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fix/687-tr
...
feat/622-t
| Author | SHA1 | Date | |
|---|---|---|---|
| 89e46680df |
@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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"""
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Full Nostr agent-to-agent communication demo - FINAL WORKING
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"""
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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"""
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Soul Eval Gate — The Conscience of the Training Pipeline
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@@ -1,9 +0,0 @@
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- name: Nightly Pipeline Scheduler
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schedule: '*/30 18-23,0-8 * * *' # Every 30 min, off-peak hours only
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tasks:
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- name: Check and start pipelines
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shell: "bash scripts/nightly-pipeline-scheduler.sh"
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env:
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PIPELINE_TOKEN_LIMIT: "500000"
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PIPELINE_PEAK_START: "9"
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PIPELINE_PEAK_END: "18"
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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import json
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from hermes_tools import browser_navigate, browser_vision
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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import json
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from hermes_tools import browser_navigate, browser_vision
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@@ -1,50 +0,0 @@
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# Nightly Pipeline Scheduler
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Auto-starts batch pipelines when inference is available.
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## What It Does
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1. Checks inference provider health (OpenRouter, Ollama, RunPod)
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2. Checks if it's off-peak hours (configurable, default: after 6PM)
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3. Checks interactive session load (don't fight with live users)
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4. Checks daily token budget (configurable limit)
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5. Starts the highest-priority incomplete pipeline
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## Pipeline Priority Order
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| Priority | Pipeline | Deps | Max Tokens |
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|----------|----------|------|------------|
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| 1 | playground-factory | none | 100,000 |
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| 2 | training-factory | none | 150,000 |
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| 3 | knowledge-mine | training-factory running | 80,000 |
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| 4 | adversary | knowledge-mine running | 50,000 |
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| 5 | codebase-genome | none | 120,000 |
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## Usage
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```bash
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# Normal run (used by cron)
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./scripts/nightly-pipeline-scheduler.sh
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# Dry run (show what would start)
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./scripts/nightly-pipeline-scheduler.sh --dry-run
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# Status report
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./scripts/nightly-pipeline-scheduler.sh --status
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# Force start during peak hours
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./scripts/nightly-pipeline-scheduler.sh --force
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```
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## Configuration
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Set via environment variables:
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- `PIPELINE_TOKEN_LIMIT`: Daily token budget (default: 500,000)
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- `PIPELINE_PEAK_START`: Peak hours start (default: 9)
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- `PIPELINE_PEAK_END`: Peak hours end (default: 18)
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- `HERMES_HOME`: Hermes home directory (default: ~/.hermes)
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## Cron
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Runs every 30 minutes. Off-peak only (unless --force).
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See `cron/pipeline-scheduler.yml`.
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@@ -1,383 +0,0 @@
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#!/usr/bin/env bash
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# nightly-pipeline-scheduler.sh — Auto-start batch pipelines when inference is available.
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#
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# Checks provider health, pipeline progress, token budget, and interactive load.
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# Starts the highest-priority incomplete pipeline that can run.
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#
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# Usage:
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# ./scripts/nightly-pipeline-scheduler.sh # Normal run
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# ./scripts/nightly-pipeline-scheduler.sh --dry-run # Show what would start
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# ./scripts/nightly-pipeline-scheduler.sh --status # Pipeline status report
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set -euo pipefail
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# --- Configuration ---
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HERMES_HOME="${HERMES_HOME:-$HOME/.hermes}"
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BUDGET_FILE="${HERMES_HOME}/pipeline_budget.json"
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STATE_FILE="${HERMES_HOME}/pipeline_state.json"
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LOG_FILE="${HERMES_HOME}/logs/pipeline-scheduler.log"
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TOKEN_DAILY_LIMIT="${PIPELINE_TOKEN_LIMIT:-500000}"
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PEAK_HOURS_START="${PIPELINE_PEAK_START:-9}"
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PEAK_HOURS_END="${PIPELINE_PEAK_END:-18}"
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# Pipeline definitions (priority order)
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# Each pipeline: name, script, max_tokens, dependencies
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PIPELINES=(
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"playground-factory|scripts/pipeline_playground_factory.sh|100000|none"
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"training-factory|scripts/pipeline_training_factory.sh|150000|none"
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"knowledge-mine|scripts/pipeline_knowledge_mine.sh|80000|training-factory"
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"adversary|scripts/pipeline_adversary.sh|50000|knowledge-mine"
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"codebase-genome|scripts/pipeline_codebase_genome.sh|120000|none"
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)
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# --- Colors ---
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[0;33m'
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CYAN='\033[0;36m'
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NC='\033[0m'
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# --- Helpers ---
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now_hour() { date +%-H; }
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is_peak_hours() {
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local h=$(now_hour)
