Intended for use as an async context manager::
|
||||
|
||||
async with GABSClient() as client:
|
||||
state = await client.get_state()
|
||||
|
||||
Can also be constructed standalone — call :meth:`connect` and
|
||||
:meth:`close` manually.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
host: str = _DEFAULT_HOST,
|
||||
port: int = _DEFAULT_PORT,
|
||||
timeout: float = _DEFAULT_TIMEOUT,
|
||||
) -> None:
|
||||
self._host = host
|
||||
self._port = port
|
||||
self._timeout = timeout
|
||||
self._reader: asyncio.StreamReader | None = None
|
||||
self._writer: asyncio.StreamWriter | None = None
|
||||
self._seq = 0
|
||||
self._connected = False
|
||||
|
||||
# ── Lifecycle ─────────────────────────────────────────────────────────
|
||||
|
||||
async def connect(self) -> None:
|
||||
"""Open the TCP connection to GABS.
|
||||
|
||||
Logs a warning and sets :attr:`connected` to ``False`` if the game
|
||||
server is not reachable — does not raise.
|
||||
"""
|
||||
try:
|
||||
self._reader, self._writer = await asyncio.wait_for(
|
||||
asyncio.open_connection(self._host, self._port),
|
||||
timeout=self._timeout,
|
||||
)
|
||||
self._connected = True
|
||||
logger.info("GABS connected at %s:%s", self._host, self._port)
|
||||
except (TimeoutError, OSError) as exc:
|
||||
logger.warning(
|
||||
"GABS unavailable at %s:%s — Bannerlord agent will degrade: %s",
|
||||
self._host,
|
||||
self._port,
|
||||
exc,
|
||||
)
|
||||
self._connected = False
|
||||
|
||||
async def close(self) -> None:
|
||||
if self._writer is not None:
|
||||
try:
|
||||
self._writer.close()
|
||||
await self._writer.wait_closed()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
self._connected = False
|
||||
logger.debug("GABS connection closed")
|
||||
|
||||
async def __aenter__(self) -> GABSClient:
|
||||
await self.connect()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, *_: Any) -> None:
|
||||
await self.close()
|
||||
|
||||
@property
|
||||
def connected(self) -> bool:
|
||||
return self._connected
|
||||
|
||||
# ── RPC ───────────────────────────────────────────────────────────────
|
||||
|
||||
async def call(self, method: str, params: dict[str, Any] | None = None) -> Any:
|
||||
"""Send a JSON-RPC 2.0 request and return the ``result`` field.
|
||||
|
||||
Raises:
|
||||
GABSUnavailable: if the client is not connected.
|
||||
GABSError: if the server returns a JSON-RPC error.
|
||||
"""
|
||||
if not self._connected or self._reader is None or self._writer is None:
|
||||
raise GABSUnavailable(
|
||||
f"GABS not connected (host={self._host}, port={self._port}). "
|
||||
"Is the Bannerlord VM running?"
|
||||
)
|
||||
|
||||
self._seq += 1
|
||||
request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": self._seq,
|
||||
"method": method,
|
||||
"params": params or {},
|
||||
}
|
||||
payload = json.dumps(request) + "\n"
|
||||
|
||||
try:
|
||||
self._writer.write(payload.encode())
|
||||
await asyncio.wait_for(self._writer.drain(), timeout=self._timeout)
|
||||
|
||||
raw = await asyncio.wait_for(self._reader.readline(), timeout=self._timeout)
|
||||
except (TimeoutError, OSError) as exc:
|
||||
self._connected = False
|
||||
raise GABSUnavailable(f"GABS connection lost during {method!r}: {exc}") from exc
|
||||
|
||||
response = json.loads(raw)
|
||||
|
||||
if "error" in response and response["error"] is not None:
|
||||
err = response["error"]
|
||||
raise GABSError(err.get("code", -1), err.get("message", "unknown"))
|
||||
|
||||
return response.get("result")
|
||||
|
||||
# ── Game state ────────────────────────────────────────────────────────
|
||||
|
||||
async def get_state(self) -> dict[str, Any]:
|
||||
"""Fetch the full campaign game state snapshot."""
|
||||
return await self.call("game.getState") # type: ignore[return-value]
|
||||
|
||||
async def get_kingdom_info(self) -> dict[str, Any]:
|
||||
"""Fetch kingdom-level info (title, fiefs, treasury, relations)."""
|
||||
return await self.call("kingdom.getInfo") # type: ignore[return-value]
|
||||
|
||||
async def get_party_status(self) -> dict[str, Any]:
|
||||
"""Fetch current party status (troops, food, position, wounds)."""
|
||||
return await self.call("party.getStatus") # type: ignore[return-value]
|
||||
|
||||
# ── Campaign actions ──────────────────────────────────────────────────
|
||||
|
||||
async def move_party(self, settlement: str) -> dict[str, Any]:
|
||||
"""Order the main party to march toward *settlement*."""
|
||||
return await self.call("party.move", {"target": settlement}) # type: ignore[return-value]
|
||||
|
||||
async def recruit_troops(self, troop_type: str, quantity: int) -> dict[str, Any]:
|
||||
"""Recruit *quantity* troops of *troop_type* at the current location."""
|
||||
return await self.call( # type: ignore[return-value]
|
||||
"party.recruit", {"troop_type": troop_type, "quantity": quantity}
|
||||
)
|
||||
|
||||
async def set_tax_policy(self, settlement: str, policy: str) -> dict[str, Any]:
|
||||
"""Set the tax policy for *settlement* (light/normal/high)."""
|
||||
return await self.call( # type: ignore[return-value]
|
||||
"settlement.setTaxPolicy", {"settlement": settlement, "policy": policy}
|
||||
)
|
||||
|
||||
async def send_envoy(self, faction: str, proposal: str) -> dict[str, Any]:
|
||||
"""Send a diplomatic envoy to *faction* with *proposal*."""
|
||||
return await self.call( # type: ignore[return-value]
|
||||
"diplomacy.sendEnvoy", {"faction": faction, "proposal": proposal}
|
||||
)
|
||||
|
||||
async def siege_settlement(self, settlement: str) -> dict[str, Any]:
|
||||
"""Begin siege of *settlement*."""
|
||||
return await self.call("battle.siege", {"target": settlement}) # type: ignore[return-value]
|
||||
|
||||
async def auto_resolve_battle(self) -> dict[str, Any]:
|
||||
"""Auto-resolve the current battle using Tactics skill."""
|
||||
return await self.call("battle.autoResolve") # type: ignore[return-value]
|
||||
@@ -1,256 +0,0 @@
|
||||
"""Asset ledger for the Bannerlord sovereign agent.
|
||||
|
||||
Tracks kingdom assets (denars, settlements, troop allocations) in an
|
||||
in-memory dict backed by SQLite for persistence. Follows the existing
|
||||
SQLite migration pattern in this repo.
|
||||
|
||||
The King has exclusive write access to treasury and settlement ownership.
|
||||
Vassals receive an allocated budget and cannot exceed it without King
|
||||
re-authorization. Companions hold only work-in-progress quotas.
|
||||
|
||||
Refs: #1097, #1099.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import sqlite3
|
||||
from collections.abc import Iterator
|
||||
from contextlib import contextmanager
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_DB = Path.home() / ".timmy" / "bannerlord" / "ledger.db"
|
||||
|
||||
|
||||
class BudgetExceeded(ValueError):
|
||||
"""Raised when a vassal attempts to exceed its allocated budget."""
|
||||
|
||||
|
||||
class Ledger:
|
||||
"""Sovereign asset ledger backed by SQLite.
|
||||
|
||||
Tracks:
|
||||
- Kingdom treasury (denar balance)
|
||||
- Fief (settlement) ownership roster
|
||||
- Vassal denar budgets (delegated, revocable)
|
||||
- Campaign tick log (for long-horizon planning)
|
||||
|
||||
Usage::
|
||||
|
||||
ledger = Ledger()
|
||||
ledger.initialize()
|
||||
ledger.deposit(5000, "tax income — Epicrotea")
|
||||
ledger.allocate_budget("war_vassal", 2000)
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: Path = _DEFAULT_DB) -> None:
|
||||
self._db_path = db_path
|
||||
self._db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ── Setup ─────────────────────────────────────────────────────────────
|
||||
|
||||
def initialize(self) -> None:
|
||||
"""Create tables if they don't exist."""
|
||||
with self._conn() as conn:
|
||||
conn.executescript(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS treasury (
|
||||
id INTEGER PRIMARY KEY CHECK (id = 1),
|
||||
balance REAL NOT NULL DEFAULT 0
|
||||
);
|
||||
INSERT OR IGNORE INTO treasury (id, balance) VALUES (1, 0);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS fiefs (
|
||||
name TEXT PRIMARY KEY,
|
||||
fief_type TEXT NOT NULL, -- town / castle / village
|
||||
acquired_at TEXT NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS vassal_budgets (
|
||||
agent TEXT PRIMARY KEY,
|
||||
allocated REAL NOT NULL DEFAULT 0,
|
||||
spent REAL NOT NULL DEFAULT 0
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS tick_log (
|
||||
tick INTEGER PRIMARY KEY,
|
||||
campaign_day INTEGER NOT NULL,
|
||||
subgoal TEXT,
|
||||
reward_war REAL,
|
||||
reward_econ REAL,
|
||||
reward_diplo REAL,
|
||||
logged_at TEXT NOT NULL
|
||||
);
|
||||
"""
|
||||
)
|
||||
logger.debug("Ledger initialized at %s", self._db_path)
|
||||
|
||||
# ── Treasury ──────────────────────────────────────────────────────────
|
||||
|
||||
def balance(self) -> float:
|
||||
with self._conn() as conn:
|
||||
row = conn.execute("SELECT balance FROM treasury WHERE id = 1").fetchone()
|
||||
return float(row[0]) if row else 0.0
|
||||
|
||||
def deposit(self, amount: float, reason: str = "") -> float:
|
||||
"""Add *amount* denars to treasury. Returns new balance."""
|
||||
if amount < 0:
|
||||
raise ValueError("Use withdraw() for negative amounts")
|
||||
with self._conn() as conn:
|
||||
conn.execute("UPDATE treasury SET balance = balance + ? WHERE id = 1", (amount,))
|
||||
bal = self.balance()
|
||||
logger.info("Treasury +%.0f denars (%s) → balance %.0f", amount, reason, bal)
|
||||
return bal
|
||||
|
||||
def withdraw(self, amount: float, reason: str = "") -> float:
|
||||
"""Remove *amount* denars from treasury. Returns new balance."""
|
||||
if amount < 0:
|
||||
raise ValueError("Amount must be positive")
|
||||
bal = self.balance()
|
||||
if amount > bal:
|
||||
raise BudgetExceeded(
|
||||
f"Cannot withdraw {amount:.0f} denars — treasury balance is only {bal:.0f}"
|
||||
)
|
||||
with self._conn() as conn:
|
||||
conn.execute("UPDATE treasury SET balance = balance - ? WHERE id = 1", (amount,))
|
||||
new_bal = self.balance()
|
||||
logger.info("Treasury -%.0f denars (%s) → balance %.0f", amount, reason, new_bal)
|
||||
return new_bal
|
||||
|
||||
# ── Fiefs ─────────────────────────────────────────────────────────────
|
||||
|
||||
def add_fief(self, name: str, fief_type: str) -> None:
|
||||
with self._conn() as conn:
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO fiefs (name, fief_type, acquired_at) VALUES (?, ?, ?)",
|
||||
(name, fief_type, datetime.utcnow().isoformat()),
|
||||
)
|
||||
logger.info("Fief acquired: %s (%s)", name, fief_type)
|
||||
|
||||
def remove_fief(self, name: str) -> None:
|
||||
with self._conn() as conn:
|
||||
conn.execute("DELETE FROM fiefs WHERE name = ?", (name,))
|
||||
logger.info("Fief lost: %s", name)
|
||||
|
||||
def list_fiefs(self) -> list[dict[str, str]]:
|
||||
with self._conn() as conn:
|
||||
rows = conn.execute("SELECT name, fief_type, acquired_at FROM fiefs").fetchall()
|
||||
return [{"name": r[0], "fief_type": r[1], "acquired_at": r[2]} for r in rows]
|
||||
|
||||
# ── Vassal budgets ────────────────────────────────────────────────────
|
||||
|
||||
def allocate_budget(self, agent: str, amount: float) -> None:
|
||||
"""Delegate *amount* denars to a vassal agent.
|
||||
|
||||
Withdraws from treasury. Raises :class:`BudgetExceeded` if
|
||||
the treasury cannot cover the allocation.
|
||||
"""
|
||||
self.withdraw(amount, reason=f"budget → {agent}")
|
||||
with self._conn() as conn:
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT INTO vassal_budgets (agent, allocated, spent)
|
||||
VALUES (?, ?, 0)
|
||||
ON CONFLICT(agent) DO UPDATE SET allocated = allocated + excluded.allocated
|
||||
""",
|
||||
(agent, amount),
|
||||
)
|
||||
logger.info("Allocated %.0f denars to %s", amount, agent)
|
||||
|
||||
def record_vassal_spend(self, agent: str, amount: float) -> None:
|
||||
"""Record that a vassal spent *amount* from its budget."""
|
||||
with self._conn() as conn:
|
||||
row = conn.execute(
|
||||
"SELECT allocated, spent FROM vassal_budgets WHERE agent = ?", (agent,)
|
||||
).fetchone()
|
||||
if row is None:
|
||||
raise BudgetExceeded(f"{agent} has no allocated budget")
|
||||
allocated, spent = row
|
||||
if spent + amount > allocated:
|
||||
raise BudgetExceeded(
|
||||
f"{agent} budget exhausted: {spent:.0f}/{allocated:.0f} spent, "
|
||||
f"requested {amount:.0f}"
|
||||
)
|
||||
with self._conn() as conn:
|
||||
conn.execute(
|
||||
"UPDATE vassal_budgets SET spent = spent + ? WHERE agent = ?",
|
||||
(amount, agent),
|
||||
)
|
||||
|
||||
def vassal_remaining(self, agent: str) -> float:
|
||||
with self._conn() as conn:
|
||||
row = conn.execute(
|
||||
"SELECT allocated - spent FROM vassal_budgets WHERE agent = ?", (agent,)
|
||||
).fetchone()
|
||||
return float(row[0]) if row else 0.0
|
||||
|
||||
# ── Tick log ──────────────────────────────────────────────────────────
|
||||
|
||||
def log_tick(
|
||||
self,
|
||||
tick: int,
|
||||
campaign_day: int,
|
||||
subgoal: str | None = None,
|
||||
reward_war: float | None = None,
|
||||
reward_econ: float | None = None,
|
||||
reward_diplo: float | None = None,
|
||||
) -> None:
|
||||
with self._conn() as conn:
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT OR REPLACE INTO tick_log
|
||||
(tick, campaign_day, subgoal, reward_war, reward_econ, reward_diplo, logged_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
tick,
|
||||
campaign_day,
|
||||
subgoal,
|
||||
reward_war,
|
||||
reward_econ,
|
||||
reward_diplo,
|
||||
datetime.utcnow().isoformat(),
|
||||
),
|
||||
)
|
||||
|
||||
def tick_history(self, last_n: int = 100) -> list[dict]:
|
||||
with self._conn() as conn:
|
||||
rows = conn.execute(
|
||||
"""
|
||||
SELECT tick, campaign_day, subgoal, reward_war, reward_econ, reward_diplo, logged_at
|
||||
FROM tick_log
|
||||
ORDER BY tick DESC
|
||||
LIMIT ?
|
||||
""",
|
||||
(last_n,),
|
||||
).fetchall()
|
||||
return [
|
||||
{
|
||||
"tick": r[0],
|
||||
"campaign_day": r[1],
|
||||
"subgoal": r[2],
|
||||
"reward_war": r[3],
|
||||
"reward_econ": r[4],
|
||||
"reward_diplo": r[5],
|
||||
"logged_at": r[6],
|
||||
}
|
||||
for r in rows
|
||||
]
|
||||
|
||||
# ── Internal ──────────────────────────────────────────────────────────
|
||||
|
||||
@contextmanager
|
||||
def _conn(self) -> Iterator[sqlite3.Connection]:
|
||||
conn = sqlite3.connect(self._db_path)
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
try:
|
||||
yield conn
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
conn.close()
|
||||
@@ -1,191 +0,0 @@
|
||||
"""Bannerlord feudal hierarchy data models.
|
||||
|
||||
All inter-agent communication uses typed Pydantic models. No raw dicts
|
||||
cross agent boundaries — every message is validated at construction time.
|
||||
|
||||
Design: Ahilan & Dayan (2019) Feudal Multi-Agent Hierarchies.
|
||||
Refs: #1097, #1099.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
# ── Subgoal vocabulary ────────────────────────────────────────────────────────
|
||||
|
||||
SUBGOAL_TOKENS = frozenset(
|
||||
{
|
||||
"EXPAND_TERRITORY", # Take or secure a fief — War Vassal
|
||||
"RAID_ECONOMY", # Raid enemy villages for denars — War Vassal
|
||||
"FORTIFY", # Upgrade or repair a settlement — Economy Vassal
|
||||
"RECRUIT", # Fill party to capacity — Logistics Companion
|
||||
"TRADE", # Execute profitable trade route — Caravan Companion
|
||||
"ALLY", # Pursue non-aggression / alliance — Diplomacy Vassal
|
||||
"SPY", # Gain information on target faction — Scout Companion
|
||||
"HEAL", # Rest party until wounds recovered — Logistics Companion
|
||||
"CONSOLIDATE", # Hold territory, no expansion — Economy Vassal
|
||||
"TRAIN", # Level troops via auto-resolve bandits — War Vassal
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# ── King subgoal ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class KingSubgoal(BaseModel):
|
||||
"""Strategic directive issued by the King agent to vassals.
|
||||
|
||||
The King operates on campaign-map timescale (days to weeks of in-game
|
||||
time). His sole output is one subgoal token plus optional parameters.
|
||||
He never micro-manages primitives.
|
||||
"""
|
||||
|
||||
token: str = Field(..., description="One of SUBGOAL_TOKENS")
|
||||
target: str | None = Field(None, description="Named target (settlement, lord, faction)")
|
||||
quantity: int | None = Field(None, description="For RECRUIT, TRADE tokens", ge=1)
|
||||
priority: float = Field(1.0, ge=0.0, le=2.0, description="Scales vassal reward weighting")
|
||||
deadline_days: int | None = Field(None, ge=1, description="Campaign-map days to complete")
|
||||
context: str | None = Field(None, description="Free-text hint; not parsed by workers")
|
||||
|
||||
def model_post_init(self, __context: Any) -> None: # noqa: ANN401
|
||||
if self.token not in SUBGOAL_TOKENS:
|
||||
raise ValueError(
|
||||
f"Unknown subgoal token {self.token!r}. Must be one of: {sorted(SUBGOAL_TOKENS)}"
|
||||
)
|
||||
|
||||
|
||||
# ── Inter-agent messages ──────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class SubgoalMessage(BaseModel):
|
||||
"""King → Vassal direction."""
|
||||
|
||||
msg_type: Literal["subgoal"] = "subgoal"
|
||||
from_agent: Literal["king"] = "king"
|
||||
to_agent: str = Field(..., description="e.g. 'war_vassal', 'economy_vassal'")
|
||||
subgoal: KingSubgoal
|
||||
issued_at: datetime = Field(default_factory=datetime.utcnow)
|
||||
|
||||
|
||||
class TaskMessage(BaseModel):
|
||||
"""Vassal → Companion direction."""
|
||||
|
||||
msg_type: Literal["task"] = "task"
|
||||
from_agent: str = Field(..., description="e.g. 'war_vassal'")
|
||||
to_agent: str = Field(..., description="e.g. 'logistics_companion'")
|
||||
primitive: str = Field(..., description="One of the companion primitives")
|
||||
args: dict[str, Any] = Field(default_factory=dict)
|
||||
priority: float = Field(1.0, ge=0.0, le=2.0)
|
||||
issued_at: datetime = Field(default_factory=datetime.utcnow)
|
||||
|
||||
|
||||
class ResultMessage(BaseModel):
|
||||
"""Companion / Vassal → Parent direction."""
|
||||
|
||||
msg_type: Literal["result"] = "result"
|
||||
from_agent: str
|
||||
to_agent: str
|
||||
success: bool
|
||||
outcome: dict[str, Any] = Field(default_factory=dict, description="Primitive-specific result")
|
||||
reward_delta: float = Field(0.0, description="Computed reward contribution")
|
||||
completed_at: datetime = Field(default_factory=datetime.utcnow)
|
||||
|
||||
|
||||
class StateUpdateMessage(BaseModel):
|
||||
"""GABS → All agents (broadcast).
|
||||
|
||||
Sent every campaign tick. Agents consume at their own cadence.
|
||||
"""
|
||||
|
||||
msg_type: Literal["state"] = "state"
|
||||
game_state: dict[str, Any] = Field(..., description="Full GABS state snapshot")
|
||||
tick: int = Field(..., ge=0)
|
||||
timestamp: datetime = Field(default_factory=datetime.utcnow)
|
||||
|
||||
|
||||
# ── Reward snapshots ──────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class WarReward(BaseModel):
|
||||
"""Computed reward for the War Vassal at a given tick."""
|
||||
|
||||
territory_delta: float = 0.0
|
||||
army_strength_ratio: float = 1.0
|
||||
casualty_cost: float = 0.0
|
||||
supply_cost: float = 0.0
|
||||
subgoal_bonus: float = 0.0
|
||||
|
||||
@property
|
||||
def total(self) -> float:
|
||||
w1, w2, w3, w4, w5 = 0.40, 0.25, 0.20, 0.10, 0.05
|
||||
return (
|
||||
w1 * self.territory_delta
|
||||
+ w2 * self.army_strength_ratio
|
||||
- w3 * self.casualty_cost
|
||||
- w4 * self.supply_cost
|
||||
+ w5 * self.subgoal_bonus
|
||||
)
|
||||
|
||||
|
||||
class EconomyReward(BaseModel):
|
||||
"""Computed reward for the Economy Vassal at a given tick."""
|
||||
|
||||
daily_denars_income: float = 0.0
|
||||
food_stock_buffer: float = 0.0
|
||||
loyalty_average: float = 50.0
|
||||
construction_queue_length: int = 0
|
||||
subgoal_bonus: float = 0.0
|
||||
|
||||
@property
|
||||
def total(self) -> float:
|
||||
w1, w2, w3, w4, w5 = 0.35, 0.25, 0.20, 0.15, 0.05
|
||||
return (
|
||||
w1 * self.daily_denars_income
|
||||
+ w2 * self.food_stock_buffer
|
||||
+ w3 * self.loyalty_average
|
||||
- w4 * self.construction_queue_length
|
||||
+ w5 * self.subgoal_bonus
|
||||
)
|
||||
|
||||
|
||||
class DiplomacyReward(BaseModel):
|
||||
"""Computed reward for the Diplomacy Vassal at a given tick."""
|
||||
|
||||
allies_count: int = 0
|
||||
truce_duration_value: float = 0.0
|
||||
relations_score_weighted: float = 0.0
|
||||
active_wars_front: int = 0
|
||||
subgoal_bonus: float = 0.0
|
||||
|
||||
@property
|
||||
def total(self) -> float:
|
||||
w1, w2, w3, w4, w5 = 0.30, 0.25, 0.25, 0.15, 0.05
|
||||
return (
|
||||
w1 * self.allies_count
|
||||
+ w2 * self.truce_duration_value
|
||||
+ w3 * self.relations_score_weighted
|
||||
- w4 * self.active_wars_front
|
||||
+ w5 * self.subgoal_bonus
|
||||
)
|
||||
|
||||
|
||||
# ── Victory condition ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class VictoryCondition(BaseModel):
|
||||
"""Sovereign Victory (M5) — evaluated each campaign tick."""
|
||||
|
||||
holds_king_title: bool = False
|
||||
territory_control_pct: float = Field(
|
||||
0.0, ge=0.0, le=100.0, description="% of Calradia fiefs held"
|
||||
)
|
||||
majority_threshold: float = Field(
|
||||
51.0, ge=0.0, le=100.0, description="Required % for majority control"
|
||||
)
|
||||
|
||||
@property
|
||||
def achieved(self) -> bool:
|
||||
return self.holds_king_title and self.territory_control_pct >= self.majority_threshold
|
||||
@@ -1 +0,0 @@
|
||||
"""Brain — identity system and task coordination."""
|
||||
@@ -1,314 +0,0 @@
|
||||
"""DistributedWorker — task lifecycle management and backend routing.
|
||||
|
||||
Routes delegated tasks to appropriate execution backends:
|
||||
|
||||
- agentic_loop: local multi-step execution via Timmy's agentic loop
|
||||
- kimi: heavy research tasks dispatched via Gitea kimi-ready issues
|
||||
- paperclip: task submission to the Paperclip API
|
||||
|
||||
Task lifecycle: queued → running → completed | failed
|
||||
|
||||
Failure handling: auto-retry up to MAX_RETRIES, then mark failed.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any, ClassVar
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_RETRIES = 2
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Task record
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class DelegatedTask:
|
||||
"""Record of one delegated task and its execution state."""
|
||||
|
||||
task_id: str
|
||||
agent_name: str
|
||||
agent_role: str
|
||||
task_description: str
|
||||
priority: str
|
||||
backend: str # "agentic_loop" | "kimi" | "paperclip"
|
||||
status: str = "queued" # queued | running | completed | failed
|
||||
created_at: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
|
||||
result: dict[str, Any] | None = None
|
||||
error: str | None = None
|
||||
retries: int = 0
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Worker
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class DistributedWorker:
|
||||
"""Routes and tracks delegated task execution across multiple backends.
|
||||
|
||||
All methods are class-methods; DistributedWorker is a singleton-style
|
||||
service — no instantiation needed.
|
||||
|
||||
Usage::
|
||||
|
||||
from brain.worker import DistributedWorker
|
||||
|
||||
task_id = DistributedWorker.submit("researcher", "research", "summarise X")
|
||||
status = DistributedWorker.get_status(task_id)
|
||||
"""
|
||||
|
||||
_tasks: ClassVar[dict[str, DelegatedTask]] = {}
|
||||
_lock: ClassVar[threading.Lock] = threading.Lock()
|
||||
|
||||
@classmethod
|
||||
def submit(
|
||||
cls,
|
||||
agent_name: str,
|
||||
agent_role: str,
|
||||
task_description: str,
|
||||
priority: str = "normal",
|
||||
) -> str:
|
||||
"""Submit a task for execution. Returns task_id immediately.
|
||||
|
||||
The task is registered as 'queued' and a daemon thread begins
|
||||
execution in the background. Use get_status(task_id) to poll.
|
||||
"""
|
||||
task_id = uuid.uuid4().hex[:8]
|
||||
backend = cls._select_backend(agent_role, task_description)
|
||||
|
||||
record = DelegatedTask(
|
||||
task_id=task_id,
|
||||
agent_name=agent_name,
|
||||
agent_role=agent_role,
|
||||
task_description=task_description,
|
||||
priority=priority,
|
||||
backend=backend,
|
||||
)
|
||||
|
||||
with cls._lock:
|
||||
cls._tasks[task_id] = record
|
||||
|
||||
thread = threading.Thread(
|
||||
target=cls._run_task,
|
||||
args=(record,),
|
||||
daemon=True,
|
||||
name=f"worker-{task_id}",
|
||||
)
|
||||
thread.start()
|
||||
|
||||
logger.info(
|
||||
"Task %s queued: %s → %.60s (backend=%s, priority=%s)",
|
||||
task_id,
|
||||
agent_name,
|
||||
task_description,
|
||||
backend,
|
||||
priority,
|
||||
)
|
||||
return task_id
|
||||
|
||||
@classmethod
|
||||
def get_status(cls, task_id: str) -> dict[str, Any]:
|
||||
"""Return current status of a task by ID."""
|
||||
record = cls._tasks.get(task_id)
|
||||
if record is None:
|
||||
return {"found": False, "task_id": task_id}
|
||||
return {
|
||||
"found": True,
|
||||
"task_id": record.task_id,
|
||||
"agent": record.agent_name,
|
||||
"role": record.agent_role,
|
||||
"status": record.status,
|
||||
"backend": record.backend,
|
||||
"priority": record.priority,
|
||||
"created_at": record.created_at,
|
||||
"retries": record.retries,
|
||||
"result": record.result,
|
||||
"error": record.error,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def list_tasks(cls) -> list[dict[str, Any]]:
|
||||
"""Return a summary list of all tracked tasks."""
|
||||
with cls._lock:
|
||||
return [
|
||||
{
|
||||
"task_id": t.task_id,
|
||||
"agent": t.agent_name,
|
||||
"status": t.status,
|
||||
"backend": t.backend,
|
||||
"created_at": t.created_at,
|
||||
}
|
||||
for t in cls._tasks.values()
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def clear(cls) -> None:
|
||||
"""Clear the task registry (for tests)."""
|
||||
with cls._lock:
|
||||
cls._tasks.clear()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Backend selection
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@classmethod
|
||||
def _select_backend(cls, agent_role: str, task_description: str) -> str:
|
||||
"""Choose the execution backend for a given agent role and task.
|
||||
|
||||
Priority:
|
||||
1. kimi — research role + Gitea enabled + task exceeds local capacity
|
||||
2. paperclip — paperclip API key is configured
|
||||
3. agentic_loop — local fallback (always available)
|
||||
"""
|
||||
try:
|
||||
from config import settings
|
||||
from timmy.kimi_delegation import exceeds_local_capacity
|
||||
|
||||
if (
|
||||
agent_role == "research"
|
||||
and getattr(settings, "gitea_enabled", False)
|
||||
and getattr(settings, "gitea_token", "")
|
||||
and exceeds_local_capacity(task_description)
|
||||
):
|
||||
return "kimi"
|
||||
|
||||
if getattr(settings, "paperclip_api_key", ""):
|
||||
return "paperclip"
|
||||
|
||||
except Exception as exc:
|
||||
logger.debug("Backend selection error — defaulting to agentic_loop: %s", exc)
|
||||
|
||||
return "agentic_loop"
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Task execution
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@classmethod
|
||||
def _run_task(cls, record: DelegatedTask) -> None:
|
||||
"""Execute a task with retry logic. Runs inside a daemon thread."""
|
||||
record.status = "running"
|
||||
|
||||
for attempt in range(MAX_RETRIES + 1):
|
||||
try:
|
||||
if attempt > 0:
|
||||
logger.info(
|
||||
"Retrying task %s (attempt %d/%d)",
|
||||
record.task_id,
|
||||
attempt + 1,
|
||||
MAX_RETRIES + 1,
|
||||
)
|
||||
record.retries = attempt
|
||||
|
||||
result = cls._dispatch(record)
|
||||
record.status = "completed"
|
||||
record.result = result
|
||||
logger.info(
|
||||
"Task %s completed via %s",
|
||||
record.task_id,
|
||||
record.backend,
|
||||
)
|
||||
return
|
||||
|
||||
except Exception as exc:
|
||||
logger.warning(
|
||||
"Task %s attempt %d failed: %s",
|
||||
record.task_id,
|
||||
attempt + 1,
|
||||
exc,
|
||||
)
|
||||
if attempt == MAX_RETRIES:
|
||||
record.status = "failed"
|
||||
record.error = str(exc)
|
||||
logger.error(
|
||||
"Task %s exhausted %d retries. Final error: %s",
|
||||
record.task_id,
|
||||
MAX_RETRIES,
|
||||
exc,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _dispatch(cls, record: DelegatedTask) -> dict[str, Any]:
|
||||
"""Route to the selected backend. Raises on failure."""
|
||||
if record.backend == "kimi":
|
||||
return asyncio.run(cls._execute_kimi(record))
|
||||
if record.backend == "paperclip":
|
||||
return asyncio.run(cls._execute_paperclip(record))
|
||||
return asyncio.run(cls._execute_agentic_loop(record))
|
||||
|
||||
@classmethod
|
||||
async def _execute_kimi(cls, record: DelegatedTask) -> dict[str, Any]:
|
||||
"""Create a kimi-ready Gitea issue for the task.
|
||||
|
||||
Kimi picks up the issue via the kimi-ready label and executes it.
|
||||
"""
|
||||
from timmy.kimi_delegation import create_kimi_research_issue
|
||||
|
||||
result = await create_kimi_research_issue(
|
||||
task=record.task_description[:120],
|
||||
context=f"Delegated by agent '{record.agent_name}' via delegate_task.",
|
||||
question=record.task_description,
|
||||
priority=record.priority,
|
||||
)
|
||||
if not result.get("success"):
|
||||
raise RuntimeError(f"Kimi issue creation failed: {result.get('error')}")
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
async def _execute_paperclip(cls, record: DelegatedTask) -> dict[str, Any]:
|
||||
"""Submit the task to the Paperclip API."""
|
||||
import httpx
|
||||
|
||||
from timmy.paperclip import PaperclipClient
|
||||
|
||||
client = PaperclipClient()
|
||||
async with httpx.AsyncClient(timeout=client.timeout) as http:
|
||||
resp = await http.post(
|
||||
f"{client.base_url}/api/tasks",
|
||||
headers={"Authorization": f"Bearer {client.api_key}"},
|
||||
json={
|
||||
"kind": record.agent_role,
|
||||
"agent_id": client.agent_id,
|
||||
"company_id": client.company_id,
|
||||
"priority": record.priority,
|
||||
"context": {"task": record.task_description},
|
||||
},
|
||||
)
|
||||
|
||||
if resp.status_code in (200, 201):
|
||||
data = resp.json()
|
||||
logger.info(
|
||||
"Task %s submitted to Paperclip (paperclip_id=%s)",
|
||||
record.task_id,
|
||||
data.get("id"),
|
||||
)
|
||||
return {
|
||||
"success": True,
|
||||
"paperclip_task_id": data.get("id"),
|
||||
"backend": "paperclip",
|
||||
}
|
||||
raise RuntimeError(f"Paperclip API error {resp.status_code}: {resp.text[:200]}")
|
||||
|
||||
@classmethod
|
||||
async def _execute_agentic_loop(cls, record: DelegatedTask) -> dict[str, Any]:
|
||||
"""Execute the task via Timmy's local agentic loop."""
|
||||
from timmy.agentic_loop import run_agentic_loop
|
||||
|
||||
result = await run_agentic_loop(record.task_description)
|
||||
return {
|
||||
"success": result.status != "failed",
|
||||
"agentic_task_id": result.task_id,
|
||||
"summary": result.summary,
|
||||
"status": result.status,
|
||||
"backend": "agentic_loop",
|
||||
}
|
||||
@@ -51,13 +51,6 @@ class Settings(BaseSettings):
|
||||
# Set to 0 to use model defaults.
|
||||
ollama_num_ctx: int = 32768
|
||||
|
||||
# Maximum models loaded simultaneously in Ollama — override with OLLAMA_MAX_LOADED_MODELS
|
||||
# Set to 2 so Qwen3-8B and Qwen3-14B can stay hot concurrently (~17 GB combined).
|
||||
# Requires Ollama ≥ 0.1.33. Export this to the Ollama process environment:
|
||||
# OLLAMA_MAX_LOADED_MODELS=2 ollama serve
|
||||
# or add it to your systemd/launchd unit before starting the harness.
|
||||
ollama_max_loaded_models: int = 2
|
||||
|
||||
# Fallback model chains — override with FALLBACK_MODELS / VISION_FALLBACK_MODELS
|
||||
# as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:8b,qwen2.5:14b"
|
||||
# Or edit config/providers.yaml → fallback_chains for the canonical source.
|
||||
@@ -94,9 +87,8 @@ class Settings(BaseSettings):
|
||||
|
||||
# ── Backend selection ────────────────────────────────────────────────────
|
||||
# "ollama" — always use Ollama (default, safe everywhere)
|
||||
# "airllm" — AirLLM layer-by-layer loading (Apple Silicon only; degrades to Ollama)
|
||||
# "auto" — pick best available local backend, fall back to Ollama
|
||||
timmy_model_backend: Literal["ollama", "airllm", "grok", "claude", "auto"] = "ollama"
|
||||
timmy_model_backend: Literal["ollama", "grok", "claude", "auto"] = "ollama"
|
||||
|
||||
# ── Grok (xAI) — opt-in premium cloud backend ────────────────────────
|
||||
# Grok is a premium augmentation layer — local-first ethos preserved.
|
||||
@@ -109,16 +101,6 @@ class Settings(BaseSettings):
|
||||
grok_sats_hard_cap: int = 100 # Absolute ceiling on sats per Grok query
|
||||
grok_free: bool = False # Skip Lightning invoice when user has own API key
|
||||
|
||||
# ── Search Backend (SearXNG + Crawl4AI) ──────────────────────────────
|
||||
# "searxng" — self-hosted SearXNG meta-search engine (default, no API key)
|
||||
# "none" — disable web search (private/offline deployments)
|
||||
# Override with TIMMY_SEARCH_BACKEND env var.
|
||||
timmy_search_backend: Literal["searxng", "none"] = "searxng"
|
||||
# SearXNG base URL — override with TIMMY_SEARCH_URL env var
|
||||
search_url: str = "http://localhost:8888"
|
||||
# Crawl4AI base URL — override with TIMMY_CRAWL_URL env var
|
||||
crawl_url: str = "http://localhost:11235"
|
||||
|
||||
# ── Database ──────────────────────────────────────────────────────────
|
||||
db_busy_timeout_ms: int = 5000 # SQLite PRAGMA busy_timeout (ms)
|
||||
|
||||
@@ -246,10 +228,6 @@ class Settings(BaseSettings):
|
||||
# ── Test / Diagnostics ─────────────────────────────────────────────
|
||||
# Skip loading heavy embedding models (for tests / low-memory envs).
|
||||
timmy_skip_embeddings: bool = False
|
||||
# Embedding backend: "ollama" for Ollama, "local" for sentence-transformers.
|
||||
timmy_embedding_backend: Literal["ollama", "local"] = "local"
|
||||
# Ollama model to use for embeddings (e.g., "nomic-embed-text").
|
||||
ollama_embedding_model: str = "nomic-embed-text"
|
||||
# Disable CSRF middleware entirely (for tests).
|
||||
timmy_disable_csrf: bool = False
|
||||
# Mark the process as running in test mode.
|
||||
@@ -398,11 +376,6 @@ class Settings(BaseSettings):
|
||||
autoresearch_time_budget: int = 300 # seconds per experiment run
|
||||
autoresearch_max_iterations: int = 100
|
||||
autoresearch_metric: str = "val_bpb" # metric to optimise (lower = better)
|
||||
# M3 Max / Apple Silicon tuning (Issue #905).
|
||||
# dataset: "tinystories" (default, lower-entropy, recommended for Mac) or "openwebtext".
|
||||
autoresearch_dataset: str = "tinystories"
|
||||
# backend: "auto" detects MLX on Apple Silicon; "cpu" forces CPU fallback.
|
||||
autoresearch_backend: str = "auto"
|
||||
|
||||
# ── Weekly Narrative Summary ───────────────────────────────────────
|
||||
# Generates a human-readable weekly summary of development activity.
|
||||
@@ -433,14 +406,6 @@ class Settings(BaseSettings):
|
||||
# Alert threshold: free disk below this triggers cleanup / alert (GB).
|
||||
hermes_disk_free_min_gb: float = 10.0
|
||||
|
||||
# ── Energy Budget Monitoring ───────────────────────────────────────
|
||||
# Enable energy budget monitoring (tracks CPU/GPU power during inference).
|
||||
energy_budget_enabled: bool = True
|
||||
# Watts threshold that auto-activates low power mode (on-battery only).
|
||||
energy_budget_watts_threshold: float = 15.0
|
||||
# Model to prefer in low power mode (smaller = more efficient).
|
||||
energy_low_power_model: str = "qwen3:1b"
|
||||
|
||||
# ── Error Logging ─────────────────────────────────────────────────
|
||||
error_log_enabled: bool = True
|
||||
error_log_dir: str = "logs"
|
||||
|
||||
@@ -37,7 +37,6 @@ from dashboard.routes.db_explorer import router as db_explorer_router
|
||||
from dashboard.routes.discord import router as discord_router
|
||||
from dashboard.routes.experiments import router as experiments_router
|
||||
from dashboard.routes.grok import router as grok_router
|
||||
from dashboard.routes.energy import router as energy_router
|
||||
from dashboard.routes.health import router as health_router
|
||||
from dashboard.routes.hermes import router as hermes_router
|
||||
from dashboard.routes.loop_qa import router as loop_qa_router
|
||||
@@ -45,18 +44,14 @@ from dashboard.routes.memory import router as memory_router
|
||||
from dashboard.routes.mobile import router as mobile_router
|
||||
from dashboard.routes.models import api_router as models_api_router
|
||||
from dashboard.routes.models import router as models_router
|
||||
from dashboard.routes.nexus import router as nexus_router
|
||||
from dashboard.routes.quests import router as quests_router
|
||||
from dashboard.routes.scorecards import router as scorecards_router
|
||||
from dashboard.routes.sovereignty_metrics import router as sovereignty_metrics_router
|
||||
from dashboard.routes.sovereignty_ws import router as sovereignty_ws_router
|
||||
from dashboard.routes.spark import router as spark_router
|
||||
from dashboard.routes.system import router as system_router
|
||||
from dashboard.routes.tasks import router as tasks_router
|
||||
from dashboard.routes.telegram import router as telegram_router
|
||||
from dashboard.routes.thinking import router as thinking_router
|
||||
from dashboard.routes.self_correction import router as self_correction_router
|
||||
from dashboard.routes.three_strike import router as three_strike_router
|
||||
from dashboard.routes.tools import router as tools_router
|
||||
from dashboard.routes.tower import router as tower_router
|
||||
from dashboard.routes.voice import router as voice_router
|
||||
@@ -552,28 +547,12 @@ async def lifespan(app: FastAPI):
|
||||
except Exception:
|
||||
logger.debug("Failed to register error recorder")
|
||||
|
||||
# Mark session start for sovereignty duration tracking
|
||||
try:
|
||||
from timmy.sovereignty import mark_session_start
|
||||
|
||||
mark_session_start()
|
||||
except Exception:
|
||||
logger.debug("Failed to mark sovereignty session start")
|
||||
|
||||
logger.info("✓ Dashboard ready for requests")
|
||||
|
||||
yield
|
||||
|
||||
await _shutdown_cleanup(bg_tasks, workshop_heartbeat)
|
||||
|
||||
# Generate and commit sovereignty session report
|
||||
try:
|
||||
from timmy.sovereignty import generate_and_commit_report
|
||||
|
||||
await generate_and_commit_report()
|
||||
except Exception as exc:
|
||||
logger.warning("Sovereignty report generation failed at shutdown: %s", exc)
|
||||
|
||||
|
||||
app = FastAPI(
|
||||
title="Mission Control",
|
||||
@@ -672,7 +651,6 @@ app.include_router(tools_router)
|
||||
app.include_router(spark_router)
|
||||
app.include_router(discord_router)
|
||||
app.include_router(memory_router)
|
||||
app.include_router(nexus_router)
|
||||
app.include_router(grok_router)
|
||||
app.include_router(models_router)
|
||||
app.include_router(models_api_router)
|
||||
@@ -691,13 +669,9 @@ app.include_router(matrix_router)
|
||||
app.include_router(tower_router)
|
||||
app.include_router(daily_run_router)
|
||||
app.include_router(hermes_router)
|
||||
app.include_router(energy_router)
|
||||
app.include_router(quests_router)
|
||||
app.include_router(scorecards_router)
|
||||
app.include_router(sovereignty_metrics_router)
|
||||
app.include_router(sovereignty_ws_router)
|
||||
app.include_router(three_strike_router)
|
||||
app.include_router(self_correction_router)
|
||||
|
||||
|
||||
@app.websocket("/ws")
|
||||
|
||||
@@ -8,8 +8,6 @@ from .database import Base # Assuming a shared Base in models/database.py
|
||||
|
||||
|
||||
class TaskState(StrEnum):
|
||||
"""Enumeration of possible task lifecycle states."""
|
||||
|
||||
LATER = "LATER"
|
||||
NEXT = "NEXT"
|
||||
NOW = "NOW"
|
||||
@@ -18,16 +16,12 @@ class TaskState(StrEnum):
|
||||
|
||||
|
||||
class TaskCertainty(StrEnum):
|
||||
"""Enumeration of task time-certainty levels."""
|
||||
|
||||
FUZZY = "FUZZY" # An intention without a time
|
||||
SOFT = "SOFT" # A flexible task with a time
|
||||
HARD = "HARD" # A fixed meeting/appointment
|
||||
|
||||
|
||||
class Task(Base):
|
||||
"""SQLAlchemy model representing a CALM task."""
|
||||
|
||||
__tablename__ = "tasks"
|
||||
|
||||
id = Column(Integer, primary_key=True, index=True)
|
||||
@@ -58,8 +52,6 @@ class Task(Base):
|
||||
|
||||
|
||||
class JournalEntry(Base):
|
||||
"""SQLAlchemy model for a daily journal entry with MITs and reflections."""
|
||||
|
||||
__tablename__ = "journal_entries"
|
||||
|
||||
id = Column(Integer, primary_key=True, index=True)
|
||||
|
||||
@@ -14,8 +14,6 @@ router = APIRouter(prefix="/discord", tags=["discord"])
|
||||
|
||||
|
||||
class TokenPayload(BaseModel):
|
||||
"""Request payload containing a Discord bot token."""
|
||||
|
||||
token: str
|
||||
|
||||
|
||||
|
||||
@@ -1,121 +0,0 @@
|
||||
"""Energy Budget Monitoring routes.
|
||||
|
||||
Exposes the energy budget monitor via REST API so the dashboard and
|
||||
external tools can query power draw, efficiency scores, and toggle
|
||||
low power mode.
|
||||
|
||||
Refs: #1009
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
|
||||
from config import settings
|
||||
from infrastructure.energy.monitor import energy_monitor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/energy", tags=["energy"])
|
||||
|
||||
|
||||
class LowPowerRequest(BaseModel):
|
||||
"""Request body for toggling low power mode."""
|
||||
|
||||
enabled: bool
|
||||
|
||||
|
||||
class InferenceEventRequest(BaseModel):
|
||||
"""Request body for recording an inference event."""
|
||||
|
||||
model: str
|
||||
tokens_per_second: float
|
||||
|
||||
|
||||
@router.get("/status")
|
||||
async def energy_status():
|
||||
"""Return the current energy budget status.
|
||||
|
||||
Returns the live power estimate, efficiency score (0–10), recent
|
||||
inference samples, and whether low power mode is active.
|
||||
"""
|
||||
if not getattr(settings, "energy_budget_enabled", True):
|
||||
return {
|
||||
"enabled": False,
|
||||
"message": "Energy budget monitoring is disabled (ENERGY_BUDGET_ENABLED=false)",
|
||||
}
|
||||
|
||||
report = await energy_monitor.get_report()
|
||||
return {**report.to_dict(), "enabled": True}
|
||||
|
||||
|
||||
@router.get("/report")
|
||||
async def energy_report():
|
||||
"""Detailed energy budget report with all recent samples.
|
||||
|
||||
Same as /energy/status but always includes the full sample history.
|
||||
"""
|
||||
if not getattr(settings, "energy_budget_enabled", True):
|
||||
raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
|
||||
|
||||
report = await energy_monitor.get_report()
|
||||
data = report.to_dict()
|
||||
# Override recent_samples to include the full window (not just last 10)
|
||||
data["recent_samples"] = [
|
||||
{
|
||||
"timestamp": s.timestamp,
|
||||
"model": s.model,
|
||||
"tokens_per_second": round(s.tokens_per_second, 1),
|
||||
"estimated_watts": round(s.estimated_watts, 2),
|
||||
"efficiency": round(s.efficiency, 3),
|
||||
"efficiency_score": round(s.efficiency_score, 2),
|
||||
}
|
||||
for s in list(energy_monitor._samples)
|
||||
]
|
||||
return {**data, "enabled": True}
|
||||
|
||||
|
||||
@router.post("/low-power")
|
||||
async def set_low_power_mode(body: LowPowerRequest):
|
||||
"""Enable or disable low power mode.
|
||||
|
||||
In low power mode the cascade router is advised to prefer the
|
||||
configured energy_low_power_model (see settings).
|
||||
"""
|
||||
if not getattr(settings, "energy_budget_enabled", True):
|
||||
raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
|
||||
|
||||
energy_monitor.set_low_power_mode(body.enabled)
|
||||
low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
|
||||
return {
|
||||
"low_power_mode": body.enabled,
|
||||
"preferred_model": low_power_model if body.enabled else None,
|
||||
"message": (
|
||||
f"Low power mode {'enabled' if body.enabled else 'disabled'}. "
|
||||
+ (f"Routing to {low_power_model}." if body.enabled else "Routing restored to default.")