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[[ $h -ge $PEAK_HOURS_START && $h -lt $PEAK_HOURS_END ]]
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}
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ensure_dirs() {
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mkdir -p "$(dirname "$LOG_FILE")" "$(dirname "$BUDGET_FILE")" "$(dirname "$STATE_FILE")"
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}
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log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG_FILE"; }
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get_budget_used_today() {
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if [[ -f "$BUDGET_FILE" ]]; then
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local today=$(date +%Y-%m-%d)
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python3 -c "
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import json, sys
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with open('$BUDGET_FILE') as f:
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d = json.load(f)
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print(d.get('daily', {}).get('$today', {}).get('tokens_used', 0))
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" 2>/dev/null || echo 0
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else
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echo 0
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fi
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}
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get_budget_remaining() {
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local used=$(get_budget_used_today)
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echo $((TOKEN_DAILY_LIMIT - used))
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}
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update_budget() {
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local pipeline="$1"
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local tokens="$2"
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local today=$(date +%Y-%m-%d)
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python3 -c "
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import json, os
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path = '$BUDGET_FILE'
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d = {}
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if os.path.exists(path):
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with open(path) as f:
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d = json.load(f)
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daily = d.setdefault('daily', {})
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day = daily.setdefault('$today', {'tokens_used': 0, 'pipelines': {}})
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day['tokens_used'] = day.get('tokens_used', 0) + $tokens
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day['pipelines']['$pipeline'] = day['pipelines'].get('$pipeline', 0) + $tokens
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with open(path, 'w') as f:
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json.dump(d, f, indent=2)
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"
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}
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get_pipeline_state() {
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if [[ -f "$STATE_FILE" ]]; then
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cat "$STATE_FILE"
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else
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echo "{}"
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fi
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}
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set_pipeline_state() {
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local pipeline="$1"
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local state="$2" # running, complete, failed, skipped
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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d = {}
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if os.path.exists(path):
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with open(path) as f:
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d = json.load(f)
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d['$pipeline'] = {'state': '$state', 'updated': '$(date -Iseconds)'}
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with open(path, 'w') as f:
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json.dump(d, f, indent=2)
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"
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}
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is_pipeline_complete() {
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local pipeline="$1"
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('false')
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else:
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with open(path) as f:
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d = json.load(f)
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state = d.get('$pipeline', {}).get('state', 'not_started')
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print('true' if state == 'complete' else 'false')
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" 2>/dev/null || echo false
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}
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is_pipeline_running() {
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local pipeline="$1"
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('false')
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else:
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with open(path) as f:
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d = json.load(f)
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state = d.get('$pipeline', {}).get('state', 'not_started')
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print('true' if state == 'running' else 'false')
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" 2>/dev/null || echo false
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}
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check_dependency() {
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local dep="$1"
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if [[ "$dep" == "none" ]]; then
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return 0
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fi
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# For knowledge-mine: training-factory must be running or complete
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if [[ "$dep" == "training-factory" ]]; then
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local state=$(python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('not_started')
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else:
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with open(path) as f:
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d = json.load(f)
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print(d.get('training-factory', {}).get('state', 'not_started'))
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" 2>/dev/null || echo "not_started")
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[[ "$state" == "running" || "$state" == "complete" ]]
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return $?
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fi
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# For adversary: knowledge-mine must be at least 50% done
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# Simplified: check if it's running (we'd need progress tracking for 50%)
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if [[ "$dep" == "knowledge-mine" ]]; then
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local state=$(python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('not_started')
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else:
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with open(path) as f:
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d = json.load(f)
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print(d.get('knowledge-mine', {}).get('state', 'not_started'))
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" 2>/dev/null || echo "not_started")
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[[ "$state" == "running" || "$state" == "complete" ]]
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return $?
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fi
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return 0
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}
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check_inference_available() {
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# Check if any inference provider is responding
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# 1. Check OpenRouter
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local or_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "https://openrouter.ai/api/v1/models" 2>/dev/null || echo "000")
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# 2. Check local Ollama
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local ollama_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "http://localhost:11434/api/tags" 2>/dev/null || echo "000")
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# 3. Check RunPod (if configured)
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local runpod_ok="000"
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if [[ -n "${RUNPOD_ENDPOINT:-}" ]]; then
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runpod_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "$RUNPOD_ENDPOINT/health" 2>/dev/null || echo "000")
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fi
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if [[ "$or_ok" == "200" || "$ollama_ok" == "200" || "$runpod_ok" == "200" ]]; then
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return 0
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fi
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return 1
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}
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check_interactive_load() {
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# Check if there are active interactive sessions (don't fight with live users)
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# Look for tmux panes with active hermes sessions
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local active=$(tmux list-panes -a -F '#{pane_pid} #{pane_current_command}' 2>/dev/null \
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| grep -c "hermes\|python3" || echo 0)
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|
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# If more than 3 interactive sessions, skip pipeline start
|
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if [[ $active -gt 3 ]]; then
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return 1
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fi
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return 0
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}
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start_pipeline() {
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local name="$1"
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local script="$2"
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local max_tokens="$3"
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local budget_remaining="$4"
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local mode="${5:-run}"
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if [[ "$budget_remaining" -lt "$max_tokens" ]]; then
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log "SKIP $name: insufficient budget ($budget_remaining < $max_tokens tokens)"
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return 1
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fi
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if [[ ! -f "$script" ]]; then
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log "SKIP $name: script not found ($script)"
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return 1
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fi
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if [[ "$mode" == "dry-run" ]]; then
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log "DRY-RUN: Would start $name (budget: $budget_remaining, needs: $max_tokens)"
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return 0
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fi
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log "START $name (budget: $budget_remaining, max_tokens: $max_tokens)"
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set_pipeline_state "$name" "running"
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# Run in background, capture output
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local log_path="${HERMES_HOME}/logs/pipeline-${name}.log"
|
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bash "$script" --max-tokens "$max_tokens" >> "$log_path" 2>&1 &
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local pid=$!
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# Wait a moment to check if it started OK
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sleep 2
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if kill -0 $pid 2>/dev/null; then
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log "RUNNING $name (PID: $pid, log: $log_path)"
|
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# Record the PID
|
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python3 -c "
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import json, os
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path = '$STATE_FILE'
|
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d = {}
|
||||
if os.path.exists(path):
|
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with open(path) as f:
|
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d = json.load(f)
|
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d['$name']['pid'] = $pid
|
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with open(path, 'w') as f:
|
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json.dump(d, f, indent=2)
|
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"
|
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return 0
|
||||
else
|
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log "FAIL $name: script exited immediately"
|
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set_pipeline_state "$name" "failed"
|
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return 1
|
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fi
|
||||
}
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|
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# --- Main ---
|
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main() {
|
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local mode="${1:-run}"
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ensure_dirs
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|
||||
log "=== Pipeline Scheduler ($mode) ==="
|
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|
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# Check 1: Is inference available?
|
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if ! check_inference_available; then
|
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log "No inference provider available. Skipping all pipelines."
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exit 0
|
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fi
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log "Inference: AVAILABLE"
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|
||||
# Check 2: Is it peak hours?
|
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if is_peak_hours && [[ "$mode" != "--force" ]]; then
|
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local h=$(now_hour)
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log "Peak hours ($h:00). Skipping pipeline start. Use --force to override."
|
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exit 0
|
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fi
|
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log "Off-peak: OK"
|
||||
|
||||
# Check 3: Interactive load
|
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if ! check_interactive_load && [[ "$mode" != "--force" ]]; then
|
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log "High interactive load. Skipping pipeline start."
|
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exit 0
|
||||
fi
|
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log "Interactive load: OK"
|
||||
|
||||
# Check 4: Token budget
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local budget=$(get_budget_remaining)
|
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log "Token budget remaining: $budget / $TOKEN_DAILY_LIMIT"
|
||||
|
||||
if [[ $budget -le 0 ]]; then
|
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log "Daily token budget exhausted. Stopping."