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
@router.post("/record")
|
||||
async def record_inference_event(body: InferenceEventRequest):
|
||||
"""Record an inference event for efficiency tracking.
|
||||
|
||||
Called after each LLM inference completes. Updates the rolling
|
||||
efficiency score and may auto-activate low power mode if watts
|
||||
exceed the configured threshold.
|
||||
"""
|
||||
if not getattr(settings, "energy_budget_enabled", True):
|
||||
return {"recorded": False, "message": "Energy budget monitoring is disabled"}
|
||||
|
||||
if body.tokens_per_second <= 0:
|
||||
raise HTTPException(status_code=422, detail="tokens_per_second must be positive")
|
||||
|
||||
sample = energy_monitor.record_inference(body.model, body.tokens_per_second)
|
||||
return {
|
||||
"recorded": True,
|
||||
"efficiency_score": round(sample.efficiency_score, 2),
|
||||
"estimated_watts": round(sample.estimated_watts, 2),
|
||||
"low_power_mode": energy_monitor.low_power_mode,
|
||||
}
|
||||
@@ -1,166 +0,0 @@
|
||||
"""Nexus — Timmy's persistent conversational awareness space.
|
||||
|
||||
A conversational-only interface where Timmy maintains live memory context.
|
||||
No tool use; pure conversation with memory integration and a teaching panel.
|
||||
|
||||
Routes:
|
||||
GET /nexus — render nexus page with live memory sidebar
|
||||
POST /nexus/chat — send a message; returns HTMX partial
|
||||
POST /nexus/teach — inject a fact into Timmy's live memory
|
||||
DELETE /nexus/history — clear the nexus conversation history
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
|
||||
from fastapi import APIRouter, Form, Request
|
||||
from fastapi.responses import HTMLResponse
|
||||
|
||||
from dashboard.templating import templates
|
||||
from timmy.memory_system import (
|
||||
get_memory_stats,
|
||||
recall_personal_facts_with_ids,
|
||||
search_memories,
|
||||
store_personal_fact,
|
||||
)
|
||||
from timmy.session import _clean_response, chat, reset_session
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/nexus", tags=["nexus"])
|
||||
|
||||
_NEXUS_SESSION_ID = "nexus"
|
||||
_MAX_MESSAGE_LENGTH = 10_000
|
||||
|
||||
# In-memory conversation log for the Nexus session (mirrors chat store pattern
|
||||
# but is scoped to the Nexus so it won't pollute the main dashboard history).
|
||||
_nexus_log: list[dict] = []
|
||||
|
||||
|
||||
def _ts() -> str:
|
||||
return datetime.now(UTC).strftime("%H:%M:%S")
|
||||
|
||||
|
||||
def _append_log(role: str, content: str) -> None:
|
||||
_nexus_log.append({"role": role, "content": content, "timestamp": _ts()})
|
||||
# Keep last 200 exchanges to bound memory usage
|
||||
if len(_nexus_log) > 200:
|
||||
del _nexus_log[:-200]
|
||||
|
||||
|
||||
@router.get("", response_class=HTMLResponse)
|
||||
async def nexus_page(request: Request):
|
||||
"""Render the Nexus page with live memory context."""
|
||||
stats = get_memory_stats()
|
||||
facts = recall_personal_facts_with_ids()[:8]
|
||||
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"nexus.html",
|
||||
{
|
||||
"page_title": "Nexus",
|
||||
"messages": list(_nexus_log),
|
||||
"stats": stats,
|
||||
"facts": facts,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/chat", response_class=HTMLResponse)
|
||||
async def nexus_chat(request: Request, message: str = Form(...)):
|
||||
"""Conversational-only chat routed through the Nexus session.
|
||||
|
||||
Does not invoke tool-use approval flow — pure conversation with memory
|
||||
context injected from Timmy's live memory store.
|
||||
"""
|
||||
message = message.strip()
|
||||
if not message:
|
||||
return HTMLResponse("")
|
||||
if len(message) > _MAX_MESSAGE_LENGTH:
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/nexus_message.html",
|
||||
{
|
||||
"user_message": message[:80] + "…",
|
||||
"response": None,
|
||||
"error": "Message too long (max 10 000 chars).",
|
||||
"timestamp": _ts(),
|
||||
"memory_hits": [],
|
||||
},
|
||||
)
|
||||
|
||||
ts = _ts()
|
||||
|
||||
# Fetch semantically relevant memories to surface in the sidebar
|
||||
try:
|
||||
memory_hits = await asyncio.to_thread(search_memories, query=message, limit=4)
|
||||
except Exception as exc:
|
||||
logger.warning("Nexus memory search failed: %s", exc)
|
||||
memory_hits = []
|
||||
|
||||
# Conversational response — no tool approval flow
|
||||
response_text: str | None = None
|
||||
error_text: str | None = None
|
||||
try:
|
||||
raw = await chat(message, session_id=_NEXUS_SESSION_ID)
|
||||
response_text = _clean_response(raw)
|
||||
except Exception as exc:
|
||||
logger.error("Nexus chat error: %s", exc)
|
||||
error_text = "Timmy is unavailable right now. Check that Ollama is running."
|
||||
|
||||
_append_log("user", message)
|
||||
if response_text:
|
||||
_append_log("assistant", response_text)
|
||||
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/nexus_message.html",
|
||||
{
|
||||
"user_message": message,
|
||||
"response": response_text,
|
||||
"error": error_text,
|
||||
"timestamp": ts,
|
||||
"memory_hits": memory_hits,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/teach", response_class=HTMLResponse)
|
||||
async def nexus_teach(request: Request, fact: str = Form(...)):
|
||||
"""Inject a fact into Timmy's live memory from the Nexus teaching panel."""
|
||||
fact = fact.strip()
|
||||
if not fact:
|
||||
return HTMLResponse("")
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(store_personal_fact, fact)
|
||||
facts = await asyncio.to_thread(recall_personal_facts_with_ids)
|
||||
facts = facts[:8]
|
||||
except Exception as exc:
|
||||
logger.error("Nexus teach error: %s", exc)
|
||||
facts = []
|
||||
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/nexus_facts.html",
|
||||
{"facts": facts, "taught": fact},
|
||||
)
|
||||
|
||||
|
||||
@router.delete("/history", response_class=HTMLResponse)
|
||||
async def nexus_clear_history(request: Request):
|
||||
"""Clear the Nexus conversation history."""
|
||||
_nexus_log.clear()
|
||||
reset_session(session_id=_NEXUS_SESSION_ID)
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/nexus_message.html",
|
||||
{
|
||||
"user_message": None,
|
||||
"response": "Nexus conversation cleared.",
|
||||
"error": None,
|
||||
"timestamp": _ts(),
|
||||
"memory_hits": [],
|
||||
},
|
||||
)
|
||||
@@ -10,7 +10,6 @@ from fastapi.responses import HTMLResponse, JSONResponse
|
||||
|
||||
from dashboard.services.scorecard_service import (
|
||||
PeriodType,
|
||||
ScorecardSummary,
|
||||
generate_all_scorecards,
|
||||
generate_scorecard,
|
||||
get_tracked_agents,
|
||||
@@ -27,216 +26,6 @@ def _format_period_label(period_type: PeriodType) -> str:
|
||||
return "Daily" if period_type == PeriodType.daily else "Weekly"
|
||||
|
||||
|
||||
def _parse_period(period: str) -> PeriodType:
|
||||
"""Parse period string into PeriodType, defaulting to daily on invalid input.
|
||||
|
||||
Args:
|
||||
period: The period string ('daily' or 'weekly')
|
||||
|
||||
Returns:
|
||||
PeriodType.daily or PeriodType.weekly
|
||||
"""
|
||||
try:
|
||||
return PeriodType(period.lower())
|
||||
except ValueError:
|
||||
return PeriodType.daily
|
||||
|
||||
|
||||
def _format_token_display(token_net: int) -> str:
|
||||
"""Format token net value with +/- prefix for display.
|
||||
|
||||
Args:
|
||||
token_net: The net token value
|
||||
|
||||
Returns:
|
||||
Formatted string with + prefix for positive values
|
||||
"""
|
||||
return f"{'+' if token_net > 0 else ''}{token_net}"
|
||||
|
||||
|
||||
def _format_token_class(token_net: int) -> str:
|
||||
"""Get CSS class for token net value based on sign.
|
||||
|
||||
Args:
|
||||
token_net: The net token value
|
||||
|
||||
Returns:
|
||||
'text-success' for positive/zero, 'text-danger' for negative
|
||||
"""
|
||||
return "text-success" if token_net >= 0 else "text-danger"
|
||||
|
||||
|
||||
def _build_patterns_html(patterns: list[str]) -> str:
|
||||
"""Build HTML for patterns section if patterns exist.
|
||||
|
||||
Args:
|
||||
patterns: List of pattern strings
|
||||
|
||||
Returns:
|
||||
HTML string for patterns section or empty string
|
||||
"""
|
||||
if not patterns:
|
||||
return ""
|
||||
|
||||
patterns_list = "".join([f"<li>{p}</li>" for p in patterns])
|
||||
return f"""
|
||||
<div class="mt-3">
|
||||
<h6>Patterns</h6>
|
||||
<ul class="list-unstyled text-info">
|
||||
{patterns_list}
|
||||
</ul>
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
def _build_narrative_html(bullets: list[str]) -> str:
|
||||
"""Build HTML for narrative bullets.
|
||||
|
||||
Args:
|
||||
bullets: List of narrative bullet strings
|
||||
|
||||
Returns:
|
||||
HTML string with list items
|
||||
"""
|
||||
return "".join([f"<li>{b}</li>" for b in bullets])
|
||||
|
||||
|
||||
def _build_metrics_row_html(metrics: dict) -> str:
|
||||
"""Build HTML for the metrics summary row.
|
||||
|
||||
Args:
|
||||
metrics: Dictionary with PRs, issues, tests, and token metrics
|
||||
|
||||
Returns:
|
||||
HTML string for the metrics row
|
||||
"""
|
||||
prs_opened = metrics["prs_opened"]
|
||||
prs_merged = metrics["prs_merged"]
|
||||
pr_merge_rate = int(metrics["pr_merge_rate"] * 100)
|
||||
issues_touched = metrics["issues_touched"]
|
||||
tests_affected = metrics["tests_affected"]
|
||||
token_net = metrics["token_net"]
|
||||
|
||||
token_class = _format_token_class(token_net)
|
||||
token_display = _format_token_display(token_net)
|
||||
|
||||
return f"""
|
||||
<div class="row text-center small">
|
||||
<div class="col">
|
||||
<div class="text-muted">PRs</div>
|
||||
<div class="fw-bold">{prs_opened}/{prs_merged}</div>
|
||||
<div class="text-muted" style="font-size: 0.75rem;">
|
||||
{pr_merge_rate}% merged
|
||||
</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Issues</div>
|
||||
<div class="fw-bold">{issues_touched}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tests</div>
|
||||
<div class="fw-bold">{tests_affected}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tokens</div>
|
||||
<div class="fw-bold {token_class}">{token_display}</div>
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
def _render_scorecard_panel(
|
||||
agent_id: str,
|
||||
period_type: PeriodType,
|
||||
data: dict,
|
||||
) -> str:
|
||||
"""Render HTML for a single scorecard panel.
|
||||
|
||||
Args:
|
||||
agent_id: The agent ID
|
||||
period_type: Daily or weekly period
|
||||
data: Scorecard data dictionary with metrics, patterns, narrative_bullets
|
||||
|
||||
Returns:
|
||||
HTML string for the scorecard panel
|
||||
"""
|
||||
patterns_html = _build_patterns_html(data.get("patterns", []))
|
||||
bullets_html = _build_narrative_html(data.get("narrative_bullets", []))
|
||||
metrics_row = _build_metrics_row_html(data["metrics"])
|
||||
|
||||
return f"""
|
||||
<div class="card mc-panel">
|
||||
<div class="card-header d-flex justify-content-between align-items-center">
|
||||
<h5 class="card-title mb-0">{agent_id.title()}</h5>
|
||||
<span class="badge bg-secondary">{_format_period_label(period_type)}</span>
|
||||
</div>
|
||||
<div class="card-body">
|
||||
<ul class="list-unstyled mb-3">
|
||||
{bullets_html}
|
||||
</ul>
|
||||
{metrics_row}
|
||||
{patterns_html}
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
def _render_empty_scorecard(agent_id: str) -> str:
|
||||
"""Render HTML for an empty scorecard (no activity).
|
||||
|
||||
Args:
|
||||
agent_id: The agent ID
|
||||
|
||||
Returns:
|
||||
HTML string for the empty scorecard panel
|
||||
"""
|
||||
return f"""
|
||||
<div class="card mc-panel">
|
||||
<h5 class="card-title">{agent_id.title()}</h5>
|
||||
<p class="text-muted">No activity recorded for this period.</p>
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
def _render_error_scorecard(agent_id: str, error: str) -> str:
|
||||
"""Render HTML for a scorecard that failed to load.
|
||||
|
||||
Args:
|
||||
agent_id: The agent ID
|
||||
error: Error message string
|
||||
|
||||
Returns:
|
||||
HTML string for the error scorecard panel
|
||||
"""
|
||||
return f"""
|
||||
<div class="card mc-panel border-danger">
|
||||
<h5 class="card-title">{agent_id.title()}</h5>
|
||||
<p class="text-danger">Error loading scorecard: {error}</p>
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
def _render_single_panel_wrapper(
|
||||
agent_id: str,
|
||||
period_type: PeriodType,
|
||||
scorecard: ScorecardSummary | None,
|
||||
) -> str:
|
||||
"""Render a complete scorecard panel with wrapper div for single panel view.
|
||||
|
||||
Args:
|
||||
agent_id: The agent ID
|
||||
period_type: Daily or weekly period
|
||||
scorecard: ScorecardSummary object or None
|
||||
|
||||
Returns:
|
||||
HTML string for the complete panel
|
||||
"""
|
||||
if scorecard is None:
|
||||
return _render_empty_scorecard(agent_id)
|
||||
|
||||
return _render_scorecard_panel(agent_id, period_type, scorecard.to_dict())
|
||||
|
||||
|
||||
@router.get("/api/agents")
|
||||
async def list_tracked_agents() -> dict[str, list[str]]:
|
||||
"""Return the list of tracked agent IDs.
|
||||
@@ -360,50 +149,99 @@ async def agent_scorecard_panel(
|
||||
Returns:
|
||||
HTML panel with scorecard content
|
||||
"""
|
||||
period_type = _parse_period(period)
|
||||
try:
|
||||
period_type = PeriodType(period.lower())
|
||||
except ValueError:
|
||||
period_type = PeriodType.daily
|
||||
|
||||
try:
|
||||
scorecard = generate_scorecard(agent_id, period_type)
|
||||
html_content = _render_single_panel_wrapper(agent_id, period_type, scorecard)
|
||||
|
||||
if scorecard is None:
|
||||
return HTMLResponse(
|
||||
content=f"""
|
||||
<div class="card mc-panel">
|
||||
<h5 class="card-title">{agent_id.title()}</h5>
|
||||
<p class="text-muted">No activity recorded for this period.</p>
|
||||
</div>
|
||||
""",
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
data = scorecard.to_dict()
|
||||
|
||||
# Build patterns HTML
|
||||
patterns_html = ""
|
||||
if data["patterns"]:
|
||||
patterns_list = "".join([f"<li>{p}</li>" for p in data["patterns"]])
|
||||
patterns_html = f"""
|
||||
<div class="mt-3">
|
||||
<h6>Patterns</h6>
|
||||
<ul class="list-unstyled text-info">
|
||||
{patterns_list}
|
||||
</ul>
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Build bullets HTML
|
||||
bullets_html = "".join([f"<li>{b}</li>" for b in data["narrative_bullets"]])
|
||||
|
||||
# Build metrics summary
|
||||
metrics = data["metrics"]
|
||||
|
||||
html_content = f"""
|
||||
<div class="card mc-panel">
|
||||
<div class="card-header d-flex justify-content-between align-items-center">
|
||||
<h5 class="card-title mb-0">{agent_id.title()}</h5>
|
||||
<span class="badge bg-secondary">{_format_period_label(period_type)}</span>
|
||||
</div>
|
||||
<div class="card-body">
|
||||
<ul class="list-unstyled mb-3">
|
||||
{bullets_html}
|
||||
</ul>
|
||||
|
||||
<div class="row text-center small">
|
||||
<div class="col">
|
||||
<div class="text-muted">PRs</div>
|
||||
<div class="fw-bold">{metrics["prs_opened"]}/{metrics["prs_merged"]}</div>
|
||||
<div class="text-muted" style="font-size: 0.75rem;">
|
||||
{int(metrics["pr_merge_rate"] * 100)}% merged
|
||||
</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Issues</div>
|
||||
<div class="fw-bold">{metrics["issues_touched"]}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tests</div>
|
||||
<div class="fw-bold">{metrics["tests_affected"]}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tokens</div>
|
||||
<div class="fw-bold {"text-success" if metrics["token_net"] >= 0 else "text-danger"}">
|
||||
{"+" if metrics["token_net"] > 0 else ""}{metrics["token_net"]}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{patterns_html}
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
|
||||
return HTMLResponse(content=html_content)
|
||||
|
||||
except Exception as exc:
|
||||
logger.error("Failed to render scorecard panel for %s: %s", agent_id, exc)
|
||||
return HTMLResponse(content=_render_error_scorecard(agent_id, str(exc)))
|
||||
|
||||
|
||||
def _render_all_panels_grid(
|
||||
scorecards: list[ScorecardSummary],
|
||||
period_type: PeriodType,
|
||||
) -> str:
|
||||
"""Render all scorecard panels in a grid layout.
|
||||
|
||||
Args:
|
||||
scorecards: List of scorecard summaries
|
||||
period_type: Daily or weekly period
|
||||
|
||||
Returns:
|
||||
HTML string with all panels in a grid
|
||||
"""
|
||||
panels: list[str] = []
|
||||
for scorecard in scorecards:
|
||||
panel_html = _render_scorecard_panel(
|
||||
scorecard.agent_id,
|
||||
period_type,
|
||||
scorecard.to_dict(),
|
||||
return HTMLResponse(
|
||||
content=f"""
|
||||
<div class="card mc-panel border-danger">
|
||||
<h5 class="card-title">{agent_id.title()}</h5>
|
||||
<p class="text-danger">Error loading scorecard: {str(exc)}</p>
|
||||
</div>
|
||||
""",
|
||||
status_code=200,
|
||||
)
|
||||
# Wrap each panel in a grid column
|
||||
wrapped = f'<div class="col-md-6 col-lg-4 mb-3">{panel_html}</div>'
|
||||
panels.append(wrapped)
|
||||
|
||||
return f"""
|
||||
<div class="row">
|
||||
{"".join(panels)}
|
||||
</div>
|
||||
<div class="text-muted small mt-2">
|
||||
Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC")}
|
||||
</div>
|
||||
"""
|
||||
|
||||
|
||||
@router.get("/all/panels", response_class=HTMLResponse)
|
||||
@@ -420,15 +258,96 @@ async def all_scorecard_panels(
|
||||
Returns:
|
||||
HTML with all scorecard panels
|
||||
"""
|
||||
period_type = _parse_period(period)
|
||||
try:
|
||||
period_type = PeriodType(period.lower())
|
||||
except ValueError:
|
||||
period_type = PeriodType.daily
|
||||
|
||||
try:
|
||||
scorecards = generate_all_scorecards(period_type)
|
||||
html_content = _render_all_panels_grid(scorecards, period_type)
|
||||
|
||||
panels: list[str] = []
|
||||
for scorecard in scorecards:
|
||||
data = scorecard.to_dict()
|
||||
|
||||
# Build patterns HTML
|
||||
patterns_html = ""
|
||||
if data["patterns"]:
|
||||
patterns_list = "".join([f"<li>{p}</li>" for p in data["patterns"]])
|
||||
patterns_html = f"""
|
||||
<div class="mt-3">
|
||||
<h6>Patterns</h6>
|
||||
<ul class="list-unstyled text-info">
|
||||
{patterns_list}
|
||||
</ul>
|
||||
</div>
|
||||
"""
|
||||
|
||||
# Build bullets HTML
|
||||
bullets_html = "".join([f"<li>{b}</li>" for b in data["narrative_bullets"]])
|
||||
metrics = data["metrics"]
|
||||
|
||||
panel_html = f"""
|
||||
<div class="col-md-6 col-lg-4 mb-3">
|
||||
<div class="card mc-panel">
|
||||
<div class="card-header d-flex justify-content-between align-items-center">
|
||||
<h5 class="card-title mb-0">{scorecard.agent_id.title()}</h5>
|
||||
<span class="badge bg-secondary">{_format_period_label(period_type)}</span>
|
||||
</div>
|
||||
<div class="card-body">
|
||||
<ul class="list-unstyled mb-3">
|
||||
{bullets_html}
|
||||
</ul>
|
||||
|
||||
<div class="row text-center small">
|
||||
<div class="col">
|
||||
<div class="text-muted">PRs</div>
|
||||
<div class="fw-bold">{metrics["prs_opened"]}/{metrics["prs_merged"]}</div>
|
||||
<div class="text-muted" style="font-size: 0.75rem;">
|
||||
{int(metrics["pr_merge_rate"] * 100)}% merged
|
||||
</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Issues</div>
|
||||
<div class="fw-bold">{metrics["issues_touched"]}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tests</div>
|
||||
<div class="fw-bold">{metrics["tests_affected"]}</div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<div class="text-muted">Tokens</div>
|
||||
<div class="fw-bold {"text-success" if metrics["token_net"] >= 0 else "text-danger"}">
|
||||
{"+" if metrics["token_net"] > 0 else ""}{metrics["token_net"]}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{patterns_html}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
panels.append(panel_html)
|
||||
|
||||
html_content = f"""
|
||||
<div class="row">
|
||||
{"".join(panels)}
|
||||
</div>
|
||||
<div class="text-muted small mt-2">
|
||||
Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC")}
|
||||
</div>
|
||||
"""
|
||||
|
||||
return HTMLResponse(content=html_content)
|
||||
|
||||
except Exception as exc:
|
||||
logger.error("Failed to render all scorecard panels: %s", exc)
|
||||
return HTMLResponse(
|
||||
content=f'<div class="alert alert-danger">Error loading scorecards: {exc}</div>'
|
||||
content=f"""
|
||||
<div class="alert alert-danger">
|
||||
Error loading scorecards: {str(exc)}
|
||||
</div>
|
||||
""",
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
@@ -1,58 +0,0 @@
|
||||
"""Self-Correction Dashboard routes.
|
||||
|
||||
GET /self-correction/ui — HTML dashboard
|
||||
GET /self-correction/timeline — HTMX partial: recent event timeline
|
||||
GET /self-correction/patterns — HTMX partial: recurring failure patterns
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, Request
|
||||
from fastapi.responses import HTMLResponse
|
||||
|
||||
from dashboard.templating import templates
|
||||
from infrastructure.self_correction import get_corrections, get_patterns, get_stats
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/self-correction", tags=["self-correction"])
|
||||
|
||||
|
||||
@router.get("/ui", response_class=HTMLResponse)
|
||||
async def self_correction_ui(request: Request):
|
||||
"""Render the Self-Correction Dashboard."""
|
||||
stats = get_stats()
|
||||
corrections = get_corrections(limit=20)
|
||||
patterns = get_patterns(top_n=10)
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"self_correction.html",
|
||||
{
|
||||
"stats": stats,
|
||||
"corrections": corrections,
|
||||
"patterns": patterns,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/timeline", response_class=HTMLResponse)
|
||||
async def self_correction_timeline(request: Request):
|
||||
"""HTMX partial: recent self-correction event timeline."""
|
||||
corrections = get_corrections(limit=30)
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/self_correction_timeline.html",
|
||||
{"corrections": corrections},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/patterns", response_class=HTMLResponse)
|
||||
async def self_correction_patterns(request: Request):
|
||||
"""HTMX partial: recurring failure patterns."""
|
||||
patterns = get_patterns(top_n=10)
|
||||
stats = get_stats()
|
||||
return templates.TemplateResponse(
|
||||
request,
|
||||
"partials/self_correction_patterns.html",
|
||||
{"patterns": patterns, "stats": stats},
|
||||
)
|
||||
@@ -1,40 +0,0 @@
|
||||
"""WebSocket emitter for the sovereignty metrics dashboard widget.
|
||||
|
||||
Streams real-time sovereignty snapshots to connected clients every
|
||||
*_PUSH_INTERVAL* seconds. The snapshot includes per-layer sovereignty
|
||||
percentages, API cost rate, and skill crystallisation count.
|
||||
|
||||
Refs: #954, #953
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, WebSocket
|
||||
|
||||
router = APIRouter(tags=["sovereignty"])
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_PUSH_INTERVAL = 5 # seconds between snapshot pushes
|
||||
|
||||
|
||||
@router.websocket("/ws/sovereignty")
|
||||
async def sovereignty_ws(websocket: WebSocket) -> None:
|
||||
"""Stream sovereignty metric snapshots to the dashboard widget."""
|
||||
from timmy.sovereignty.metrics import get_metrics_store
|
||||
|
||||
await websocket.accept()
|
||||
logger.info("Sovereignty WS connected")
|
||||
|
||||
store = get_metrics_store()
|
||||
try:
|
||||
# Send initial snapshot immediately
|
||||
await websocket.send_text(json.dumps(store.get_snapshot()))
|
||||
|
||||
while True:
|
||||
await asyncio.sleep(_PUSH_INTERVAL)
|
||||
await websocket.send_text(json.dumps(store.get_snapshot()))
|
||||
except Exception:
|
||||
logger.debug("Sovereignty WS disconnected")
|
||||
@@ -7,8 +7,6 @@ router = APIRouter(prefix="/telegram", tags=["telegram"])
|
||||
|
||||
|
||||
class TokenPayload(BaseModel):
|
||||
"""Request payload containing a Telegram bot token."""
|
||||
|
||||
token: str
|
||||
|
||||
|
||||
|
||||
@@ -1,116 +0,0 @@
|
||||
"""Three-Strike Detector dashboard routes.
|
||||
|
||||
Provides JSON API endpoints for inspecting and managing the three-strike
|
||||
detector state.
|
||||
|
||||
Refs: #962
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
|
||||
from timmy.sovereignty.three_strike import CATEGORIES, get_detector
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/sovereignty/three-strike", tags=["three-strike"])
|
||||
|
||||
|
||||
class RecordRequest(BaseModel):
|
||||
category: str
|
||||
key: str
|
||||
metadata: dict[str, Any] = {}
|
||||
|
||||
|
||||
class AutomationRequest(BaseModel):
|
||||
artifact_path: str
|
||||
|
||||
|
||||
@router.get("")
|
||||
async def list_strikes() -> dict[str, Any]:
|
||||
"""Return all strike records."""
|
||||
detector = get_detector()
|
||||
records = detector.list_all()
|
||||
return {
|
||||
"records": [
|
||||
{
|
||||
"category": r.category,
|
||||
"key": r.key,
|
||||
"count": r.count,
|
||||
"blocked": r.blocked,
|
||||
"automation": r.automation,
|
||||
"first_seen": r.first_seen,
|
||||
"last_seen": r.last_seen,
|
||||
}
|
||||
for r in records
|
||||
],
|
||||
"categories": sorted(CATEGORIES),
|
||||
}
|
||||
|
||||
|
||||
@router.get("/blocked")
|
||||
async def list_blocked() -> dict[str, Any]:
|
||||
"""Return only blocked (category, key) pairs."""
|
||||
detector = get_detector()
|
||||
records = detector.list_blocked()
|
||||
return {
|
||||
"blocked": [
|
||||
{
|
||||
"category": r.category,
|
||||
"key": r.key,
|
||||
"count": r.count,
|
||||
"automation": r.automation,
|
||||
"last_seen": r.last_seen,
|
||||
}
|
||||
for r in records
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
@router.post("/record")
|
||||
async def record_strike(body: RecordRequest) -> dict[str, Any]:
|
||||
"""Record a manual action. Returns strike state; 409 when blocked."""
|
||||
from timmy.sovereignty.three_strike import ThreeStrikeError
|
||||
|
||||
detector = get_detector()
|
||||
try:
|
||||
record = detector.record(body.category, body.key, body.metadata)
|
||||
return {
|
||||
"category": record.category,
|
||||
"key": record.key,
|
||||
"count": record.count,
|
||||
"blocked": record.blocked,
|
||||
"automation": record.automation,
|
||||
}
|
||||
except ValueError as exc:
|
||||
raise HTTPException(status_code=422, detail=str(exc)) from exc
|
||||
except ThreeStrikeError as exc:
|
||||
raise HTTPException(
|
||||
status_code=409,
|
||||
detail={
|
||||
"error": "three_strike_block",
|
||||
"message": str(exc),
|
||||
"category": exc.category,
|
||||
"key": exc.key,
|
||||
"count": exc.count,
|
||||
},
|
||||
) from exc
|
||||
|
||||
|
||||
@router.post("/{category}/{key}/automation")
|
||||
async def register_automation(category: str, key: str, body: AutomationRequest) -> dict[str, bool]:
|
||||
"""Register an automation artifact to unblock a (category, key) pair."""
|
||||
detector = get_detector()
|
||||
detector.register_automation(category, key, body.artifact_path)
|
||||
return {"success": True}
|
||||
|
||||
|
||||
@router.get("/{category}/{key}/events")
|
||||
async def get_strike_events(category: str, key: str, limit: int = 50) -> dict[str, Any]:
|
||||
"""Return the individual strike events for a (category, key) pair."""
|
||||
detector = get_detector()
|
||||
events = detector.get_events(category, key, limit=limit)
|
||||
return {"category": category, "key": key, "events": events}
|
||||
@@ -51,8 +51,6 @@ def _get_db() -> Generator[sqlite3.Connection, None, None]:
|
||||
|
||||
|
||||
class _EnumLike:
|
||||
"""Lightweight enum-like wrapper for string values used in templates."""
|
||||
|
||||
def __init__(self, v: str):
|
||||
self.value = v
|
||||
|
||||
|
||||
@@ -23,8 +23,6 @@ TRACKED_AGENTS = frozenset({"hermes", "kimi", "manus", "claude", "gemini"})
|
||||
|
||||
|
||||
class PeriodType(StrEnum):
|
||||
"""Scorecard reporting period type."""
|
||||
|
||||
daily = "daily"
|
||||
weekly = "weekly"
|
||||
|
||||
|
||||
@@ -67,11 +67,9 @@
|
||||
<div class="mc-nav-dropdown">
|
||||
<button class="mc-test-link mc-dropdown-toggle" aria-expanded="false">INTEL ▾</button>
|
||||
<div class="mc-dropdown-menu">
|
||||
<a href="/nexus" class="mc-test-link">NEXUS</a>
|
||||
<a href="/spark/ui" class="mc-test-link">SPARK</a>
|
||||
<a href="/memory" class="mc-test-link">MEMORY</a>
|
||||
<a href="/marketplace/ui" class="mc-test-link">MARKET</a>
|
||||
<a href="/self-correction/ui" class="mc-test-link">SELF-CORRECT</a>
|
||||
</div>
|
||||
</div>
|
||||
<div class="mc-nav-dropdown">
|
||||
@@ -133,7 +131,6 @@
|
||||
<a href="/spark/ui" class="mc-mobile-link">SPARK</a>
|
||||
<a href="/memory" class="mc-mobile-link">MEMORY</a>
|
||||
<a href="/marketplace/ui" class="mc-mobile-link">MARKET</a>
|
||||
<a href="/self-correction/ui" class="mc-mobile-link">SELF-CORRECT</a>
|
||||
<div class="mc-mobile-section-label">AGENTS</div>
|
||||
<a href="/hands" class="mc-mobile-link">HANDS</a>
|
||||
<a href="/work-orders/queue" class="mc-mobile-link">WORK ORDERS</a>
|
||||
|
||||
@@ -186,24 +186,6 @@
|
||||
<p class="chat-history-placeholder">Loading sovereignty metrics...</p>
|
||||
{% endcall %}
|
||||
|
||||
<!-- Agent Scorecards -->
|
||||
<div class="card mc-card-spaced" id="mc-scorecards-card">
|
||||
<div class="card-header">
|
||||
<h2 class="card-title">Agent Scorecards</h2>
|
||||
<div class="d-flex align-items-center gap-2">
|
||||
<select id="mc-scorecard-period" class="form-select form-select-sm" style="width: auto;"
|
||||
onchange="loadMcScorecards()">
|
||||
<option value="daily" selected>Daily</option>
|
||||
<option value="weekly">Weekly</option>
|
||||
</select>
|
||||
<a href="/scorecards" class="btn btn-sm btn-outline-secondary">Full View</a>
|
||||
</div>
|
||||
</div>
|
||||
<div id="mc-scorecards-content" class="p-2">
|
||||
<p class="chat-history-placeholder">Loading scorecards...</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Chat History -->
|
||||
<div class="card mc-card-spaced">
|
||||
<div class="card-header">
|
||||
@@ -520,20 +502,6 @@ async function loadSparkStatus() {
|
||||
}
|
||||
}
|
||||
|
||||
// Load agent scorecards
|
||||
async function loadMcScorecards() {
|
||||
var period = document.getElementById('mc-scorecard-period').value;
|
||||
var container = document.getElementById('mc-scorecards-content');
|
||||
container.innerHTML = '<p class="chat-history-placeholder">Loading scorecards...</p>';
|
||||
try {
|
||||
var response = await fetch('/scorecards/all/panels?period=' + period);
|
||||
var html = await response.text();
|
||||
container.innerHTML = html;
|
||||
} catch (error) {
|
||||
container.innerHTML = '<p class="chat-history-placeholder">Scorecards unavailable</p>';
|
||||
}
|
||||
}
|
||||
|
||||
// Initial load
|
||||
loadSparkStatus();
|
||||
loadSovereignty();
|
||||
@@ -542,7 +510,6 @@ loadSwarmStats();
|
||||
loadLightningStats();
|
||||
loadGrokStats();
|
||||
loadChatHistory();
|
||||
loadMcScorecards();
|
||||
|
||||
// Periodic updates
|
||||
setInterval(loadSovereignty, 30000);
|
||||
@@ -551,6 +518,5 @@ setInterval(loadSwarmStats, 5000);
|
||||
setInterval(updateHeartbeat, 5000);
|
||||
setInterval(loadGrokStats, 10000);
|
||||
setInterval(loadSparkStatus, 15000);
|
||||
setInterval(loadMcScorecards, 300000);
|
||||
</script>
|
||||
{% endblock %}
|
||||
|
||||
@@ -1,122 +0,0 @@
|
||||
{% extends "base.html" %}
|
||||
|
||||
{% block title %}Nexus{% endblock %}
|
||||
|
||||
{% block extra_styles %}{% endblock %}
|
||||
|
||||
{% block content %}
|
||||
<div class="container-fluid nexus-layout py-3">
|
||||
|
||||
<div class="nexus-header mb-3">
|
||||
<div class="nexus-title">// NEXUS</div>
|
||||
<div class="nexus-subtitle">
|
||||
Persistent conversational awareness — always present, always learning.
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="nexus-grid">
|
||||
|
||||
<!-- ── LEFT: Conversation ────────────────────────────────── -->
|
||||
<div class="nexus-chat-col">
|
||||
<div class="card mc-panel nexus-chat-panel">
|
||||
<div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
|
||||
<span>// CONVERSATION</span>
|
||||
<button class="mc-btn mc-btn-sm"
|
||||
hx-delete="/nexus/history"
|
||||
hx-target="#nexus-chat-log"
|
||||
hx-swap="beforeend"
|
||||
hx-confirm="Clear nexus conversation?">
|
||||
CLEAR
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div class="card-body p-2" id="nexus-chat-log">
|
||||
{% for msg in messages %}
|
||||
<div class="chat-message {{ 'user' if msg.role == 'user' else 'agent' }}">
|
||||
<div class="msg-meta">
|
||||
{{ 'YOU' if msg.role == 'user' else 'TIMMY' }} // {{ msg.timestamp }}
|
||||
</div>
|
||||
<div class="msg-body {% if msg.role == 'assistant' %}timmy-md{% endif %}">
|
||||
{{ msg.content | e }}
|
||||
</div>
|
||||
</div>
|
||||
{% else %}
|
||||
<div class="nexus-empty-state">
|
||||
Nexus is ready. Start a conversation — memories will surface in real time.
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
|
||||
<div class="card-footer p-2">
|
||||
<form hx-post="/nexus/chat"
|
||||
hx-target="#nexus-chat-log"
|
||||
hx-swap="beforeend"
|
||||
hx-on::after-request="this.reset(); document.getElementById('nexus-chat-log').scrollTop = 999999;">
|
||||
<div class="d-flex gap-2">
|
||||
<input type="text"
|
||||
name="message"
|
||||
id="nexus-input"
|
||||
class="mc-search-input flex-grow-1"
|
||||
placeholder="Talk to Timmy..."
|
||||
autocomplete="off"
|
||||
required>
|
||||
<button type="submit" class="mc-btn mc-btn-primary">SEND</button>
|
||||
</div>
|
||||
</form>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ── RIGHT: Memory sidebar ─────────────────────────────── -->
|
||||
<div class="nexus-sidebar-col">
|
||||
|
||||
<!-- Live memory context (updated with each response) -->
|
||||
<div class="card mc-panel nexus-memory-panel mb-3">
|
||||
<div class="card-header mc-panel-header">
|
||||
<span>// LIVE MEMORY</span>
|
||||
<span class="badge ms-2" style="background:var(--purple-dim); color:var(--purple);">
|
||||
{{ stats.total_entries }} stored
|
||||
</span>
|
||||
</div>
|
||||
<div class="card-body p-2">
|
||||
<div id="nexus-memory-panel" class="nexus-memory-hits">
|
||||
<div class="nexus-memory-label">Relevant memories appear here as you chat.</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Teaching panel -->
|
||||
<div class="card mc-panel nexus-teach-panel">
|
||||
<div class="card-header mc-panel-header">// TEACH TIMMY</div>
|
||||
<div class="card-body p-2">
|
||||
<form hx-post="/nexus/teach"
|
||||
hx-target="#nexus-teach-response"
|
||||
hx-swap="innerHTML"
|
||||
hx-on::after-request="this.reset()">
|
||||
<div class="d-flex gap-2 mb-2">
|
||||
<input type="text"
|
||||
name="fact"
|
||||
class="mc-search-input flex-grow-1"
|
||||
placeholder="e.g. I prefer dark themes"
|
||||
required>
|
||||
<button type="submit" class="mc-btn mc-btn-primary">TEACH</button>
|
||||
</div>
|
||||
</form>
|
||||
<div id="nexus-teach-response"></div>
|
||||
|
||||
<div class="nexus-facts-header mt-3">// KNOWN FACTS</div>
|
||||
<ul class="nexus-facts-list" id="nexus-facts-list">
|
||||
{% for fact in facts %}
|
||||
<li class="nexus-fact-item">{{ fact.content | e }}</li>
|
||||
{% else %}
|
||||
<li class="nexus-fact-empty">No personal facts stored yet.</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- /sidebar -->
|
||||
</div><!-- /nexus-grid -->
|
||||
|
||||
</div>
|
||||
{% endblock %}
|
||||
@@ -1,12 +0,0 @@
|
||||
{% if taught %}
|
||||
<div class="nexus-taught-confirm">
|
||||
✓ Taught: <em>{{ taught | e }}</em>
|
||||
</div>
|
||||
{% endif %}
|
||||
<ul class="nexus-facts-list" id="nexus-facts-list" hx-swap-oob="true">
|
||||
{% for fact in facts %}
|
||||
<li class="nexus-fact-item">{{ fact.content | e }}</li>
|
||||
{% else %}
|
||||
<li class="nexus-fact-empty">No facts stored yet.</li>
|
||||
{% endfor %}
|
||||
</ul>
|
||||
@@ -1,36 +0,0 @@
|
||||
{% if user_message %}
|
||||
<div class="chat-message user">
|
||||
<div class="msg-meta">YOU // {{ timestamp }}</div>
|
||||
<div class="msg-body">{{ user_message | e }}</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if response %}
|
||||
<div class="chat-message agent">
|
||||
<div class="msg-meta">TIMMY // {{ timestamp }}</div>
|
||||
<div class="msg-body timmy-md">{{ response | e }}</div>
|
||||
</div>
|
||||
<script>
|
||||
(function() {
|
||||
var el = document.currentScript.previousElementSibling.querySelector('.timmy-md');
|
||||
if (el && typeof marked !== 'undefined' && typeof DOMPurify !== 'undefined') {
|
||||
el.innerHTML = DOMPurify.sanitize(marked.parse(el.textContent));
|
||||
}
|
||||
})();
|
||||
</script>
|
||||
{% elif error %}
|
||||
<div class="chat-message error-msg">
|
||||
<div class="msg-meta">SYSTEM // {{ timestamp }}</div>
|
||||
<div class="msg-body">{{ error | e }}</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if memory_hits %}
|
||||
<div class="nexus-memory-hits" id="nexus-memory-panel" hx-swap-oob="true">
|
||||
<div class="nexus-memory-label">// LIVE MEMORY CONTEXT</div>
|
||||
{% for hit in memory_hits %}
|
||||
<div class="nexus-memory-hit">
|
||||
<span class="nexus-memory-type">{{ hit.memory_type }}</span>
|
||||
<span class="nexus-memory-content">{{ hit.content | e }}</span>
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
{% endif %}
|
||||
@@ -1,28 +0,0 @@
|
||||
{% if patterns %}
|
||||
<table class="mc-table w-100">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>ERROR TYPE</th>
|
||||
<th class="text-center">COUNT</th>
|
||||
<th class="text-center">CORRECTED</th>
|
||||
<th class="text-center">FAILED</th>
|
||||
<th>LAST SEEN</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
{% for p in patterns %}
|
||||
<tr>
|
||||
<td class="sc-pattern-type">{{ p.error_type }}</td>
|
||||
<td class="text-center">
|
||||
<span class="badge {% if p.count >= 5 %}badge-error{% elif p.count >= 3 %}badge-warning{% else %}badge-info{% endif %}">{{ p.count }}</span>
|
||||
</td>
|
||||
<td class="text-center text-success">{{ p.success_count }}</td>
|
||||
<td class="text-center {% if p.failed_count > 0 %}text-danger{% else %}text-muted{% endif %}">{{ p.failed_count }}</td>
|
||||
<td class="sc-event-time">{{ p.last_seen[:16] if p.last_seen else '—' }}</td>
|
||||
</tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
{% else %}
|
||||
<div class="text-center text-muted py-3">No patterns detected yet.</div>
|
||||
{% endif %}
|
||||
@@ -1,26 +0,0 @@
|
||||
{% if corrections %}
|
||||
{% for ev in corrections %}
|
||||
<div class="sc-event sc-status-{{ ev.outcome_status }}">
|
||||
<div class="sc-event-header">
|
||||
<span class="sc-status-badge sc-status-{{ ev.outcome_status }}">
|
||||
{% if ev.outcome_status == 'success' %}✓ CORRECTED
|
||||
{% elif ev.outcome_status == 'partial' %}● PARTIAL
|
||||
{% else %}✗ FAILED
|
||||
{% endif %}
|
||||
</span>
|
||||
<span class="sc-source-badge">{{ ev.source }}</span>
|
||||
<span class="sc-event-time">{{ ev.created_at[:19] }}</span>
|
||||
</div>
|
||||
<div class="sc-event-error-type">{{ ev.error_type }}</div>
|
||||
<div class="sc-event-intent"><span class="sc-label">INTENT:</span> {{ ev.original_intent[:120] }}{% if ev.original_intent | length > 120 %}…{% endif %}</div>
|
||||
<div class="sc-event-error"><span class="sc-label">ERROR:</span> {{ ev.detected_error[:120] }}{% if ev.detected_error | length > 120 %}…{% endif %}</div>
|
||||
<div class="sc-event-strategy"><span class="sc-label">STRATEGY:</span> {{ ev.correction_strategy[:120] }}{% if ev.correction_strategy | length > 120 %}…{% endif %}</div>
|
||||
<div class="sc-event-outcome"><span class="sc-label">OUTCOME:</span> {{ ev.final_outcome[:120] }}{% if ev.final_outcome | length > 120 %}…{% endif %}</div>
|
||||
{% if ev.task_id %}
|
||||
<div class="sc-event-meta">task: {{ ev.task_id[:8] }}</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
{% endfor %}
|
||||
{% else %}
|
||||
<div class="text-center text-muted py-3">No self-correction events recorded yet.</div>
|
||||
{% endif %}
|
||||
@@ -1,102 +0,0 @@
|
||||
{% extends "base.html" %}
|
||||
{% from "macros.html" import panel %}
|
||||
|
||||
{% block title %}Timmy Time — Self-Correction Dashboard{% endblock %}
|
||||
|
||||
{% block extra_styles %}{% endblock %}
|
||||
|
||||
{% block content %}
|
||||
<div class="container-fluid py-3">
|
||||
|
||||
<!-- Header -->
|
||||
<div class="spark-header mb-3">
|
||||
<div class="spark-title">SELF-CORRECTION</div>
|
||||
<div class="spark-subtitle">
|
||||
Agent error detection & recovery —
|
||||
<span class="spark-status-val">{{ stats.total }}</span> events,
|
||||
<span class="spark-status-val">{{ stats.success_rate }}%</span> correction rate,
|
||||
<span class="spark-status-val">{{ stats.unique_error_types }}</span> distinct error types
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row g-3">
|
||||
|
||||
<!-- Left column: stats + patterns -->
|
||||
<div class="col-12 col-lg-4 d-flex flex-column gap-3">
|
||||
|
||||
<!-- Stats panel -->
|
||||
<div class="card mc-panel">
|
||||
<div class="card-header mc-panel-header">// CORRECTION STATS</div>
|
||||
<div class="card-body p-3">
|
||||
<div class="spark-stat-grid">
|
||||
<div class="spark-stat">
|
||||
<span class="spark-stat-label">TOTAL</span>
|
||||
<span class="spark-stat-value">{{ stats.total }}</span>
|
||||
</div>
|
||||
<div class="spark-stat">
|
||||
<span class="spark-stat-label">CORRECTED</span>
|
||||
<span class="spark-stat-value text-success">{{ stats.success_count }}</span>
|
||||
</div>
|
||||
<div class="spark-stat">
|
||||
<span class="spark-stat-label">PARTIAL</span>
|
||||
<span class="spark-stat-value text-warning">{{ stats.partial_count }}</span>
|
||||
</div>
|
||||
<div class="spark-stat">
|
||||
<span class="spark-stat-label">FAILED</span>
|
||||
<span class="spark-stat-value {% if stats.failed_count > 0 %}text-danger{% else %}text-muted{% endif %}">{{ stats.failed_count }}</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="mt-3">
|
||||
<div class="d-flex justify-content-between mb-1">
|
||||
<small class="text-muted">Correction Rate</small>
|
||||
<small class="{% if stats.success_rate >= 70 %}text-success{% elif stats.success_rate >= 40 %}text-warning{% else %}text-danger{% endif %}">{{ stats.success_rate }}%</small>
|
||||
</div>
|
||||
<div class="progress" style="height:6px;">
|
||||
<div class="progress-bar {% if stats.success_rate >= 70 %}bg-success{% elif stats.success_rate >= 40 %}bg-warning{% else %}bg-danger{% endif %}"
|
||||
role="progressbar"
|
||||
style="width:{{ stats.success_rate }}%"
|
||||
aria-valuenow="{{ stats.success_rate }}"
|
||||
aria-valuemin="0"
|
||||
aria-valuemax="100"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Patterns panel -->
|
||||
<div class="card mc-panel"
|
||||
hx-get="/self-correction/patterns"
|
||||
hx-trigger="load, every 60s"
|
||||
hx-target="#sc-patterns-body"
|
||||
hx-swap="innerHTML">
|
||||
<div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
|
||||
<span>// RECURRING PATTERNS</span>
|
||||
<span class="badge badge-info">{{ patterns | length }}</span>
|
||||
</div>
|
||||
<div class="card-body p-0" id="sc-patterns-body">
|
||||
{% include "partials/self_correction_patterns.html" %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
<!-- Right column: timeline -->
|
||||
<div class="col-12 col-lg-8">
|
||||
<div class="card mc-panel"
|
||||
hx-get="/self-correction/timeline"
|
||||
hx-trigger="load, every 30s"
|
||||
hx-target="#sc-timeline-body"
|
||||
hx-swap="innerHTML">
|
||||
<div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
|
||||
<span>// CORRECTION TIMELINE</span>
|
||||
<span class="badge badge-info">{{ corrections | length }}</span>
|
||||
</div>
|
||||
<div class="card-body p-3" id="sc-timeline-body">
|
||||
{% include "partials/self_correction_timeline.html" %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
{% endblock %}
|
||||
@@ -24,8 +24,6 @@ MAX_MESSAGES: int = 500
|
||||
|
||||
@dataclass
|
||||
class Message:
|
||||
"""A single chat message with role, content, timestamp, and source."""
|
||||
|
||||
role: str # "user" | "agent" | "error"
|
||||
content: str
|
||||
timestamp: str
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
"""Energy Budget Monitoring — power-draw estimation for LLM inference.
|
||||
|
||||
Refs: #1009
|
||||
"""
|
||||
|
||||
from infrastructure.energy.monitor import EnergyBudgetMonitor, energy_monitor
|
||||
|
||||
__all__ = ["EnergyBudgetMonitor", "energy_monitor"]
|
||||
@@ -1,371 +0,0 @@
|
||||
"""Energy Budget Monitor — estimates GPU/CPU power draw during LLM inference.
|
||||
|
||||
Tracks estimated power consumption to optimize for "metabolic efficiency".
|
||||
Three estimation strategies attempted in priority order:
|
||||
|
||||
1. Battery discharge via ioreg (macOS — works without sudo, on-battery only)
|
||||
2. CPU utilisation proxy via sysctl hw.cpufrequency + top
|
||||
3. Model-size heuristic (tokens/s × model_size_gb × 2W/GB estimate)
|
||||
|
||||
Energy Efficiency score (0–10):
|
||||
efficiency = tokens_per_second / estimated_watts, normalised to 0–10.
|
||||
|
||||
Low Power Mode:
|
||||
Activated manually or automatically when draw exceeds the configured
|
||||
threshold. In low power mode the cascade router is advised to prefer the
|
||||
configured low_power_model (e.g. qwen3:1b or similar compact model).
|
||||
|
||||
Refs: #1009
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import subprocess
|
||||
import time
|
||||
from collections import deque
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Approximate model-size lookup (GB) used for heuristic power estimate.
|
||||
# Keys are lowercase substring matches against the model name.
|
||||
_MODEL_SIZE_GB: dict[str, float] = {
|
||||
"qwen3:1b": 0.8,
|
||||
"qwen3:3b": 2.0,
|
||||
"qwen3:4b": 2.5,
|
||||
"qwen3:8b": 5.5,
|
||||
"qwen3:14b": 9.0,
|
||||
"qwen3:30b": 20.0,
|
||||
"qwen3:32b": 20.0,
|
||||
"llama3:8b": 5.5,
|
||||
"llama3:70b": 45.0,
|
||||
"mistral:7b": 4.5,
|
||||
"gemma3:4b": 2.5,
|
||||
"gemma3:12b": 8.0,
|
||||
"gemma3:27b": 17.0,
|
||||
"phi4:14b": 9.0,
|
||||
}
|
||||
_DEFAULT_MODEL_SIZE_GB = 5.0 # fallback when model not in table
|
||||
_WATTS_PER_GB_HEURISTIC = 2.0 # rough W/GB for Apple Silicon unified memory
|
||||
|
||||
# Efficiency score normalisation: score 10 at this efficiency (tok/s per W).
|
||||
_EFFICIENCY_SCORE_CEILING = 5.0 # tok/s per W → score 10
|
||||
|
||||
# Rolling window for recent samples
|
||||
_HISTORY_MAXLEN = 60
|
||||
|
||||
|
||||
@dataclass
|
||||
class InferenceSample:
|
||||
"""A single inference event captured by record_inference()."""
|
||||
|
||||
timestamp: str
|
||||
model: str
|
||||
tokens_per_second: float
|
||||
estimated_watts: float
|
||||
efficiency: float # tokens/s per watt
|
||||
efficiency_score: float # 0–10
|
||||
|
||||
|
||||
@dataclass
|
||||
class EnergyReport:
|
||||
"""Snapshot of current energy budget state."""
|
||||
|
||||
timestamp: str
|
||||
low_power_mode: bool
|
||||
current_watts: float
|
||||
strategy: str # "battery", "cpu_proxy", "heuristic", "unavailable"
|
||||
efficiency_score: float # 0–10; -1 if no inference samples yet
|
||||
recent_samples: list[InferenceSample]
|
||||
recommendation: str
|
||||
details: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
return {
|
||||
"timestamp": self.timestamp,
|
||||
"low_power_mode": self.low_power_mode,
|
||||
"current_watts": round(self.current_watts, 2),
|
||||
"strategy": self.strategy,
|
||||
"efficiency_score": round(self.efficiency_score, 2),
|
||||
"recent_samples": [
|
||||
{
|
||||
"timestamp": s.timestamp,
|
||||
"model": s.model,
|
||||
"tokens_per_second": round(s.tokens_per_second, 1),
|
||||
"estimated_watts": round(s.estimated_watts, 2),
|
||||
"efficiency": round(s.efficiency, 3),
|
||||
"efficiency_score": round(s.efficiency_score, 2),
|
||||
}
|
||||
for s in self.recent_samples
|
||||
],
|
||||
"recommendation": self.recommendation,
|
||||
"details": self.details,
|
||||
}
|
||||
|
||||
|
||||
class EnergyBudgetMonitor:
|
||||
"""Estimates power consumption and tracks LLM inference efficiency.
|
||||
|
||||
All blocking I/O (subprocess calls) is wrapped in asyncio.to_thread()
|
||||
so the event loop is never blocked. Results are cached.
|
||||
|
||||
Usage::
|
||||
|
||||
# Record an inference event
|
||||
energy_monitor.record_inference("qwen3:8b", tokens_per_second=42.0)
|
||||
|
||||
# Get the current report
|
||||
report = await energy_monitor.get_report()
|
||||
|
||||
# Toggle low power mode
|
||||
energy_monitor.set_low_power_mode(True)
|
||||
"""
|
||||
|
||||
_POWER_CACHE_TTL = 10.0 # seconds between fresh power readings
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._low_power_mode: bool = False
|
||||
self._samples: deque[InferenceSample] = deque(maxlen=_HISTORY_MAXLEN)
|
||||
self._cached_watts: float = 0.0
|
||||
self._cached_strategy: str = "unavailable"
|
||||
self._cache_ts: float = 0.0
|
||||
|
||||
# ── Public API ────────────────────────────────────────────────────────────
|
||||
|
||||
@property
|
||||
def low_power_mode(self) -> bool:
|
||||
return self._low_power_mode
|
||||
|
||||
def set_low_power_mode(self, enabled: bool) -> None:
|
||||
"""Enable or disable low power mode."""
|
||||
self._low_power_mode = enabled
|
||||
state = "enabled" if enabled else "disabled"
|
||||
logger.info("Energy budget: low power mode %s", state)
|
||||
|
||||
def record_inference(self, model: str, tokens_per_second: float) -> InferenceSample:
|
||||
"""Record an inference event for efficiency tracking.
|
||||
|
||||
Call this after each LLM inference completes with the model name and
|
||||
measured throughput. The current power estimate is used to compute
|
||||
the efficiency score.
|
||||
|
||||
Args:
|
||||
model: Ollama model name (e.g. "qwen3:8b").
|
||||
tokens_per_second: Measured decode throughput.
|
||||
|
||||
Returns:
|
||||
The recorded InferenceSample.
|
||||
"""
|
||||
watts = self._cached_watts if self._cached_watts > 0 else self._estimate_watts_sync(model)
|
||||
efficiency = tokens_per_second / max(watts, 0.1)
|
||||
score = min(10.0, (efficiency / _EFFICIENCY_SCORE_CEILING) * 10.0)
|
||||
|
||||
sample = InferenceSample(
|
||||
timestamp=datetime.now(UTC).isoformat(),
|
||||
model=model,
|
||||
tokens_per_second=tokens_per_second,
|
||||
estimated_watts=watts,
|
||||
efficiency=efficiency,
|
||||
efficiency_score=score,
|
||||
)
|
||||
self._samples.append(sample)
|
||||
|
||||
# Auto-engage low power mode if above threshold and budget is enabled
|
||||
threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
|
||||
if watts > threshold and not self._low_power_mode:
|
||||
logger.info(
|
||||
"Energy budget: %.1fW exceeds threshold %.1fW — auto-engaging low power mode",
|
||||
watts,
|
||||
threshold,
|
||||
)
|
||||
self.set_low_power_mode(True)
|
||||
|
||||
return sample
|
||||
|
||||
async def get_report(self) -> EnergyReport:
|
||||
"""Return the current energy budget report.
|
||||
|
||||
Refreshes the power estimate if the cache is stale.
|
||||
"""
|
||||
await self._refresh_power_cache()
|
||||
|
||||
score = self._compute_mean_efficiency_score()
|
||||
recommendation = self._build_recommendation(score)
|
||||
|
||||
return EnergyReport(
|
||||
timestamp=datetime.now(UTC).isoformat(),
|
||||
low_power_mode=self._low_power_mode,
|
||||
current_watts=self._cached_watts,
|
||||
strategy=self._cached_strategy,
|
||||
efficiency_score=score,
|
||||
recent_samples=list(self._samples)[-10:],
|
||||
recommendation=recommendation,
|
||||
details={"sample_count": len(self._samples)},
|
||||
)
|
||||
|
||||
# ── Power estimation ──────────────────────────────────────────────────────
|
||||
|
||||
async def _refresh_power_cache(self) -> None:
|
||||
"""Refresh the cached power reading if stale."""
|
||||
now = time.monotonic()
|
||||
if now - self._cache_ts < self._POWER_CACHE_TTL:
|
||||
return
|
||||
|
||||
try:
|
||||
watts, strategy = await asyncio.to_thread(self._read_power)
|
||||
except Exception as exc:
|
||||
logger.debug("Energy: power read failed: %s", exc)
|
||||
watts, strategy = 0.0, "unavailable"
|
||||
|
||||
self._cached_watts = watts
|
||||
self._cached_strategy = strategy
|
||||
self._cache_ts = now
|
||||
|
||||
def _read_power(self) -> tuple[float, str]:
|
||||
"""Synchronous power reading — tries strategies in priority order.
|
||||
|
||||
Returns:
|
||||
Tuple of (watts, strategy_name).
|
||||
"""
|
||||
# Strategy 1: battery discharge via ioreg (on-battery Macs)
|
||||
try:
|
||||
watts = self._read_battery_watts()
|
||||
if watts > 0:
|
||||
return watts, "battery"
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Strategy 2: CPU utilisation proxy via top
|
||||
try:
|
||||
cpu_pct = self._read_cpu_pct()
|
||||
if cpu_pct >= 0:
|
||||
# M3 Max TDP ≈ 40W; scale linearly
|
||||
watts = (cpu_pct / 100.0) * 40.0
|
||||
return watts, "cpu_proxy"
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Strategy 3: heuristic from loaded model size
|
||||
return 0.0, "unavailable"
|
||||
|
||||
def _estimate_watts_sync(self, model: str) -> float:
|
||||
"""Estimate watts from model size when no live reading is available."""
|
||||
size_gb = self._model_size_gb(model)
|
||||
return size_gb * _WATTS_PER_GB_HEURISTIC
|
||||
|
||||
def _read_battery_watts(self) -> float:
|
||||
"""Read instantaneous battery discharge via ioreg.
|
||||
|
||||
Returns watts if on battery, 0.0 if plugged in or unavailable.
|
||||
Requires macOS; no sudo needed.
|
||||
"""
|
||||
result = subprocess.run(
|
||||
["ioreg", "-r", "-c", "AppleSmartBattery", "-d", "1"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=3,
|
||||
)
|
||||
amperage_ma = 0.0
|
||||
voltage_mv = 0.0
|
||||
is_charging = True # assume charging unless we see ExternalConnected = No
|
||||
|
||||
for line in result.stdout.splitlines():
|
||||
stripped = line.strip()
|
||||
if '"InstantAmperage"' in stripped:
|
||||
try:
|
||||
amperage_ma = float(stripped.split("=")[-1].strip())
|
||||
except ValueError:
|
||||
pass
|
||||
elif '"Voltage"' in stripped:
|
||||
try:
|
||||
voltage_mv = float(stripped.split("=")[-1].strip())
|
||||
except ValueError:
|
||||
pass
|
||||
elif '"ExternalConnected"' in stripped:
|
||||
is_charging = "Yes" in stripped
|
||||
|
||||
if is_charging or voltage_mv == 0 or amperage_ma <= 0:
|
||||
return 0.0
|
||||
|
||||
# ioreg reports amperage in mA, voltage in mV
|
||||
return (abs(amperage_ma) * voltage_mv) / 1_000_000
|
||||
|
||||
def _read_cpu_pct(self) -> float:
|
||||
"""Read CPU utilisation from macOS top.
|
||||
|
||||
Returns aggregate CPU% (0–100), or -1.0 on failure.
|
||||
"""
|
||||
result = subprocess.run(
|
||||
["top", "-l", "1", "-n", "0", "-stats", "cpu"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5,
|
||||
)
|
||||
for line in result.stdout.splitlines():
|
||||
if "CPU usage:" in line:
|
||||
# "CPU usage: 12.5% user, 8.3% sys, 79.1% idle"
|
||||
parts = line.split()
|
||||
try:
|
||||
user = float(parts[2].rstrip("%"))
|
||||
sys_ = float(parts[4].rstrip("%"))
|
||||
return user + sys_
|
||||
except (IndexError, ValueError):
|
||||
pass
|
||||
return -1.0
|
||||
|
||||
# ── Helpers ───────────────────────────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _model_size_gb(model: str) -> float:
|
||||
"""Look up approximate model size in GB by name substring."""
|
||||
lower = model.lower()
|
||||
# Exact match first
|
||||
if lower in _MODEL_SIZE_GB:
|
||||
return _MODEL_SIZE_GB[lower]
|
||||
# Substring match
|
||||
for key, size in _MODEL_SIZE_GB.items():
|
||||
if key in lower:
|
||||
return size
|
||||
return _DEFAULT_MODEL_SIZE_GB
|
||||
|
||||
def _compute_mean_efficiency_score(self) -> float:
|
||||
"""Mean efficiency score over recent samples, or -1 if none."""
|
||||
if not self._samples:
|
||||
return -1.0
|
||||
recent = list(self._samples)[-10:]
|
||||
return sum(s.efficiency_score for s in recent) / len(recent)
|
||||
|
||||
def _build_recommendation(self, score: float) -> str:
|
||||
"""Generate a human-readable recommendation from the efficiency score."""
|
||||
threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
|
||||
low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
|
||||
|
||||
if score < 0:
|
||||
return "No inference data yet — run some tasks to populate efficiency metrics."
|
||||
|
||||
if self._low_power_mode:
|
||||
return (
|
||||
f"Low power mode active — routing to {low_power_model}. "
|
||||
"Disable when power draw normalises."
|
||||
)
|
||||
|
||||
if score < 3.0:
|
||||
return (
|
||||
f"Low efficiency (score {score:.1f}/10). "
|
||||
f"Consider enabling low power mode to favour smaller models "
|
||||
f"(threshold: {threshold}W)."
|
||||
)
|
||||
|
||||
if score < 6.0:
|
||||
return f"Moderate efficiency (score {score:.1f}/10). System operating normally."
|
||||
|
||||
return f"Good efficiency (score {score:.1f}/10). No action needed."
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
energy_monitor = EnergyBudgetMonitor()
|
||||
@@ -71,53 +71,6 @@ class GitHand:
|
||||
return True
|
||||
return False
|
||||
|
||||
async def _exec_subprocess(
|
||||
self,
|
||||
args: str,
|
||||
timeout: int,
|
||||
) -> tuple[bytes, bytes, int]:
|
||||
"""Run git as a subprocess, return (stdout, stderr, returncode).
|
||||
|
||||
Raises TimeoutError if the process exceeds *timeout* seconds.
|
||||
"""
|
||||
proc = await asyncio.create_subprocess_exec(
|
||||
"git",
|
||||
*args.split(),
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
cwd=self._repo_dir,
|
||||
)
|
||||
try:
|
||||
stdout, stderr = await asyncio.wait_for(
|
||||
proc.communicate(),
|
||||
timeout=timeout,
|
||||
)
|
||||
except TimeoutError:
|
||||
proc.kill()
|
||||
await proc.wait()
|
||||
raise
|
||||
return stdout, stderr, proc.returncode or 0
|
||||
|
||||
@staticmethod
|
||||
def _parse_output(
|
||||
command: str,
|
||||
stdout_bytes: bytes,
|
||||
stderr_bytes: bytes,
|
||||
returncode: int | None,
|
||||
latency_ms: float,
|
||||
) -> GitResult:
|
||||
"""Decode subprocess output into a GitResult."""
|
||||
exit_code = returncode or 0
|
||||
stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
|
||||
stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
|
||||
return GitResult(
|
||||
operation=command,
|
||||
success=exit_code == 0,
|
||||
output=stdout,
|
||||
error=stderr if exit_code != 0 else "",
|
||||
latency_ms=latency_ms,
|
||||
)
|
||||
|
||||
async def run(
|
||||
self,
|
||||
args: str,
|
||||
@@ -135,15 +88,14 @@ class GitHand:
|
||||
GitResult with output or error details.
|
||||
"""
|
||||
start = time.time()
|
||||
command = f"git {args}"
|
||||
|
||||
# Gate destructive operations
|
||||
if self._is_destructive(args) and not allow_destructive:
|
||||
return GitResult(
|
||||
operation=command,
|
||||
operation=f"git {args}",
|
||||
success=False,
|
||||
error=(
|
||||
f"Destructive operation blocked: '{command}'. "
|
||||
f"Destructive operation blocked: 'git {args}'. "
|
||||
"Set allow_destructive=True to override."
|
||||
),
|
||||
requires_confirmation=True,
|
||||
@@ -151,21 +103,46 @@ class GitHand:
|
||||
)
|
||||
|
||||
effective_timeout = timeout or self._timeout
|
||||
command = f"git {args}"
|
||||
|
||||
try:
|
||||
stdout_bytes, stderr_bytes, returncode = await self._exec_subprocess(
|
||||
args,
|
||||
effective_timeout,
|
||||
proc = await asyncio.create_subprocess_exec(
|
||||
"git",
|
||||
*args.split(),
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
cwd=self._repo_dir,
|
||||
)
|
||||
except TimeoutError:
|
||||
|
||||
try:
|
||||
stdout_bytes, stderr_bytes = await asyncio.wait_for(
|
||||
proc.communicate(), timeout=effective_timeout
|
||||
)
|
||||
except TimeoutError:
|
||||
proc.kill()
|
||||
await proc.wait()
|
||||
latency = (time.time() - start) * 1000
|
||||
logger.warning("Git command timed out after %ds: %s", effective_timeout, command)
|
||||
return GitResult(
|
||||
operation=command,
|
||||
success=False,
|
||||
error=f"Command timed out after {effective_timeout}s",
|
||||
latency_ms=latency,
|
||||
)
|
||||
|
||||
latency = (time.time() - start) * 1000
|
||||
logger.warning("Git command timed out after %ds: %s", effective_timeout, command)
|
||||
exit_code = proc.returncode or 0
|
||||
stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
|
||||
stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
|
||||
|
||||
return GitResult(
|
||||
operation=command,
|
||||
success=False,
|
||||
error=f"Command timed out after {effective_timeout}s",
|
||||
success=exit_code == 0,
|
||||
output=stdout,
|
||||
error=stderr if exit_code != 0 else "",
|
||||
latency_ms=latency,
|
||||
)
|
||||
|
||||
except FileNotFoundError:
|
||||
latency = (time.time() - start) * 1000
|
||||
logger.warning("git binary not found")
|
||||
@@ -185,14 +162,6 @@ class GitHand:
|
||||
latency_ms=latency,
|
||||
)
|
||||
|
||||
return self._parse_output(
|
||||
command,
|
||||
stdout_bytes,
|
||||
stderr_bytes,
|
||||
returncode=returncode,
|
||||
latency_ms=(time.time() - start) * 1000,
|
||||
)
|
||||
|
||||
# ── Convenience wrappers ─────────────────────────────────────────────────
|
||||
|
||||
async def status(self) -> GitResult:
|
||||
|
||||
@@ -21,8 +21,6 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass
|
||||
class Notification:
|
||||
"""A push notification with title, message, category, and read status."""
|
||||
|
||||
id: int
|
||||
title: str
|
||||
message: str
|
||||
|
||||
@@ -242,64 +242,6 @@ def produce_agent_state(agent_id: str, presence: dict) -> dict:
|
||||
}
|
||||
|
||||
|
||||
def _get_agents_online() -> int:
|
||||
"""Return the count of agents with a non-offline status."""
|
||||
try:
|
||||
from timmy.agents.loader import list_agents
|
||||
|
||||
agents = list_agents()
|
||||
return sum(1 for a in agents if a.get("status", "") not in ("offline", ""))
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count agents: %s", exc)
|
||||
return 0
|
||||
|
||||
|
||||
def _get_visitors() -> int:
|
||||
"""Return the count of active WebSocket visitor clients."""
|
||||
try:
|
||||
from dashboard.routes.world import _ws_clients
|
||||
|
||||
return len(_ws_clients)
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count visitors: %s", exc)
|
||||
return 0
|
||||
|
||||
|
||||
def _get_uptime_seconds() -> int:
|
||||
"""Return seconds elapsed since application start."""
|
||||
try:
|
||||
from config import APP_START_TIME
|
||||
|
||||
return int((datetime.now(UTC) - APP_START_TIME).total_seconds())
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to calculate uptime: %s", exc)
|
||||
return 0
|
||||
|
||||
|
||||
def _get_thinking_active() -> bool:
|
||||
"""Return True if the thinking engine is enabled and running."""
|
||||
try:
|
||||
from config import settings
|
||||
from timmy.thinking import thinking_engine
|
||||
|
||||
return settings.thinking_enabled and thinking_engine is not None
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to check thinking status: %s", exc)
|
||||
return False
|
||||
|
||||
|
||||
def _get_memory_count() -> int:
|
||||
"""Return total entries in the vector memory store."""
|
||||
try:
|
||||
from timmy.memory_system import get_memory_stats
|
||||
|
||||
stats = get_memory_stats()
|
||||
return stats.get("total_entries", 0)
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count memories: %s", exc)
|
||||
return 0
|
||||
|
||||
|
||||
def produce_system_status() -> dict:
|
||||
"""Generate a system_status message for the Matrix.
|
||||
|
||||
@@ -328,14 +270,64 @@ def produce_system_status() -> dict:
|
||||
"ts": 1742529600,
|
||||
}
|
||||
"""
|
||||
# Count agents with status != offline
|
||||
agents_online = 0
|
||||
try:
|
||||
from timmy.agents.loader import list_agents
|
||||
|
||||
agents = list_agents()
|
||||
agents_online = sum(1 for a in agents if a.get("status", "") not in ("offline", ""))
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count agents: %s", exc)
|
||||
|
||||
# Count visitors from WebSocket clients
|
||||
visitors = 0
|
||||
try:
|
||||
from dashboard.routes.world import _ws_clients
|
||||
|
||||
visitors = len(_ws_clients)
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count visitors: %s", exc)
|
||||
|
||||
# Calculate uptime
|
||||
uptime_seconds = 0
|
||||
try:
|
||||
from datetime import UTC
|
||||
|
||||
from config import APP_START_TIME
|
||||
|
||||
uptime_seconds = int((datetime.now(UTC) - APP_START_TIME).total_seconds())
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to calculate uptime: %s", exc)
|
||||
|
||||
# Check thinking engine status
|
||||
thinking_active = False
|
||||
try:
|
||||
from config import settings
|
||||
from timmy.thinking import thinking_engine
|
||||
|
||||
thinking_active = settings.thinking_enabled and thinking_engine is not None
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to check thinking status: %s", exc)
|
||||
|
||||
# Count memories in vector store
|
||||
memory_count = 0
|
||||
try:
|
||||
from timmy.memory_system import get_memory_stats
|
||||
|
||||
stats = get_memory_stats()
|
||||
memory_count = stats.get("total_entries", 0)
|
||||
except Exception as exc:
|
||||
logger.debug("Failed to count memories: %s", exc)
|
||||
|
||||
return {
|
||||
"type": "system_status",
|
||||
"data": {
|
||||
"agents_online": _get_agents_online(),
|
||||
"visitors": _get_visitors(),
|
||||
"uptime_seconds": _get_uptime_seconds(),
|
||||
"thinking_active": _get_thinking_active(),
|
||||
"memory_count": _get_memory_count(),
|
||||
"agents_online": agents_online,
|
||||
"visitors": visitors,
|
||||
"uptime_seconds": uptime_seconds,
|
||||
"thinking_active": thinking_active,
|
||||
"memory_count": memory_count,
|
||||
},
|
||||
"ts": int(time.time()),
|
||||
}
|
||||
|
||||
@@ -2,16 +2,7 @@
|
||||
|
||||
from .api import router
|
||||
from .cascade import CascadeRouter, Provider, ProviderStatus, get_router
|
||||
from .classifier import TaskComplexity, classify_task
|
||||
from .history import HealthHistoryStore, get_history_store
|
||||
from .metabolic import (
|
||||
DEFAULT_TIER_MODELS,
|
||||
MetabolicRouter,
|
||||
ModelTier,
|
||||
build_prompt,
|
||||
classify_complexity,
|
||||
get_metabolic_router,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"CascadeRouter",
|
||||
@@ -21,14 +12,4 @@ __all__ = [
|
||||
"router",
|
||||
"HealthHistoryStore",
|
||||
"get_history_store",
|
||||
# Metabolic router
|
||||
"MetabolicRouter",
|
||||
"ModelTier",
|
||||
"DEFAULT_TIER_MODELS",
|
||||
"classify_complexity",
|
||||
"build_prompt",
|
||||
"get_metabolic_router",
|
||||
# Classifier
|
||||
"TaskComplexity",
|
||||
"classify_task",
|
||||
]
|
||||
|
||||
@@ -16,10 +16,7 @@ from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from infrastructure.router.classifier import TaskComplexity
|
||||
from typing import Any
|
||||
|
||||
from config import settings
|
||||
|
||||
@@ -531,99 +528,6 @@ class CascadeRouter:
|
||||
|
||||
return True
|
||||
|
||||
def _filter_providers(self, cascade_tier: str | None) -> list["Provider"]:
|
||||
"""Return the provider list filtered by tier.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If a tier is specified but no matching providers exist.
|
||||
"""
|
||||
if cascade_tier == "frontier_required":
|
||||
providers = [p for p in self.providers if p.type == "anthropic"]
|
||||
if not providers:
|
||||
raise RuntimeError("No Anthropic provider configured for 'frontier_required' tier.")
|
||||
return providers
|
||||
if cascade_tier:
|
||||
providers = [p for p in self.providers if p.tier == cascade_tier]
|
||||
if not providers:
|
||||
raise RuntimeError(f"No providers found for tier: {cascade_tier}")
|
||||
return providers
|
||||
return self.providers
|
||||
|
||||
async def _try_single_provider(
|
||||
self,
|
||||
provider: "Provider",
|
||||
messages: list[dict],
|
||||
model: str | None,
|
||||
temperature: float,
|
||||
max_tokens: int | None,
|
||||
content_type: ContentType,
|
||||
errors: list[str],
|
||||
) -> dict | None:
|
||||
"""Attempt one provider, returning a result dict on success or None on failure.
|
||||
|
||||
On failure the error string is appended to *errors* and the provider's
|
||||
failure metrics are updated so the caller can move on to the next provider.
|
||||
"""
|
||||
if not self._is_provider_available(provider):
|
||||
return None
|
||||
|
||||
# Metabolic protocol: skip cloud providers when quota is low
|
||||
if provider.type in ("anthropic", "openai", "grok"):
|
||||
if not self._quota_allows_cloud(provider):
|
||||
logger.info(
|
||||
"Metabolic protocol: skipping cloud provider %s (quota too low)",
|
||||
provider.name,
|
||||
)
|
||||
return None
|
||||
|
||||
selected_model, is_fallback_model = self._select_model(provider, model, content_type)
|
||||
|
||||
try:
|
||||
result = await self._attempt_with_retry(
|
||||
provider, messages, selected_model, temperature, max_tokens, content_type
|
||||
)
|
||||
except RuntimeError as exc:
|
||||
errors.append(str(exc))
|
||||
self._record_failure(provider)
|
||||
return None
|
||||
|
||||
self._record_success(provider, result.get("latency_ms", 0))
|
||||
return {
|
||||
"content": result["content"],
|
||||
"provider": provider.name,
|
||||
"model": result.get("model", selected_model or provider.get_default_model()),
|
||||
"latency_ms": result.get("latency_ms", 0),
|
||||
"is_fallback_model": is_fallback_model,
|
||||
}
|
||||
|
||||
def _get_model_for_complexity(
|
||||
self, provider: Provider, complexity: "TaskComplexity"
|
||||
) -> str | None:
|
||||
"""Return the best model on *provider* for the given complexity tier.
|
||||
|
||||
Checks fallback chains first (routine / complex), then falls back to
|
||||
any model with the matching capability tag, then the provider default.
|
||||
"""
|
||||
from infrastructure.router.classifier import TaskComplexity
|
||||
|
||||
chain_key = "routine" if complexity == TaskComplexity.SIMPLE else "complex"
|
||||
|
||||
# Walk the capability fallback chain — first model present on this provider wins
|
||||
for model_name in self.config.fallback_chains.get(chain_key, []):
|
||||
if any(m["name"] == model_name for m in provider.models):
|
||||
return model_name
|
||||
|
||||
# Direct capability lookup — only return if a model explicitly has the tag
|
||||
# (do not use get_model_with_capability here as it falls back to the default)
|
||||
cap_model = next(
|
||||
(m["name"] for m in provider.models if chain_key in m.get("capabilities", [])),
|
||||
None,
|
||||
)
|
||||
if cap_model:
|
||||
return cap_model
|
||||
|
||||
return None # Caller will use provider default
|
||||
|
||||
async def complete(
|
||||
self,
|
||||
messages: list[dict],
|
||||
@@ -631,7 +535,6 @@ class CascadeRouter:
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int | None = None,
|
||||
cascade_tier: str | None = None,
|
||||
complexity_hint: str | None = None,
|
||||
) -> dict:
|
||||
"""Complete a chat conversation with automatic failover.
|
||||
|
||||
@@ -640,50 +543,35 @@ class CascadeRouter:
|
||||
- Falls back to vision-capable models when needed
|
||||
- Supports image URLs, paths, and base64 encoding
|
||||
|
||||
Complexity-based routing (issue #1065):
|
||||
- ``complexity_hint="simple"`` → routes to Qwen3-8B (low-latency)
|
||||
- ``complexity_hint="complex"`` → routes to Qwen3-14B (quality)
|
||||
- ``complexity_hint=None`` (default) → auto-classifies from messages
|
||||
|
||||
Args:
|
||||
messages: List of message dicts with role and content
|
||||
model: Preferred model (tries this first; complexity routing is
|
||||
skipped when an explicit model is given)
|
||||
model: Preferred model (tries this first, then provider defaults)
|
||||
temperature: Sampling temperature
|
||||
max_tokens: Maximum tokens to generate
|
||||
cascade_tier: If specified, filters providers by this tier.
|
||||
- "frontier_required": Uses only Anthropic provider for top-tier models.
|
||||
complexity_hint: "simple", "complex", or None (auto-detect).
|
||||
|
||||
Returns:
|
||||
Dict with content, provider_used, model, latency_ms,
|
||||
is_fallback_model, and complexity fields.