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Check 5: Pipeline status
|
||||
if [[ "$mode" == "--status" ]]; then
|
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echo -e "${CYAN}Pipeline Status:${NC}"
|
||||
echo "────────────────────────────────────────────────────"
|
||||
for entry in "${PIPELINES[@]}"; do
|
||||
IFS='|' read -r name script max_tokens dep <<< "$entry"
|
||||
local state=$(python3 -c "
|
||||
import json, os
|
||||
path = '$STATE_FILE'
|
||||
if not os.path.exists(path):
|
||||
print('not_started')
|
||||
else:
|
||||
with open(path) as f:
|
||||
d = json.load(f)
|
||||
print(d.get('$name', {}).get('state', 'not_started'))
|
||||
" 2>/dev/null || echo "not_started")
|
||||
|
||||
local color=$NC
|
||||
case "$state" in
|
||||
running) color=$YELLOW ;;
|
||||
complete) color=$GREEN ;;
|
||||
failed) color=$RED ;;
|
||||
esac
|
||||
printf " %-25s %b%s%b (max: %s tokens, dep: %s)\n" "$name" "$color" "$state" "$NC" "$max_tokens" "$dep"
|
||||
done
|
||||
echo "────────────────────────────────────────────────────"
|
||||
echo " Budget: $budget / $TOKEN_DAILY_LIMIT tokens remaining"
|
||||
echo " Peak hours: $PEAK_HOURS_START:00 - $PEAK_HOURS_END:00"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Find and start the highest-priority incomplete pipeline
|
||||
local started=0
|
||||
for entry in "${PIPELINES[@]}"; do
|
||||
IFS='|' read -r name script max_tokens dep <<< "$entry"
|
||||
|
||||
# Skip if already running or complete
|
||||
if [[ "$(is_pipeline_running $name)" == "true" ]]; then
|
||||
log "SKIP $name: already running"
|
||||
continue
|
||||
fi
|
||||
if [[ "$(is_pipeline_complete $name)" == "true" ]]; then
|
||||
log "SKIP $name: already complete"
|
||||
continue
|
||||
fi
|
||||
|
||||
# Check dependency
|
||||
if ! check_dependency "$dep"; then
|
||||
log "SKIP $name: dependency $dep not met"
|
||||
continue
|
||||
fi
|
||||
|
||||
# Try to start
|
||||
if start_pipeline "$name" "$script" "$max_tokens" "$budget" "$mode"; then
|
||||
started=1
|
||||
# Only start one pipeline per run (let it claim tokens before next check)
|
||||
# Exception: playground-factory and training-factory can run in parallel
|
||||
if [[ "$name" != "playground-factory" && "$name" != "training-factory" ]]; then
|
||||
break
|
||||
fi
|
||||
fi
|
||||
done
|
||||
|
||||
if [[ $started -eq 0 ]]; then
|
||||
log "No pipelines to start (all complete, running, or blocked)."
|
||||
fi
|
||||
|
||||
log "=== Pipeline Scheduler done ==="
|
||||
}
|
||||
|
||||
main "$@"
|
||||
194
scripts/token-tracker.py
Normal file
194
scripts/token-tracker.py
Normal file
@@ -0,0 +1,194 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Token Budget Tracker -- real-time spend dashboard for pipelines."""
|
||||
|
||||
import argparse, json, os, sqlite3, sys, time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
DB_PATH = Path.home() / ".hermes" / "pipelines" / "token_usage.db"
|
||||
ALERT_THRESHOLDS = [0.5, 0.8, 1.0]
|
||||
DEFAULT_BUDGETS = {
|
||||
"knowledge-mine": 200_000_000,
|
||||
"training-factory": 215_000_000,
|
||||
"playground": 16_000_000,
|
||||
"adversary": 17_000_000,
|
||||
}
|
||||
|
||||
def get_db():
|
||||
DB_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
conn.executescript("""
|
||||
CREATE TABLE IF NOT EXISTS token_usage (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
pipeline TEXT NOT NULL,
|
||||
worker TEXT NOT NULL,
|
||||
tokens INTEGER NOT NULL,
|
||||
recorded_at REAL NOT NULL,
|
||||
hour_bucket TEXT NOT NULL
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS pipeline_budgets (
|
||||
pipeline TEXT PRIMARY KEY,
|
||||
target_tokens INTEGER NOT NULL,
|
||||
updated_at REAL NOT NULL
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS alerts_sent (