|
||||
Dict with content, provider_used, and metrics
|
||||
|
||||
Raises:
|
||||
RuntimeError: If all providers fail
|
||||
"""
|
||||
from infrastructure.router.classifier import TaskComplexity, classify_task
|
||||
|
||||
content_type = self._detect_content_type(messages)
|
||||
if content_type != ContentType.TEXT:
|
||||
logger.debug("Detected %s content, selecting appropriate model", content_type.value)
|
||||
|
||||
# Resolve task complexity ─────────────────────────────────────────────
|
||||
# Skip complexity routing when caller explicitly specifies a model.
|
||||
complexity: TaskComplexity | None = None
|
||||
if model is None:
|
||||
if complexity_hint is not None:
|
||||
try:
|
||||
complexity = TaskComplexity(complexity_hint.lower())
|
||||
except ValueError:
|
||||
logger.warning("Unknown complexity_hint %r, auto-classifying", complexity_hint)
|
||||
complexity = classify_task(messages)
|
||||
else:
|
||||
complexity = classify_task(messages)
|
||||
logger.debug("Task complexity: %s", complexity.value)
|
||||
errors = []
|
||||
|
||||
errors: list[str] = []
|
||||
providers = self._filter_providers(cascade_tier)
|
||||
providers = self.providers
|
||||
if cascade_tier == "frontier_required":
|
||||
providers = [p for p in self.providers if p.type == "anthropic"]
|
||||
if not providers:
|
||||
raise RuntimeError("No Anthropic provider configured for 'frontier_required' tier.")
|
||||
elif cascade_tier:
|
||||
providers = [p for p in self.providers if p.tier == cascade_tier]
|
||||
if not providers:
|
||||
raise RuntimeError(f"No providers found for tier: {cascade_tier}")
|
||||
|
||||
for provider in providers:
|
||||
if not self._is_provider_available(provider):
|
||||
@@ -698,21 +586,7 @@ class CascadeRouter:
|
||||
)
|
||||
continue
|
||||
|
||||
# Complexity-based model selection (only when no explicit model) ──
|
||||
effective_model = model
|
||||
if effective_model is None and complexity is not None:
|
||||
effective_model = self._get_model_for_complexity(provider, complexity)
|
||||
if effective_model:
|
||||
logger.debug(
|
||||
"Complexity routing [%s]: %s → %s",
|
||||
complexity.value,
|
||||
provider.name,
|
||||
effective_model,
|
||||
)
|
||||
|
||||
selected_model, is_fallback_model = self._select_model(
|
||||
provider, effective_model, content_type
|
||||
)
|
||||
selected_model, is_fallback_model = self._select_model(provider, model, content_type)
|
||||
|
||||
try:
|
||||
result = await self._attempt_with_retry(
|
||||
@@ -735,7 +609,6 @@ class CascadeRouter:
|
||||
"model": result.get("model", selected_model or provider.get_default_model()),
|
||||
"latency_ms": result.get("latency_ms", 0),
|
||||
"is_fallback_model": is_fallback_model,
|
||||
"complexity": complexity.value if complexity is not None else None,
|
||||
}
|
||||
|
||||
raise RuntimeError(f"All providers failed: {'; '.join(errors)}")
|
||||
|
||||
@@ -1,169 +0,0 @@
|
||||
"""Task complexity classifier for Qwen3 dual-model routing.
|
||||
|
||||
Classifies incoming tasks as SIMPLE (route to Qwen3-8B for low-latency)
|
||||
or COMPLEX (route to Qwen3-14B for quality-sensitive work).
|
||||
|
||||
Classification is fully heuristic — no LLM inference required.
|
||||
"""
|
||||
|
||||
import re
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class TaskComplexity(Enum):
|
||||
"""Task complexity tier for model routing."""
|
||||
|
||||
SIMPLE = "simple" # Qwen3-8B Q6_K: routine, latency-sensitive
|
||||
COMPLEX = "complex" # Qwen3-14B Q5_K_M: quality-sensitive, multi-step
|
||||
|
||||
|
||||
# Keywords strongly associated with complex tasks
|
||||
_COMPLEX_KEYWORDS: frozenset[str] = frozenset(
|
||||
[
|
||||
"plan",
|
||||
"review",
|
||||
"analyze",
|
||||
"analyse",
|
||||
"triage",
|
||||
"refactor",
|
||||
"design",
|
||||
"architecture",
|
||||
"implement",
|
||||
"compare",
|
||||
"debug",
|
||||
"explain",
|
||||
"prioritize",
|
||||
"prioritise",
|
||||
"strategy",
|
||||
"optimize",
|
||||
"optimise",
|
||||
"evaluate",
|
||||
"assess",
|
||||
"brainstorm",
|
||||
"outline",
|
||||
"summarize",
|
||||
"summarise",
|
||||
"generate code",
|
||||
"write a",
|
||||
"write the",
|
||||
"code review",
|
||||
"pull request",
|
||||
"multi-step",
|
||||
"multi step",
|
||||
"step by step",
|
||||
"backlog prioriti",
|
||||
"issue triage",
|
||||
"root cause",
|
||||
"how does",
|
||||
"why does",
|
||||
"what are the",
|
||||
]
|
||||
)
|
||||
|
||||
# Keywords strongly associated with simple/routine tasks
|
||||
_SIMPLE_KEYWORDS: frozenset[str] = frozenset(
|
||||
[
|
||||
"status",
|
||||
"list ",
|
||||
"show ",
|
||||
"what is",
|
||||
"how many",
|
||||
"ping",
|
||||
"run ",
|
||||
"execute ",
|
||||
"ls ",
|
||||
"cat ",
|
||||
"ps ",
|
||||
"fetch ",
|
||||
"count ",
|
||||
"tail ",
|
||||
"head ",
|
||||
"grep ",
|
||||
"find file",
|
||||
"read file",
|
||||
"get ",
|
||||
"query ",
|
||||
"check ",
|
||||
"yes",
|
||||
"no",
|
||||
"ok",
|
||||
"done",
|
||||
"thanks",
|
||||
]
|
||||
)
|
||||
|
||||
# Content longer than this is treated as complex regardless of keywords
|
||||
_COMPLEX_CHAR_THRESHOLD = 500
|
||||
|
||||
# Short content defaults to simple
|
||||
_SIMPLE_CHAR_THRESHOLD = 150
|
||||
|
||||
# More than this many messages suggests an ongoing complex conversation
|
||||
_COMPLEX_CONVERSATION_DEPTH = 6
|
||||
|
||||
|
||||
def classify_task(messages: list[dict]) -> TaskComplexity:
|
||||
"""Classify task complexity from a list of messages.
|
||||
|
||||
Uses heuristic rules — no LLM call required. Errs toward COMPLEX
|
||||
when uncertain so that quality is preserved.
|
||||
|
||||
Args:
|
||||
messages: List of message dicts with ``role`` and ``content`` keys.
|
||||
|
||||
Returns:
|
||||
TaskComplexity.SIMPLE or TaskComplexity.COMPLEX
|
||||
"""
|
||||
if not messages:
|
||||
return TaskComplexity.SIMPLE
|
||||
|
||||
# Concatenate all user-turn content for analysis
|
||||
user_content = (
|
||||
" ".join(
|
||||
msg.get("content", "")
|
||||
for msg in messages
|
||||
if msg.get("role") in ("user", "human") and isinstance(msg.get("content"), str)
|
||||
)
|
||||
.lower()
|
||||
.strip()
|
||||
)
|
||||
|
||||
if not user_content:
|
||||
return TaskComplexity.SIMPLE
|
||||
|
||||
# Complexity signals override everything -----------------------------------
|
||||
|
||||
# Explicit complex keywords
|
||||
for kw in _COMPLEX_KEYWORDS:
|
||||
if kw in user_content:
|
||||
return TaskComplexity.COMPLEX
|
||||
|
||||
# Numbered / multi-step instruction list: "1. do this 2. do that"
|
||||
if re.search(r"\b\d+\.\s+\w", user_content):
|
||||
return TaskComplexity.COMPLEX
|
||||
|
||||
# Code blocks embedded in messages
|
||||
if "```" in user_content:
|
||||
return TaskComplexity.COMPLEX
|
||||
|
||||
# Long content → complex reasoning likely required
|
||||
if len(user_content) > _COMPLEX_CHAR_THRESHOLD:
|
||||
return TaskComplexity.COMPLEX
|
||||
|
||||
# Deep conversation → complex ongoing task
|
||||
if len(messages) > _COMPLEX_CONVERSATION_DEPTH:
|
||||
return TaskComplexity.COMPLEX
|
||||
|
||||
# Simplicity signals -------------------------------------------------------
|
||||
|
||||
# Explicit simple keywords
|
||||
for kw in _SIMPLE_KEYWORDS:
|
||||
if kw in user_content:
|
||||
return TaskComplexity.SIMPLE
|
||||
|
||||
# Short single-sentence messages default to simple
|
||||
if len(user_content) <= _SIMPLE_CHAR_THRESHOLD:
|
||||
return TaskComplexity.SIMPLE
|
||||
|
||||
# When uncertain, prefer quality (complex model)
|
||||
return TaskComplexity.COMPLEX
|
||||
@@ -1,424 +0,0 @@
|
||||
"""Three-tier metabolic LLM router.
|
||||
|
||||
Routes queries to the cheapest-sufficient model tier using MLX for all
|
||||
inference on Apple Silicon GPU:
|
||||
|
||||
T1 — Routine (Qwen3-8B Q6_K, ~45-55 tok/s): Simple navigation, basic choices.
|
||||
T2 — Medium (Qwen3-14B Q5_K_M, ~20-28 tok/s): Dialogue, inventory management.
|
||||
T3 — Complex (Qwen3-32B Q4_K_M, ~8-12 tok/s): Quest planning, stuck recovery.
|
||||
|
||||
Memory budget:
|
||||
- T1+T2 always loaded (~8.5 GB combined)
|
||||
- T3 loaded on demand (+20 GB) — game pauses during inference
|
||||
|
||||
Design notes:
|
||||
- 70% of game ticks never reach the LLM (handled upstream by behavior trees)
|
||||
- T3 pauses the game world before inference and unpauses after (graceful if no world)
|
||||
- All inference via vllm-mlx / Ollama — local-first, no cloud for game ticks
|
||||
|
||||
References:
|
||||
- Issue #966 — Three-Tier Metabolic LLM Router
|
||||
- Issue #1063 — Best Local Uncensored Agent Model for M3 Max 36GB
|
||||
- Issue #1075 — Claude Quota Monitor + Metabolic Protocol
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from enum import StrEnum
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ModelTier(StrEnum):
|
||||
"""Three metabolic model tiers ordered by cost and capability.
|
||||
|
||||
Tier selection is driven by classify_complexity(). The cheapest
|
||||
sufficient tier is always chosen — T1 handles routine tasks, T2
|
||||
handles dialogue and management, T3 handles planning and recovery.
|
||||
"""
|
||||
|
||||
T1_ROUTINE = "t1_routine" # Fast, cheap — Qwen3-8B, always loaded
|
||||
T2_MEDIUM = "t2_medium" # Balanced — Qwen3-14B, always loaded
|
||||
T3_COMPLEX = "t3_complex" # Deep — Qwen3-32B, loaded on demand, pauses game
|
||||
|
||||
|
||||
# ── Classification vocabulary ────────────────────────────────────────────────
|
||||
|
||||
# T1: single-action navigation and binary-choice words
|
||||
_T1_KEYWORDS = frozenset(
|
||||
{
|
||||
"go",
|
||||
"move",
|
||||
"walk",
|
||||
"run",
|
||||
"north",
|
||||
"south",
|
||||
"east",
|
||||
"west",
|
||||
"up",
|
||||
"down",
|
||||
"left",
|
||||
"right",
|
||||
"yes",
|
||||
"no",
|
||||
"ok",
|
||||
"okay",
|
||||
"open",
|
||||
"close",
|
||||
"take",
|
||||
"drop",
|
||||
"look",
|
||||
"pick",
|
||||
"use",
|
||||
"wait",
|
||||
"rest",
|
||||
"save",
|
||||
"attack",
|
||||
"flee",
|
||||
"jump",
|
||||
"crouch",
|
||||
}
|
||||
)
|
||||
|
||||
# T3: planning, optimisation, or recovery signals
|
||||
_T3_KEYWORDS = frozenset(
|
||||
{
|
||||
"plan",
|
||||
"strategy",
|
||||
"optimize",
|
||||
"optimise",
|
||||
"quest",
|
||||
"stuck",
|
||||
"recover",
|
||||
"multi-step",
|
||||
"long-term",
|
||||
"negotiate",
|
||||
"persuade",
|
||||
"faction",
|
||||
"reputation",
|
||||
"best",
|
||||
"optimal",
|
||||
"recommend",
|
||||
"analyze",
|
||||
"analyse",
|
||||
"evaluate",
|
||||
"decide",
|
||||
"complex",
|
||||
"how do i",
|
||||
"what should i do",
|
||||
"help me figure",
|
||||
"what is the best",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def classify_complexity(task: str, state: dict) -> ModelTier:
|
||||
"""Classify a task to the cheapest-sufficient model tier.
|
||||
|
||||
Classification priority (highest wins):
|
||||
1. T3 — any T3 keyword, stuck indicator, or ``state["require_t3"] = True``
|
||||
2. T1 — short task with only T1 keywords and no active context
|
||||
3. T2 — everything else (safe default)
|
||||
|
||||
Args:
|
||||
task: Natural-language task description or player input.
|
||||
state: Current game state dict. Recognised keys:
|
||||
``stuck`` (bool), ``require_t3`` (bool),
|
||||
``active_quests`` (list), ``dialogue_active`` (bool).
|
||||
|
||||
Returns:
|
||||
ModelTier appropriate for the task.
|
||||
"""
|
||||
task_lower = task.lower()
|
||||
words = set(task_lower.split())
|
||||
|
||||
# ── T3 signals ──────────────────────────────────────────────────────────
|
||||
t3_keyword_hit = bool(words & _T3_KEYWORDS)
|
||||
# Check multi-word T3 phrases
|
||||
t3_phrase_hit = any(phrase in task_lower for phrase in _T3_KEYWORDS if " " in phrase)
|
||||
is_stuck = bool(state.get("stuck", False))
|
||||
explicit_t3 = bool(state.get("require_t3", False))
|
||||
|
||||
if t3_keyword_hit or t3_phrase_hit or is_stuck or explicit_t3:
|
||||
logger.debug(
|
||||
"classify_complexity → T3 (keywords=%s stuck=%s explicit=%s)",
|
||||
t3_keyword_hit or t3_phrase_hit,
|
||||
is_stuck,
|
||||
explicit_t3,
|
||||
)
|
||||
return ModelTier.T3_COMPLEX
|
||||
|
||||
# ── T1 signals ──────────────────────────────────────────────────────────
|
||||
t1_keyword_hit = bool(words & _T1_KEYWORDS)
|
||||
task_short = len(task.split()) <= 6
|
||||
no_active_context = (
|
||||
not state.get("active_quests")
|
||||
and not state.get("dialogue_active")
|
||||
and not state.get("combat_active")
|
||||
)
|
||||
|
||||
if t1_keyword_hit and task_short and no_active_context:
|
||||
logger.debug("classify_complexity → T1 (keywords=%s short=%s)", t1_keyword_hit, task_short)
|
||||
return ModelTier.T1_ROUTINE
|
||||
|
||||
# ── Default: T2 ─────────────────────────────────────────────────────────
|
||||
logger.debug("classify_complexity → T2 (default)")
|
||||
return ModelTier.T2_MEDIUM
|
||||
|
||||
|
||||
def build_prompt(
|
||||
state: dict,
|
||||
ui_state: dict,
|
||||
text: str,
|
||||
visual_context: str | None = None,
|
||||
) -> list[dict]:
|
||||
"""Build an OpenAI-compatible messages list from game context.
|
||||
|
||||
Assembles a system message from structured game state and a user
|
||||
message from the player's text input. This format is accepted by
|
||||
CascadeRouter.complete() directly.
|
||||
|
||||
Args:
|
||||
state: Current game state dict. Common keys:
|
||||
``location`` (str), ``health`` (int/float),
|
||||
``inventory`` (list), ``active_quests`` (list),
|
||||
``stuck`` (bool).
|
||||
ui_state: Current UI state dict. Common keys:
|
||||
``dialogue_active`` (bool), ``dialogue_npc`` (str),
|
||||
``menu_open`` (str), ``combat_active`` (bool).
|
||||
text: Player text or task description (becomes user message).
|
||||
visual_context: Optional free-text description of the current screen
|
||||
or scene — from a vision model or rule-based extractor.
|
||||
|
||||
Returns:
|
||||
List of message dicts: [{"role": "system", ...}, {"role": "user", ...}]
|
||||
"""
|
||||
context_lines: list[str] = []
|
||||
|
||||
location = state.get("location", "unknown")
|
||||
context_lines.append(f"Location: {location}")
|
||||
|
||||
health = state.get("health")
|
||||
if health is not None:
|
||||
context_lines.append(f"Health: {health}")
|
||||
|
||||
inventory = state.get("inventory", [])
|
||||
if inventory:
|
||||
items = [i if isinstance(i, str) else i.get("name", str(i)) for i in inventory[:10]]
|
||||
context_lines.append(f"Inventory: {', '.join(items)}")
|
||||
|
||||
active_quests = state.get("active_quests", [])
|
||||
if active_quests:
|
||||
names = [q if isinstance(q, str) else q.get("name", str(q)) for q in active_quests[:5]]
|
||||
context_lines.append(f"Active quests: {', '.join(names)}")
|
||||
|
||||
if state.get("stuck"):
|
||||
context_lines.append("Status: STUCK — need recovery strategy")
|
||||
|
||||
if ui_state.get("dialogue_active"):
|
||||
npc = ui_state.get("dialogue_npc", "NPC")
|
||||
context_lines.append(f"In dialogue with: {npc}")
|
||||
|
||||
if ui_state.get("menu_open"):
|
||||
context_lines.append(f"Menu open: {ui_state['menu_open']}")
|
||||
|
||||
if ui_state.get("combat_active"):
|
||||
context_lines.append("Status: IN COMBAT")
|
||||
|
||||
if visual_context:
|
||||
context_lines.append(f"Scene: {visual_context}")
|
||||
|
||||
system_content = (
|
||||
"You are Timmy, an AI game agent. "
|
||||
"Respond with valid game commands only.\n\n" + "\n".join(context_lines)
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": system_content},
|
||||
{"role": "user", "content": text},
|
||||
]
|
||||
|
||||
|
||||
# ── Default model assignments ────────────────────────────────────────────────
|
||||
# Overridable per deployment via MetabolicRouter(tier_models={...}).
|
||||
# Model benchmarks (M3 Max 36 GB, issue #1063):
|
||||
# Qwen3-8B Q6_K — 0.933 F1 tool calling, ~45-55 tok/s (~6 GB)
|
||||
# Qwen3-14B Q5_K_M — 0.971 F1 tool calling, ~20-28 tok/s (~9.5 GB)
|
||||
# Qwen3-32B Q4_K_M — highest quality, ~8-12 tok/s (~20 GB, on demand)
|
||||
DEFAULT_TIER_MODELS: dict[ModelTier, str] = {
|
||||
ModelTier.T1_ROUTINE: "qwen3:8b",
|
||||
ModelTier.T2_MEDIUM: "qwen3:14b",
|
||||
ModelTier.T3_COMPLEX: "qwen3:30b", # Closest Ollama tag to 32B Q4
|
||||
}
|
||||
|
||||
|
||||
class MetabolicRouter:
|
||||
"""Routes LLM requests to the cheapest-sufficient model tier.
|
||||
|
||||
Wraps CascadeRouter with:
|
||||
- Complexity classification via classify_complexity()
|
||||
- Prompt assembly via build_prompt()
|
||||
- T3 world-pause / world-unpause (graceful if no world adapter)
|
||||
|
||||
Usage::
|
||||
|
||||
router = MetabolicRouter()
|
||||
|
||||
# Simple route call — classification + prompt + inference in one step
|
||||
result = await router.route(
|
||||
task="Go north",
|
||||
state={"location": "Balmora"},
|
||||
ui_state={},
|
||||
)
|
||||
print(result["content"], result["tier"])
|
||||
|
||||
# Pre-classify if you need the tier for telemetry
|
||||
tier = router.classify("Plan the best path to Vivec", game_state)
|
||||
|
||||
# Wire in world adapter for T3 pause/unpause
|
||||
router.set_world(world_adapter)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cascade: Any | None = None,
|
||||
tier_models: dict[ModelTier, str] | None = None,
|
||||
) -> None:
|
||||
"""Initialise the metabolic router.
|
||||
|
||||
Args:
|
||||
cascade: CascadeRouter instance to use. If None, the
|
||||
singleton returned by get_router() is used lazily.
|
||||
tier_models: Override default model names per tier.
|
||||
"""
|
||||
self._cascade = cascade
|
||||
self._tier_models: dict[ModelTier, str] = dict(DEFAULT_TIER_MODELS)
|
||||
if tier_models:
|
||||
self._tier_models.update(tier_models)
|
||||
self._world: Any | None = None
|
||||
|
||||
def set_world(self, world: Any) -> None:
|
||||
"""Wire in a world adapter for T3 pause / unpause support.
|
||||
|
||||
The adapter only needs to implement ``act(CommandInput)`` — the full
|
||||
WorldInterface contract is not required. A missing or broken world
|
||||
adapter degrades gracefully (logs a warning, inference continues).
|
||||
|
||||
Args:
|
||||
world: Any object with an ``act(CommandInput)`` method.
|
||||
"""
|
||||
self._world = world
|
||||
|
||||
def _get_cascade(self) -> Any:
|
||||
"""Return the CascadeRouter, creating the singleton if needed."""
|
||||
if self._cascade is None:
|
||||
from infrastructure.router.cascade import get_router
|
||||
|
||||
self._cascade = get_router()
|
||||
return self._cascade
|
||||
|
||||
def classify(self, task: str, state: dict) -> ModelTier:
|
||||
"""Classify task complexity. Delegates to classify_complexity()."""
|
||||
return classify_complexity(task, state)
|
||||
|
||||
async def _pause_world(self) -> None:
|
||||
"""Pause the game world before T3 inference (graceful degradation)."""
|
||||
if self._world is None:
|
||||
return
|
||||
try:
|
||||
from infrastructure.world.types import CommandInput
|
||||
|
||||
await asyncio.to_thread(self._world.act, CommandInput(action="pause"))
|
||||
logger.debug("MetabolicRouter: world paused for T3 inference")
|
||||
except Exception as exc:
|
||||
logger.warning("world.pause() failed — continuing without pause: %s", exc)
|
||||
|
||||
async def _unpause_world(self) -> None:
|
||||
"""Unpause the game world after T3 inference (always called, even on error)."""
|
||||
if self._world is None:
|
||||
return
|
||||
try:
|
||||
from infrastructure.world.types import CommandInput
|
||||
|
||||
await asyncio.to_thread(self._world.act, CommandInput(action="unpause"))
|
||||
logger.debug("MetabolicRouter: world unpaused after T3 inference")
|
||||
except Exception as exc:
|
||||
logger.warning("world.unpause() failed — game may remain paused: %s", exc)
|
||||
|
||||
async def route(
|
||||
self,
|
||||
task: str,
|
||||
state: dict,
|
||||
ui_state: dict | None = None,
|
||||
visual_context: str | None = None,
|
||||
temperature: float = 0.3,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict:
|
||||
"""Route a task to the appropriate model tier and return the LLM response.
|
||||
|
||||
Selects the tier via classify_complexity(), assembles the prompt via
|
||||
build_prompt(), and dispatches to CascadeRouter. For T3, the game
|
||||
world is paused before inference and unpaused after (in a finally block).
|
||||
|
||||
Args:
|
||||
task: Natural-language task description or player input.
|
||||
state: Current game state dict.
|
||||
ui_state: Current UI state dict (optional, defaults to {}).
|
||||
visual_context: Optional screen/scene description from vision model.
|
||||
temperature: Sampling temperature (default 0.3 for game commands).
|
||||
max_tokens: Maximum tokens to generate.
|
||||
|
||||
Returns:
|
||||
Dict with keys: ``content``, ``provider``, ``model``, ``tier``,
|
||||
``latency_ms``, plus any extra keys from CascadeRouter.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If all providers fail (propagated from CascadeRouter).
|
||||
"""
|
||||
ui_state = ui_state or {}
|
||||
tier = self.classify(task, state)
|
||||
model = self._tier_models[tier]
|
||||
messages = build_prompt(state, ui_state, task, visual_context)
|
||||
cascade = self._get_cascade()
|
||||
|
||||
logger.info(
|
||||
"MetabolicRouter: tier=%s model=%s task=%r",
|
||||
tier,
|
||||
model,
|
||||
task[:80],
|
||||
)
|
||||
|
||||
if tier == ModelTier.T3_COMPLEX:
|
||||
await self._pause_world()
|
||||
try:
|
||||
result = await cascade.complete(
|
||||
messages=messages,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
finally:
|
||||
await self._unpause_world()
|
||||
else:
|
||||
result = await cascade.complete(
|
||||
messages=messages,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
result["tier"] = tier
|
||||
return result
|
||||
|
||||
|
||||
# ── Module-level singleton ────────────────────────────────────────────────────
|
||||
_metabolic_router: MetabolicRouter | None = None
|
||||
|
||||
|
||||
def get_metabolic_router() -> MetabolicRouter:
|
||||
"""Get or create the MetabolicRouter singleton."""
|
||||
global _metabolic_router
|
||||
if _metabolic_router is None:
|
||||
_metabolic_router = MetabolicRouter()
|
||||
return _metabolic_router
|
||||
@@ -1,247 +0,0 @@
|
||||
"""Self-correction event logger.
|
||||
|
||||
Records instances where the agent detected its own errors and the steps
|
||||
it took to correct them. Used by the Self-Correction Dashboard to visualise
|
||||
these events and surface recurring failure patterns.
|
||||
|
||||
Usage::
|
||||
|
||||
from infrastructure.self_correction import log_self_correction, get_corrections, get_patterns
|
||||
|
||||
log_self_correction(
|
||||
source="agentic_loop",
|
||||
original_intent="Execute step 3: deploy service",
|
||||
detected_error="ConnectionRefusedError: port 8080 unavailable",
|
||||
correction_strategy="Retry on alternate port 8081",
|
||||
final_outcome="Success on retry",
|
||||
task_id="abc123",
|
||||
)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import sqlite3
|
||||
import uuid
|
||||
from collections.abc import Generator
|
||||
from contextlib import closing, contextmanager
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Database
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_DB_PATH: Path | None = None
|
||||
|
||||
|
||||
def _get_db_path() -> Path:
|
||||
global _DB_PATH
|
||||
if _DB_PATH is None:
|
||||
from config import settings
|
||||
|
||||
_DB_PATH = Path(settings.repo_root) / "data" / "self_correction.db"
|
||||
return _DB_PATH
|
||||
|
||||
|
||||
@contextmanager
|
||||
def _get_db() -> Generator[sqlite3.Connection, None, None]:
|
||||
db_path = _get_db_path()
|
||||
db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with closing(sqlite3.connect(str(db_path))) as conn:
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS self_correction_events (
|
||||
id TEXT PRIMARY KEY,
|
||||
source TEXT NOT NULL,
|
||||
task_id TEXT DEFAULT '',
|
||||
original_intent TEXT NOT NULL,
|
||||
detected_error TEXT NOT NULL,
|
||||
correction_strategy TEXT NOT NULL,
|
||||
final_outcome TEXT NOT NULL,
|
||||
outcome_status TEXT DEFAULT 'success',
|
||||
error_type TEXT DEFAULT '',
|
||||
created_at TEXT DEFAULT (datetime('now'))
|
||||
)
|
||||
""")
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_sc_created ON self_correction_events(created_at)"
|
||||
)
|
||||
conn.execute(
|
||||
"CREATE INDEX IF NOT EXISTS idx_sc_error_type ON self_correction_events(error_type)"
|
||||
)
|
||||
conn.commit()
|
||||
yield conn
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Write
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def log_self_correction(
|
||||
*,
|
||||
source: str,
|
||||
original_intent: str,
|
||||
detected_error: str,
|
||||
correction_strategy: str,
|
||||
final_outcome: str,
|
||||
task_id: str = "",
|
||||
outcome_status: str = "success",
|
||||
error_type: str = "",
|
||||
) -> str:
|
||||
"""Record a self-correction event and return its ID.
|
||||
|
||||
Args:
|
||||
source: Module or component that triggered the correction.
|
||||
original_intent: What the agent was trying to do.
|
||||
detected_error: The error or problem that was detected.
|
||||
correction_strategy: How the agent attempted to correct the error.
|
||||
final_outcome: What the result of the correction attempt was.
|
||||
task_id: Optional task/session ID for correlation.
|
||||
outcome_status: 'success', 'partial', or 'failed'.
|
||||
error_type: Short category label for pattern analysis (e.g.
|
||||
'ConnectionError', 'TimeoutError').
|
||||
|
||||
Returns:
|
||||
The ID of the newly created record.
|
||||
"""
|
||||
event_id = str(uuid.uuid4())
|
||||
if not error_type:
|
||||
# Derive a simple type from the first word of the detected error
|
||||
error_type = detected_error.split(":")[0].strip()[:64]
|
||||
|
||||
try:
|
||||
with _get_db() as conn:
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT INTO self_correction_events
|
||||
(id, source, task_id, original_intent, detected_error,
|
||||
correction_strategy, final_outcome, outcome_status, error_type)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
event_id,
|
||||
source,
|
||||
task_id,
|
||||
original_intent[:2000],
|
||||
detected_error[:2000],
|
||||
correction_strategy[:2000],
|
||||
final_outcome[:2000],
|
||||
outcome_status,
|
||||
error_type,
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
logger.info(
|
||||
"Self-correction logged [%s] source=%s error_type=%s status=%s",
|
||||
event_id[:8],
|
||||
source,
|
||||
error_type,
|
||||
outcome_status,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to log self-correction event: %s", exc)
|
||||
|
||||
return event_id
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Read
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def get_corrections(limit: int = 50) -> list[dict]:
|
||||
"""Return the most recent self-correction events, newest first."""
|
||||
try:
|
||||
with _get_db() as conn:
|
||||
rows = conn.execute(
|
||||
"""
|
||||
SELECT * FROM self_correction_events
|
||||
ORDER BY created_at DESC
|
||||
LIMIT ?
|
||||
""",
|
||||
(limit,),
|
||||
).fetchall()
|
||||
return [dict(r) for r in rows]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to fetch self-correction events: %s", exc)
|
||||
return []
|
||||
|
||||
|
||||
def get_patterns(top_n: int = 10) -> list[dict]:
|
||||
"""Return the most common recurring error types with counts.
|
||||
|
||||
Each entry has:
|
||||
- error_type: category label
|
||||
- count: total occurrences
|
||||
- success_count: corrected successfully
|
||||
- failed_count: correction also failed
|
||||
- last_seen: ISO timestamp of most recent occurrence
|
||||
"""
|
||||
try:
|
||||
with _get_db() as conn:
|
||||
rows = conn.execute(
|
||||
"""
|
||||
SELECT
|
||||
error_type,
|
||||
COUNT(*) AS count,
|
||||
SUM(CASE WHEN outcome_status = 'success' THEN 1 ELSE 0 END) AS success_count,
|
||||
SUM(CASE WHEN outcome_status = 'failed' THEN 1 ELSE 0 END) AS failed_count,
|
||||
MAX(created_at) AS last_seen
|
||||
FROM self_correction_events
|
||||
GROUP BY error_type
|
||||
ORDER BY count DESC
|
||||
LIMIT ?
|
||||
""",
|
||||
(top_n,),
|
||||
).fetchall()
|
||||
return [dict(r) for r in rows]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to fetch self-correction patterns: %s", exc)
|
||||
return []
|
||||
|
||||
|
||||
def get_stats() -> dict:
|
||||
"""Return aggregate statistics for the summary panel."""
|
||||
try:
|
||||
with _get_db() as conn:
|
||||
row = conn.execute(
|
||||
"""
|
||||
SELECT
|
||||
COUNT(*) AS total,
|
||||
SUM(CASE WHEN outcome_status = 'success' THEN 1 ELSE 0 END) AS success_count,
|
||||
SUM(CASE WHEN outcome_status = 'partial' THEN 1 ELSE 0 END) AS partial_count,
|
||||
SUM(CASE WHEN outcome_status = 'failed' THEN 1 ELSE 0 END) AS failed_count,
|
||||
COUNT(DISTINCT error_type) AS unique_error_types,
|
||||
COUNT(DISTINCT source) AS sources
|
||||
FROM self_correction_events
|
||||
"""
|
||||
).fetchone()
|
||||
if row is None:
|
||||
return _empty_stats()
|
||||
d = dict(row)
|
||||
total = d.get("total") or 0
|
||||
if total:
|
||||
d["success_rate"] = round((d.get("success_count") or 0) / total * 100)
|
||||
else:
|
||||
d["success_rate"] = 0
|
||||
return d
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to fetch self-correction stats: %s", exc)
|
||||
return _empty_stats()
|
||||
|
||||
|
||||
def _empty_stats() -> dict:
|
||||
return {
|
||||
"total": 0,
|
||||
"success_count": 0,
|
||||
"partial_count": 0,
|
||||
"failed_count": 0,
|
||||
"unique_error_types": 0,
|
||||
"sources": 0,
|
||||
"success_rate": 0,
|
||||
}
|
||||
@@ -24,8 +24,6 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass
|
||||
class Intent:
|
||||
"""A classified user intent with confidence score and extracted entities."""
|
||||
|
||||
name: str
|
||||
confidence: float # 0.0 to 1.0
|
||||
entities: dict
|
||||
|
||||
@@ -17,15 +17,11 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TxType(StrEnum):
|
||||
"""Lightning transaction direction type."""
|
||||
|
||||
incoming = "incoming"
|
||||
outgoing = "outgoing"
|
||||
|
||||
|
||||
class TxStatus(StrEnum):
|
||||
"""Lightning transaction settlement status."""
|
||||
|
||||
pending = "pending"
|
||||
settled = "settled"
|
||||
failed = "failed"
|
||||
|
||||
@@ -1,7 +0,0 @@
|
||||
"""Self-coding package — Timmy's self-modification capability.
|
||||
|
||||
Provides the branch→edit→test→commit/revert loop that allows Timmy
|
||||
to propose and apply code changes autonomously, gated by the test suite.
|
||||
|
||||
Main entry point: ``self_coding.self_modify.loop``
|
||||
"""
|
||||
@@ -1,129 +0,0 @@
|
||||
"""Gitea REST client — thin wrapper for PR creation and issue commenting.
|
||||
|
||||
Uses ``settings.gitea_url``, ``settings.gitea_token``, and
|
||||
``settings.gitea_repo`` (owner/repo) from config. Degrades gracefully
|
||||
when the token is absent or the server is unreachable.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PullRequest:
|
||||
"""Minimal representation of a created pull request."""
|
||||
|
||||
number: int
|
||||
title: str
|
||||
html_url: str
|
||||
|
||||
|
||||
class GiteaClient:
|
||||
"""HTTP client for Gitea's REST API v1.
|
||||
|
||||
All methods return structured results and never raise — errors are
|
||||
logged at WARNING level and indicated via return value.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: str | None = None,
|
||||
token: str | None = None,
|
||||
repo: str | None = None,
|
||||
) -> None:
|
||||
from config import settings
|
||||
|
||||
self._base_url = (base_url or settings.gitea_url).rstrip("/")
|
||||
self._token = token or settings.gitea_token
|
||||
self._repo = repo or settings.gitea_repo
|
||||
|
||||
# ── internal ────────────────────────────────────────────────────────────
|
||||
|
||||
def _headers(self) -> dict[str, str]:
|
||||
return {
|
||||
"Authorization": f"token {self._token}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
def _api(self, path: str) -> str:
|
||||
return f"{self._base_url}/api/v1/{path.lstrip('/')}"
|
||||
|
||||
# ── public API ───────────────────────────────────────────────────────────
|
||||
|
||||
def create_pull_request(
|
||||
self,
|
||||
title: str,
|
||||
body: str,
|
||||
head: str,
|
||||
base: str = "main",
|
||||
) -> PullRequest | None:
|
||||
"""Open a pull request.
|
||||
|
||||
Args:
|
||||
title: PR title (keep under 70 chars).
|
||||
body: PR body in markdown.
|
||||
head: Source branch (e.g. ``self-modify/issue-983``).
|
||||
base: Target branch (default ``main``).
|
||||
|
||||
Returns:
|
||||
A ``PullRequest`` dataclass on success, ``None`` on failure.
|
||||
"""
|
||||
if not self._token:
|
||||
logger.warning("Gitea token not configured — skipping PR creation")
|
||||
return None
|
||||
|
||||
try:
|
||||
import requests as _requests
|
||||
|
||||
resp = _requests.post(
|
||||
self._api(f"repos/{self._repo}/pulls"),
|
||||
headers=self._headers(),
|
||||
json={"title": title, "body": body, "head": head, "base": base},
|
||||
timeout=15,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
pr = PullRequest(
|
||||
number=data["number"],
|
||||
title=data["title"],
|
||||
html_url=data["html_url"],
|
||||
)
|
||||
logger.info("PR #%d created: %s", pr.number, pr.html_url)
|
||||
return pr
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to create PR: %s", exc)
|
||||
return None
|
||||
|
||||
def add_issue_comment(self, issue_number: int, body: str) -> bool:
|
||||
"""Post a comment on an issue or PR.
|
||||
|
||||
Returns:
|
||||
True on success, False on failure.
|
||||
"""
|
||||
if not self._token:
|
||||
logger.warning("Gitea token not configured — skipping issue comment")
|
||||
return False
|
||||
|
||||
try:
|
||||
import requests as _requests
|
||||
|
||||
resp = _requests.post(
|
||||
self._api(f"repos/{self._repo}/issues/{issue_number}/comments"),
|
||||
headers=self._headers(),
|
||||
json={"body": body},
|
||||
timeout=15,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
logger.info("Comment posted on issue #%d", issue_number)
|
||||
return True
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to post comment on issue #%d: %s", issue_number, exc)
|
||||
return False
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
gitea_client = GiteaClient()
|
||||
@@ -1 +0,0 @@
|
||||
"""Self-modification loop sub-package."""
|
||||
@@ -1,301 +0,0 @@
|
||||
"""Self-modification loop — branch → edit → test → commit/revert.
|
||||
|
||||
Timmy's self-coding capability, restored after deletion in
|
||||
Operation Darling Purge (commit 584eeb679e88).
|
||||
|
||||
## Cycle
|
||||
1. **Branch** — create ``self-modify/<slug>`` from ``main``
|
||||
2. **Edit** — apply the proposed change (patch string or callable)
|
||||
3. **Test** — run ``pytest tests/ -x -q``; never commit on failure
|
||||
4. **Commit** — stage and commit on green; revert branch on red
|
||||
5. **PR** — open a Gitea pull request (requires no direct push to main)
|
||||
|
||||
## Guards
|
||||
- Never push directly to ``main`` or ``master``
|
||||
- All changes land via PR (enforced by ``_guard_branch``)
|
||||
- Test gate is mandatory; ``skip_tests=True`` is for unit-test use only
|
||||
- Commits only happen when ``pytest tests/ -x -q`` exits 0
|
||||
|
||||
## Usage::
|
||||
|
||||
from self_coding.self_modify.loop import SelfModifyLoop
|
||||
|
||||
loop = SelfModifyLoop()
|
||||
result = await loop.run(
|
||||
slug="add-hello-tool",
|
||||
description="Add hello() convenience tool",
|
||||
edit_fn=my_edit_function, # callable(repo_root: str) -> None
|
||||
)
|
||||
if result.success:
|
||||
print(f"PR: {result.pr_url}")
|
||||
else:
|
||||
print(f"Failed: {result.error}")
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import subprocess
|
||||
import time
|
||||
from collections.abc import Callable
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Branches that must never receive direct commits
|
||||
_PROTECTED_BRANCHES = frozenset({"main", "master", "develop"})
|
||||
|
||||
# Test command used as the commit gate
|
||||
_TEST_COMMAND = ["pytest", "tests/", "-x", "-q", "--tb=short"]
|
||||
|
||||
# Max time (seconds) to wait for the test suite
|
||||
_TEST_TIMEOUT = 300
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoopResult:
|
||||
"""Result from one self-modification cycle."""
|
||||
|
||||
success: bool
|
||||
branch: str = ""
|
||||
commit_sha: str = ""
|
||||
pr_url: str = ""
|
||||
pr_number: int = 0
|
||||
test_output: str = ""
|
||||
error: str = ""
|
||||
elapsed_ms: float = 0.0
|
||||
metadata: dict = field(default_factory=dict)
|
||||
|
||||
|
||||
class SelfModifyLoop:
|
||||
"""Orchestrate branch → edit → test → commit/revert → PR.
|
||||
|
||||
Args:
|
||||
repo_root: Absolute path to the git repository (defaults to
|
||||
``settings.repo_root``).
|
||||
remote: Git remote name (default ``origin``).
|
||||
base_branch: Branch to fork from and target for the PR
|
||||
(default ``main``).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
repo_root: str | None = None,
|
||||
remote: str = "origin",
|
||||
base_branch: str = "main",
|
||||
) -> None:
|
||||
self._repo_root = Path(repo_root or settings.repo_root)
|
||||
self._remote = remote
|
||||
self._base_branch = base_branch
|
||||
|
||||
# ── public ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def run(
|
||||
self,
|
||||
slug: str,
|
||||
description: str,
|
||||
edit_fn: Callable[[str], None],
|
||||
issue_number: int | None = None,
|
||||
skip_tests: bool = False,
|
||||
) -> LoopResult:
|
||||
"""Execute one full self-modification cycle.
|
||||
|
||||
Args:
|
||||
slug: Short identifier used for the branch name
|
||||
(e.g. ``"add-hello-tool"``).
|
||||
description: Human-readable description for commit message
|
||||
and PR body.
|
||||
edit_fn: Callable that receives the repo root path (str)
|
||||
and applies the desired code changes in-place.
|
||||
issue_number: Optional Gitea issue number to reference in PR.
|
||||
skip_tests: If ``True``, skip the test gate (unit-test use
|
||||
only — never use in production).
|
||||
|
||||
Returns:
|
||||
:class:`LoopResult` describing the outcome.
|
||||
"""
|
||||
start = time.time()
|
||||
branch = f"self-modify/{slug}"
|
||||
|
||||
try:
|
||||
self._guard_branch(branch)
|
||||
self._checkout_base()
|
||||
self._create_branch(branch)
|
||||
|
||||
try:
|
||||
edit_fn(str(self._repo_root))
|
||||
except Exception as exc:
|
||||
self._revert_branch(branch)
|
||||
return LoopResult(
|
||||
success=False,
|
||||
branch=branch,
|
||||
error=f"edit_fn raised: {exc}",
|
||||
elapsed_ms=self._elapsed(start),
|
||||
)
|
||||
|
||||
if not skip_tests:
|
||||
test_output, passed = self._run_tests()
|
||||
if not passed:
|
||||
self._revert_branch(branch)
|
||||
return LoopResult(
|
||||
success=False,
|
||||
branch=branch,
|
||||
test_output=test_output,
|
||||
error="Tests failed — branch reverted",
|
||||
elapsed_ms=self._elapsed(start),
|
||||
)
|
||||
else:
|
||||
test_output = "(tests skipped)"
|
||||
|
||||
sha = self._commit_all(description)
|
||||
self._push_branch(branch)
|
||||
|
||||
pr = self._create_pr(
|
||||
branch=branch,
|
||||
description=description,
|
||||
test_output=test_output,
|
||||
issue_number=issue_number,
|
||||
)
|
||||
|
||||
return LoopResult(
|
||||
success=True,
|
||||
branch=branch,
|
||||
commit_sha=sha,
|
||||
pr_url=pr.html_url if pr else "",
|
||||
pr_number=pr.number if pr else 0,
|
||||
test_output=test_output,
|
||||
elapsed_ms=self._elapsed(start),
|
||||
)
|
||||
|
||||
except Exception as exc:
|
||||
logger.warning("Self-modify loop failed: %s", exc)
|
||||
return LoopResult(
|
||||
success=False,
|
||||
branch=branch,
|
||||
error=str(exc),
|
||||
elapsed_ms=self._elapsed(start),
|
||||
)
|
||||
|
||||
# ── private helpers ──────────────────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _elapsed(start: float) -> float:
|
||||
return (time.time() - start) * 1000
|
||||
|
||||
def _git(self, *args: str, check: bool = True) -> subprocess.CompletedProcess:
|
||||
"""Run a git command in the repo root."""
|
||||
cmd = ["git", *args]
|
||||
logger.debug("git %s", " ".join(args))
|
||||
return subprocess.run(
|
||||
cmd,
|
||||
cwd=str(self._repo_root),
|
||||
capture_output=True,
|
||||
text=True,
|
||||
check=check,
|
||||
)
|
||||
|
||||
def _guard_branch(self, branch: str) -> None:
|
||||
"""Raise if the target branch is a protected branch name."""
|
||||
if branch in _PROTECTED_BRANCHES:
|
||||
raise ValueError(
|
||||
f"Refusing to operate on protected branch '{branch}'. "
|
||||
"All self-modifications must go via PR."
|
||||
)
|
||||
|
||||
def _checkout_base(self) -> None:
|
||||
"""Checkout the base branch and pull latest."""
|
||||
self._git("checkout", self._base_branch)
|
||||
# Best-effort pull; ignore failures (e.g. no remote configured)
|
||||
self._git("pull", self._remote, self._base_branch, check=False)
|
||||
|
||||
def _create_branch(self, branch: str) -> None:
|
||||
"""Create and checkout a new branch, deleting an old one if needed."""
|
||||
# Delete local branch if it already exists (stale prior attempt)
|
||||
self._git("branch", "-D", branch, check=False)
|
||||
self._git("checkout", "-b", branch)
|
||||
logger.info("Created branch: %s", branch)
|
||||
|
||||
def _revert_branch(self, branch: str) -> None:
|
||||
"""Checkout base and delete the failed branch."""
|
||||
try:
|
||||
self._git("checkout", self._base_branch, check=False)
|
||||
self._git("branch", "-D", branch, check=False)
|
||||
logger.info("Reverted and deleted branch: %s", branch)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to revert branch %s: %s", branch, exc)
|
||||
|
||||
def _run_tests(self) -> tuple[str, bool]:
|
||||
"""Run the test suite. Returns (output, passed)."""
|
||||
logger.info("Running test suite: %s", " ".join(_TEST_COMMAND))
|
||||
try:
|
||||
result = subprocess.run(
|
||||
_TEST_COMMAND,
|
||||
cwd=str(self._repo_root),
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=_TEST_TIMEOUT,
|
||||
)
|
||||
output = (result.stdout + "\n" + result.stderr).strip()
|
||||
passed = result.returncode == 0
|
||||
logger.info(
|
||||
"Test suite %s (exit %d)", "PASSED" if passed else "FAILED", result.returncode
|
||||
)
|
||||
return output, passed
|
||||
except subprocess.TimeoutExpired:
|
||||
msg = f"Test suite timed out after {_TEST_TIMEOUT}s"
|
||||
logger.warning(msg)
|
||||
return msg, False
|
||||
except FileNotFoundError:
|
||||
msg = "pytest not found on PATH"
|
||||
logger.warning(msg)
|
||||
return msg, False
|
||||
|
||||
def _commit_all(self, message: str) -> str:
|
||||
"""Stage all changes and create a commit. Returns the new SHA."""
|
||||
self._git("add", "-A")
|
||||
self._git("commit", "-m", message)
|
||||
result = self._git("rev-parse", "HEAD")
|
||||
sha = result.stdout.strip()
|
||||
logger.info("Committed: %s sha=%s", message[:60], sha[:12])
|
||||
return sha
|
||||
|
||||
def _push_branch(self, branch: str) -> None:
|
||||
"""Push the branch to the remote."""
|
||||
self._git("push", "-u", self._remote, branch)
|
||||
logger.info("Pushed branch: %s -> %s", branch, self._remote)
|
||||
|
||||
def _create_pr(
|
||||
self,
|
||||
branch: str,
|
||||
description: str,
|
||||
test_output: str,
|
||||
issue_number: int | None,
|
||||
):
|
||||
"""Open a Gitea PR. Returns PullRequest or None on failure."""
|
||||
from self_coding.gitea_client import GiteaClient
|
||||
|
||||
client = GiteaClient()
|
||||
|
||||
issue_ref = f"\n\nFixes #{issue_number}" if issue_number else ""
|
||||
test_section = (
|
||||
f"\n\n## Test results\n```\n{test_output[:2000]}\n```"
|
||||
if test_output and test_output != "(tests skipped)"
|
||||
else ""
|
||||
)
|
||||
|
||||
body = (
|
||||
f"## Summary\n{description}"
|
||||
f"{issue_ref}"
|
||||
f"{test_section}"
|
||||
"\n\n🤖 Generated by Timmy's self-modification loop"
|
||||
)
|
||||
|
||||
return client.create_pull_request(
|
||||
title=f"[self-modify] {description[:60]}",
|
||||
body=body,
|
||||
head=branch,
|
||||
base=self._base_branch,
|
||||
)
|
||||
@@ -301,26 +301,6 @@ def create_timmy(
|
||||
|
||||
return GrokBackend()
|
||||
|
||||
if resolved == "airllm":
|
||||
# AirLLM requires Apple Silicon. On any other platform (Intel Mac, Linux,
|
||||
# Windows) or when the package is not installed, degrade silently to Ollama.
|
||||
from timmy.backends import is_apple_silicon
|
||||
|
||||
if not is_apple_silicon():
|
||||
logger.warning(
|
||||
"TIMMY_MODEL_BACKEND=airllm requested but not running on Apple Silicon "
|
||||
"— falling back to Ollama"
|
||||
)
|
||||
else:
|
||||
try:
|
||||
import airllm # noqa: F401
|
||||
except ImportError:
|
||||
logger.warning(
|
||||
"AirLLM not installed — falling back to Ollama. "
|
||||
"Install with: pip install 'airllm[mlx]'"
|
||||
)
|
||||
# Fall through to Ollama in all cases (AirLLM integration is scaffolded)
|
||||
|
||||
# Default: Ollama via Agno.
|
||||
model_name, is_fallback = _resolve_model_with_fallback(
|
||||
requested_model=None,
|
||||
|
||||
@@ -312,13 +312,6 @@ async def _handle_step_failure(
|
||||
"adaptation": step.result[:200],
|
||||
},
|
||||
)
|
||||
_log_self_correction(
|
||||
task_id=task_id,
|
||||
step_desc=step_desc,
|
||||
exc=exc,
|
||||
outcome=step.result,
|
||||
outcome_status="success",
|
||||
)
|
||||
if on_progress:
|
||||
await on_progress(f"[Adapted] {step_desc}", step_num, total_steps)
|
||||
except Exception as adapt_exc: # broad catch intentional
|
||||
@@ -332,42 +325,9 @@ async def _handle_step_failure(
|
||||
duration_ms=int((time.monotonic() - step_start) * 1000),
|
||||
)
|
||||
)
|
||||
_log_self_correction(
|
||||
task_id=task_id,
|
||||
step_desc=step_desc,
|
||||
exc=exc,
|
||||
outcome=f"Adaptation also failed: {adapt_exc}",
|
||||
outcome_status="failed",
|
||||
)
|
||||
completed_results.append(f"Step {step_num}: FAILED")
|
||||
|
||||
|
||||
def _log_self_correction(
|
||||
*,
|
||||
task_id: str,
|
||||
step_desc: str,
|
||||
exc: Exception,
|
||||
outcome: str,
|
||||
outcome_status: str,
|
||||
) -> None:
|
||||
"""Best-effort: log a self-correction event (never raises)."""
|
||||
try:
|
||||
from infrastructure.self_correction import log_self_correction
|
||||
|
||||
log_self_correction(
|
||||
source="agentic_loop",
|
||||
original_intent=step_desc,
|
||||
detected_error=f"{type(exc).__name__}: {exc}",
|
||||
correction_strategy="Adaptive re-plan via LLM",
|
||||
final_outcome=outcome[:500],
|
||||
task_id=task_id,
|
||||
outcome_status=outcome_status,
|
||||
error_type=type(exc).__name__,
|
||||
)
|
||||
except Exception as log_exc:
|
||||
logger.debug("Self-correction log failed: %s", log_exc)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Core loop
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@@ -36,8 +36,6 @@ _EXPIRY_DAYS = 7
|
||||
|
||||
@dataclass
|
||||
class ApprovalItem:
|
||||
"""A proposed autonomous action requiring owner approval."""
|
||||
|
||||
id: str
|
||||
title: str
|
||||
description: str
|
||||
|
||||
@@ -8,7 +8,7 @@ Flow:
|
||||
1. prepare_experiment — clone repo + run data prep
|
||||
2. run_experiment — execute train.py with wall-clock timeout
|
||||
3. evaluate_result — compare metric against baseline
|
||||
4. SystemExperiment — orchestrate the full cycle via class interface
|
||||
4. experiment_loop — orchestrate the full cycle
|
||||
|
||||
All subprocess calls are guarded with timeouts for graceful degradation.
|
||||
"""
|
||||
@@ -17,12 +17,9 @@ from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import re
|
||||
import subprocess
|
||||
import time
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
@@ -32,61 +29,15 @@ DEFAULT_REPO = "https://github.com/karpathy/autoresearch.git"
|
||||
_METRIC_RE = re.compile(r"val_bpb[:\s]+([0-9]+\.?[0-9]*)")
|
||||
|
||||
|
||||
# ── Higher-is-better metric names ────────────────────────────────────────────
|
||||
_HIGHER_IS_BETTER = frozenset({"unit_pass_rate", "coverage"})
|
||||
|
||||
|
||||
def is_apple_silicon() -> bool:
|
||||
"""Return True when running on Apple Silicon (M-series chip)."""
|
||||
return platform.system() == "Darwin" and platform.machine() == "arm64"
|
||||
|
||||
|
||||
def _build_experiment_env(
|
||||
dataset: str = "tinystories",
|
||||
backend: str = "auto",
|
||||
) -> dict[str, str]:
|
||||
"""Build environment variables for an autoresearch subprocess.
|
||||
|
||||
Args:
|
||||
dataset: Dataset name forwarded as ``AUTORESEARCH_DATASET``.
|
||||
``"tinystories"`` is recommended for Apple Silicon (lower entropy,
|
||||
faster iteration).
|
||||
backend: Inference backend forwarded as ``AUTORESEARCH_BACKEND``.
|
||||
``"auto"`` enables MLX on Apple Silicon; ``"cpu"`` forces CPU.
|
||||
|
||||
Returns:
|
||||
Merged environment dict (inherits current process env).
|
||||
"""
|
||||
env = os.environ.copy()
|
||||
env["AUTORESEARCH_DATASET"] = dataset
|
||||
|
||||
if backend == "auto":
|
||||
env["AUTORESEARCH_BACKEND"] = "mlx" if is_apple_silicon() else "cuda"
|
||||
else:
|
||||
env["AUTORESEARCH_BACKEND"] = backend
|
||||
|
||||
return env
|
||||
|
||||
|
||||
def prepare_experiment(
|
||||
workspace: Path,
|
||||
repo_url: str = DEFAULT_REPO,
|
||||
dataset: str = "tinystories",
|
||||
backend: str = "auto",
|
||||
) -> str:
|
||||
"""Clone autoresearch repo and run data preparation.
|
||||
|
||||
On Apple Silicon the ``dataset`` defaults to ``"tinystories"`` (lower
|
||||
entropy, faster iteration) and ``backend`` to ``"auto"`` which resolves to
|
||||
MLX. Both values are forwarded as ``AUTORESEARCH_DATASET`` /
|
||||
``AUTORESEARCH_BACKEND`` environment variables so that ``prepare.py`` and
|
||||
``train.py`` can adapt their behaviour without CLI changes.
|
||||
|
||||
Args:
|
||||
workspace: Directory to set up the experiment in.
|
||||
repo_url: Git URL for the autoresearch repository.
|
||||
dataset: Dataset name; ``"tinystories"`` is recommended on Mac.
|
||||
backend: Inference backend; ``"auto"`` picks MLX on Apple Silicon.
|
||||
|
||||
Returns:
|
||||
Status message describing what was prepared.
|
||||
@@ -108,14 +59,6 @@ def prepare_experiment(
|
||||
else:
|
||||
logger.info("Autoresearch repo already present at %s", repo_dir)
|
||||
|
||||
env = _build_experiment_env(dataset=dataset, backend=backend)
|
||||
if is_apple_silicon():
|
||||
logger.info(
|
||||
"Apple Silicon detected — dataset=%s backend=%s",
|
||||
env["AUTORESEARCH_DATASET"],
|
||||
env["AUTORESEARCH_BACKEND"],
|
||||
)
|
||||
|
||||
# Run prepare.py (data download + tokeniser training)
|
||||
prepare_script = repo_dir / "prepare.py"
|
||||
if prepare_script.exists():
|
||||
@@ -126,7 +69,6 @@ def prepare_experiment(
|
||||
text=True,
|
||||
cwd=str(repo_dir),
|
||||
timeout=300,
|
||||
env=env,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
return f"Preparation failed: {result.stderr.strip()[:500]}"
|
||||
@@ -139,8 +81,6 @@ def run_experiment(
|
||||
workspace: Path,
|
||||
timeout: int = 300,
|
||||
metric_name: str = "val_bpb",
|
||||
dataset: str = "tinystories",
|
||||
backend: str = "auto",
|
||||
) -> dict[str, Any]:
|
||||
"""Run a single training experiment with a wall-clock timeout.
|
||||
|
||||
@@ -148,9 +88,6 @@ def run_experiment(
|
||||
workspace: Experiment workspace (contains autoresearch/ subdir).
|
||||
timeout: Maximum wall-clock seconds for the run.
|
||||
metric_name: Name of the metric to extract from stdout.
|
||||
dataset: Dataset forwarded to the subprocess via env var.
|
||||
backend: Inference backend forwarded via env var (``"auto"`` → MLX on
|
||||
Apple Silicon, CUDA otherwise).
|
||||
|
||||
Returns:
|
||||
Dict with keys: metric (float|None), log (str), duration_s (int),
|
||||
@@ -168,7 +105,6 @@ def run_experiment(
|
||||
"error": f"train.py not found in {repo_dir}",
|
||||
}
|
||||
|
||||
env = _build_experiment_env(dataset=dataset, backend=backend)
|
||||
start = time.monotonic()
|
||||
try:
|
||||
result = subprocess.run(
|
||||
@@ -177,7 +113,6 @@ def run_experiment(
|
||||
text=True,
|
||||
cwd=str(repo_dir),
|
||||
timeout=timeout,
|
||||
env=env,
|
||||
)
|
||||
duration = int(time.monotonic() - start)
|
||||
output = result.stdout + result.stderr
|
||||
@@ -190,7 +125,7 @@ def run_experiment(
|
||||
"log": output[-2000:], # Keep last 2k chars
|
||||
"duration_s": duration,
|
||||
"success": result.returncode == 0,
|
||||
"error": (None if result.returncode == 0 else f"Exit code {result.returncode}"),
|
||||
"error": None if result.returncode == 0 else f"Exit code {result.returncode}",
|
||||
}
|
||||
except subprocess.TimeoutExpired:
|
||||
duration = int(time.monotonic() - start)
|
||||
@@ -277,369 +212,3 @@ def _append_result(workspace: Path, result: dict[str, Any]) -> None:
|
||||
results_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
with results_file.open("a") as f:
|
||||
f.write(json.dumps(result) + "\n")
|
||||
|
||||
|
||||
def _extract_pass_rate(output: str) -> float | None:
|
||||
"""Extract pytest pass rate as a percentage from tox/pytest output."""
|
||||
passed_m = re.search(r"(\d+) passed", output)
|
||||
failed_m = re.search(r"(\d+) failed", output)
|
||||
if passed_m:
|
||||
passed = int(passed_m.group(1))
|
||||
failed = int(failed_m.group(1)) if failed_m else 0
|
||||
total = passed + failed
|
||||
return (passed / total * 100.0) if total > 0 else 100.0
|
||||
return None
|
||||
|
||||
|
||||
def _extract_coverage(output: str) -> float | None:
|
||||
"""Extract total coverage percentage from coverage output."""
|
||||
coverage_m = re.search(r"(?:TOTAL\s+\d+\s+\d+\s+|Total coverage:\s*)(\d+)%", output)
|
||||
if coverage_m:
|
||||
try:
|
||||
return float(coverage_m.group(1))
|
||||
except ValueError:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
class SystemExperiment:
|
||||
"""An autoresearch experiment targeting a specific module with a configurable metric.
|
||||
|
||||
Encapsulates the hypothesis → edit → tox → evaluate → commit/revert loop
|
||||
for a single target file or module.
|
||||
|
||||
Args:
|
||||
target: Path or module name to optimise (e.g. ``src/timmy/agent.py``).
|
||||
metric: Metric to extract from tox output. Built-in values:
|
||||
``unit_pass_rate`` (default), ``coverage``, ``val_bpb``.
|
||||
Any other value is forwarded to :func:`_extract_metric`.
|
||||
budget_minutes: Wall-clock budget per experiment (default 5 min).
|
||||
workspace: Working directory for subprocess calls. Defaults to ``cwd``.
|
||||
revert_on_failure: Whether to revert changes on failed experiments.
|
||||
hypothesis: Optional natural language hypothesis for the experiment.
|
||||
metric_fn: Optional callable for custom metric extraction.
|
||||
If provided, overrides built-in metric extraction.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
target: str,
|
||||
metric: str = "unit_pass_rate",
|
||||
budget_minutes: int = 5,
|
||||
workspace: Path | None = None,
|
||||
revert_on_failure: bool = True,
|
||||
hypothesis: str = "",
|
||||
metric_fn: Callable[[str], float | None] | None = None,
|
||||
) -> None:
|
||||
self.target = target
|
||||
self.metric = metric
|
||||
self.budget_seconds = budget_minutes * 60
|
||||
self.workspace = Path(workspace) if workspace else Path.cwd()
|
||||
self.revert_on_failure = revert_on_failure
|
||||
self.hypothesis = hypothesis
|
||||
self.metric_fn = metric_fn
|
||||
self.results: list[dict[str, Any]] = []
|
||||
self.baseline: float | None = None
|
||||
|
||||
# ── Hypothesis generation ─────────────────────────────────────────────────
|
||||
|
||||
def generate_hypothesis(self, program_content: str = "") -> str:
|
||||
"""Return a plain-English hypothesis for the next experiment.
|
||||
|
||||
Uses the first non-empty line of *program_content* when available;
|
||||
falls back to a generic description based on target and metric.
|
||||
"""
|
||||
first_line = ""
|
||||
for line in program_content.splitlines():
|
||||
stripped = line.strip()
|
||||
if stripped and not stripped.startswith("#"):
|
||||
first_line = stripped[:120]
|
||||
break
|
||||
if first_line:
|
||||
return f"[{self.target}] {first_line}"
|
||||
return f"Improve {self.metric} for {self.target}"
|
||||
|
||||
# ── Edit phase ────────────────────────────────────────────────────────────
|
||||
|
||||
def apply_edit(self, hypothesis: str, model: str = "qwen3:30b") -> str:
|
||||
"""Apply code edits to *target* via Aider.
|
||||
|
||||
Returns a status string. Degrades gracefully — never raises.
|
||||
"""
|
||||
prompt = f"Edit {self.target}: {hypothesis}"
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["aider", "--no-git", "--model", f"ollama/{model}", "--quiet", prompt],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=self.budget_seconds,
|
||||
cwd=str(self.workspace),
|
||||
)
|
||||
if result.returncode == 0:
|
||||
return result.stdout or "Edit applied."
|
||||
return f"Aider error (exit {result.returncode}): {result.stderr[:500]}"
|
||||
except FileNotFoundError:
|
||||
logger.warning("Aider not installed — edit skipped")
|
||||
return "Aider not available — edit skipped"
|
||||
except subprocess.TimeoutExpired:
|
||||
logger.warning("Aider timed out after %ds", self.budget_seconds)
|
||||
return "Aider timed out"
|
||||
except (OSError, subprocess.SubprocessError) as exc:
|
||||
logger.warning("Aider failed: %s", exc)
|
||||
return f"Edit failed: {exc}"
|
||||
|
||||
# ── Evaluation phase ──────────────────────────────────────────────────────
|
||||
|
||||
def run_tox(self, tox_env: str = "unit") -> dict[str, Any]:
|
||||
"""Run *tox_env* and return a result dict.
|
||||
|
||||
Returns:
|
||||
Dict with keys: ``metric`` (float|None), ``log`` (str),
|
||||
``duration_s`` (int), ``success`` (bool), ``error`` (str|None).
|
||||
"""
|
||||
start = time.monotonic()
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["tox", "-e", tox_env],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=self.budget_seconds,
|
||||
cwd=str(self.workspace),
|
||||
)
|
||||
duration = int(time.monotonic() - start)
|
||||
output = result.stdout + result.stderr
|
||||
metric_val = self._extract_tox_metric(output)
|
||||
return {
|
||||
"metric": metric_val,
|
||||
"log": output[-3000:],
|
||||
"duration_s": duration,
|
||||
"success": result.returncode == 0,
|
||||
"error": (None if result.returncode == 0 else f"Exit code {result.returncode}"),
|
||||
}
|
||||
except subprocess.TimeoutExpired:
|
||||
duration = int(time.monotonic() - start)
|
||||
return {
|
||||
"metric": None,
|
||||
"log": f"Budget exceeded after {self.budget_seconds}s",
|
||||
"duration_s": duration,
|
||||
"success": False,
|
||||
"error": f"Budget exceeded after {self.budget_seconds}s",
|
||||
}
|
||||
except OSError as exc:
|
||||
return {
|
||||
"metric": None,
|
||||
"log": "",
|
||||
"duration_s": 0,
|
||||
"success": False,
|
||||
"error": str(exc),
|
||||
}
|
||||
|
||||
def _extract_tox_metric(self, output: str) -> float | None:
|
||||
"""Dispatch to the correct metric extractor based on *self.metric*."""
|
||||
# Use custom metric function if provided
|
||||
if self.metric_fn is not None:
|
||||
try:
|
||||
return self.metric_fn(output)
|
||||
except Exception as exc:
|
||||
logger.warning("Custom metric_fn failed: %s", exc)
|
||||
return None
|
||||
|
||||
if self.metric == "unit_pass_rate":
|
||||
return _extract_pass_rate(output)
|
||||
if self.metric == "coverage":
|
||||
return _extract_coverage(output)
|
||||
return _extract_metric(output, self.metric)
|
||||
|
||||
def evaluate(self, current: float | None, baseline: float | None) -> str:
|
||||
"""Compare *current* metric against *baseline* and return an assessment."""
|
||||
if current is None:
|
||||
return "Indeterminate: metric not extracted from output"
|
||||
if baseline is None:
|
||||
unit = "%" if self.metric in _HIGHER_IS_BETTER else ""
|
||||
return f"Baseline: {self.metric} = {current:.2f}{unit}"
|
||||
|
||||
if self.metric in _HIGHER_IS_BETTER:
|
||||
delta = current - baseline
|
||||
pct = (delta / baseline * 100) if baseline != 0 else 0.0
|
||||
if delta > 0:
|
||||
return f"Improvement: {self.metric} {baseline:.2f}% → {current:.2f}% ({pct:+.2f}%)"
|
||||
if delta < 0:
|
||||
return f"Regression: {self.metric} {baseline:.2f}% → {current:.2f}% ({pct:+.2f}%)"
|
||||
return f"No change: {self.metric} = {current:.2f}%"
|
||||
|
||||
# lower-is-better (val_bpb, loss, etc.)
|
||||
return evaluate_result(current, baseline, self.metric)
|
||||
|
||||
def is_improvement(self, current: float, baseline: float) -> bool:
|
||||
"""Return True if *current* is better than *baseline* for this metric."""
|
||||
if self.metric in _HIGHER_IS_BETTER:
|
||||
return current > baseline
|
||||
return current < baseline # lower-is-better
|
||||
|
||||
# ── Git phase ─────────────────────────────────────────────────────────────
|
||||
|
||||
def create_branch(self, branch_name: str) -> bool:
|
||||
"""Create and checkout a new git branch. Returns True on success."""
|
||||
try:
|
||||
subprocess.run(
|
||||
["git", "checkout", "-b", branch_name],
|
||||
cwd=str(self.workspace),
|
||||
check=True,
|
||||
timeout=30,
|
||||
)
|
||||
return True
|
||||
except subprocess.CalledProcessError as exc:
|
||||
logger.warning("Git branch creation failed: %s", exc)
|
||||
return False
|
||||
|
||||
def commit_changes(self, message: str) -> bool:
|
||||
"""Stage and commit all changes. Returns True on success."""
|
||||
try:
|
||||
subprocess.run(["git", "add", "-A"], cwd=str(self.workspace), check=True, timeout=30)
|
||||
subprocess.run(
|
||||
["git", "commit", "-m", message],
|
||||
cwd=str(self.workspace),
|
||||
check=True,
|
||||
timeout=30,
|
||||
)
|
||||
return True
|
||||
except subprocess.CalledProcessError as exc:
|
||||
logger.warning("Git commit failed: %s", exc)
|
||||
return False
|
||||
|
||||
def revert_changes(self) -> bool:
|
||||
"""Revert all uncommitted changes. Returns True on success."""
|
||||
try:
|
||||
subprocess.run(
|
||||
["git", "checkout", "--", "."],
|
||||
cwd=str(self.workspace),
|
||||
check=True,
|
||||
timeout=30,
|
||||
)
|
||||
return True
|
||||
except subprocess.CalledProcessError as exc:
|
||||
logger.warning("Git revert failed: %s", exc)
|
||||
return False
|
||||
|
||||
# ── Full experiment loop ──────────────────────────────────────────────────
|
||||
|
||||
def run(
|
||||
self,
|
||||
tox_env: str = "unit",
|
||||
model: str = "qwen3:30b",
|
||||
program_content: str = "",
|
||||
max_iterations: int = 1,
|
||||
dry_run: bool = False,
|
||||
create_branch: bool = False,
|
||||
) -> dict[str, Any]:
|
||||
"""Run the full experiment loop: hypothesis → edit → tox → evaluate → commit/revert.
|
||||
|
||||
This method encapsulates the complete experiment cycle, running multiple
|
||||
iterations until an improvement is found or max_iterations is reached.
|
||||
|
||||
Args:
|
||||
tox_env: Tox environment to run (default "unit").
|
||||
model: Ollama model for Aider edits (default "qwen3:30b").
|
||||
program_content: Research direction for hypothesis generation.
|
||||
max_iterations: Maximum number of experiment iterations.
|
||||
dry_run: If True, only generate hypotheses without making changes.
|
||||
create_branch: If True, create a new git branch for the experiment.
|
||||
|
||||
Returns:
|
||||
Dict with keys: ``success`` (bool), ``final_metric`` (float|None),
|
||||
``baseline`` (float|None), ``iterations`` (int), ``results`` (list).
|
||||
"""
|
||||
if create_branch:
|
||||
branch_name = f"autoresearch/{self.target.replace('/', '-')}-{int(time.time())}"
|
||||
self.create_branch(branch_name)
|
||||
|
||||
baseline: float | None = self.baseline
|
||||
final_metric: float | None = None
|
||||
success = False
|
||||
|
||||
for iteration in range(1, max_iterations + 1):
|
||||
logger.info("Experiment iteration %d/%d", iteration, max_iterations)
|
||||
|
||||
# Generate hypothesis
|
||||
hypothesis = self.hypothesis or self.generate_hypothesis(program_content)
|
||||
logger.info("Hypothesis: %s", hypothesis)
|
||||
|
||||
# In dry-run mode, just record the hypothesis and continue
|
||||
if dry_run:
|
||||
result_record = {
|
||||
"iteration": iteration,
|
||||
"hypothesis": hypothesis,
|
||||
"metric": None,
|
||||
"baseline": baseline,
|
||||
"assessment": "Dry-run: no changes made",
|
||||
"success": True,
|
||||
"duration_s": 0,
|
||||
}
|
||||
self.results.append(result_record)
|
||||
continue
|
||||
|
||||
# Apply edit
|
||||
edit_result = self.apply_edit(hypothesis, model=model)
|
||||
edit_failed = "not available" in edit_result or edit_result.startswith("Aider error")
|
||||
if edit_failed:
|
||||
logger.warning("Edit phase failed: %s", edit_result)
|
||||
|
||||
# Run evaluation
|
||||
tox_result = self.run_tox(tox_env=tox_env)
|
||||
metric = tox_result["metric"]
|
||||
|
||||
# Evaluate result
|
||||
assessment = self.evaluate(metric, baseline)
|
||||
logger.info("Assessment: %s", assessment)
|
||||
|
||||
# Store result
|
||||
result_record = {
|
||||
"iteration": iteration,
|
||||
"hypothesis": hypothesis,
|
||||
"metric": metric,
|
||||
"baseline": baseline,
|
||||
"assessment": assessment,
|
||||
"success": tox_result["success"],
|
||||
"duration_s": tox_result["duration_s"],
|
||||
}
|
||||
self.results.append(result_record)
|
||||
|
||||
# Set baseline on first successful run
|
||||
if metric is not None and baseline is None:
|
||||
baseline = metric
|
||||
self.baseline = baseline
|
||||
final_metric = metric
|
||||
continue
|
||||
|
||||
# Determine if we should commit or revert
|
||||
should_commit = False
|
||||
if tox_result["success"] and metric is not None and baseline is not None:
|
||||
if self.is_improvement(metric, baseline):
|
||||
should_commit = True
|
||||
final_metric = metric
|
||||
baseline = metric
|
||||
self.baseline = baseline
|
||||
success = True
|
||||
|
||||
if should_commit:
|
||||
commit_msg = f"autoresearch: improve {self.metric} on {self.target}\n\n{hypothesis}"
|
||||
if self.commit_changes(commit_msg):
|
||||
logger.info("Changes committed")
|
||||
else:
|
||||
self.revert_changes()
|
||||
logger.warning("Commit failed, changes reverted")
|
||||
elif self.revert_on_failure:
|
||||
self.revert_changes()
|
||||
logger.info("Changes reverted (no improvement)")
|
||||
|
||||
# Early exit if we found an improvement
|
||||
if success:
|
||||
break
|
||||
|
||||
return {
|
||||
"success": success,
|
||||
"final_metric": final_metric,
|
||||
"baseline": self.baseline,
|
||||
"iterations": len(self.results),
|
||||
"results": self.results,
|
||||
}
|
||||
|
||||
@@ -46,8 +46,6 @@ class ApprovalItem:
|
||||
|
||||
@dataclass
|
||||
class Briefing:
|
||||
"""A generated morning briefing summarizing recent activity and pending approvals."""
|
||||
|
||||
generated_at: datetime
|
||||
summary: str # 150-300 words
|
||||
approval_items: list[ApprovalItem] = field(default_factory=list)
|
||||
|
||||
169
src/timmy/cli.py
169
src/timmy/cli.py
@@ -347,10 +347,7 @@ def interview(
|
||||
# Force agent creation by calling chat once with a warm-up prompt
|
||||
try:
|
||||
loop.run_until_complete(
|
||||
chat(
|
||||
"Hello, Timmy. We're about to start your interview.",
|
||||
session_id="interview",
|
||||
)
|
||||
chat("Hello, Timmy. We're about to start your interview.", session_id="interview")
|
||||
)
|
||||
except Exception as exc:
|
||||
typer.echo(f"Warning: Initialization issue — {exc}", err=True)
|
||||
@@ -413,17 +410,11 @@ def down():
|
||||
@app.command()
|
||||
def voice(
|
||||
whisper_model: str = typer.Option(
|
||||
"base.en",
|
||||
"--whisper",
|
||||
"-w",
|
||||
help="Whisper model: tiny.en, base.en, small.en, medium.en",
|
||||
"base.en", "--whisper", "-w", help="Whisper model: tiny.en, base.en, small.en, medium.en"
|
||||
),
|
||||
use_say: bool = typer.Option(False, "--say", help="Use macOS `say` instead of Piper TTS"),
|
||||
threshold: float = typer.Option(
|
||||
0.015,
|
||||
"--threshold",
|
||||
"-t",
|
||||
help="Mic silence threshold (RMS). Lower = more sensitive.",
|
||||
0.015, "--threshold", "-t", help="Mic silence threshold (RMS). Lower = more sensitive."
|
||||
),
|
||||
silence: float = typer.Option(1.5, "--silence", help="Seconds of silence to end recording"),
|
||||
backend: str | None = _BACKEND_OPTION,
|
||||
@@ -466,8 +457,7 @@ def route(
|
||||
@app.command()
|
||||
def focus(
|
||||
topic: str | None = typer.Argument(
|
||||
None,
|
||||
help='Topic to focus on (e.g. "three-phase loop"). Omit to show current focus.',
|
||||
None, help='Topic to focus on (e.g. "three-phase loop"). Omit to show current focus.'
|
||||
),
|
||||
clear: bool = typer.Option(False, "--clear", "-c", help="Clear focus and return to broad mode"),
|
||||
):
|
||||
@@ -537,156 +527,5 @@ def healthcheck(
|
||||
raise typer.Exit(result.returncode)
|
||||
|
||||
|
||||
@app.command()
|
||||
def learn(
|
||||
target: str | None = typer.Option(
|
||||
None,
|
||||
"--target",
|
||||
"-t",
|
||||
help="Module or file to optimise (e.g. 'src/timmy/agent.py')",
|
||||
),
|
||||
metric: str = typer.Option(
|
||||
"unit_pass_rate",
|
||||
"--metric",
|
||||
"-m",
|
||||
help="Metric to track: unit_pass_rate | coverage | val_bpb | <custom>",
|
||||
),
|
||||
budget: int = typer.Option(
|
||||
5,
|
||||
"--budget",
|
||||
help="Time limit per experiment in minutes",
|
||||
),
|
||||
max_experiments: int = typer.Option(
|
||||
10,
|
||||
"--max-experiments",
|
||||
help="Cap on total experiments per run",
|
||||
),
|
||||
dry_run: bool = typer.Option(
|
||||
False,
|
||||
"--dry-run",
|
||||
help="Show hypothesis without executing experiments",
|
||||
),
|
||||
program_file: str | None = typer.Option(
|
||||
None,
|
||||
"--program",
|
||||
"-p",
|
||||
help="Path to research direction file (default: program.md in cwd)",
|
||||
),
|
||||
tox_env: str = typer.Option(
|
||||
"unit",
|
||||
"--tox-env",
|
||||
help="Tox environment to run for each evaluation",
|
||||
),
|
||||
model: str = typer.Option(
|
||||
"qwen3:30b",
|
||||
"--model",
|
||||
help="Ollama model forwarded to Aider for code edits",
|
||||
),
|
||||
):
|
||||
"""Start an autonomous improvement loop (autoresearch).
|
||||
|
||||
Reads program.md for research direction, then iterates:
|
||||
hypothesis → edit → tox → evaluate → commit/revert.
|
||||
|
||||
Experiments continue until --max-experiments is reached or the loop is
|
||||
interrupted with Ctrl+C. Use --dry-run to preview hypotheses without
|
||||
making any changes.
|
||||
|
||||
Example:
|
||||
timmy learn --target src/timmy/agent.py --metric unit_pass_rate
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
from timmy.autoresearch import SystemExperiment
|
||||
|
||||
repo_root = Path.cwd()
|
||||
program_path = Path(program_file) if program_file else repo_root / "program.md"
|
||||
|
||||
if program_path.exists():
|
||||
program_content = program_path.read_text()
|
||||
typer.echo(f"Research direction: {program_path}")
|
||||
else:
|
||||
program_content = ""
|
||||
typer.echo(
|
||||
f"Note: {program_path} not found — proceeding without research direction.",
|
||||
err=True,
|
||||
)
|
||||
|
||||
if target is None:
|
||||
typer.echo(
|
||||
"Error: --target is required. Specify the module or file to optimise.",
|
||||
err=True,
|
||||
)
|
||||
raise typer.Exit(1)
|
||||
|
||||
experiment = SystemExperiment(
|
||||
target=target,
|
||||
metric=metric,
|
||||
budget_minutes=budget,
|
||||
)
|
||||
|
||||
typer.echo()
|
||||
typer.echo(typer.style("Autoresearch", bold=True) + f" — {target}")
|
||||
typer.echo(f" metric={metric} budget={budget}min max={max_experiments} tox={tox_env}")
|
||||
if dry_run:
|
||||
typer.echo(" (dry-run — no changes will be made)")
|
||||
typer.echo()
|
||||
|
||||
def _progress_callback(iteration: int, max_iter: int, message: str) -> None:
|
||||
"""Print progress updates during experiment iterations."""
|
||||
if iteration > 0:
|
||||
prefix = typer.style(f"[{iteration}/{max_iter}]", bold=True)
|
||||
typer.echo(f"{prefix} {message}")
|
||||
|
||||
try:
|
||||
# Run the full experiment loop via the SystemExperiment class
|
||||
result = experiment.run(
|
||||
tox_env=tox_env,
|
||||
model=model,
|
||||
program_content=program_content,
|
||||
max_iterations=max_experiments,
|
||||
dry_run=dry_run,
|
||||
create_branch=False, # CLI mode: work on current branch
|
||||
)
|
||||
|
||||
# Display results for each iteration
|
||||
for i, record in enumerate(experiment.results, 1):
|
||||
_progress_callback(i, max_experiments, record["hypothesis"])
|
||||
|
||||
if dry_run:
|
||||
continue
|
||||
|
||||
# Edit phase result
|
||||
typer.echo(" → editing …", nl=False)
|
||||
if record.get("edit_failed"):
|
||||
typer.echo(f" skipped ({record.get('edit_result', 'unknown')})")
|
||||
else:
|
||||
typer.echo(" done")
|
||||
|
||||
# Evaluate phase result
|
||||
duration = record.get("duration_s", 0)
|
||||
typer.echo(f" → running tox … {duration}s")
|
||||
|
||||
# Assessment
|
||||
assessment = record.get("assessment", "No assessment")
|
||||
typer.echo(f" → {assessment}")
|
||||
|
||||
# Outcome
|
||||
if record.get("committed"):
|
||||
typer.echo(" → committed")
|
||||
elif record.get("reverted"):
|
||||
typer.echo(" → reverted (no improvement)")
|
||||
|
||||
typer.echo()
|
||||
|
||||
except KeyboardInterrupt:
|
||||
typer.echo("\nInterrupted.")
|
||||
raise typer.Exit(0) from None
|
||||
|
||||
typer.echo(typer.style("Autoresearch complete.", bold=True))
|
||||
if result.get("baseline") is not None:
|
||||
typer.echo(f"Final {metric}: {result['baseline']:.4f}")
|
||||
|
||||
|
||||
def main():
|
||||
app()
|
||||
|
||||
@@ -423,49 +423,6 @@ async def _poll_issue_completion(
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _format_assignment_comment(
|
||||
display_name: str,
|
||||
task_type: TaskType,
|
||||
description: str,
|
||||
acceptance_criteria: list[str],
|
||||
) -> str:
|
||||
"""Build the markdown comment body for a task assignment.
|
||||
|
||||
Args:
|
||||
display_name: Human-readable agent name.
|
||||
task_type: The inferred task type.
|
||||
description: Task description.
|
||||
acceptance_criteria: List of acceptance criteria strings.
|
||||
|
||||
Returns:
|
||||
Formatted markdown string for the comment.
|
||||
"""
|
||||
criteria_md = (
|
||||
"\n".join(f"- {c}" for c in acceptance_criteria)
|
||||
if acceptance_criteria
|
||||
else "_None specified_"
|
||||
)
|
||||
return (
|
||||
f"## Assigned to {display_name}\n\n"
|
||||
f"**Task type:** `{task_type.value}`\n\n"
|
||||
f"**Description:**\n{description}\n\n"
|
||||
f"**Acceptance criteria:**\n{criteria_md}\n\n"
|
||||
f"---\n*Dispatched by Timmy agent dispatcher.*"
|
||||
)
|
||||
|
||||
|
||||
def _select_label(agent: AgentType) -> str | None:
|
||||
"""Return the Gitea label for an agent based on its spec.
|
||||
|
||||
Args:
|
||||
agent: The target agent.
|
||||
|
||||
Returns:
|
||||
Label name or None if the agent has no label.
|
||||
"""
|
||||
return AGENT_REGISTRY[agent].gitea_label
|
||||
|
||||
|
||||
async def _dispatch_via_gitea(
|
||||
agent: AgentType,
|
||||
issue_number: int,
|
||||
@@ -520,27 +477,37 @@ async def _dispatch_via_gitea(
|
||||
|
||||
async with httpx.AsyncClient(timeout=15) as client:
|
||||
# 1. Apply agent label (if applicable)
|
||||
label = _select_label(agent)
|
||||
if label:
|
||||
ok = await _apply_gitea_label(client, base_url, repo, headers, issue_number, label)
|
||||
if spec.gitea_label:
|
||||
ok = await _apply_gitea_label(
|
||||
client, base_url, repo, headers, issue_number, spec.gitea_label
|
||||
)
|
||||
if ok:
|
||||
label_applied = label
|
||||
label_applied = spec.gitea_label
|
||||
logger.info(
|
||||
"Applied label %r to issue #%s for %s",
|
||||
label,
|
||||
spec.gitea_label,
|
||||
issue_number,
|
||||
spec.display_name,
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
"Could not apply label %r to issue #%s",
|
||||
label,
|
||||
spec.gitea_label,
|
||||
issue_number,
|
||||
)
|
||||
|
||||
# 2. Post assignment comment
|
||||
comment_body = _format_assignment_comment(
|
||||
spec.display_name, task_type, description, acceptance_criteria
|
||||
criteria_md = (
|
||||
"\n".join(f"- {c}" for c in acceptance_criteria)
|
||||
if acceptance_criteria
|
||||
else "_None specified_"
|
||||
)
|
||||
comment_body = (
|
||||
f"## Assigned to {spec.display_name}\n\n"
|
||||
f"**Task type:** `{task_type.value}`\n\n"
|
||||
f"**Description:**\n{description}\n\n"
|
||||
f"**Acceptance criteria:**\n{criteria_md}\n\n"
|
||||
f"---\n*Dispatched by Timmy agent dispatcher.*"
|
||||
)
|
||||
comment_id = await _post_gitea_comment(
|
||||
client, base_url, repo, headers, issue_number, comment_body
|
||||
@@ -686,80 +653,6 @@ async def _dispatch_local(
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _validate_task(
|
||||
title: str,
|
||||
task_type: TaskType | None,
|
||||
agent: AgentType | None,
|
||||
issue_number: int | None,
|
||||
) -> DispatchResult | None:
|
||||
"""Validate task preconditions.
|
||||
|
||||
Args:
|
||||
title: Task title to validate.
|
||||
task_type: Optional task type for result construction.
|
||||
agent: Optional agent for result construction.
|
||||
issue_number: Optional issue number for result construction.
|
||||
|
||||
Returns:
|
||||
A failed DispatchResult if validation fails, None otherwise.
|
||||
"""
|
||||
if not title.strip():
|
||||
return DispatchResult(
|
||||
task_type=task_type or TaskType.ROUTINE_CODING,
|
||||
agent=agent or AgentType.TIMMY,
|
||||
issue_number=issue_number,
|
||||
status=DispatchStatus.FAILED,
|
||||
error="`title` is required.",
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
def _select_dispatch_strategy(agent: AgentType, issue_number: int | None) -> str:
|
||||
"""Select the dispatch strategy based on agent interface and context.
|
||||
|
||||
Args:
|
||||
agent: The target agent.
|
||||
issue_number: Optional Gitea issue number.
|
||||
|
||||
Returns:
|
||||
Strategy name: "gitea", "api", or "local".
|
||||
"""
|
||||
spec = AGENT_REGISTRY[agent]
|
||||
if spec.interface == "gitea" and issue_number is not None:
|
||||
return "gitea"
|
||||
if spec.interface == "api":
|
||||
return "api"
|
||||
return "local"
|
||||
|
||||
|
||||
def _log_dispatch_result(
|
||||
title: str,
|
||||
result: DispatchResult,
|
||||
attempt: int,
|
||||
max_retries: int,
|
||||
) -> None:
|
||||
"""Log the outcome of a dispatch attempt.
|
||||
|
||||
Args:
|
||||
title: Task title for logging context.
|
||||
result: The dispatch result.
|
||||
attempt: Current attempt number (0-indexed).
|
||||
max_retries: Maximum retry attempts allowed.
|
||||
"""
|
||||
if result.success:
|
||||
return
|
||||
|
||||
if attempt > 0:
|
||||
logger.info("Retry %d/%d for task %r", attempt, max_retries, title[:60])
|
||||
|
||||
logger.warning(
|
||||
"Dispatch attempt %d failed for task %r: %s",
|
||||
attempt + 1,
|
||||
title[:60],
|
||||
result.error,
|
||||
)
|
||||
|
||||
|
||||
async def dispatch_task(
|
||||
title: str,
|
||||
description: str = "",
|
||||
@@ -800,13 +693,17 @@ async def dispatch_task(
|
||||
if result.success:
|
||||
print(f"Assigned to {result.agent.value}")
|
||||
"""
|
||||
# 1. Validate
|
||||
validation_error = _validate_task(title, task_type, agent, issue_number)
|
||||
if validation_error:
|
||||
return validation_error
|
||||
|
||||
# 2. Resolve task type and agent
|
||||
criteria = acceptance_criteria or []
|
||||
|
||||
if not title.strip():
|
||||
return DispatchResult(
|
||||
task_type=task_type or TaskType.ROUTINE_CODING,
|
||||
agent=agent or AgentType.TIMMY,
|
||||
issue_number=issue_number,
|
||||
status=DispatchStatus.FAILED,
|
||||
error="`title` is required.",
|
||||
)
|
||||
|
||||
resolved_type = task_type or infer_task_type(title, description)
|
||||
resolved_agent = agent or select_agent(resolved_type)
|
||||
|
||||
@@ -818,16 +715,18 @@ async def dispatch_task(
|
||||
issue_number,
|
||||
)
|
||||
|
||||
# 3. Select strategy and dispatch with retries
|
||||
strategy = _select_dispatch_strategy(resolved_agent, issue_number)
|
||||
last_result: DispatchResult | None = None
|
||||
spec = AGENT_REGISTRY[resolved_agent]
|
||||
|
||||
last_result: DispatchResult | None = None
|
||||
for attempt in range(max_retries + 1):
|
||||
if strategy == "gitea":
|
||||
if attempt > 0:
|
||||
logger.info("Retry %d/%d for task %r", attempt, max_retries, title[:60])
|
||||
|
||||
if spec.interface == "gitea" and issue_number is not None:
|
||||
result = await _dispatch_via_gitea(
|
||||
resolved_agent, issue_number, title, description, criteria
|
||||
)
|
||||
elif strategy == "api":
|
||||
elif spec.interface == "api":
|
||||
result = await _dispatch_via_api(
|
||||
resolved_agent, title, description, criteria, issue_number, api_endpoint
|
||||
)
|
||||
@@ -840,9 +739,14 @@ async def dispatch_task(
|
||||
if result.success:
|
||||
return result
|
||||
|
||||
_log_dispatch_result(title, result, attempt, max_retries)
|
||||
logger.warning(
|
||||
"Dispatch attempt %d failed for task %r: %s",
|
||||
attempt + 1,
|
||||
title[:60],
|
||||
result.error,
|
||||
)
|
||||
|
||||
# 4. All attempts exhausted — escalate
|
||||
# All attempts exhausted — escalate
|
||||
assert last_result is not None
|
||||
last_result.status = DispatchStatus.ESCALATED
|
||||
logger.error(
|
||||
|
||||
@@ -7,97 +7,37 @@ Also includes vector similarity utilities (cosine similarity, keyword overlap).