|
||||
pipeline TEXT NOT NULL,
|
||||
threshold REAL NOT NULL,
|
||||
sent_at REAL NOT NULL,
|
||||
PRIMARY KEY (pipeline, threshold)
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_usage_pipeline_hour
|
||||
ON token_usage(pipeline, hour_bucket);
|
||||
""")
|
||||
for name, target in DEFAULT_BUDGETS.items():
|
||||
conn.execute(
|
||||
"INSERT OR IGNORE INTO pipeline_budgets (pipeline, target_tokens, updated_at) VALUES (?, ?, ?)",
|
||||
(name, target, time.time())
|
||||
)
|
||||
conn.commit()
|
||||
return conn
|
||||
|
||||
def log_usage(conn, pipeline, worker, tokens):
|
||||
now = time.time()
|
||||
hour = datetime.now().strftime("%Y-%m-%d %H:00")
|
||||
conn.execute(
|
||||
"INSERT INTO token_usage (pipeline, worker, tokens, recorded_at, hour_bucket) VALUES (?, ?, ?, ?, ?)",
|
||||
(pipeline, worker, tokens, now, hour)
|
||||
)
|
||||
conn.commit()
|
||||
check_alerts(conn, pipeline)
|
||||
|
||||
def get_pipeline_stats(conn):
|
||||
rows = conn.execute("""
|
||||
SELECT u.pipeline, COALESCE(b.target_tokens, 0) as target,
|
||||
SUM(u.tokens) as used, MIN(u.recorded_at) as started_at,
|
||||
COUNT(DISTINCT u.worker) as workers
|
||||
FROM token_usage u
|
||||
LEFT JOIN pipeline_budgets b ON u.pipeline = b.pipeline
|
||||
GROUP BY u.pipeline ORDER BY used DESC
|
||||
""").fetchall()
|
||||
return [dict(r) for r in rows]
|
||||
|
||||
def fmt(n):
|
||||
if n >= 1_000_000_000: return f"{n/1_000_000_000:.1f}B"
|
||||
if n >= 1_000_000: return f"{n/1_000_000:.1f}M"
|
||||
if n >= 1_000: return f"{n/1_000:.1f}K"
|
||||
return str(n)
|
||||
|
||||
def bar(ratio, w=8):
|
||||
filled = int(ratio * w)
|
||||
return "█" * filled + "░" * (w - filled)
|
||||
|
||||
def eta(used, target, started):
|
||||
if used <= 0 or started <= 0: return "--"
|
||||
elapsed = (time.time() - started) / 3600
|
||||
if elapsed <= 0: return "--"
|
||||
rate = used / elapsed
|
||||
remaining = target - used
|
||||
if remaining <= 0: return "DONE"
|
||||
h = remaining / rate
|
||||
return f"{h/24:.1f}d" if h >= 24 else f"{h:.1f}h"
|
||||
|
||||
def render_dashboard(conn):
|
||||
stats = get_pipeline_stats(conn)
|
||||
if not stats:
|
||||
print("No pipeline data recorded yet.")
|
||||
return
|
||||
print()
|
||||
print(f"{'Pipeline':<20} {'Tokens Used':>12} {'Target':>10} {'Progress':>10} {'ETA':>8} {'Workers':>8}")
|
||||
print("-" * 72)
|
||||
total_used = total_target = 0
|
||||
for s in stats:
|
||||
used = s["used"] or 0
|
||||
target = s["target"] or 1
|
||||
ratio = min(used / target, 1.0) if target > 0 else 0
|
||||
print(f"{s['pipeline']:<20} {fmt(used):>12} {fmt(target):>10} {bar(ratio):>10} {eta(used, target, s['started_at'] or 0):>8} {s['workers'] or 0:>8}")
|
||||
total_used += used
|
||||
total_target += target
|
||||
print("-" * 72)
|
||||
r = min(total_used / total_target, 1.0) if total_target > 0 else 0
|
||||
print(f"{'TOTAL':<20} {fmt(total_used):>12} {fmt(total_target):>10} {bar(r):>10}")
|
||||
print()
|
||||
|
||||
def render_watch(conn, interval=5):
|
||||
try:
|
||||
while True:
|
||||
os.system("clear" if os.name != "nt" else "cls")
|
||||
print(f"Token Budget Tracker -- {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print("Press Ctrl+C to exit")
|
||||
render_dashboard(conn)
|
||||
time.sleep(interval)
|
||||
except KeyboardInterrupt:
|
||||
print("\nExiting.")
|
||||
|
||||
def render_daily_summary(conn):
|
||||
since = time.time() - 86400
|
||||
rows = conn.execute("""
|
||||
SELECT pipeline, SUM(tokens) as total, COUNT(DISTINCT worker) as workers, COUNT(*) as entries
|
||||
FROM token_usage WHERE recorded_at >= ? GROUP BY pipeline ORDER BY total DESC
|
||||
""", (since,)).fetchall()
|
||||
if not rows:
|
||||
print("No usage in last 24 hours.")