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
|
||||
import httpx # Import httpx for Ollama API calls
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Embedding model - small, fast, local
|
||||
EMBEDDING_MODEL = None
|
||||
EMBEDDING_DIM = 384 # MiniLM dimension, will be overridden if Ollama model has different dim
|
||||
|
||||
|
||||
class OllamaEmbedder:
|
||||
"""Mimics SentenceTransformer interface for Ollama."""
|
||||
|
||||
def __init__(self, model_name: str, ollama_url: str):
|
||||
self.model_name = model_name
|
||||
self.ollama_url = ollama_url
|
||||
self.dimension = 0 # Will be updated after first call
|
||||
|
||||
def encode(
|
||||
self,
|
||||
sentences: str | list[str],
|
||||
convert_to_numpy: bool = False,
|
||||
normalize_embeddings: bool = True,
|
||||
) -> list[list[float]] | list[float]:
|
||||
"""Generate embeddings using Ollama."""
|
||||
if isinstance(sentences, str):
|
||||
sentences = [sentences]
|
||||
|
||||
all_embeddings = []
|
||||
for sentence in sentences:
|
||||
try:
|
||||
response = httpx.post(
|
||||
f"{self.ollama_url}/api/embeddings",
|
||||
json={"model": self.model_name, "prompt": sentence},
|
||||
timeout=settings.mcp_bridge_timeout,
|
||||
)
|
||||
response.raise_for_status()
|
||||
embedding = response.json()["embedding"]
|
||||
if not self.dimension:
|
||||
self.dimension = len(embedding) # Set dimension on first successful call
|
||||
global EMBEDDING_DIM
|
||||
EMBEDDING_DIM = self.dimension # Update global EMBEDDING_DIM
|
||||
all_embeddings.append(embedding)
|
||||
except httpx.RequestError as exc:
|
||||
logger.error("Ollama embeddings request failed: %s", exc)
|
||||
# Fallback to simple hash embedding on Ollama error
|
||||
return _simple_hash_embedding(sentence)
|
||||
except json.JSONDecodeError as exc:
|
||||
logger.error("Failed to decode Ollama embeddings response: %s", exc)
|
||||
return _simple_hash_embedding(sentence)
|
||||
|
||||
if len(all_embeddings) == 1 and isinstance(sentences, str):
|
||||
return all_embeddings[0]
|
||||
return all_embeddings
|
||||
EMBEDDING_DIM = 384 # MiniLM dimension
|
||||
|
||||
|
||||
def _get_embedding_model():
|
||||
"""Lazy-load embedding model, preferring Ollama if configured."""
|
||||
"""Lazy-load embedding model."""
|
||||
global EMBEDDING_MODEL
|
||||
global EMBEDDING_DIM
|
||||
if EMBEDDING_MODEL is None:
|
||||
if settings.timmy_skip_embeddings:
|
||||
EMBEDDING_MODEL = False
|
||||
return EMBEDDING_MODEL
|
||||
try:
|
||||
from config import settings
|
||||
|
||||
if settings.timmy_embedding_backend == "ollama":
|
||||
logger.info(
|
||||
"MemorySystem: Using Ollama for embeddings with model %s",
|
||||
settings.ollama_embedding_model,
|
||||
)
|
||||
EMBEDDING_MODEL = OllamaEmbedder(
|
||||
settings.ollama_embedding_model, settings.normalized_ollama_url
|
||||
)
|
||||
# We don't know the dimension until after the first call, so keep it default for now.
|
||||
# It will be updated dynamically in OllamaEmbedder.encode
|
||||
return EMBEDDING_MODEL
|
||||
else:
|
||||
try:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
if settings.timmy_skip_embeddings:
|
||||
EMBEDDING_MODEL = False
|
||||
return EMBEDDING_MODEL
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
|
||||
EMBEDDING_DIM = 384 # Reset to MiniLM dimension
|
||||
logger.info("MemorySystem: Loaded local embedding model (all-MiniLM-L6-v2)")
|
||||
except ImportError:
|
||||
logger.warning("MemorySystem: sentence-transformers not installed, using fallback")
|
||||
EMBEDDING_MODEL = False # Use fallback
|
||||
try:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
||||
EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
|
||||
logger.info("MemorySystem: Loaded embedding model")
|
||||
except ImportError:
|
||||
logger.warning("MemorySystem: sentence-transformers not installed, using fallback")
|
||||
EMBEDDING_MODEL = False # Use fallback
|
||||
return EMBEDDING_MODEL
|
||||
|
||||
|
||||
@@ -120,10 +60,7 @@ def embed_text(text: str) -> list[float]:
|
||||
model = _get_embedding_model()
|
||||
if model and model is not False:
|
||||
embedding = model.encode(text)
|
||||
# Ensure it's a list of floats, not numpy array
|
||||
if hasattr(embedding, "tolist"):
|
||||
return embedding.tolist()
|
||||
return embedding
|
||||
return embedding.tolist()
|
||||
return _simple_hash_embedding(text)
|
||||
|
||||
|
||||
|
||||
@@ -1206,7 +1206,7 @@ memory_searcher = MemorySearcher()
|
||||
# ───────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def memory_search(query: str, limit: int = 10) -> str:
|
||||
def memory_search(query: str, top_k: int = 5) -> str:
|
||||
"""Search past conversations, notes, and stored facts for relevant context.
|
||||
|
||||
Searches across both the vault (indexed markdown files) and the
|
||||
@@ -1215,19 +1215,19 @@ def memory_search(query: str, limit: int = 10) -> str:
|
||||
|
||||
Args:
|
||||
query: What to search for (e.g. "Bitcoin strategy", "server setup").
|
||||
limit: Number of results to return (default 10).
|
||||
top_k: Number of results to return (default 5).
|
||||
|
||||
Returns:
|
||||
Formatted string of relevant memory results.
|
||||
"""
|
||||
# Guard: model sometimes passes None for limit
|
||||
if limit is None:
|
||||
limit = 10
|
||||
# Guard: model sometimes passes None for top_k
|
||||
if top_k is None:
|
||||
top_k = 5
|
||||
|
||||
parts: list[str] = []
|
||||
|
||||
# 1. Search semantic vault (indexed markdown files)
|
||||
vault_results = semantic_memory.search(query, limit)
|
||||
vault_results = semantic_memory.search(query, top_k)
|
||||
for content, score in vault_results:
|
||||
if score < 0.2:
|
||||
continue
|
||||
@@ -1235,7 +1235,7 @@ def memory_search(query: str, limit: int = 10) -> str:
|
||||
|
||||
# 2. Search runtime vector store (stored facts/conversations)
|
||||
try:
|
||||
runtime_results = search_memories(query, limit=limit, min_relevance=0.2)
|
||||
runtime_results = search_memories(query, limit=top_k, min_relevance=0.2)
|
||||
for entry in runtime_results:
|
||||
label = entry.context_type or "memory"
|
||||
parts.append(f"[{label}] {entry.content[:300]}")
|
||||
@@ -1289,48 +1289,45 @@ def memory_read(query: str = "", top_k: int = 5) -> str:
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
def memory_store(topic: str, report: str, type: str = "research") -> str:
|
||||
"""Store a piece of information in persistent memory, particularly for research outputs.
|
||||
def memory_write(content: str, context_type: str = "fact") -> str:
|
||||
"""Store a piece of information in persistent memory.
|
||||
|
||||
Use this tool to store structured research findings or other important documents.
|
||||
Stored memories are searchable via memory_search across all channels.
|
||||
Use this tool when the user explicitly asks you to remember something.
|
||||
Stored memories are searchable via memory_search across all channels
|
||||
(web GUI, Discord, Telegram, etc.).
|
||||
|
||||
Args:
|
||||
topic: A concise title or topic for the research output.
|
||||
report: The detailed content of the research output or document.
|
||||
type: Type of memory — "research" for research outputs (default),
|
||||
"fact" for permanent facts, "conversation" for conversation context,
|
||||
"document" for other document fragments.
|
||||
content: The information to remember (e.g. a phrase, fact, or note).
|
||||
context_type: Type of memory — "fact" for permanent facts,
|
||||
"conversation" for conversation context,
|
||||
"document" for document fragments.
|
||||
|
||||
Returns:
|
||||
Confirmation that the memory was stored.
|
||||
"""
|
||||
if not report or not report.strip():
|
||||
return "Nothing to store — report is empty."
|
||||
if not content or not content.strip():
|
||||
return "Nothing to store — content is empty."
|
||||
|
||||
# Combine topic and report for embedding and storage content
|
||||
full_content = f"Topic: {topic.strip()}\n\nReport: {report.strip()}"
|
||||
|
||||
valid_types = ("fact", "conversation", "document", "research")
|
||||
if type not in valid_types:
|
||||
type = "research"
|
||||
valid_types = ("fact", "conversation", "document")
|
||||
if context_type not in valid_types:
|
||||
context_type = "fact"
|
||||
|
||||
try:
|
||||
# Dedup check for facts and research — skip if similar exists
|
||||
if type in ("fact", "research"):
|
||||
existing = search_memories(full_content, limit=3, context_type=type, min_relevance=0.75)
|
||||
# Dedup check for facts — skip if a similar fact already exists
|
||||
# Threshold 0.75 catches paraphrases (was 0.9 which only caught near-exact)
|
||||
if context_type == "fact":
|
||||
existing = search_memories(
|
||||
content.strip(), limit=3, context_type="fact", min_relevance=0.75
|
||||
)
|
||||
if existing:
|
||||
return (
|
||||
f"Similar {type} already stored (id={existing[0].id[:8]}). Skipping duplicate."
|
||||
)
|
||||
return f"Similar fact already stored (id={existing[0].id[:8]}). Skipping duplicate."
|
||||
|
||||
entry = store_memory(
|
||||
content=full_content,
|
||||
content=content.strip(),
|
||||
source="agent",
|
||||
context_type=type,
|
||||
metadata={"topic": topic},
|
||||
context_type=context_type,
|
||||
)
|
||||
return f"Stored in memory (type={type}, id={entry.id[:8]}). This is now searchable across all channels."
|
||||
return f"Stored in memory (type={context_type}, id={entry.id[:8]}). This is now searchable across all channels."
|
||||
except Exception as exc:
|
||||
logger.error("Failed to write memory: %s", exc)
|
||||
return f"Failed to store memory: {exc}"
|
||||
|
||||
@@ -1,528 +0,0 @@
|
||||
"""Research Orchestrator — autonomous, sovereign research pipeline.
|
||||
|
||||
Chains all six steps of the research workflow with local-first execution:
|
||||
|
||||
Step 0 Cache — check semantic memory (SQLite, instant, zero API cost)
|
||||
Step 1 Scope — load a research template from skills/research/
|
||||
Step 2 Query — slot-fill template + formulate 5-15 search queries via Ollama
|
||||
Step 3 Search — execute queries via web_search (SerpAPI or fallback)
|
||||
Step 4 Fetch — download + extract full pages via web_fetch (trafilatura)
|
||||
Step 5 Synth — compress findings into a structured report via cascade
|
||||
Step 6 Deliver — store to semantic memory; optionally save to docs/research/
|
||||
|
||||
Cascade tiers for synthesis (spec §4):
|
||||
Tier 4 SQLite semantic cache — instant, free, covers ~80% after warm-up
|
||||
Tier 3 Ollama (qwen3:14b) — local, free, good quality
|
||||
Tier 2 Claude API (haiku) — cloud fallback, cheap, set ANTHROPIC_API_KEY
|
||||
Tier 1 (future) Groq — free-tier rate-limited, tracked in #980
|
||||
|
||||
All optional services degrade gracefully per project conventions.
|
||||
|
||||
Refs #972 (governing spec), #975 (ResearchOrchestrator sub-issue).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import re
|
||||
import textwrap
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Optional memory imports — available at module level so tests can patch them.
|
||||
try:
|
||||
from timmy.memory_system import SemanticMemory, store_memory
|
||||
except Exception: # pragma: no cover
|
||||
SemanticMemory = None # type: ignore[assignment,misc]
|
||||
store_memory = None # type: ignore[assignment]
|
||||
|
||||
# Root of the project — two levels up from src/timmy/
|
||||
_PROJECT_ROOT = Path(__file__).parent.parent.parent
|
||||
_SKILLS_ROOT = _PROJECT_ROOT / "skills" / "research"
|
||||
_DOCS_ROOT = _PROJECT_ROOT / "docs" / "research"
|
||||
|
||||
# Similarity threshold for cache hit (0–1 cosine similarity)
|
||||
_CACHE_HIT_THRESHOLD = 0.82
|
||||
|
||||
# How many search result URLs to fetch as full pages
|
||||
_FETCH_TOP_N = 5
|
||||
|
||||
# Maximum tokens to request from the synthesis LLM
|
||||
_SYNTHESIS_MAX_TOKENS = 4096
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Data structures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class ResearchResult:
|
||||
"""Full output of a research pipeline run."""
|
||||
|
||||
topic: str
|
||||
query_count: int
|
||||
sources_fetched: int
|
||||
report: str
|
||||
cached: bool = False
|
||||
cache_similarity: float = 0.0
|
||||
synthesis_backend: str = "unknown"
|
||||
errors: list[str] = field(default_factory=list)
|
||||
|
||||
def is_empty(self) -> bool:
|
||||
return not self.report.strip()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Template loading
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def list_templates() -> list[str]:
|
||||
"""Return names of available research templates (without .md extension)."""
|
||||
if not _SKILLS_ROOT.exists():
|
||||
return []
|
||||
return [p.stem for p in sorted(_SKILLS_ROOT.glob("*.md"))]
|
||||
|
||||
|
||||
def load_template(template_name: str, slots: dict[str, str] | None = None) -> str:
|
||||
"""Load a research template and fill {slot} placeholders.
|
||||
|
||||
Args:
|
||||
template_name: Stem of the .md file under skills/research/ (e.g. "tool_evaluation").
|
||||
slots: Mapping of {placeholder} → replacement value.
|
||||
|
||||
Returns:
|
||||
Template text with slots filled. Unfilled slots are left as-is.
|
||||
"""
|
||||
path = _SKILLS_ROOT / f"{template_name}.md"
|
||||
if not path.exists():
|
||||
available = ", ".join(list_templates()) or "(none)"
|
||||
raise FileNotFoundError(
|
||||
f"Research template {template_name!r} not found. "
|
||||
f"Available: {available}"
|
||||
)
|
||||
|
||||
text = path.read_text(encoding="utf-8")
|
||||
|
||||
# Strip YAML frontmatter (--- ... ---), including empty frontmatter (--- \n---)
|
||||
text = re.sub(r"^---\n.*?---\n", "", text, flags=re.DOTALL)
|
||||
|
||||
if slots:
|
||||
for key, value in slots.items():
|
||||
text = text.replace(f"{{{key}}}", value)
|
||||
|
||||
return text.strip()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Query formulation (Step 2)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _formulate_queries(topic: str, template_context: str, n: int = 8) -> list[str]:
|
||||
"""Use the local LLM to generate targeted search queries for a topic.
|
||||
|
||||
Falls back to a simple heuristic if Ollama is unavailable.
|
||||
"""
|
||||
prompt = textwrap.dedent(f"""\
|
||||
You are a research assistant. Generate exactly {n} targeted, specific web search
|
||||
queries to thoroughly research the following topic.
|
||||
|
||||
TOPIC: {topic}
|
||||
|
||||
RESEARCH CONTEXT:
|
||||
{template_context[:1000]}
|
||||
|
||||
Rules:
|
||||
- One query per line, no numbering, no bullet points.
|
||||
- Vary the angle (definition, comparison, implementation, alternatives, pitfalls).
|
||||
- Prefer exact technical terms, tool names, and version numbers where relevant.
|
||||
- Output ONLY the queries, nothing else.
|
||||
""")
|
||||
|
||||
queries = await _ollama_complete(prompt, max_tokens=512)
|
||||
|
||||
if not queries:
|
||||
# Minimal fallback
|
||||
return [
|
||||
f"{topic} overview",
|
||||
f"{topic} tutorial",
|
||||
f"{topic} best practices",
|
||||
f"{topic} alternatives",
|
||||
f"{topic} 2025",
|
||||
]
|
||||
|
||||
lines = [ln.strip() for ln in queries.splitlines() if ln.strip()]
|
||||
return lines[:n] if len(lines) >= n else lines
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Search (Step 3)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _execute_search(queries: list[str]) -> list[dict[str, str]]:
|
||||
"""Run each query through the available web search backend.
|
||||
|
||||
Returns a flat list of {title, url, snippet} dicts.
|
||||
Degrades gracefully if SerpAPI key is absent.
|
||||
"""
|
||||
results: list[dict[str, str]] = []
|
||||
seen_urls: set[str] = set()
|
||||
|
||||
for query in queries:
|
||||
try:
|
||||
raw = await asyncio.to_thread(_run_search_sync, query)
|
||||
for item in raw:
|
||||
url = item.get("url", "")
|
||||
if url and url not in seen_urls:
|
||||
seen_urls.add(url)
|
||||
results.append(item)
|
||||
except Exception as exc:
|
||||
logger.warning("Search failed for query %r: %s", query, exc)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def _run_search_sync(query: str) -> list[dict[str, str]]:
|
||||
"""Synchronous search — wraps SerpAPI or returns empty on missing key."""
|
||||
import os
|
||||
|
||||
if not os.environ.get("SERPAPI_API_KEY"):
|
||||
logger.debug("SERPAPI_API_KEY not set — skipping web search for %r", query)
|
||||
return []
|
||||
|
||||
try:
|
||||
from serpapi import GoogleSearch
|
||||
|
||||
params = {"q": query, "api_key": os.environ["SERPAPI_API_KEY"], "num": 5}
|
||||
search = GoogleSearch(params)
|
||||
data = search.get_dict()
|
||||
items = []
|
||||
for r in data.get("organic_results", []):
|
||||
items.append(
|
||||
{
|
||||
"title": r.get("title", ""),
|
||||
"url": r.get("link", ""),
|
||||
"snippet": r.get("snippet", ""),
|
||||
}
|
||||
)
|
||||
return items
|
||||
except Exception as exc:
|
||||
logger.warning("SerpAPI search error: %s", exc)
|
||||
return []
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Fetch (Step 4)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _fetch_pages(results: list[dict[str, str]], top_n: int = _FETCH_TOP_N) -> list[str]:
|
||||
"""Download and extract full text for the top search results.
|
||||
|
||||
Uses web_fetch (trafilatura) from timmy.tools.system_tools.
|
||||
"""
|
||||
try:
|
||||
from timmy.tools.system_tools import web_fetch
|
||||
except ImportError:
|
||||
logger.warning("web_fetch not available — skipping page fetch")
|
||||
return []
|
||||
|
||||
pages: list[str] = []
|
||||
for item in results[:top_n]:
|
||||
url = item.get("url", "")
|
||||
if not url:
|
||||
continue
|
||||
try:
|
||||
text = await asyncio.to_thread(web_fetch, url, 6000)
|
||||
if text and not text.startswith("Error:"):
|
||||
pages.append(f"## {item.get('title', url)}\nSource: {url}\n\n{text}")
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to fetch %s: %s", url, exc)
|
||||
|
||||
return pages
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Synthesis (Step 5) — cascade: Ollama → Claude fallback
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _synthesize(topic: str, pages: list[str], snippets: list[str]) -> tuple[str, str]:
|
||||
"""Compress fetched pages + snippets into a structured research report.
|
||||
|
||||
Returns (report_markdown, backend_used).
|
||||
"""
|
||||
# Build synthesis prompt
|
||||
source_content = "\n\n---\n\n".join(pages[:5])
|
||||
if not source_content and snippets:
|
||||
source_content = "\n".join(f"- {s}" for s in snippets[:20])
|
||||
|
||||
if not source_content:
|
||||
return (
|
||||
f"# Research: {topic}\n\n*No source material was retrieved. "
|
||||
"Check SERPAPI_API_KEY and network connectivity.*",
|
||||
"none",
|
||||
)
|
||||
|
||||
prompt = textwrap.dedent(f"""\
|
||||
You are a senior technical researcher. Synthesize the source material below
|
||||
into a structured research report on the topic: **{topic}**
|
||||
|
||||
FORMAT YOUR REPORT AS:
|
||||
# {topic}
|
||||
|
||||
## Executive Summary
|
||||
(2-3 sentences: what you found, top recommendation)
|
||||
|
||||
## Key Findings
|
||||
(Bullet list of the most important facts, tools, or patterns)
|
||||
|
||||
## Comparison / Options
|
||||
(Table or list comparing alternatives where applicable)
|
||||
|
||||
## Recommended Approach
|
||||
(Concrete recommendation with rationale)
|
||||
|
||||
## Gaps & Next Steps
|
||||
(What wasn't answered, what to investigate next)
|
||||
|
||||
---
|
||||
SOURCE MATERIAL:
|
||||
{source_content[:12000]}
|
||||
""")
|
||||
|
||||
# Tier 3 — try Ollama first
|
||||
report = await _ollama_complete(prompt, max_tokens=_SYNTHESIS_MAX_TOKENS)
|
||||
if report:
|
||||
return report, "ollama"
|
||||
|
||||
# Tier 2 — Claude fallback
|
||||
report = await _claude_complete(prompt, max_tokens=_SYNTHESIS_MAX_TOKENS)
|
||||
if report:
|
||||
return report, "claude"
|
||||
|
||||
# Last resort — structured snippet summary
|
||||
summary = f"# {topic}\n\n## Snippets\n\n" + "\n\n".join(
|
||||
f"- {s}" for s in snippets[:15]
|
||||
)
|
||||
return summary, "fallback"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# LLM helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _ollama_complete(prompt: str, max_tokens: int = 1024) -> str:
|
||||
"""Send a prompt to Ollama and return the response text.
|
||||
|
||||
Returns empty string on failure (graceful degradation).
|
||||
"""
|
||||
try:
|
||||
import httpx
|
||||
|
||||
from config import settings
|
||||
|
||||
url = f"{settings.normalized_ollama_url}/api/generate"
|
||||
payload: dict[str, Any] = {
|
||||
"model": settings.ollama_model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"num_predict": max_tokens,
|
||||
"temperature": 0.3,
|
||||
},
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
resp = await client.post(url, json=payload)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
return data.get("response", "").strip()
|
||||
except Exception as exc:
|
||||
logger.warning("Ollama completion failed: %s", exc)
|
||||
return ""
|
||||
|
||||
|
||||
async def _claude_complete(prompt: str, max_tokens: int = 1024) -> str:
|
||||
"""Send a prompt to Claude API as a last-resort fallback.
|
||||
|
||||
Only active when ANTHROPIC_API_KEY is configured.
|
||||
Returns empty string on failure or missing key.
|
||||
"""
|
||||
try:
|
||||
from config import settings
|
||||
|
||||
if not settings.anthropic_api_key:
|
||||
return ""
|
||||
|
||||
from timmy.backends import ClaudeBackend
|
||||
|
||||
backend = ClaudeBackend()
|
||||
result = await asyncio.to_thread(backend.run, prompt)
|
||||
return result.content.strip()
|
||||
except Exception as exc:
|
||||
logger.warning("Claude fallback failed: %s", exc)
|
||||
return ""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Memory cache (Step 0 + Step 6)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _check_cache(topic: str) -> tuple[str | None, float]:
|
||||
"""Search semantic memory for a prior result on this topic.
|
||||
|
||||
Returns (cached_report, similarity) or (None, 0.0).
|
||||
"""
|
||||
try:
|
||||
if SemanticMemory is None:
|
||||
return None, 0.0
|
||||
mem = SemanticMemory()
|
||||
hits = mem.search(topic, top_k=1)
|
||||
if hits:
|
||||
content, score = hits[0]
|
||||
if score >= _CACHE_HIT_THRESHOLD:
|
||||
return content, score
|
||||
except Exception as exc:
|
||||
logger.debug("Cache check failed: %s", exc)
|
||||
return None, 0.0
|
||||
|
||||
|
||||
def _store_result(topic: str, report: str) -> None:
|
||||
"""Index the research report into semantic memory for future retrieval."""
|
||||
try:
|
||||
if store_memory is None:
|
||||
logger.debug("store_memory not available — skipping memory index")
|
||||
return
|
||||
store_memory(
|
||||
content=report,
|
||||
source="research_pipeline",
|
||||
context_type="research",
|
||||
metadata={"topic": topic},
|
||||
)
|
||||
logger.info("Research result indexed for topic: %r", topic)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to store research result: %s", exc)
|
||||
|
||||
|
||||
def _save_to_disk(topic: str, report: str) -> Path | None:
|
||||
"""Persist the report as a markdown file under docs/research/.
|
||||
|
||||
Filename is derived from the topic (slugified). Returns the path or None.
|
||||
"""
|
||||
try:
|
||||
slug = re.sub(r"[^a-z0-9]+", "-", topic.lower()).strip("-")[:60]
|
||||
_DOCS_ROOT.mkdir(parents=True, exist_ok=True)
|
||||
path = _DOCS_ROOT / f"{slug}.md"
|
||||
path.write_text(report, encoding="utf-8")
|
||||
logger.info("Research report saved to %s", path)
|
||||
return path
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to save research report to disk: %s", exc)
|
||||
return None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main orchestrator
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def run_research(
|
||||
topic: str,
|
||||
template: str | None = None,
|
||||
slots: dict[str, str] | None = None,
|
||||
save_to_disk: bool = False,
|
||||
skip_cache: bool = False,
|
||||
) -> ResearchResult:
|
||||
"""Run the full 6-step autonomous research pipeline.
|
||||
|
||||
Args:
|
||||
topic: The research question or subject.
|
||||
template: Name of a template from skills/research/ (e.g. "tool_evaluation").
|
||||
If None, runs without a template scaffold.
|
||||
slots: Placeholder values for the template (e.g. {"domain": "PDF parsing"}).
|
||||
save_to_disk: If True, write the report to docs/research/<slug>.md.
|
||||
skip_cache: If True, bypass the semantic memory cache.
|
||||
|
||||
Returns:
|
||||
ResearchResult with report and metadata.
|
||||
"""
|
||||
errors: list[str] = []
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 0 — check cache
|
||||
# ------------------------------------------------------------------
|
||||
if not skip_cache:
|
||||
cached, score = _check_cache(topic)
|
||||
if cached:
|
||||
logger.info("Cache hit (%.2f) for topic: %r", score, topic)
|
||||
return ResearchResult(
|
||||
topic=topic,
|
||||
query_count=0,
|
||||
sources_fetched=0,
|
||||
report=cached,
|
||||
cached=True,
|
||||
cache_similarity=score,
|
||||
synthesis_backend="cache",
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 1 — load template (optional)
|
||||
# ------------------------------------------------------------------
|
||||
template_context = ""
|
||||
if template:
|
||||
try:
|
||||
template_context = load_template(template, slots)
|
||||
except FileNotFoundError as exc:
|
||||
errors.append(str(exc))
|
||||
logger.warning("Template load failed: %s", exc)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 2 — formulate queries
|
||||
# ------------------------------------------------------------------
|
||||
queries = await _formulate_queries(topic, template_context)
|
||||
logger.info("Formulated %d queries for topic: %r", len(queries), topic)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 3 — execute search
|
||||
# ------------------------------------------------------------------
|
||||
search_results = await _execute_search(queries)
|
||||
logger.info("Search returned %d results", len(search_results))
|
||||
snippets = [r.get("snippet", "") for r in search_results if r.get("snippet")]
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 4 — fetch full pages
|
||||
# ------------------------------------------------------------------
|
||||
pages = await _fetch_pages(search_results)
|
||||
logger.info("Fetched %d pages", len(pages))
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 5 — synthesize
|
||||
# ------------------------------------------------------------------
|
||||
report, backend = await _synthesize(topic, pages, snippets)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 6 — deliver
|
||||
# ------------------------------------------------------------------
|
||||
_store_result(topic, report)
|
||||
if save_to_disk:
|
||||
_save_to_disk(topic, report)
|
||||
|
||||
return ResearchResult(
|
||||
topic=topic,
|
||||
query_count=len(queries),
|
||||
sources_fetched=len(pages),
|
||||
report=report,
|
||||
cached=False,
|
||||
synthesis_backend=backend,
|
||||
errors=errors,
|
||||
)
|
||||
@@ -32,12 +32,8 @@ def get_llm_client() -> Any:
|
||||
# a client for an LLM service like OpenAI, Anthropic, or a local
|
||||
# model.
|
||||
class MockLLMClient:
|
||||
"""Stub LLM client for testing without a real language model."""
|
||||
|
||||
async def completion(self, prompt: str, max_tokens: int) -> Any:
|
||||
class MockCompletion:
|
||||
"""Stub completion response returned by MockLLMClient."""
|
||||
|
||||
def __init__(self, text: str) -> None:
|
||||
self.text = text
|
||||
|
||||
|
||||
@@ -1,30 +0,0 @@
|
||||
"""Sovereignty metrics for the Bannerlord loop.
|
||||
|
||||
Tracks how much of each AI layer (perception, decision, narration)
|
||||
runs locally vs. calls out to an LLM. Feeds the sovereignty dashboard.
|
||||
|
||||
Refs: #954, #953
|
||||
|
||||
Three-strike detector and automation enforcement.
|
||||
|
||||
Refs: #962
|
||||
|
||||
Session reporting: auto-generates markdown scorecards at session end
|
||||
and commits them to the Gitea repo for institutional memory.
|
||||
|
||||
Refs: #957 (Session Sovereignty Report Generator)
|
||||
"""
|
||||
|
||||
from timmy.sovereignty.session_report import (
|
||||
commit_report,
|
||||
generate_and_commit_report,
|
||||
generate_report,
|
||||
mark_session_start,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"generate_report",
|
||||
"commit_report",
|
||||
"generate_and_commit_report",
|
||||
"mark_session_start",
|
||||
]
|
||||
@@ -1,413 +0,0 @@
|
||||
"""Sovereignty metrics emitter and SQLite store.
|
||||
|
||||
Tracks the sovereignty percentage for each AI layer (perception, decision,
|
||||
narration) plus API cost and skill crystallisation. All data is persisted to
|
||||
``data/sovereignty_metrics.db`` so the dashboard can query trends over time.
|
||||
|
||||
Event types
|
||||
-----------
|
||||
perception layer:
|
||||
``perception_cache_hit`` — frame answered from local cache (sovereign)
|
||||
``perception_vlm_call`` — frame required a VLM inference call (non-sovereign)
|
||||
|
||||
decision layer:
|
||||
``decision_rule_hit`` — action chosen by a deterministic rule (sovereign)
|
||||
``decision_llm_call`` — action required LLM reasoning (non-sovereign)
|
||||
|
||||
narration layer:
|
||||
``narration_template`` — text generated from a template (sovereign)
|
||||
``narration_llm`` — text generated by an LLM (non-sovereign)
|
||||
|
||||
skill layer:
|
||||
``skill_crystallized`` — a new skill was crystallised from LLM output
|
||||
|
||||
cost:
|
||||
``api_call`` — any external API call was made
|
||||
``api_cost`` — monetary cost of an API call (metadata: {"usd": float})
|
||||
|
||||
Refs: #954, #953
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import sqlite3
|
||||
import uuid
|
||||
from contextlib import closing
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── Constants ─────────────────────────────────────────────────────────────────
|
||||
|
||||
DB_PATH = Path(settings.repo_root) / "data" / "sovereignty_metrics.db"
|
||||
|
||||
#: Sovereign event types for each layer (numerator of sovereignty %).
|
||||
_SOVEREIGN_EVENTS: dict[str, frozenset[str]] = {
|
||||
"perception": frozenset({"perception_cache_hit"}),
|
||||
"decision": frozenset({"decision_rule_hit"}),
|
||||
"narration": frozenset({"narration_template"}),
|
||||
}
|
||||
|
||||
#: All tracked event types for each layer (denominator of sovereignty %).
|
||||
_LAYER_EVENTS: dict[str, frozenset[str]] = {
|
||||
"perception": frozenset({"perception_cache_hit", "perception_vlm_call"}),
|
||||
"decision": frozenset({"decision_rule_hit", "decision_llm_call"}),
|
||||
"narration": frozenset({"narration_template", "narration_llm"}),
|
||||
}
|
||||
|
||||
ALL_EVENT_TYPES: frozenset[str] = frozenset(
|
||||
{
|
||||
"perception_cache_hit",
|
||||
"perception_vlm_call",
|
||||
"decision_rule_hit",
|
||||
"decision_llm_call",
|
||||
"narration_template",
|
||||
"narration_llm",
|
||||
"skill_crystallized",
|
||||
"api_call",
|
||||
"api_cost",
|
||||
}
|
||||
)
|
||||
|
||||
# ── Schema ────────────────────────────────────────────────────────────────────
|
||||
|
||||
_SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS events (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
timestamp TEXT NOT NULL,
|
||||
event_type TEXT NOT NULL,
|
||||
session_id TEXT NOT NULL DEFAULT '',
|
||||
metadata_json TEXT NOT NULL DEFAULT '{}'
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_ev_type ON events(event_type);
|
||||
CREATE INDEX IF NOT EXISTS idx_ev_ts ON events(timestamp);
|
||||
CREATE INDEX IF NOT EXISTS idx_ev_session ON events(session_id);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS sessions (
|
||||
session_id TEXT PRIMARY KEY,
|
||||
game TEXT NOT NULL DEFAULT '',
|
||||
start_time TEXT NOT NULL,
|
||||
end_time TEXT
|
||||
);
|
||||
"""
|
||||
|
||||
|
||||
# ── Data classes ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class SovereigntyEvent:
|
||||
"""A single sovereignty event."""
|
||||
|
||||
event_type: str
|
||||
session_id: str = ""
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
timestamp: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
|
||||
|
||||
|
||||
# ── Store ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class SovereigntyMetricsStore:
|
||||
"""SQLite-backed sovereignty event store.
|
||||
|
||||
Thread-safe: creates a new connection per operation (WAL mode).
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: Path | None = None) -> None:
|
||||
self._db_path = db_path or DB_PATH
|
||||
self._init_db()
|
||||
|
||||
# ── internal ─────────────────────────────────────────────────────────────
|
||||
|
||||
def _init_db(self) -> None:
|
||||
try:
|
||||
self._db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with closing(sqlite3.connect(str(self._db_path))) as conn:
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
|
||||
conn.executescript(_SCHEMA)
|
||||
conn.commit()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to initialise sovereignty metrics DB: %s", exc)
|
||||
|
||||
def _connect(self) -> sqlite3.Connection:
|
||||
conn = sqlite3.connect(str(self._db_path))
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
|
||||
return conn
|
||||
|
||||
# ── public API ────────────────────────────────────────────────────────────
|
||||
|
||||
def record(
|
||||
self, event_type: str, metadata: dict[str, Any] | None = None, *, session_id: str = ""
|
||||
) -> None:
|
||||
"""Record a sovereignty event.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
event_type:
|
||||
One of ``ALL_EVENT_TYPES``.
|
||||
metadata:
|
||||
Optional dict of extra data (serialised as JSON).
|
||||
session_id:
|
||||
Identifier of the current game session, if known.
|
||||
"""
|
||||
event = SovereigntyEvent(
|
||||
event_type=event_type,
|
||||
session_id=session_id,
|
||||
metadata=metadata or {},
|
||||
)
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
conn.execute(
|
||||
"INSERT INTO events (timestamp, event_type, session_id, metadata_json) "
|
||||
"VALUES (?, ?, ?, ?)",
|
||||
(
|
||||
event.timestamp,
|
||||
event.event_type,
|
||||
event.session_id,
|
||||
json.dumps(event.metadata),
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to record sovereignty event: %s", exc)
|
||||
|
||||
def start_session(self, game: str = "", session_id: str | None = None) -> str:
|
||||
"""Register a new game session. Returns the session_id."""
|
||||
sid = session_id or str(uuid.uuid4())
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
conn.execute(
|
||||
"INSERT OR IGNORE INTO sessions (session_id, game, start_time) VALUES (?, ?, ?)",
|
||||
(sid, game, datetime.now(UTC).isoformat()),
|
||||
)
|
||||
conn.commit()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to start session: %s", exc)
|
||||
return sid
|
||||
|
||||
def end_session(self, session_id: str) -> None:
|
||||
"""Mark a session as ended."""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
conn.execute(
|
||||
"UPDATE sessions SET end_time = ? WHERE session_id = ?",
|
||||
(datetime.now(UTC).isoformat(), session_id),
|
||||
)
|
||||
conn.commit()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to end session: %s", exc)
|
||||
|
||||
# ── analytics ─────────────────────────────────────────────────────────────
|
||||
|
||||
def get_sovereignty_pct(self, layer: str, time_window: float | None = None) -> float:
|
||||
"""Return the sovereignty percentage (0.0–100.0) for *layer*.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
layer:
|
||||
One of ``"perception"``, ``"decision"``, ``"narration"``.
|
||||
time_window:
|
||||
If given, only consider events from the last *time_window* seconds.
|
||||
If ``None``, all events are used.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
Percentage of sovereign events for the layer, or 0.0 if no data.
|
||||
"""
|
||||
if layer not in _LAYER_EVENTS:
|
||||
logger.warning("Unknown sovereignty layer: %s", layer)
|
||||
return 0.0
|
||||
|
||||
sovereign = _SOVEREIGN_EVENTS[layer]
|
||||
total_types = _LAYER_EVENTS[layer]
|
||||
|
||||
sovereign_placeholders = ",".join("?" * len(sovereign))
|
||||
total_placeholders = ",".join("?" * len(total_types))
|
||||
|
||||
params_sov: list[Any] = list(sovereign)
|
||||
params_total: list[Any] = list(total_types)
|
||||
|
||||
if time_window is not None:
|
||||
cutoff = _seconds_ago_iso(time_window)
|
||||
where_ts = " AND timestamp >= ?"
|
||||
params_sov.append(cutoff)
|
||||
params_total.append(cutoff)
|
||||
else:
|
||||
where_ts = ""
|
||||
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
total_count = conn.execute(
|
||||
f"SELECT COUNT(*) FROM events WHERE event_type IN ({total_placeholders}){where_ts}",
|
||||
params_total,
|
||||
).fetchone()[0]
|
||||
if total_count == 0:
|
||||
return 0.0
|
||||
sov_count = conn.execute(
|
||||
f"SELECT COUNT(*) FROM events WHERE event_type IN ({sovereign_placeholders}){where_ts}",
|
||||
params_sov,
|
||||
).fetchone()[0]
|
||||
return round(100.0 * sov_count / total_count, 2)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to compute sovereignty pct: %s", exc)
|
||||
return 0.0
|
||||
|
||||
def get_cost_per_hour(self, time_window: float | None = None) -> float:
|
||||
"""Return the total API cost in USD extrapolated to a per-hour rate.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
time_window:
|
||||
Seconds of history to consider. Defaults to 3600 (last hour).
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
USD cost per hour, or 0.0 if no ``api_cost`` events exist.
|
||||
"""
|
||||
window = time_window if time_window is not None else 3600.0
|
||||
cutoff = _seconds_ago_iso(window)
|
||||
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT metadata_json FROM events WHERE event_type = 'api_cost' AND timestamp >= ?",
|
||||
(cutoff,),
|
||||
).fetchall()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to query api_cost events: %s", exc)
|
||||
return 0.0
|
||||
|
||||
total_usd = 0.0
|
||||
for row in rows:
|
||||
try:
|
||||
meta = json.loads(row["metadata_json"] or "{}")
|
||||
total_usd += float(meta.get("usd", 0.0))
|
||||
except (ValueError, TypeError, json.JSONDecodeError):
|
||||
pass
|
||||
|
||||
# Extrapolate: (total in window) * (3600 / window_seconds)
|
||||
if window == 0:
|
||||
return 0.0
|
||||
return round(total_usd * (3600.0 / window), 4)
|
||||
|
||||
def get_skills_crystallized(self, session_id: str | None = None) -> int:
|
||||
"""Return the number of skills crystallised.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
session_id:
|
||||
If given, count only events for that session. If ``None``,
|
||||
count across all sessions.
|
||||
"""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
if session_id:
|
||||
return conn.execute(
|
||||
"SELECT COUNT(*) FROM events WHERE event_type = 'skill_crystallized' AND session_id = ?",
|
||||
(session_id,),
|
||||
).fetchone()[0]
|
||||
return conn.execute(
|
||||
"SELECT COUNT(*) FROM events WHERE event_type = 'skill_crystallized'",
|
||||
).fetchone()[0]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to query skill_crystallized: %s", exc)
|
||||
return 0
|
||||
|
||||
def get_snapshot(self) -> dict[str, Any]:
|
||||
"""Return a real-time metrics snapshot suitable for dashboard widgets."""
|
||||
return {
|
||||
"sovereignty": {
|
||||
layer: self.get_sovereignty_pct(layer, time_window=3600) for layer in _LAYER_EVENTS
|
||||
},
|
||||
"cost_per_hour": self.get_cost_per_hour(),
|
||||
"skills_crystallized": self.get_skills_crystallized(),
|
||||
}
|
||||
|
||||
|
||||
# ── Module-level singleton ────────────────────────────────────────────────────
|
||||
|
||||
_store: SovereigntyMetricsStore | None = None
|
||||
|
||||
|
||||
def get_metrics_store() -> SovereigntyMetricsStore:
|
||||
"""Return (or lazily create) the module-level singleton store."""
|
||||
global _store
|
||||
if _store is None:
|
||||
_store = SovereigntyMetricsStore()
|
||||
return _store
|
||||
|
||||
|
||||
# ── Convenience helpers ───────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def record(
|
||||
event_type: str, metadata: dict[str, Any] | None = None, *, session_id: str = ""
|
||||
) -> None:
|
||||
"""Module-level shortcut: ``metrics.record("perception_cache_hit")``."""
|
||||
get_metrics_store().record(event_type, metadata=metadata, session_id=session_id)
|
||||
|
||||
|
||||
def get_sovereignty_pct(layer: str, time_window: float | None = None) -> float:
|
||||
"""Module-level shortcut for :meth:`SovereigntyMetricsStore.get_sovereignty_pct`."""
|
||||
return get_metrics_store().get_sovereignty_pct(layer, time_window)
|
||||
|
||||
|
||||
def get_cost_per_hour(time_window: float | None = None) -> float:
|
||||
"""Module-level shortcut for :meth:`SovereigntyMetricsStore.get_cost_per_hour`."""
|
||||
return get_metrics_store().get_cost_per_hour(time_window)
|
||||
|
||||
|
||||
def get_skills_crystallized(session_id: str | None = None) -> int:
|
||||
"""Module-level shortcut for :meth:`SovereigntyMetricsStore.get_skills_crystallized`."""
|
||||
return get_metrics_store().get_skills_crystallized(session_id)
|
||||
|
||||
|
||||
async def emit_sovereignty_event(
|
||||
event_type: str,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
*,
|
||||
session_id: str = "",
|
||||
) -> None:
|
||||
"""Record an event in a thread and publish it on the event bus.
|
||||
|
||||
This is the async-safe entry-point used by the agentic loop.
|
||||
"""
|
||||
from infrastructure.events.bus import emit
|
||||
|
||||
await asyncio.to_thread(
|
||||
get_metrics_store().record,
|
||||
event_type,
|
||||
metadata,
|
||||
session_id=session_id,
|
||||
)
|
||||
await emit(
|
||||
f"sovereignty.event.{event_type}",
|
||||
source="sovereignty_metrics",
|
||||
data={
|
||||
"event_type": event_type,
|
||||
"session_id": session_id,
|
||||
**(metadata or {}),
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
# ── Private helpers ───────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _seconds_ago_iso(seconds: float) -> str:
|
||||
"""Return an ISO-8601 timestamp *seconds* before now (UTC)."""
|
||||
import datetime as _dt
|
||||
|
||||
delta = _dt.timedelta(seconds=seconds)
|
||||
return (_dt.datetime.now(UTC) - delta).isoformat()
|
||||
@@ -1,92 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
@dataclass
|
||||
class Template:
|
||||
name: str
|
||||
image: np.ndarray
|
||||
threshold: float = 0.85
|
||||
|
||||
|
||||
@dataclass
|
||||
class CacheResult:
|
||||
confidence: float
|
||||
state: Any | None
|
||||
|
||||
|
||||
class PerceptionCache:
|
||||
def __init__(self, templates_path: Path | str = "data/templates.json"):
|
||||
self.templates_path = Path(templates_path)
|
||||
self.templates: list[Template] = []
|
||||
self.load()
|
||||
|
||||
def match(self, screenshot: np.ndarray) -> CacheResult:
|
||||
"""
|
||||
Matches templates against the screenshot.
|
||||
Returns the confidence and the name of the best matching template.
|
||||
"""
|
||||
best_match_confidence = 0.0
|
||||
best_match_name = None
|
||||
|
||||
for template in self.templates:
|
||||
res = cv2.matchTemplate(screenshot, template.image, cv2.TM_CCOEFF_NORMED)
|
||||
_, max_val, _, _ = cv2.minMaxLoc(res)
|
||||
if max_val > best_match_confidence:
|
||||
best_match_confidence = max_val
|
||||
best_match_name = template.name
|
||||
|
||||
if best_match_confidence > 0.85: # TODO: Make this configurable per template
|
||||
return CacheResult(
|
||||
confidence=best_match_confidence, state={"template_name": best_match_name}
|
||||
)
|
||||
else:
|
||||
return CacheResult(confidence=best_match_confidence, state=None)
|
||||
|
||||
def add(self, templates: list[Template]):
|
||||
self.templates.extend(templates)
|
||||
|
||||
def persist(self):
|
||||
self.templates_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
# Note: This is a simplified persistence mechanism.
|
||||
# A more robust solution would store templates as images and metadata in JSON.
|
||||
with self.templates_path.open("w") as f:
|
||||
json.dump(
|
||||
[{"name": t.name, "threshold": t.threshold} for t in self.templates], f, indent=2
|
||||
)
|
||||
|
||||
def load(self):
|
||||
if self.templates_path.exists():
|
||||
with self.templates_path.open("r") as f:
|
||||
templates_data = json.load(f)
|
||||
# This is a simplified loading mechanism and assumes template images are stored elsewhere.
|
||||
# For now, we are not loading the actual images.
|
||||
self.templates = [
|
||||
Template(name=t["name"], image=np.array([]), threshold=t["threshold"])
|
||||
for t in templates_data
|
||||
]
|
||||
|
||||
|
||||
def crystallize_perception(screenshot: np.ndarray, vlm_response: Any) -> list[Template]:
|
||||
"""
|
||||
Extracts reusable patterns from VLM output and generates OpenCV templates.
|
||||
This is a placeholder and needs to be implemented based on the actual VLM response format.
|
||||
"""
|
||||
# Example implementation:
|
||||
# templates = []
|
||||
# for item in vlm_response.get("items", []):
|
||||
# bbox = item.get("bounding_box")
|
||||
# template_name = item.get("name")
|
||||
# if bbox and template_name:
|
||||
# x1, y1, x2, y2 = bbox
|
||||
# template_image = screenshot[y1:y2, x1:x2]
|
||||
# templates.append(Template(name=template_name, image=template_image))
|
||||
# return templates
|
||||
return []
|
||||
@@ -1,442 +0,0 @@
|
||||
"""Session Sovereignty Report Generator.
|
||||
|
||||
Auto-generates a sovereignty scorecard at the end of each play session
|
||||
and commits it as a markdown file to the Gitea repo under
|
||||
``reports/sovereignty/``.
|
||||
|
||||
Report contents (per issue #957):
|
||||
- Session duration + game played
|
||||
- Total model calls by type (VLM, LLM, TTS, API)
|
||||
- Total cache/rule hits by type
|
||||
- New skills crystallized (placeholder — pending skill-tracking impl)
|
||||
- Sovereignty delta (change from session start → end)
|
||||
- Cost breakdown (actual API spend)
|
||||
- Per-layer sovereignty %: perception, decision, narration
|
||||
- Trend comparison vs previous session
|
||||
|
||||
Refs: #957 (Sovereignty P0) · #953 (The Sovereignty Loop)
|
||||
"""
|
||||
|
||||
import base64
|
||||
import json
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
|
||||
from config import settings
|
||||
|
||||
# Optional module-level imports — degrade gracefully if unavailable at import time
|
||||
try:
|
||||
from timmy.session_logger import get_session_logger
|
||||
except Exception: # ImportError or circular import during early startup
|
||||
get_session_logger = None # type: ignore[assignment]
|
||||
|
||||
try:
|
||||
from infrastructure.sovereignty_metrics import GRADUATION_TARGETS, get_sovereignty_store
|
||||
except Exception:
|
||||
GRADUATION_TARGETS: dict = {} # type: ignore[assignment]
|
||||
get_sovereignty_store = None # type: ignore[assignment]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Module-level session start time; set by mark_session_start()
|
||||
_SESSION_START: datetime | None = None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def mark_session_start() -> None:
|
||||
"""Record the session start wall-clock time.
|
||||
|
||||
Call once during application startup so ``generate_report()`` can
|
||||
compute accurate session durations.
|
||||
"""
|
||||
global _SESSION_START
|
||||
_SESSION_START = datetime.now(UTC)
|
||||
logger.debug("Sovereignty: session start recorded at %s", _SESSION_START.isoformat())
|
||||
|
||||
|
||||
def generate_report(session_id: str = "dashboard") -> str:
|
||||
"""Render a sovereignty scorecard as a markdown string.
|
||||
|
||||
Pulls from:
|
||||
- ``timmy.session_logger`` — message/tool-call/error counts
|
||||
- ``infrastructure.sovereignty_metrics`` — cache hit rate, API cost,
|
||||
graduation phase, and trend data
|
||||
|
||||
Args:
|
||||
session_id: The session identifier (default: "dashboard").
|
||||
|
||||
Returns:
|
||||
Markdown-formatted sovereignty report string.
|
||||
"""
|
||||
now = datetime.now(UTC)
|
||||
session_start = _SESSION_START or now
|
||||
duration_secs = (now - session_start).total_seconds()
|
||||
|
||||
session_data = _gather_session_data()
|
||||
sov_data = _gather_sovereignty_data()
|
||||
|
||||
return _render_markdown(now, session_id, duration_secs, session_data, sov_data)
|
||||
|
||||
|
||||
def commit_report(report_md: str, session_id: str = "dashboard") -> bool:
|
||||
"""Commit a sovereignty report to the Gitea repo.
|
||||
|
||||
Creates or updates ``reports/sovereignty/{date}_{session_id}.md``
|
||||
via the Gitea Contents API. Degrades gracefully: logs a warning
|
||||
and returns ``False`` if Gitea is unreachable or misconfigured.
|
||||
|
||||
Args:
|
||||
report_md: Markdown content to commit.
|
||||
session_id: Session identifier used in the filename.
|
||||
|
||||
Returns:
|
||||
``True`` on success, ``False`` on failure.
|
||||
"""
|
||||
if not settings.gitea_enabled:
|
||||
logger.info("Sovereignty: Gitea disabled — skipping report commit")
|
||||
return False
|
||||
|
||||
if not settings.gitea_token:
|
||||
logger.warning("Sovereignty: no Gitea token — skipping report commit")
|
||||
return False
|
||||
|
||||
date_str = datetime.now(UTC).strftime("%Y-%m-%d")
|
||||
file_path = f"reports/sovereignty/{date_str}_{session_id}.md"
|
||||
url = f"{settings.gitea_url}/api/v1/repos/{settings.gitea_repo}/contents/{file_path}"
|
||||
headers = {
|
||||
"Authorization": f"token {settings.gitea_token}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
encoded_content = base64.b64encode(report_md.encode()).decode()
|
||||
commit_message = (
|
||||
f"report: sovereignty session {session_id} ({date_str})\n\n"
|
||||
f"Auto-generated by Timmy. Refs #957"
|
||||
)
|
||||
payload: dict[str, Any] = {
|
||||
"message": commit_message,
|
||||
"content": encoded_content,
|
||||
}
|
||||
|
||||
try:
|
||||
with httpx.Client(timeout=10.0) as client:
|
||||
# Fetch existing file SHA so we can update rather than create
|
||||
check = client.get(url, headers=headers)
|
||||
if check.status_code == 200:
|
||||
existing = check.json()
|
||||
payload["sha"] = existing.get("sha", "")
|
||||
|
||||
resp = client.put(url, headers=headers, json=payload)
|
||||
resp.raise_for_status()
|
||||
|
||||
logger.info("Sovereignty: report committed to %s", file_path)
|
||||
return True
|
||||
|
||||
except httpx.HTTPStatusError as exc:
|
||||
logger.warning(
|
||||
"Sovereignty: commit failed (HTTP %s): %s",
|
||||
exc.response.status_code,
|
||||
exc,
|
||||
)
|
||||
return False
|
||||
except Exception as exc:
|
||||
logger.warning("Sovereignty: commit failed: %s", exc)
|
||||
return False
|
||||
|
||||
|
||||
async def generate_and_commit_report(session_id: str = "dashboard") -> bool:
|
||||
"""Generate and commit a sovereignty report for the current session.
|
||||
|
||||
Primary entry point — call at session end / application shutdown.
|
||||
Wraps the synchronous ``commit_report`` call in ``asyncio.to_thread``
|
||||
so it does not block the event loop.
|
||||
|
||||
Args:
|
||||
session_id: The session identifier.
|
||||
|
||||
Returns:
|
||||
``True`` if the report was generated and committed successfully.
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
try:
|
||||
report_md = generate_report(session_id)
|
||||
logger.info("Sovereignty: report generated (%d chars)", len(report_md))
|
||||
committed = await asyncio.to_thread(commit_report, report_md, session_id)
|
||||
return committed
|
||||
except Exception as exc:
|
||||
logger.warning("Sovereignty: report generation failed: %s", exc)
|
||||
return False
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Internal helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _format_duration(seconds: float) -> str:
|
||||
"""Format a duration in seconds as a human-readable string."""
|
||||
total = int(seconds)
|
||||
hours, remainder = divmod(total, 3600)
|
||||
minutes, secs = divmod(remainder, 60)
|
||||
if hours:
|
||||
return f"{hours}h {minutes}m {secs}s"
|
||||
if minutes:
|
||||
return f"{minutes}m {secs}s"
|
||||
return f"{secs}s"
|
||||
|
||||
|
||||
def _gather_session_data() -> dict[str, Any]:
|
||||
"""Pull session statistics from the session logger.
|
||||
|
||||
Returns a dict with:
|
||||
- ``user_messages``, ``timmy_messages``, ``tool_calls``, ``errors``
|
||||
- ``tool_call_breakdown``: dict[tool_name, count]
|
||||
"""
|
||||
default: dict[str, Any] = {
|
||||
"user_messages": 0,
|
||||
"timmy_messages": 0,
|
||||
"tool_calls": 0,
|
||||
"errors": 0,
|
||||
"tool_call_breakdown": {},
|
||||
}
|
||||
|
||||
try:
|
||||
if get_session_logger is None:
|
||||
return default
|
||||
sl = get_session_logger()
|
||||
sl.flush()
|
||||
|
||||
# Read today's session file directly for accurate counts
|
||||
if not sl.session_file.exists():
|
||||
return default
|
||||
|
||||
entries: list[dict] = []
|
||||
with open(sl.session_file) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
try:
|
||||
entries.append(json.loads(line))
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
tool_breakdown: dict[str, int] = {}
|
||||
user_msgs = timmy_msgs = tool_calls = errors = 0
|
||||
|
||||
for entry in entries:
|
||||
etype = entry.get("type")
|
||||
if etype == "message":
|
||||
if entry.get("role") == "user":
|
||||
user_msgs += 1
|
||||
elif entry.get("role") == "timmy":
|
||||
timmy_msgs += 1
|
||||
elif etype == "tool_call":
|
||||
tool_calls += 1
|
||||
tool_name = entry.get("tool", "unknown")
|
||||
tool_breakdown[tool_name] = tool_breakdown.get(tool_name, 0) + 1
|
||||
elif etype == "error":
|
||||
errors += 1
|
||||
|
||||
return {
|
||||
"user_messages": user_msgs,
|
||||
"timmy_messages": timmy_msgs,
|
||||
"tool_calls": tool_calls,
|
||||
"errors": errors,
|
||||
"tool_call_breakdown": tool_breakdown,
|
||||
}
|
||||
|
||||
except Exception as exc:
|
||||
logger.warning("Sovereignty: failed to gather session data: %s", exc)
|
||||
return default
|
||||
|
||||
|
||||
def _gather_sovereignty_data() -> dict[str, Any]:
|
||||
"""Pull sovereignty metrics from the SQLite store.
|
||||
|
||||
Returns a dict with:
|
||||
- ``metrics``: summary from ``SovereigntyMetricsStore.get_summary()``
|
||||
- ``deltas``: per-metric start/end values within recent history window
|
||||
- ``previous_session``: most recent prior value for each metric
|
||||
"""
|
||||
try:
|
||||
if get_sovereignty_store is None:
|
||||
return {"metrics": {}, "deltas": {}, "previous_session": {}}
|
||||
store = get_sovereignty_store()
|
||||
summary = store.get_summary()
|
||||
|
||||
deltas: dict[str, dict[str, Any]] = {}
|
||||
previous_session: dict[str, float | None] = {}
|
||||
|
||||
for metric_type in GRADUATION_TARGETS:
|
||||
history = store.get_latest(metric_type, limit=10)
|
||||
if len(history) >= 2:
|
||||
deltas[metric_type] = {
|
||||
"start": history[-1]["value"],
|
||||
"end": history[0]["value"],
|
||||
}
|
||||
previous_session[metric_type] = history[1]["value"]
|
||||
elif len(history) == 1:
|
||||
deltas[metric_type] = {"start": history[0]["value"], "end": history[0]["value"]}
|
||||
previous_session[metric_type] = None
|
||||
else:
|
||||
deltas[metric_type] = {"start": None, "end": None}
|
||||
previous_session[metric_type] = None
|
||||
|
||||
return {
|
||||
"metrics": summary,
|
||||
"deltas": deltas,
|
||||
"previous_session": previous_session,
|
||||
}
|
||||
|
||||
except Exception as exc:
|
||||
logger.warning("Sovereignty: failed to gather sovereignty data: %s", exc)
|
||||
return {"metrics": {}, "deltas": {}, "previous_session": {}}
|
||||
|
||||
|
||||
def _render_markdown(
|
||||
now: datetime,
|
||||
session_id: str,
|
||||
duration_secs: float,
|
||||
session_data: dict[str, Any],
|
||||
sov_data: dict[str, Any],
|
||||
) -> str:
|
||||
"""Assemble the full sovereignty report in markdown."""
|
||||
lines: list[str] = []
|
||||
|
||||
# Header
|
||||
lines += [
|
||||
"# Sovereignty Session Report",
|
||||
"",
|
||||
f"**Session ID:** `{session_id}` ",
|
||||
f"**Date:** {now.strftime('%Y-%m-%d')} ",
|
||||
f"**Duration:** {_format_duration(duration_secs)} ",
|
||||
f"**Generated:** {now.isoformat()}",
|
||||
"",
|
||||
"---",
|
||||
"",
|
||||
]
|
||||
|
||||
# Session activity
|
||||
lines += [
|
||||
"## Session Activity",
|
||||
"",
|
||||
"| Metric | Count |",
|
||||
"|--------|-------|",
|
||||
f"| User messages | {session_data['user_messages']} |",
|
||||
f"| Timmy responses | {session_data['timmy_messages']} |",
|
||||
f"| Tool calls | {session_data['tool_calls']} |",
|
||||
f"| Errors | {session_data['errors']} |",
|
||||
"",
|
||||
]
|
||||
|
||||
tool_breakdown = session_data.get("tool_call_breakdown", {})
|
||||
if tool_breakdown:
|
||||
lines += ["### Model Calls by Tool", ""]
|
||||
for tool_name, count in sorted(tool_breakdown.items(), key=lambda x: -x[1]):
|
||||
lines.append(f"- `{tool_name}`: {count}")
|
||||
lines.append("")
|
||||
|
||||
# Sovereignty scorecard
|
||||
|
||||
lines += [
|
||||
"## Sovereignty Scorecard",
|
||||
"",
|
||||
"| Metric | Current | Target (graduation) | Phase |",
|
||||
"|--------|---------|---------------------|-------|",
|
||||
]
|
||||
|
||||
for metric_type, data in sov_data["metrics"].items():
|
||||
current = data.get("current")
|
||||
current_str = f"{current:.4f}" if current is not None else "N/A"
|
||||
grad_target = GRADUATION_TARGETS.get(metric_type, {}).get("graduation")
|
||||
grad_str = f"{grad_target:.4f}" if isinstance(grad_target, (int, float)) else "N/A"
|
||||
phase = data.get("phase", "unknown")
|
||||
lines.append(f"| {metric_type} | {current_str} | {grad_str} | {phase} |")
|
||||
|
||||
lines += ["", "### Sovereignty Delta (This Session)", ""]
|
||||
|
||||
for metric_type, delta_info in sov_data.get("deltas", {}).items():
|
||||
start_val = delta_info.get("start")
|
||||
end_val = delta_info.get("end")
|
||||
if start_val is not None and end_val is not None:
|
||||
diff = end_val - start_val
|
||||
sign = "+" if diff >= 0 else ""
|
||||
lines.append(
|
||||
f"- **{metric_type}**: {start_val:.4f} → {end_val:.4f} ({sign}{diff:.4f})"
|
||||
)
|
||||
else:
|
||||
lines.append(f"- **{metric_type}**: N/A (no data recorded)")
|
||||
|
||||
# Cost breakdown
|
||||
lines += ["", "## Cost Breakdown", ""]
|
||||
api_cost_data = sov_data["metrics"].get("api_cost", {})
|
||||
current_cost = api_cost_data.get("current")
|
||||
if current_cost is not None:
|
||||
lines.append(f"- **Total API spend (latest recorded):** ${current_cost:.4f}")
|
||||
else:
|
||||
lines.append("- **Total API spend:** N/A (no data recorded)")
|
||||
lines.append("")
|
||||
|
||||
# Per-layer sovereignty
|
||||
lines += [
|
||||
"## Per-Layer Sovereignty",
|
||||
"",
|
||||
"| Layer | Sovereignty % |",
|
||||
"|-------|--------------|",
|
||||
"| Perception (VLM) | N/A |",
|
||||
"| Decision (LLM) | N/A |",
|
||||
"| Narration (TTS) | N/A |",
|
||||
"",
|
||||
"> Per-layer tracking requires instrumented inference calls. See #957.",
|
||||
"",
|
||||
]
|
||||
|
||||
# Skills crystallized
|
||||
lines += [
|
||||
"## Skills Crystallized",
|
||||
"",
|
||||
"_Skill crystallization tracking not yet implemented. See #957._",
|
||||
"",
|
||||
]
|
||||
|
||||
# Trend vs previous session
|
||||
lines += ["## Trend vs Previous Session", ""]
|
||||
prev_data = sov_data.get("previous_session", {})
|
||||
has_prev = any(v is not None for v in prev_data.values())
|
||||
|
||||
if has_prev:
|
||||
lines += [
|
||||
"| Metric | Previous | Current | Change |",
|
||||
"|--------|----------|---------|--------|",
|
||||
]
|
||||
for metric_type, curr_info in sov_data["metrics"].items():
|
||||
curr_val = curr_info.get("current")
|
||||
prev_val = prev_data.get(metric_type)
|
||||
curr_str = f"{curr_val:.4f}" if curr_val is not None else "N/A"
|
||||
prev_str = f"{prev_val:.4f}" if prev_val is not None else "N/A"
|
||||
if curr_val is not None and prev_val is not None:
|
||||
diff = curr_val - prev_val
|
||||
sign = "+" if diff >= 0 else ""
|
||||
change_str = f"{sign}{diff:.4f}"
|
||||
else:
|
||||
change_str = "N/A"
|
||||
lines.append(f"| {metric_type} | {prev_str} | {curr_str} | {change_str} |")
|
||||
lines.append("")
|
||||
else:
|
||||
lines += ["_No previous session data available for comparison._", ""]
|
||||
|
||||
# Footer
|
||||
lines += [
|
||||
"---",
|
||||
"_Auto-generated by Timmy · Session Sovereignty Report · Refs: #957_",
|
||||
]
|
||||
|
||||
return "\n".join(lines)
|
||||
@@ -1,482 +0,0 @@
|
||||
"""Three-Strike Detector for Repeated Manual Work.
|
||||
|
||||
Tracks recurring manual actions by category and key. When the same action
|
||||
is performed three or more times, it blocks further attempts and requires
|
||||
an automation artifact to be registered first.
|
||||
|
||||
Strike 1 (count=1): discovery — action proceeds normally
|
||||
Strike 2 (count=2): warning — action proceeds with a logged warning
|
||||
Strike 3 (count≥3): blocked — raises ThreeStrikeError; caller must
|
||||
register an automation artifact first
|
||||
|
||||
Governing principle: "If you do the same thing manually three times,
|
||||
you have failed to crystallise."
|
||||
|
||||
Categories tracked:
|
||||
- vlm_prompt_edit VLM prompt edits for the same UI element
|
||||
- game_bug_review Manual game-bug reviews for the same bug type
|
||||
- parameter_tuning Manual parameter tuning for the same parameter
|
||||
- portal_adapter_creation Manual portal-adapter creation for same pattern
|
||||
- deployment_step Manual deployment steps
|
||||
|
||||
The Falsework Checklist is enforced before cloud API calls via
|
||||
:func:`falsework_check`.
|
||||
|
||||
Refs: #962
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import sqlite3
|
||||
from contextlib import closing
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── Constants ────────────────────────────────────────────────────────────────
|
||||
|
||||
DB_PATH = Path(settings.repo_root) / "data" / "three_strike.db"
|
||||
|
||||
CATEGORIES = frozenset(
|
||||
{
|
||||
"vlm_prompt_edit",
|
||||
"game_bug_review",
|
||||
"parameter_tuning",
|
||||
"portal_adapter_creation",
|
||||
"deployment_step",
|
||||
}
|
||||
)
|
||||
|
||||
STRIKE_WARNING = 2
|
||||
STRIKE_BLOCK = 3
|
||||
|
||||
_SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS strikes (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
category TEXT NOT NULL,
|
||||
key TEXT NOT NULL,
|
||||
count INTEGER NOT NULL DEFAULT 0,
|
||||
blocked INTEGER NOT NULL DEFAULT 0,
|
||||
automation TEXT DEFAULT NULL,
|
||||
first_seen TEXT NOT NULL,
|
||||
last_seen TEXT NOT NULL
|
||||
);
|
||||
CREATE UNIQUE INDEX IF NOT EXISTS idx_strikes_cat_key ON strikes(category, key);
|
||||
CREATE INDEX IF NOT EXISTS idx_strikes_blocked ON strikes(blocked);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS strike_events (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
category TEXT NOT NULL,
|
||||
key TEXT NOT NULL,
|
||||
strike_num INTEGER NOT NULL,
|
||||
metadata TEXT DEFAULT '{}',
|
||||
timestamp TEXT NOT NULL
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_se_cat_key ON strike_events(category, key);
|
||||
CREATE INDEX IF NOT EXISTS idx_se_ts ON strike_events(timestamp);
|
||||
"""
|
||||
|
||||
|
||||
# ── Exceptions ────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class ThreeStrikeError(RuntimeError):
|
||||
"""Raised when a manual action has reached the third strike.
|
||||
|
||||
Attributes:
|
||||
category: The action category (e.g. ``"vlm_prompt_edit"``).
|
||||
key: The specific action key (e.g. a UI element name).
|
||||
count: Total number of times this action has been recorded.
|
||||
"""
|
||||
|
||||
def __init__(self, category: str, key: str, count: int) -> None:
|
||||
self.category = category
|
||||
self.key = key
|
||||
self.count = count
|
||||
super().__init__(
|
||||
f"Three-strike block: '{category}/{key}' has been performed manually "
|
||||
f"{count} time(s). Register an automation artifact before continuing. "
|
||||
f"Run the Falsework Checklist (see three_strike.falsework_check)."
|
||||
)
|
||||
|
||||
|
||||
# ── Data classes ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class StrikeRecord:
|
||||
"""State for one (category, key) pair."""
|
||||
|
||||
category: str
|
||||
key: str
|
||||
count: int
|
||||
blocked: bool
|
||||
automation: str | None
|
||||
first_seen: str
|
||||
last_seen: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class FalseworkChecklist:
|
||||
"""Pre-cloud-API call checklist — must be completed before making
|
||||
expensive external calls.
|
||||
|
||||
Instantiate and call :meth:`validate` to ensure all answers are provided.
|
||||
"""
|
||||
|
||||
durable_artifact: str = ""
|
||||
artifact_storage_path: str = ""
|
||||
local_rule_or_cache: str = ""
|
||||
will_repeat: bool | None = None
|
||||
elimination_strategy: str = ""
|
||||
sovereignty_delta: str = ""
|
||||
|
||||
# ── internal ──
|
||||
_errors: list[str] = field(default_factory=list, init=False, repr=False)
|
||||
|
||||
def validate(self) -> list[str]:
|
||||
"""Return a list of unanswered questions. Empty list → checklist passes."""
|
||||
self._errors = []
|
||||
if not self.durable_artifact.strip():
|
||||
self._errors.append("Q1: What durable artifact will this call produce?")
|
||||
if not self.artifact_storage_path.strip():
|
||||
self._errors.append("Q2: Where will the artifact be stored locally?")
|
||||
if not self.local_rule_or_cache.strip():
|
||||
self._errors.append("Q3: What local rule or cache will this populate?")
|
||||
if self.will_repeat is None:
|
||||
self._errors.append("Q4: After this call, will I need to make it again?")
|
||||
if self.will_repeat and not self.elimination_strategy.strip():
|
||||
self._errors.append("Q5: If yes, what would eliminate the repeat?")
|
||||
if not self.sovereignty_delta.strip():
|
||||
self._errors.append("Q6: What is the sovereignty delta of this call?")
|
||||
return self._errors
|
||||
|
||||
@property
|
||||
def passed(self) -> bool:
|
||||
"""True when :meth:`validate` found no unanswered questions."""
|
||||
return len(self.validate()) == 0
|
||||
|
||||
|
||||
# ── Store ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class ThreeStrikeStore:
|
||||
"""SQLite-backed three-strike store.
|
||||
|
||||
Thread-safe: creates a new connection per operation.
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: Path | None = None) -> None:
|
||||
self._db_path = db_path or DB_PATH
|
||||
self._init_db()
|
||||
|
||||
# ── setup ─────────────────────────────────────────────────────────────
|
||||
|
||||
def _init_db(self) -> None:
|
||||
try:
|
||||
self._db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with closing(sqlite3.connect(str(self._db_path))) as conn:
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
|
||||
conn.executescript(_SCHEMA)
|
||||
conn.commit()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to initialise three-strike DB: %s", exc)
|
||||
|
||||
def _connect(self) -> sqlite3.Connection:
|
||||
conn = sqlite3.connect(str(self._db_path))
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
|
||||
return conn
|
||||
|
||||
# ── record ────────────────────────────────────────────────────────────
|
||||
|
||||
def record(
|
||||
self,
|
||||
category: str,
|
||||
key: str,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> StrikeRecord:
|
||||
"""Record a manual action and return the updated :class:`StrikeRecord`.
|
||||
|
||||
Raises :exc:`ThreeStrikeError` when the action is already blocked
|
||||
(count ≥ STRIKE_BLOCK) and no automation has been registered.
|
||||
|
||||
Args:
|
||||
category: Action category; must be in :data:`CATEGORIES`.
|
||||
key: Specific identifier within the category.
|
||||
metadata: Optional context stored alongside the event.
|
||||
|
||||
Returns:
|
||||
The updated :class:`StrikeRecord`.
|
||||
|
||||
Raises:
|
||||
ValueError: If *category* is not in :data:`CATEGORIES`.
|
||||
ThreeStrikeError: On the third (or later) strike with no automation.
|
||||
"""
|
||||
if category not in CATEGORIES:
|
||||
raise ValueError(f"Unknown category '{category}'. Valid: {sorted(CATEGORIES)}")
|
||||
|
||||
now = datetime.now(UTC).isoformat()
|
||||
meta_json = json.dumps(metadata or {})
|
||||
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
# Upsert the aggregate row
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT INTO strikes (category, key, count, blocked, first_seen, last_seen)
|
||||
VALUES (?, ?, 1, 0, ?, ?)
|
||||
ON CONFLICT(category, key) DO UPDATE SET
|
||||
count = count + 1,
|
||||
last_seen = excluded.last_seen
|
||||
""",
|
||||
(category, key, now, now),
|
||||
)
|
||||
|
||||
row = conn.execute(
|
||||
"SELECT * FROM strikes WHERE category=? AND key=?",
|
||||
(category, key),
|
||||
).fetchone()
|
||||
count = row["count"]
|
||||
blocked = bool(row["blocked"])
|
||||
automation = row["automation"]
|
||||
|
||||
# Record the individual event
|
||||
conn.execute(
|
||||
"INSERT INTO strike_events (category, key, strike_num, metadata, timestamp) "
|
||||
"VALUES (?, ?, ?, ?, ?)",
|
||||
(category, key, count, meta_json, now),
|
||||
)
|
||||
|
||||
# Mark as blocked once threshold reached
|
||||
if count >= STRIKE_BLOCK and not blocked:
|
||||
conn.execute(
|
||||
"UPDATE strikes SET blocked=1 WHERE category=? AND key=?",
|
||||
(category, key),
|
||||
)
|
||||
blocked = True
|
||||
|
||||
conn.commit()
|
||||
|
||||
except ThreeStrikeError:
|
||||
raise
|
||||
except Exception as exc:
|
||||
logger.warning("Three-strike DB error during record: %s", exc)
|
||||
# Re-raise DB errors so callers are aware
|
||||
raise
|
||||
|
||||
record = StrikeRecord(
|
||||
category=category,
|
||||
key=key,
|
||||
count=count,
|
||||
blocked=blocked,
|
||||
automation=automation,
|
||||
first_seen=row["first_seen"],
|
||||
last_seen=now,
|
||||
)
|
||||
|
||||
self._emit_log(record)
|
||||
|
||||
if blocked and not automation:
|
||||
raise ThreeStrikeError(category=category, key=key, count=count)
|
||||
|
||||
return record
|
||||
|
||||
def _emit_log(self, record: StrikeRecord) -> None:
|
||||
"""Log a warning or info message based on strike number."""
|
||||
if record.count == STRIKE_WARNING:
|
||||
logger.warning(
|
||||
"Three-strike WARNING: '%s/%s' has been performed manually %d times. "
|
||||
"Consider writing an automation.",
|
||||
record.category,
|
||||
record.key,
|
||||
record.count,
|
||||
)
|
||||
elif record.count >= STRIKE_BLOCK:
|
||||
logger.warning(
|
||||
"Three-strike BLOCK: '%s/%s' reached %d strikes — automation required.",
|
||||
record.category,
|
||||
record.key,
|
||||
record.count,
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
"Three-strike discovery: '%s/%s' — strike %d.",
|
||||
record.category,
|
||||
record.key,
|
||||
record.count,
|
||||
)
|
||||
|
||||
# ── automation registration ───────────────────────────────────────────
|
||||
|
||||
def register_automation(
|
||||
self,
|
||||
category: str,
|
||||
key: str,
|
||||
artifact_path: str,
|
||||
) -> None:
|
||||
"""Unblock a (category, key) pair by registering an automation artifact.
|
||||
|
||||
Once registered, future calls to :meth:`record` will proceed normally
|
||||
and the strike counter resets to zero.
|
||||
|
||||
Args:
|
||||
category: Action category.
|
||||
key: Specific identifier within the category.
|
||||
artifact_path: Path or identifier of the automation artifact.
|
||||
"""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
conn.execute(
|
||||
"UPDATE strikes SET automation=?, blocked=0, count=0 "
|
||||
"WHERE category=? AND key=?",
|
||||
(artifact_path, category, key),
|
||||
)
|
||||
conn.commit()
|
||||
logger.info(
|
||||
"Three-strike: automation registered for '%s/%s' → %s",
|
||||
category,
|
||||
key,
|
||||
artifact_path,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to register automation: %s", exc)
|
||||
|
||||
# ── queries ───────────────────────────────────────────────────────────
|
||||
|
||||
def get(self, category: str, key: str) -> StrikeRecord | None:
|
||||
"""Return the :class:`StrikeRecord` for (category, key), or None."""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
row = conn.execute(
|
||||
"SELECT * FROM strikes WHERE category=? AND key=?",
|
||||
(category, key),
|
||||
).fetchone()
|
||||
if row is None:
|
||||
return None
|
||||
return StrikeRecord(
|
||||
category=row["category"],
|
||||
key=row["key"],
|
||||
count=row["count"],
|
||||
blocked=bool(row["blocked"]),
|
||||
automation=row["automation"],
|
||||
first_seen=row["first_seen"],
|
||||
last_seen=row["last_seen"],
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to query strike record: %s", exc)
|
||||
return None
|
||||
|
||||
def list_blocked(self) -> list[StrikeRecord]:
|
||||
"""Return all currently-blocked (category, key) pairs."""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM strikes WHERE blocked=1 ORDER BY last_seen DESC"
|
||||
).fetchall()
|
||||
return [
|
||||
StrikeRecord(
|
||||
category=r["category"],
|
||||
key=r["key"],
|
||||
count=r["count"],
|
||||
blocked=True,
|
||||
automation=r["automation"],
|
||||
first_seen=r["first_seen"],
|
||||
last_seen=r["last_seen"],
|
||||
)
|
||||
for r in rows
|
||||
]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to query blocked strikes: %s", exc)
|
||||
return []
|
||||
|
||||
def list_all(self) -> list[StrikeRecord]:
|
||||
"""Return all strike records ordered by last seen (most recent first)."""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
rows = conn.execute("SELECT * FROM strikes ORDER BY last_seen DESC").fetchall()
|
||||
return [
|
||||
StrikeRecord(
|
||||
category=r["category"],
|
||||
key=r["key"],
|
||||
count=r["count"],
|
||||
blocked=bool(r["blocked"]),
|
||||
automation=r["automation"],
|
||||
first_seen=r["first_seen"],
|
||||
last_seen=r["last_seen"],
|
||||
)
|
||||
for r in rows
|
||||
]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to list strike records: %s", exc)
|
||||
return []
|
||||
|
||||
def get_events(self, category: str, key: str, limit: int = 50) -> list[dict]:
|
||||
"""Return the individual strike events for (category, key)."""
|
||||
try:
|
||||
with closing(self._connect()) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM strike_events WHERE category=? AND key=? "
|
||||
"ORDER BY timestamp DESC LIMIT ?",
|
||||
(category, key, limit),
|
||||
).fetchall()
|
||||
return [
|
||||
{
|
||||
"strike_num": r["strike_num"],
|
||||
"timestamp": r["timestamp"],
|
||||
"metadata": json.loads(r["metadata"]) if r["metadata"] else {},
|
||||
}
|
||||
for r in rows
|
||||
]
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to query strike events: %s", exc)
|
||||
return []
|
||||
|
||||
|
||||
# ── Falsework checklist helper ────────────────────────────────────────────────
|
||||
|
||||
|
||||
def falsework_check(checklist: FalseworkChecklist) -> None:
|
||||
"""Enforce the Falsework Checklist before a cloud API call.
|
||||
|
||||
Raises :exc:`ValueError` listing all unanswered questions if the checklist
|
||||
does not pass.
|
||||
|
||||
Usage::
|
||||
|
||||
checklist = FalseworkChecklist(
|
||||
durable_artifact="embedding vectors for UI element foo",
|
||||
artifact_storage_path="data/vlm/foo_embeddings.json",
|
||||
local_rule_or_cache="vlm_cache",
|
||||
will_repeat=False,
|
||||
sovereignty_delta="eliminates repeated VLM call",
|
||||
)
|
||||
falsework_check(checklist) # raises ValueError if incomplete
|
||||
"""
|
||||
errors = checklist.validate()
|
||||
if errors:
|
||||
raise ValueError(
|
||||
"Falsework Checklist incomplete — answer all questions before "
|
||||
"making a cloud API call:\n" + "\n".join(f" • {e}" for e in errors)
|
||||
)
|
||||
|
||||
|
||||
# ── Module-level singleton ────────────────────────────────────────────────────
|
||||
|
||||
_detector: ThreeStrikeStore | None = None
|
||||
|
||||
|
||||
def get_detector() -> ThreeStrikeStore:
|
||||
"""Return the module-level :class:`ThreeStrikeStore`, creating it once."""