|
||||
return
|
||||
print(f"\nDaily Summary -- last 24 hours")
|
||||
print(f"{'Pipeline':<20} {'Total Tokens':>14} {'Workers':>8} {'Entries':>8}")
|
||||
print("-" * 54)
|
||||
gt = 0
|
||||
for r in rows:
|
||||
print(f"{r['pipeline']:<20} {fmt(r['total']):>14} {r['workers']:>8} {r['entries']:>8}")
|
||||
gt += r["total"]
|
||||
print("-" * 54)
|
||||
print(f"{'TOTAL':<20} {fmt(gt):>14}\n")
|
||||
|
||||
def check_alerts(conn, pipeline):
|
||||
row = conn.execute(
|
||||
"SELECT SUM(u.tokens) as used, COALESCE(b.target_tokens, 0) as target "
|
||||
"FROM token_usage u LEFT JOIN pipeline_budgets b ON u.pipeline = b.pipeline "
|
||||
"WHERE u.pipeline = ?", (pipeline,)
|
||||
).fetchone()
|
||||
if not row or row["target"] <= 0: return
|
||||
ratio = row["used"] / row["target"]
|
||||
for t in ALERT_THRESHOLDS:
|
||||
if ratio >= t:
|
||||
existing = conn.execute("SELECT 1 FROM alerts_sent WHERE pipeline = ? AND threshold = ?", (pipeline, t)).fetchone()
|
||||
if not existing:
|
||||
print(f"⚠️ BUDGET ALERT: {pipeline} at {int(t*100)}% ({fmt(row['used'])}/{fmt(row['target'])})", file=sys.stderr)
|
||||
conn.execute("INSERT INTO alerts_sent (pipeline, threshold, sent_at) VALUES (?, ?, ?)", (pipeline, t, time.time()))
|
||||
conn.commit()
|
||||
|
||||
def set_budget(conn, pipeline, target):
|
||||
conn.execute("INSERT OR REPLACE INTO pipeline_budgets (pipeline, target_tokens, updated_at) VALUES (?, ?, ?)",
|
||||
(pipeline, int(target), time.time()))
|
||||
conn.execute("DELETE FROM alerts_sent WHERE pipeline = ?", (pipeline,))
|
||||
conn.commit()
|
||||
print(f"Budget set: {pipeline} = {fmt(int(target))} tokens")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Token Budget Tracker")
|
||||
parser.add_argument("--watch", action="store_true")
|
||||
parser.add_argument("--watch-interval", type=int, default=5)
|
||||
parser.add_argument("--summary", action="store_true")
|
||||
parser.add_argument("--log", nargs=3, metavar=("PIPELINE", "WORKER", "TOKENS"))
|
||||
parser.add_argument("--budget", nargs=2, metavar=("PIPELINE", "TARGET"))
|
||||
parser.add_argument("--db", type=str, default=str(DB_PATH))
|
||||
args = parser.parse_args()
|
||||
global DB_PATH
|
||||
DB_PATH = Path(args.db)
|
||||
conn = get_db()
|
||||
if args.log:
|
||||
log_usage(conn, args.log[0], args.log[1], int(args.log[2]))
|
||||
print(f"Logged: {args.log[0]}/{args.log[1]} = {fmt(int(args.log[2]))} tokens")
|
||||
elif args.budget:
|
||||
set_budget(conn, args.budget[0], args.budget[1])
|
||||
elif args.summary:
|
||||
render_daily_summary(conn)
|
||||
elif args.watch:
|
||||
render_watch(conn, interval=args.watch_interval)
|
||||
else:
|
||||
render_dashboard(conn)
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,266 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
[QUALITY] Training Data Quality Filter
|
||||
Part of the Timmy Foundation tooling.
|
||||
|
||||
Scores and filters JSONL training pairs on specificity, length ratio,
|
||||
and code correctness. Removes low-quality pairs and reports results.
|
||||
|
||||
Usage:
|
||||
python3 scripts/training_quality_filter.py input.jsonl -o filtered.jsonl
|
||||
python3 scripts/training_quality_filter.py input.jsonl --threshold 0.4
|
||||
cat input.jsonl | python3 scripts/training_quality_filter.py -
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
import re
|
||||
from typing import Dict, Any, Tuple
|
||||
|
||||
DEFAULT_THRESHOLD = 0.35
|
||||
MIN_TERSE_LEN = 3
|
||||
MIN_RICH_LEN = 10
|
||||
|
||||
|
||||
def score_specificity(terse: str, rich: str) -> float:
|
||||
"""Score how specific the rich response is vs the terse prompt.
|
||||
|
||||
Higher score = more specific, actionable detail in the rich version.
|
||||
"""
|
||||
if not terse or not rich:
|
||||
return 0.0
|
||||
|
||||
# Ratio of unique words (higher = more varied/specific language)
|
||||
rich_words = rich.lower().split()
|
||||
terse_words = terse.lower().split()
|
||||
|
||||
if len(rich_words) < 3:
|
||||
return 0.1
|
||||
|
||||
unique_ratio = len(set(rich_words)) / len(rich_words)
|
||||
|
||||
# Check for concrete details: numbers, file paths, commands, code refs
|
||||
concrete_patterns = [
|
||||
r"\b\d+\b", # numbers
|
||||
r"[/\\]\w+", # file paths
|
||||
r"`[^`]+`", # inline code
|
||||
r"\b(fix|add|remove|update|create|delete|check|run|use)\b", # action verbs
|
||||
]
|
||||
concrete_count = sum(
|
||||
len(re.findall(p, rich, re.IGNORECASE)) for p in concrete_patterns
|
||||
)
|
||||
concrete_score = min(concrete_count / 5.0, 1.0)
|
||||
|
||||
# Length expansion ratio (rich should be meaningfully longer than terse)
|
||||
expansion = len(rich_words) / max(len(terse_words), 1)
|
||||
expansion_score = min(expansion / 5.0, 1.0)
|
||||
|
||||
return round(0.3 * unique_ratio + 0.4 * concrete_score + 0.3 * expansion_score, 3)
|
||||
|
||||
|
||||
def score_length_ratio(terse: str, rich: str) -> float:
|
||||
"""Score the length ratio between terse and rich.
|
||||
|
||||
Too short rich = low quality. Too long = possibly padded.
|
||||
Sweet spot: 3-15x expansion.
|
||||
"""
|
||||
if not terse or not rich:
|
||||
return 0.0
|
||||
|
||||
t_len = len(terse.split())
|
||||
r_len = len(rich.split())
|
||||
|
||||
if t_len < MIN_TERSE_LEN or r_len < MIN_RICH_LEN:
|
||||
return 0.1
|
||||
|
||||
ratio = r_len / max(t_len, 1)
|
||||
|
||||
if ratio < 1.5:
|
||||
return 0.2 # barely expanded
|
||||
elif ratio < 3.0:
|
||||
return 0.5 # some expansion
|
||||
elif ratio <= 15.0:
|
||||
return 1.0 # good expansion
|
||||
elif ratio <= 30.0:
|
||||
return 0.7 # possibly padded
|
||||
else:
|
||||
return 0.4 # very padded
|
||||
|
||||
|
||||
def score_code_correctness(terse: str, rich: str) -> float:
|
||||
"""Score code blocks in the rich response for basic correctness.
|
||||
|
||||
Checks for matching brackets, valid-looking syntax patterns.
|
||||
"""
|
||||
if not rich:
|
||||
return 0.5 # no code = neutral
|
||||
|
||||
code_blocks = re.findall(r"```(?:\w*)\n(.*?)```", rich, re.DOTALL)
|
||||
if not code_blocks:
|
||||
return 0.5 # no code blocks = neutral
|
||||
|
||||
scores = []
|
||||
for block in code_blocks:
|
||||
block_score = 1.0
|
||||
|
||||
# Check bracket balance
|
||||
for open_c, close_c in [("(", ")"), ("[", "]"), ("{", "}")]:
|
||||
if block.count(open_c) != block.count(close_c):
|
||||
block_score -= 0.3
|
||||
|
||||
# Check for common syntax errors
|
||||
if re.search(r"def \w+[^:]*\n(?!\s)", block):
|
||||
block_score -= 0.2 # missing colon or body
|
||||
|
||||
# Minimum viable code length
|
||||
if len(block.strip()) < 10:
|
||||
block_score -= 0.3
|
||||
|
||||
scores.append(max(block_score, 0.0))
|
||||
|
||||
return round(sum(scores) / len(scores), 3) if scores else 0.5
|
||||
|
||||
|
||||
def score_pair(pair: Dict[str, Any]) -> Tuple[float, Dict[str, float]]:
|
||||
"""Score a single training pair. Returns (total_score, breakdown)."""
|
||||
terse = pair.get("terse", "") or pair.get("prompt", "") or ""
|
||||
rich = pair.get("rich", "") or pair.get("response", "") or ""
|
||||
|
||||
spec = score_specificity(terse, rich)
|
||||
length = score_length_ratio(terse, rich)
|
||||
code = score_code_correctness(terse, rich)
|
||||
|
||||
# Weighted total
|
||||
total = round(0.4 * spec + 0.3 * length + 0.3 * code, 3)
|
||||
|
||||
return total, {"specificity": spec, "length_ratio": length, "code_correctness": code}
|
||||
|
||||
|
||||
def filter_pairs(input_path: str, output_path: str, threshold: float,
|
||||
report: bool = False) -> Dict[str, Any]:
|
||||
"""Filter JSONL training pairs by quality score."""