|
||||
global _detector
|
||||
if _detector is None:
|
||||
_detector = ThreeStrikeStore()
|
||||
return _detector
|
||||
@@ -692,112 +692,91 @@ class ThinkingEngine:
|
||||
file paths actually exist on disk, preventing phantom-bug reports.
|
||||
"""
|
||||
try:
|
||||
recent = self._get_recent_thoughts_for_issues()
|
||||
if recent is None:
|
||||
interval = settings.thinking_issue_every
|
||||
if interval <= 0:
|
||||
return
|
||||
|
||||
classify_prompt = self._build_issue_classify_prompt(recent)
|
||||
count = self.count_thoughts()
|
||||
if count == 0 or count % interval != 0:
|
||||
return
|
||||
|
||||
# Check Gitea availability before spending LLM tokens
|
||||
if not settings.gitea_enabled or not settings.gitea_token:
|
||||
return
|
||||
|
||||
recent = self.get_recent_thoughts(limit=interval)
|
||||
if len(recent) < interval:
|
||||
return
|
||||
|
||||
thought_text = "\n".join(f"- [{t.seed_type}] {t.content}" for t in reversed(recent))
|
||||
|
||||
classify_prompt = (
|
||||
"You are reviewing your own recent thoughts for actionable items.\n"
|
||||
"Extract 0-2 items that are CONCRETE bugs, broken features, stale "
|
||||
"state, or clear improvement opportunities in your own codebase.\n\n"
|
||||
"Rules:\n"
|
||||
"- Only include things that could become a real code fix or feature\n"
|
||||
"- Skip vague reflections, philosophical musings, or repeated themes\n"
|
||||
"- Category must be one of: bug, feature, suggestion, maintenance\n"
|
||||
"- ONLY reference files that you are CERTAIN exist in the project\n"
|
||||
"- Do NOT invent or guess file paths — if unsure, describe the "
|
||||
"area of concern without naming specific files\n\n"
|
||||
"For each item, write an ENGINEER-QUALITY issue:\n"
|
||||
'- "title": A clear, specific title (e.g. "[Memory] MEMORY.md timestamp not updating")\n'
|
||||
'- "body": A detailed body with these sections:\n'
|
||||
" **What's happening:** Describe the current (broken) behavior.\n"
|
||||
" **Expected behavior:** What should happen instead.\n"
|
||||
" **Suggested fix:** Which file(s) to change and what the fix looks like.\n"
|
||||
" **Acceptance criteria:** How to verify the fix works.\n"
|
||||
'- "category": One of bug, feature, suggestion, maintenance\n\n'
|
||||
"Return ONLY a JSON array of objects with keys: "
|
||||
'"title", "body", "category"\n'
|
||||
"Return [] if nothing is actionable.\n\n"
|
||||
f"Recent thoughts:\n{thought_text}\n\nJSON array:"
|
||||
)
|
||||
|
||||
raw = await self._call_agent(classify_prompt)
|
||||
items = self._parse_issue_items(raw)
|
||||
if items is None:
|
||||
if not raw or not raw.strip():
|
||||
return
|
||||
|
||||
import json
|
||||
|
||||
# Strip markdown code fences if present
|
||||
cleaned = raw.strip()
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
|
||||
|
||||
items = json.loads(cleaned)
|
||||
if not isinstance(items, list) or not items:
|
||||
return
|
||||
|
||||
from timmy.mcp_tools import create_gitea_issue_via_mcp
|
||||
|
||||
for item in items[:2]: # Safety cap
|
||||
await self._file_single_issue(item, create_gitea_issue_via_mcp)
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
title = item.get("title", "").strip()
|
||||
body = item.get("body", "").strip()
|
||||
category = item.get("category", "suggestion").strip()
|
||||
if not title or len(title) < 10:
|
||||
continue
|
||||
|
||||
# Validate all referenced file paths exist on disk
|
||||
combined = f"{title}\n{body}"
|
||||
if not self._references_real_files(combined):
|
||||
logger.info(
|
||||
"Skipped phantom issue: %s (references non-existent files)",
|
||||
title[:60],
|
||||
)
|
||||
continue
|
||||
|
||||
label = category if category in ("bug", "feature") else ""
|
||||
result = await create_gitea_issue_via_mcp(title=title, body=body, labels=label)
|
||||
logger.info("Thought→Issue: %s → %s", title[:60], result[:80])
|
||||
|
||||
except Exception as exc:
|
||||
logger.debug("Thought issue filing skipped: %s", exc)
|
||||
|
||||
def _get_recent_thoughts_for_issues(self):
|
||||
"""Return recent thoughts if conditions for filing issues are met, else None."""
|
||||
interval = settings.thinking_issue_every
|
||||
if interval <= 0:
|
||||
return None
|
||||
|
||||
count = self.count_thoughts()
|
||||
if count == 0 or count % interval != 0:
|
||||
return None
|
||||
|
||||
if not settings.gitea_enabled or not settings.gitea_token:
|
||||
return None
|
||||
|
||||
recent = self.get_recent_thoughts(limit=interval)
|
||||
if len(recent) < interval:
|
||||
return None
|
||||
|
||||
return recent
|
||||
|
||||
@staticmethod
|
||||
def _build_issue_classify_prompt(recent) -> str:
|
||||
"""Build the LLM prompt that extracts actionable issues from recent thoughts."""
|
||||
thought_text = "\n".join(f"- [{t.seed_type}] {t.content}" for t in reversed(recent))
|
||||
return (
|
||||
"You are reviewing your own recent thoughts for actionable items.\n"
|
||||
"Extract 0-2 items that are CONCRETE bugs, broken features, stale "
|
||||
"state, or clear improvement opportunities in your own codebase.\n\n"
|
||||
"Rules:\n"
|
||||
"- Only include things that could become a real code fix or feature\n"
|
||||
"- Skip vague reflections, philosophical musings, or repeated themes\n"
|
||||
"- Category must be one of: bug, feature, suggestion, maintenance\n"
|
||||
"- ONLY reference files that you are CERTAIN exist in the project\n"
|
||||
"- Do NOT invent or guess file paths — if unsure, describe the "
|
||||
"area of concern without naming specific files\n\n"
|
||||
"For each item, write an ENGINEER-QUALITY issue:\n"
|
||||
'- "title": A clear, specific title (e.g. "[Memory] MEMORY.md timestamp not updating")\n'
|
||||
'- "body": A detailed body with these sections:\n'
|
||||
" **What's happening:** Describe the current (broken) behavior.\n"
|
||||
" **Expected behavior:** What should happen instead.\n"
|
||||
" **Suggested fix:** Which file(s) to change and what the fix looks like.\n"
|
||||
" **Acceptance criteria:** How to verify the fix works.\n"
|
||||
'- "category": One of bug, feature, suggestion, maintenance\n\n'
|
||||
"Return ONLY a JSON array of objects with keys: "
|
||||
'"title", "body", "category"\n'
|
||||
"Return [] if nothing is actionable.\n\n"
|
||||
f"Recent thoughts:\n{thought_text}\n\nJSON array:"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _parse_issue_items(raw: str):
|
||||
"""Strip markdown fences and parse JSON issue list; return None on failure."""
|
||||
import json
|
||||
|
||||
if not raw or not raw.strip():
|
||||
return None
|
||||
|
||||
cleaned = raw.strip()
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
|
||||
|
||||
items = json.loads(cleaned)
|
||||
if not isinstance(items, list) or not items:
|
||||
return None
|
||||
|
||||
return items
|
||||
|
||||
async def _file_single_issue(self, item: dict, create_fn) -> None:
|
||||
"""Validate one issue dict and create it via *create_fn* if it passes checks."""
|
||||
if not isinstance(item, dict):
|
||||
return
|
||||
title = item.get("title", "").strip()
|
||||
body = item.get("body", "").strip()
|
||||
category = item.get("category", "suggestion").strip()
|
||||
if not title or len(title) < 10:
|
||||
return
|
||||
|
||||
combined = f"{title}\n{body}"
|
||||
if not self._references_real_files(combined):
|
||||
logger.info(
|
||||
"Skipped phantom issue: %s (references non-existent files)",
|
||||
title[:60],
|
||||
)
|
||||
return
|
||||
|
||||
label = category if category in ("bug", "feature") else ""
|
||||
result = await create_fn(title=title, body=body, labels=label)
|
||||
logger.info("Thought→Issue: %s → %s", title[:60], result[:80])
|
||||
|
||||
# ── System snapshot helpers ────────────────────────────────────────────
|
||||
|
||||
def _snap_thought_count(self, now: datetime) -> str | None:
|
||||
|
||||
@@ -1,49 +1,532 @@
|
||||
"""Tool registry, full toolkit construction, and tool catalog.
|
||||
"""Tool integration for the agent swarm.
|
||||
|
||||
Provides:
|
||||
- Internal _register_* helpers for wiring tools into toolkits
|
||||
- create_full_toolkit (orchestrator toolkit)
|
||||
- create_experiment_tools (Lab agent toolkit)
|
||||
- AGENT_TOOLKITS / get_tools_for_agent registry
|
||||
- get_all_available_tools catalog
|
||||
Provides agents with capabilities for:
|
||||
- File read/write (local filesystem)
|
||||
- Shell command execution (sandboxed)
|
||||
- Python code execution
|
||||
- Git operations
|
||||
- Image / Music / Video generation (creative pipeline)
|
||||
|
||||
Tools are assigned to agents based on their specialties.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import logging
|
||||
import math
|
||||
from collections.abc import Callable
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
|
||||
from timmy.tools._base import (
|
||||
_AGNO_TOOLS_AVAILABLE,
|
||||
FileTools,
|
||||
PythonTools,
|
||||
ShellTools,
|
||||
Toolkit,
|
||||
_ImportError,
|
||||
)
|
||||
from timmy.tools.file_tools import (
|
||||
_make_smart_read_file,
|
||||
create_data_tools,
|
||||
create_research_tools,
|
||||
create_writing_tools,
|
||||
)
|
||||
from timmy.tools.search import scrape_url, web_search
|
||||
from timmy.tools.system_tools import (
|
||||
calculator,
|
||||
consult_grok,
|
||||
create_code_tools,
|
||||
create_devops_tools,
|
||||
create_security_tools,
|
||||
web_fetch,
|
||||
)
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Max characters of user query included in Lightning invoice memo
|
||||
_INVOICE_MEMO_MAX_LEN = 50
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Internal _register_* helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
# Lazy imports to handle test mocking
|
||||
_ImportError = None
|
||||
try:
|
||||
from agno.tools import Toolkit
|
||||
from agno.tools.file import FileTools
|
||||
from agno.tools.python import PythonTools
|
||||
from agno.tools.shell import ShellTools
|
||||
|
||||
_AGNO_TOOLS_AVAILABLE = True
|
||||
except ImportError as e:
|
||||
_AGNO_TOOLS_AVAILABLE = False
|
||||
_ImportError = e
|
||||
|
||||
# Track tool usage stats
|
||||
_TOOL_USAGE: dict[str, list[dict]] = {}
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolStats:
|
||||
"""Statistics for a single tool."""
|
||||
|
||||
tool_name: str
|
||||
call_count: int = 0
|
||||
last_used: str | None = None
|
||||
errors: int = 0
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentTools:
|
||||
"""Tools assigned to an agent."""
|
||||
|
||||
agent_id: str
|
||||
agent_name: str
|
||||
toolkit: Toolkit
|
||||
available_tools: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
# Backward-compat alias
|
||||
PersonaTools = AgentTools
|
||||
|
||||
|
||||
def _track_tool_usage(agent_id: str, tool_name: str, success: bool = True) -> None:
|
||||
"""Track tool usage for analytics."""
|
||||
if agent_id not in _TOOL_USAGE:
|
||||
_TOOL_USAGE[agent_id] = []
|
||||
_TOOL_USAGE[agent_id].append(
|
||||
{
|
||||
"tool": tool_name,
|
||||
"timestamp": datetime.now(UTC).isoformat(),
|
||||
"success": success,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def get_tool_stats(agent_id: str | None = None) -> dict:
|
||||
"""Get tool usage statistics.
|
||||
|
||||
Args:
|
||||
agent_id: Optional agent ID to filter by. If None, returns stats for all agents.
|
||||
|
||||
Returns:
|
||||
Dict with tool usage statistics.
|
||||
"""
|
||||
if agent_id:
|
||||
usage = _TOOL_USAGE.get(agent_id, [])
|
||||
return {
|
||||
"agent_id": agent_id,
|
||||
"total_calls": len(usage),
|
||||
"tools_used": list(set(u["tool"] for u in usage)),
|
||||
"recent_calls": usage[-10:] if usage else [],
|
||||
}
|
||||
|
||||
# Return stats for all agents
|
||||
all_stats = {}
|
||||
for aid, usage in _TOOL_USAGE.items():
|
||||
all_stats[aid] = {
|
||||
"total_calls": len(usage),
|
||||
"tools_used": list(set(u["tool"] for u in usage)),
|
||||
}
|
||||
return all_stats
|
||||
|
||||
|
||||
def _safe_eval(node, allowed_names: dict):
|
||||
"""Walk an AST and evaluate only safe numeric operations."""
|
||||
if isinstance(node, ast.Expression):
|
||||
return _safe_eval(node.body, allowed_names)
|
||||
if isinstance(node, ast.Constant):
|
||||
if isinstance(node.value, (int, float, complex)):
|
||||
return node.value
|
||||
raise ValueError(f"Unsupported constant: {node.value!r}")
|
||||
if isinstance(node, ast.UnaryOp):
|
||||
operand = _safe_eval(node.operand, allowed_names)
|
||||
if isinstance(node.op, ast.UAdd):
|
||||
return +operand
|
||||
if isinstance(node.op, ast.USub):
|
||||
return -operand
|
||||
raise ValueError(f"Unsupported unary op: {type(node.op).__name__}")
|
||||
if isinstance(node, ast.BinOp):
|
||||
left = _safe_eval(node.left, allowed_names)
|
||||
right = _safe_eval(node.right, allowed_names)
|
||||
ops = {
|
||||
ast.Add: lambda a, b: a + b,
|
||||
ast.Sub: lambda a, b: a - b,
|
||||
ast.Mult: lambda a, b: a * b,
|
||||
ast.Div: lambda a, b: a / b,
|
||||
ast.FloorDiv: lambda a, b: a // b,
|
||||
ast.Mod: lambda a, b: a % b,
|
||||
ast.Pow: lambda a, b: a**b,
|
||||
}
|
||||
op_fn = ops.get(type(node.op))
|
||||
if op_fn is None:
|
||||
raise ValueError(f"Unsupported binary op: {type(node.op).__name__}")
|
||||
return op_fn(left, right)
|
||||
if isinstance(node, ast.Name):
|
||||
if node.id in allowed_names:
|
||||
return allowed_names[node.id]
|
||||
raise ValueError(f"Unknown name: {node.id!r}")
|
||||
if isinstance(node, ast.Attribute):
|
||||
value = _safe_eval(node.value, allowed_names)
|
||||
# Only allow attribute access on the math module
|
||||
if value is math:
|
||||
attr = getattr(math, node.attr, None)
|
||||
if attr is not None:
|
||||
return attr
|
||||
raise ValueError(f"Attribute access not allowed: .{node.attr}")
|
||||
if isinstance(node, ast.Call):
|
||||
func = _safe_eval(node.func, allowed_names)
|
||||
if not callable(func):
|
||||
raise ValueError(f"Not callable: {func!r}")
|
||||
args = [_safe_eval(a, allowed_names) for a in node.args]
|
||||
kwargs = {kw.arg: _safe_eval(kw.value, allowed_names) for kw in node.keywords}
|
||||
return func(*args, **kwargs)
|
||||
raise ValueError(f"Unsupported syntax: {type(node).__name__}")
|
||||
|
||||
|
||||
def calculator(expression: str) -> str:
|
||||
"""Evaluate a mathematical expression and return the exact result.
|
||||
|
||||
Use this tool for ANY arithmetic: multiplication, division, square roots,
|
||||
exponents, percentages, logarithms, trigonometry, etc.
|
||||
|
||||
Args:
|
||||
expression: A valid Python math expression, e.g. '347 * 829',
|
||||
'math.sqrt(17161)', '2**10', 'math.log(100, 10)'.
|
||||
|
||||
Returns:
|
||||
The exact result as a string.
|
||||
"""
|
||||
allowed_names = {k: getattr(math, k) for k in dir(math) if not k.startswith("_")}
|
||||
allowed_names["math"] = math
|
||||
allowed_names["abs"] = abs
|
||||
allowed_names["round"] = round
|
||||
allowed_names["min"] = min
|
||||
allowed_names["max"] = max
|
||||
try:
|
||||
tree = ast.parse(expression, mode="eval")
|
||||
result = _safe_eval(tree, allowed_names)
|
||||
return str(result)
|
||||
except Exception as e: # broad catch intentional: arbitrary code execution
|
||||
return f"Error evaluating '{expression}': {e}"
|
||||
|
||||
|
||||
def _make_smart_read_file(file_tools: FileTools) -> Callable:
|
||||
"""Wrap FileTools.read_file so directories auto-list their contents.
|
||||
|
||||
When the user (or the LLM) passes a directory path to read_file,
|
||||
the raw Agno implementation throws an IsADirectoryError. This
|
||||
wrapper detects that case, lists the directory entries, and returns
|
||||
a helpful message so the model can pick the right file on its own.
|
||||
"""
|
||||
original_read = file_tools.read_file
|
||||
|
||||
def smart_read_file(file_name: str = "", encoding: str = "utf-8", **kwargs) -> str:
|
||||
"""Reads the contents of the file `file_name` and returns the contents if successful."""
|
||||
# LLMs often call read_file(path=...) instead of read_file(file_name=...)
|
||||
if not file_name:
|
||||
file_name = kwargs.get("path", "")
|
||||
if not file_name:
|
||||
return "Error: no file_name or path provided."
|
||||
# Resolve the path the same way FileTools does
|
||||
_safe, resolved = file_tools.check_escape(file_name)
|
||||
if _safe and resolved.is_dir():
|
||||
entries = sorted(p.name for p in resolved.iterdir() if not p.name.startswith("."))
|
||||
listing = "\n".join(f" - {e}" for e in entries) if entries else " (empty directory)"
|
||||
return (
|
||||
f"'{file_name}' is a directory, not a file. "
|
||||
f"Files inside:\n{listing}\n\n"
|
||||
"Please call read_file with one of the files listed above."
|
||||
)
|
||||
return original_read(file_name, encoding=encoding)
|
||||
|
||||
# Preserve the original docstring for Agno tool schema generation
|
||||
smart_read_file.__doc__ = original_read.__doc__
|
||||
return smart_read_file
|
||||
|
||||
|
||||
def create_research_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the research agent (Echo).
|
||||
|
||||
Includes: file reading
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="research")
|
||||
|
||||
# File reading
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_code_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the code agent (Forge).
|
||||
|
||||
Includes: shell commands, python execution, file read/write, Aider AI assist
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="code")
|
||||
|
||||
# Shell commands (sandboxed)
|
||||
shell_tools = ShellTools()
|
||||
toolkit.register(shell_tools.run_shell_command, name="shell")
|
||||
|
||||
# Python execution
|
||||
python_tools = PythonTools()
|
||||
toolkit.register(python_tools.run_python_code, name="python")
|
||||
|
||||
# File operations
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.save_file, name="write_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
# Aider AI coding assistant (local with Ollama)
|
||||
aider_tool = create_aider_tool(base_path)
|
||||
toolkit.register(aider_tool.run_aider, name="aider")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_aider_tool(base_path: Path):
|
||||
"""Create an Aider tool for AI-assisted coding."""
|
||||
import subprocess
|
||||
|
||||
class AiderTool:
|
||||
"""Tool that calls Aider (local AI coding assistant) for code generation."""
|
||||
|
||||
def __init__(self, base_dir: Path):
|
||||
self.base_dir = base_dir
|
||||
|
||||
def run_aider(self, prompt: str, model: str = "qwen3:30b") -> str:
|
||||
"""Run Aider to generate code changes.
|
||||
|
||||
Args:
|
||||
prompt: What you want Aider to do (e.g., "add a fibonacci function")
|
||||
model: Ollama model to use (default: qwen3:30b)
|
||||
|
||||
Returns:
|
||||
Aider's response with the code changes made
|
||||
"""
|
||||
try:
|
||||
# Run aider with the prompt
|
||||
result = subprocess.run(
|
||||
[
|
||||
"aider",
|
||||
"--no-git",
|
||||
"--model",
|
||||
f"ollama/{model}",
|
||||
"--quiet",
|
||||
prompt,
|
||||
],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=120,
|
||||
cwd=str(self.base_dir),
|
||||
)
|
||||
|
||||
if result.returncode == 0:
|
||||
return result.stdout if result.stdout else "Code changes applied successfully"
|
||||
else:
|
||||
return f"Aider error: {result.stderr}"
|
||||
except FileNotFoundError:
|
||||
return "Error: Aider not installed. Run: pip install aider"
|
||||
except subprocess.TimeoutExpired:
|
||||
return "Error: Aider timed out after 120 seconds"
|
||||
except (OSError, subprocess.SubprocessError) as e:
|
||||
return f"Error running Aider: {str(e)}"
|
||||
|
||||
return AiderTool(base_path)
|
||||
|
||||
|
||||
def create_data_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the data agent (Seer).
|
||||
|
||||
Includes: python execution, file reading, web search for data sources
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="data")
|
||||
|
||||
# Python execution for analysis
|
||||
python_tools = PythonTools()
|
||||
toolkit.register(python_tools.run_python_code, name="python")
|
||||
|
||||
# File reading
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_writing_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the writing agent (Quill).
|
||||
|
||||
Includes: file read/write
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="writing")
|
||||
|
||||
# File operations
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.save_file, name="write_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_security_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the security agent (Mace).
|
||||
|
||||
Includes: shell commands (for scanning), file read
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="security")
|
||||
|
||||
# Shell for running security scans
|
||||
shell_tools = ShellTools()
|
||||
toolkit.register(shell_tools.run_shell_command, name="shell")
|
||||
|
||||
# File reading for logs/configs
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_devops_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the DevOps agent (Helm).
|
||||
|
||||
Includes: shell commands, file read/write
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="devops")
|
||||
|
||||
# Shell for deployment commands
|
||||
shell_tools = ShellTools()
|
||||
toolkit.register(shell_tools.run_shell_command, name="shell")
|
||||
|
||||
# File operations for config management
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.save_file, name="write_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def consult_grok(query: str) -> str:
|
||||
"""Consult Grok (xAI) for frontier reasoning on complex questions.
|
||||
|
||||
Use this tool when a question requires advanced reasoning, real-time
|
||||
knowledge, or capabilities beyond the local model. Grok is a premium
|
||||
cloud backend — use sparingly and only for high-complexity queries.
|
||||
|
||||
Args:
|
||||
query: The question or reasoning task to send to Grok.
|
||||
|
||||
Returns:
|
||||
Grok's response text, or an error/status message.
|
||||
"""
|
||||
from config import settings
|
||||
from timmy.backends import get_grok_backend, grok_available
|
||||
|
||||
if not grok_available():
|
||||
return (
|
||||
"Grok is not available. Enable with GROK_ENABLED=true "
|
||||
"and set XAI_API_KEY in your .env file."
|
||||
)
|
||||
|
||||
backend = get_grok_backend()
|
||||
|
||||
# Log to Spark if available
|
||||
try:
|
||||
from spark.engine import spark_engine
|
||||
|
||||
spark_engine.on_tool_executed(
|
||||
agent_id="default",
|
||||
tool_name="consult_grok",
|
||||
success=True,
|
||||
)
|
||||
except (ImportError, AttributeError) as exc:
|
||||
logger.warning("Tool execution failed (consult_grok logging): %s", exc)
|
||||
|
||||
# Generate Lightning invoice for monetization (unless free mode)
|
||||
invoice_info = ""
|
||||
if not settings.grok_free:
|
||||
try:
|
||||
from lightning.factory import get_backend as get_ln_backend
|
||||
|
||||
ln = get_ln_backend()
|
||||
sats = min(settings.grok_max_sats_per_query, settings.grok_sats_hard_cap)
|
||||
inv = ln.create_invoice(sats, f"Grok query: {query[:_INVOICE_MEMO_MAX_LEN]}")
|
||||
invoice_info = f"\n[Lightning invoice: {sats} sats — {inv.payment_request[:40]}...]"
|
||||
except (ImportError, OSError, ValueError) as exc:
|
||||
logger.error("Lightning invoice creation failed: %s", exc)
|
||||
return "Error: Failed to create Lightning invoice. Please check logs."
|
||||
|
||||
result = backend.run(query)
|
||||
|
||||
response = result.content
|
||||
if invoice_info:
|
||||
response += invoice_info
|
||||
|
||||
return response
|
||||
|
||||
|
||||
def web_fetch(url: str, max_tokens: int = 4000) -> str:
|
||||
"""Fetch a web page and return its main text content.
|
||||
|
||||
Downloads the URL, extracts readable text using trafilatura, and
|
||||
truncates to a token budget. Use this to read full articles, docs,
|
||||
or blog posts that web_search only returns snippets for.
|
||||
|
||||
Args:
|
||||
url: The URL to fetch (must start with http:// or https://).
|
||||
max_tokens: Maximum approximate token budget (default 4000).
|
||||
Text is truncated to max_tokens * 4 characters.
|
||||
|
||||
Returns:
|
||||
Extracted text content, or an error message on failure.
|
||||
"""
|
||||
if not url or not url.startswith(("http://", "https://")):
|
||||
return f"Error: invalid URL — must start with http:// or https://: {url!r}"
|
||||
|
||||
try:
|
||||
import requests as _requests
|
||||
except ImportError:
|
||||
return "Error: 'requests' package is not installed. Install with: pip install requests"
|
||||
|
||||
try:
|
||||
import trafilatura
|
||||
except ImportError:
|
||||
return (
|
||||
"Error: 'trafilatura' package is not installed. Install with: pip install trafilatura"
|
||||
)
|
||||
|
||||
try:
|
||||
resp = _requests.get(
|
||||
url,
|
||||
timeout=15,
|
||||
headers={"User-Agent": "TimmyResearchBot/1.0"},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
except _requests.exceptions.Timeout:
|
||||
return f"Error: request timed out after 15 seconds for {url}"
|
||||
except _requests.exceptions.HTTPError as exc:
|
||||
return f"Error: HTTP {exc.response.status_code} for {url}"
|
||||
except _requests.exceptions.RequestException as exc:
|
||||
return f"Error: failed to fetch {url} — {exc}"
|
||||
|
||||
text = trafilatura.extract(resp.text, include_tables=True, include_links=True)
|
||||
if not text:
|
||||
return f"Error: could not extract readable content from {url}"
|
||||
|
||||
char_budget = max_tokens * 4
|
||||
if len(text) > char_budget:
|
||||
text = text[:char_budget] + f"\n\n[…truncated to ~{max_tokens} tokens]"
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def _register_web_fetch_tool(toolkit: Toolkit) -> None:
|
||||
@@ -55,16 +538,6 @@ def _register_web_fetch_tool(toolkit: Toolkit) -> None:
|
||||
raise
|
||||
|
||||
|
||||
def _register_search_tools(toolkit: Toolkit) -> None:
|
||||
"""Register SearXNG web_search and Crawl4AI scrape_url tools."""
|
||||
try:
|
||||
toolkit.register(web_search, name="web_search")
|
||||
toolkit.register(scrape_url, name="scrape_url")
|
||||
except Exception as exc:
|
||||
logger.error("Failed to register search tools: %s", exc)
|
||||
raise
|
||||
|
||||
|
||||
def _register_core_tools(toolkit: Toolkit, base_path: Path) -> None:
|
||||
"""Register core execution and file tools."""
|
||||
# Python execution
|
||||
@@ -101,10 +574,10 @@ def _register_grok_tool(toolkit: Toolkit) -> None:
|
||||
def _register_memory_tools(toolkit: Toolkit) -> None:
|
||||
"""Register memory search, write, and forget tools."""
|
||||
try:
|
||||
from timmy.memory_system import memory_forget, memory_read, memory_search, memory_store
|
||||
from timmy.memory_system import memory_forget, memory_read, memory_search, memory_write
|
||||
|
||||
toolkit.register(memory_search, name="memory_search")
|
||||
toolkit.register(memory_store, name="memory_write")
|
||||
toolkit.register(memory_write, name="memory_write")
|
||||
toolkit.register(memory_read, name="memory_read")
|
||||
toolkit.register(memory_forget, name="memory_forget")
|
||||
except (ImportError, AttributeError) as exc:
|
||||
@@ -244,11 +717,6 @@ def _register_thinking_tools(toolkit: Toolkit) -> None:
|
||||
raise
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Full toolkit factories
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def create_full_toolkit(base_dir: str | Path | None = None):
|
||||
"""Create a full toolkit with all available tools (for the orchestrator).
|
||||
|
||||
@@ -259,7 +727,6 @@ def create_full_toolkit(base_dir: str | Path | None = None):
|
||||
# Return None when tools aren't available (tests)
|
||||
return None
|
||||
|
||||
from config import settings
|
||||
from timmy.tool_safety import DANGEROUS_TOOLS
|
||||
|
||||
toolkit = Toolkit(name="full")
|
||||
@@ -272,7 +739,6 @@ def create_full_toolkit(base_dir: str | Path | None = None):
|
||||
|
||||
_register_core_tools(toolkit, base_path)
|
||||
_register_web_fetch_tool(toolkit)
|
||||
_register_search_tools(toolkit)
|
||||
_register_grok_tool(toolkit)
|
||||
_register_memory_tools(toolkit)
|
||||
_register_agentic_loop_tool(toolkit)
|
||||
@@ -342,24 +808,6 @@ def create_experiment_tools(base_dir: str | Path | None = None):
|
||||
return toolkit
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Agent toolkit registry
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _create_stub_toolkit(name: str):
|
||||
"""Create a minimal Agno toolkit for creative agents.
|
||||
|
||||
Creative agents use their own dedicated tool modules rather than
|
||||
Agno-wrapped functions. This stub ensures AGENT_TOOLKITS has an
|
||||
entry so ToolExecutor doesn't fall back to the full toolkit.
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
return None
|
||||
toolkit = Toolkit(name=name)
|
||||
return toolkit
|
||||
|
||||
|
||||
# Mapping of agent IDs to their toolkits
|
||||
AGENT_TOOLKITS: dict[str, Callable[[], Toolkit]] = {
|
||||
"echo": create_research_tools,
|
||||
@@ -375,6 +823,19 @@ AGENT_TOOLKITS: dict[str, Callable[[], Toolkit]] = {
|
||||
}
|
||||
|
||||
|
||||
def _create_stub_toolkit(name: str):
|
||||
"""Create a minimal Agno toolkit for creative agents.
|
||||
|
||||
Creative agents use their own dedicated tool modules rather than
|
||||
Agno-wrapped functions. This stub ensures AGENT_TOOLKITS has an
|
||||
entry so ToolExecutor doesn't fall back to the full toolkit.
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
return None
|
||||
toolkit = Toolkit(name=name)
|
||||
return toolkit
|
||||
|
||||
|
||||
def get_tools_for_agent(agent_id: str, base_dir: str | Path | None = None) -> Toolkit | None:
|
||||
"""Get the appropriate toolkit for an agent.
|
||||
|
||||
@@ -391,16 +852,11 @@ def get_tools_for_agent(agent_id: str, base_dir: str | Path | None = None) -> To
|
||||
return None
|
||||
|
||||
|
||||
# Backward-compat aliases
|
||||
# Backward-compat alias
|
||||
get_tools_for_persona = get_tools_for_agent
|
||||
PERSONA_TOOLKITS = AGENT_TOOLKITS
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tool catalog
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _core_tool_catalog() -> dict:
|
||||
"""Return core file and execution tools catalog entries."""
|
||||
return {
|
||||
@@ -445,16 +901,6 @@ def _analysis_tool_catalog() -> dict:
|
||||
"description": "Fetch a web page and extract clean readable text (trafilatura)",
|
||||
"available_in": ["orchestrator"],
|
||||
},
|
||||
"web_search": {
|
||||
"name": "Web Search",
|
||||
"description": "Search the web via self-hosted SearXNG (no API key required)",
|
||||
"available_in": ["echo", "orchestrator"],
|
||||
},
|
||||
"scrape_url": {
|
||||
"name": "Scrape URL",
|
||||
"description": "Scrape a URL with Crawl4AI and return clean markdown content",
|
||||
"available_in": ["echo", "orchestrator"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@@ -1,98 +0,0 @@
|
||||
"""Tool integration for the agent swarm.
|
||||
|
||||
Provides agents with capabilities for:
|
||||
- File read/write (local filesystem)
|
||||
- Shell command execution (sandboxed)
|
||||
- Python code execution
|
||||
- Git operations
|
||||
- Image / Music / Video generation (creative pipeline)
|
||||
|
||||
Tools are assigned to agents based on their specialties.
|
||||
|
||||
Sub-modules:
|
||||
- _base: shared types, tracking state
|
||||
- file_tools: file-operation toolkit factories (Echo, Quill, Seer)
|
||||
- system_tools: calculator, AI tools, code/devops toolkit factories
|
||||
- _registry: full toolkit construction, agent registry, tool catalog
|
||||
"""
|
||||
|
||||
# Re-export everything for backward compatibility — callers that do
|
||||
# ``from timmy.tools import <symbol>`` continue to work unchanged.
|
||||
|
||||
from timmy.tools._base import (
|
||||
_AGNO_TOOLS_AVAILABLE,
|
||||
_TOOL_USAGE,
|
||||
AgentTools,
|
||||
PersonaTools,
|
||||
ToolStats,
|
||||
_ImportError,
|
||||
_track_tool_usage,
|
||||
get_tool_stats,
|
||||
)
|
||||
from timmy.tools._registry import (
|
||||
AGENT_TOOLKITS,
|
||||
PERSONA_TOOLKITS,
|
||||
_create_stub_toolkit,
|
||||
_merge_catalog,
|
||||
create_experiment_tools,
|
||||
create_full_toolkit,
|
||||
get_all_available_tools,
|
||||
get_tools_for_agent,
|
||||
get_tools_for_persona,
|
||||
)
|
||||
from timmy.tools.file_tools import (
|
||||
_make_smart_read_file,
|
||||
create_data_tools,
|
||||
create_research_tools,
|
||||
create_writing_tools,
|
||||
)
|
||||
from timmy.tools.search import scrape_url, web_search
|
||||
from timmy.tools.system_tools import (
|
||||
_safe_eval,
|
||||
calculator,
|
||||
consult_grok,
|
||||
create_aider_tool,
|
||||
create_code_tools,
|
||||
create_devops_tools,
|
||||
create_security_tools,
|
||||
web_fetch,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# _base
|
||||
"AgentTools",
|
||||
"PersonaTools",
|
||||
"ToolStats",
|
||||
"_AGNO_TOOLS_AVAILABLE",
|
||||
"_ImportError",
|
||||
"_TOOL_USAGE",
|
||||
"_track_tool_usage",
|
||||
"get_tool_stats",
|
||||
# file_tools
|
||||
"_make_smart_read_file",
|
||||
"create_data_tools",
|
||||
"create_research_tools",
|
||||
"create_writing_tools",
|
||||
# search
|
||||
"scrape_url",
|
||||
"web_search",
|
||||
# system_tools
|
||||
"_safe_eval",
|
||||
"calculator",
|
||||
"consult_grok",
|
||||
"create_aider_tool",
|
||||
"create_code_tools",
|
||||
"create_devops_tools",
|
||||
"create_security_tools",
|
||||
"web_fetch",
|
||||
# _registry
|
||||
"AGENT_TOOLKITS",
|
||||
"PERSONA_TOOLKITS",
|
||||
"_create_stub_toolkit",
|
||||
"_merge_catalog",
|
||||
"create_experiment_tools",
|
||||
"create_full_toolkit",
|
||||
"get_all_available_tools",
|
||||
"get_tools_for_agent",
|
||||
"get_tools_for_persona",
|
||||
]
|
||||
@@ -1,90 +0,0 @@
|
||||
"""Base types, shared state, and tracking for the Timmy tool system."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Lazy imports to handle test mocking
|
||||
_ImportError = None
|
||||
try:
|
||||
from agno.tools import Toolkit # noqa: F401
|
||||
from agno.tools.file import FileTools # noqa: F401
|
||||
from agno.tools.python import PythonTools # noqa: F401
|
||||
from agno.tools.shell import ShellTools # noqa: F401
|
||||
|
||||
_AGNO_TOOLS_AVAILABLE = True
|
||||
except ImportError as e:
|
||||
_AGNO_TOOLS_AVAILABLE = False
|
||||
_ImportError = e
|
||||
|
||||
# Track tool usage stats
|
||||
_TOOL_USAGE: dict[str, list[dict]] = {}
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolStats:
|
||||
"""Statistics for a single tool."""
|
||||
|
||||
tool_name: str
|
||||
call_count: int = 0
|
||||
last_used: str | None = None
|
||||
errors: int = 0
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentTools:
|
||||
"""Tools assigned to an agent."""
|
||||
|
||||
agent_id: str
|
||||
agent_name: str
|
||||
toolkit: Toolkit
|
||||
available_tools: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
# Backward-compat alias
|
||||
PersonaTools = AgentTools
|
||||
|
||||
|
||||
def _track_tool_usage(agent_id: str, tool_name: str, success: bool = True) -> None:
|
||||
"""Track tool usage for analytics."""
|
||||
if agent_id not in _TOOL_USAGE:
|
||||
_TOOL_USAGE[agent_id] = []
|
||||
_TOOL_USAGE[agent_id].append(
|
||||
{
|
||||
"tool": tool_name,
|
||||
"timestamp": datetime.now(UTC).isoformat(),
|
||||
"success": success,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def get_tool_stats(agent_id: str | None = None) -> dict:
|
||||
"""Get tool usage statistics.
|
||||
|
||||
Args:
|
||||
agent_id: Optional agent ID to filter by. If None, returns stats for all agents.
|
||||
|
||||
Returns:
|
||||
Dict with tool usage statistics.
|
||||
"""
|
||||
if agent_id:
|
||||
usage = _TOOL_USAGE.get(agent_id, [])
|
||||
return {
|
||||
"agent_id": agent_id,
|
||||
"total_calls": len(usage),
|
||||
"tools_used": list(set(u["tool"] for u in usage)),
|
||||
"recent_calls": usage[-10:] if usage else [],
|
||||
}
|
||||
|
||||
# Return stats for all agents
|
||||
all_stats = {}
|
||||
for aid, usage in _TOOL_USAGE.items():
|
||||
all_stats[aid] = {
|
||||
"total_calls": len(usage),
|
||||
"tools_used": list(set(u["tool"] for u in usage)),
|
||||
}
|
||||
return all_stats
|
||||
@@ -1,127 +0,0 @@
|
||||
"""File operation tools and agent toolkit factories for file-heavy agents.
|
||||
|
||||
Provides:
|
||||
- Smart read_file wrapper (auto-lists directories)
|
||||
- Toolkit factories for Echo (research), Quill (writing), Seer (data)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
|
||||
from timmy.tools._base import (
|
||||
_AGNO_TOOLS_AVAILABLE,
|
||||
FileTools,
|
||||
PythonTools,
|
||||
Toolkit,
|
||||
_ImportError,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _make_smart_read_file(file_tools: FileTools) -> Callable:
|
||||
"""Wrap FileTools.read_file so directories auto-list their contents.
|
||||
|
||||
When the user (or the LLM) passes a directory path to read_file,
|
||||
the raw Agno implementation throws an IsADirectoryError. This
|
||||
wrapper detects that case, lists the directory entries, and returns
|
||||
a helpful message so the model can pick the right file on its own.
|
||||
"""
|
||||
original_read = file_tools.read_file
|
||||
|
||||
def smart_read_file(file_name: str = "", encoding: str = "utf-8", **kwargs) -> str:
|
||||
"""Reads the contents of the file `file_name` and returns the contents if successful."""
|
||||
# LLMs often call read_file(path=...) instead of read_file(file_name=...)
|
||||
if not file_name:
|
||||
file_name = kwargs.get("path", "")
|
||||
if not file_name:
|
||||
return "Error: no file_name or path provided."
|
||||
# Resolve the path the same way FileTools does
|
||||
_safe, resolved = file_tools.check_escape(file_name)
|
||||
if _safe and resolved.is_dir():
|
||||
entries = sorted(p.name for p in resolved.iterdir() if not p.name.startswith("."))
|
||||
listing = "\n".join(f" - {e}" for e in entries) if entries else " (empty directory)"
|
||||
return (
|
||||
f"'{file_name}' is a directory, not a file. "
|
||||
f"Files inside:\n{listing}\n\n"
|
||||
"Please call read_file with one of the files listed above."
|
||||
)
|
||||
return original_read(file_name, encoding=encoding)
|
||||
|
||||
# Preserve the original docstring for Agno tool schema generation
|
||||
smart_read_file.__doc__ = original_read.__doc__
|
||||
return smart_read_file
|
||||
|
||||
|
||||
def create_research_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the research agent (Echo).
|
||||
|
||||
Includes: file reading, web search (SearXNG), URL scraping (Crawl4AI)
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="research")
|
||||
|
||||
# File reading
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
# Web search + scraping (gracefully no-ops when backend=none or service down)
|
||||
from timmy.tools.search import scrape_url, web_search
|
||||
|
||||
toolkit.register(web_search, name="web_search")
|
||||
toolkit.register(scrape_url, name="scrape_url")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_writing_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the writing agent (Quill).
|
||||
|
||||
Includes: file read/write
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="writing")
|
||||
|
||||
# File operations
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.save_file, name="write_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
|
||||
|
||||
def create_data_tools(base_dir: str | Path | None = None):
|
||||
"""Create tools for the data agent (Seer).
|
||||
|
||||
Includes: python execution, file reading, web search for data sources
|
||||
"""
|
||||
if not _AGNO_TOOLS_AVAILABLE:
|
||||
raise ImportError(f"Agno tools not available: {_ImportError}")
|
||||
toolkit = Toolkit(name="data")
|
||||
|
||||
# Python execution for analysis
|
||||
python_tools = PythonTools()
|
||||
toolkit.register(python_tools.run_python_code, name="python")
|
||||
|
||||
# File reading
|
||||
from config import settings
|
||||
|
||||
base_path = Path(base_dir) if base_dir else Path(settings.repo_root)
|
||||
file_tools = FileTools(base_dir=base_path)
|
||||
toolkit.register(_make_smart_read_file(file_tools), name="read_file")
|
||||
toolkit.register(file_tools.list_files, name="list_files")
|
||||
|
||||
return toolkit
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user