|
||||
kept = []
|
||||
removed = []
|
||||
errors = 0
|
||||
|
||||
source = sys.stdin if input_path == "-" else open(input_path, "r")
|
||||
|
||||
try:
|
||||
for line_num, line in enumerate(source, 1):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
pair = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
errors += 1
|
||||
continue
|
||||
|
||||
score, breakdown = score_pair(pair)
|
||||
entry = {**pair, "_quality_score": score, "_quality_breakdown": breakdown}
|
||||
|
||||
if score >= threshold:
|
||||
kept.append(entry)
|
||||
else:
|
||||
removed.append(entry)
|
||||
finally:
|
||||
if source is not sys.stdin:
|
||||
source.close()
|
||||
|
||||
# Write filtered output
|
||||
if output_path:
|
||||
out = sys.stdout if output_path == "-" else open(output_path, "w")
|
||||
try:
|
||||
for pair in kept:
|
||||
# Strip internal scoring fields before output
|
||||
clean = {k: v for k, v in pair.items() if not k.startswith("_quality")}
|
||||
out.write(json.dumps(clean, ensure_ascii=False) + "\n")
|
||||
finally:
|
||||
if out is not sys.stdin:
|
||||
out.close()
|
||||
|
||||
result = {
|
||||
"total": len(kept) + len(removed),
|
||||
"kept": len(kept),
|
||||
"filtered_out": len(removed),
|
||||
"errors": errors,
|
||||
"threshold": threshold,
|
||||
"filter_rate": round(len(removed) / max(len(kept) + len(removed), 1) * 100, 1),
|
||||
}
|
||||
|
||||
if report and removed:
|
||||
# Show worst offenders
|
||||
removed_sorted = sorted(removed, key=lambda x: x["_quality_score"])
|
||||
result["worst_5"] = [
|
||||
{
|
||||
"score": e["_quality_score"],
|
||||
"terse": (e.get("terse", "") or e.get("prompt", ""))[:80],
|
||||
"breakdown": e["_quality_breakdown"],
|
||||
}
|
||||
for e in removed_sorted[:5]
|
||||
]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter training data pairs by quality")
|
||||
parser.add_argument("input", help="Input JSONL file (use - for stdin)")
|
||||
parser.add_argument("-o", "--output", default="-", help="Output JSONL file (default: stdout)")
|
||||
parser.add_argument("-t", "--threshold", type=float, default=DEFAULT_THRESHOLD,
|
||||
help=f"Quality threshold (0.0-1.0, default: {DEFAULT_THRESHOLD})")
|
||||
parser.add_argument("--report", action="store_true", help="Show quality report")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Score only, dont filter")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.dry_run:
|
||||
# Just score and report, no filtering
|
||||
source = sys.stdin if args.input == "-" else open(args.input, "r")
|
||||
scores = []
|
||||
try:
|
||||
for line in source:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
pair = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
score, breakdown = score_pair(pair)
|
||||
scores.append(score)
|
||||
finally:
|
||||
if source is not sys.stdin:
|
||||
source.close()
|
||||
|
||||
if scores:
|
||||
avg = sum(scores) / len(scores)
|
||||
below = sum(1 for s in scores if s < args.threshold)
|
||||
print(f"Total pairs: {len(scores)}")
|
||||
print(f"Average score: {avg:.3f}")
|
||||
print(f"Below threshold ({args.threshold}): {below} ({below/len(scores)*100:.1f}%)")
|
||||
print(f"Min: {min(scores):.3f} Max: {max(scores):.3f} Median: {sorted(scores)[len(scores)//2]:.3f}")
|
||||
return
|
||||
|
||||
result = filter_pairs(args.input, args.output, args.threshold, report=args.report)
|
||||
|
||||
print(f"Training Data Quality Filter", file=sys.stderr)
|
||||
print(f"{'='*40}", file=sys.stderr)
|
||||
print(f"Total pairs: {result['total']}", file=sys.stderr)
|
||||
print(f"Kept: {result['kept']}", file=sys.stderr)
|
||||
print(f"Filtered out: {result['filtered_out']} ({result['filter_rate']}%)", file=sys.stderr)
|
||||
print(f"Errors: {result['errors']}", file=sys.stderr)
|
||||
print(f"Threshold: {result['threshold']}", file=sys.stderr)
|
||||
|
||||
if args.report and "worst_5" in result:
|
||||
print(f"\nWorst 5 pairs:", file=sys.stderr)
|
||||
for w in result["worst_5"]:
|
||||
terse_preview = w["terse"][:60]
|
||||
print(f" [{w['score']:.3f}] {terse_preview}...", file=sys.stderr)
|
||||
bd = w["breakdown"]
|
||||
print(f" spec={bd['specificity']} length={bd['length_ratio']} code={bd['code_correctness']}", file=sys.stderr)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,4 +1,3 @@
|
||||
#!/usr/bin/env python3
|
||||
import json
|
||||
from hermes_tools import browser_navigate, browser_vision
|
||||
|
||||
|
||||
Reference in New Issue
Block a user