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89e46680df feat: token budget tracker — real-time spend dashboard (closes #622)
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2026-04-14 22:36:48 +00:00
10 changed files with 194 additions and 713 deletions

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#!/usr/bin/env python3
"""
Full Nostr agent-to-agent communication demo - FINAL WORKING
"""

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#!/usr/bin/env python3
"""
Soul Eval Gate — The Conscience of the Training Pipeline

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- name: Nightly Pipeline Scheduler
schedule: '*/30 18-23,0-8 * * *' # Every 30 min, off-peak hours only
tasks:
- name: Check and start pipelines
shell: "bash scripts/nightly-pipeline-scheduler.sh"
env:
PIPELINE_TOKEN_LIMIT: "500000"
PIPELINE_PEAK_START: "9"
PIPELINE_PEAK_END: "18"

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#!/usr/bin/env python3
import json
from hermes_tools import browser_navigate, browser_vision

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#!/usr/bin/env python3
import json
from hermes_tools import browser_navigate, browser_vision

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# Nightly Pipeline Scheduler
Auto-starts batch pipelines when inference is available.
## What It Does
1. Checks inference provider health (OpenRouter, Ollama, RunPod)
2. Checks if it's off-peak hours (configurable, default: after 6PM)
3. Checks interactive session load (don't fight with live users)
4. Checks daily token budget (configurable limit)
5. Starts the highest-priority incomplete pipeline
## Pipeline Priority Order
| Priority | Pipeline | Deps | Max Tokens |
|----------|----------|------|------------|
| 1 | playground-factory | none | 100,000 |
| 2 | training-factory | none | 150,000 |
| 3 | knowledge-mine | training-factory running | 80,000 |
| 4 | adversary | knowledge-mine running | 50,000 |
| 5 | codebase-genome | none | 120,000 |
## Usage
```bash
# Normal run (used by cron)
./scripts/nightly-pipeline-scheduler.sh
# Dry run (show what would start)
./scripts/nightly-pipeline-scheduler.sh --dry-run
# Status report
./scripts/nightly-pipeline-scheduler.sh --status
# Force start during peak hours
./scripts/nightly-pipeline-scheduler.sh --force
```
## Configuration
Set via environment variables:
- `PIPELINE_TOKEN_LIMIT`: Daily token budget (default: 500,000)
- `PIPELINE_PEAK_START`: Peak hours start (default: 9)
- `PIPELINE_PEAK_END`: Peak hours end (default: 18)
- `HERMES_HOME`: Hermes home directory (default: ~/.hermes)
## Cron
Runs every 30 minutes. Off-peak only (unless --force).
See `cron/pipeline-scheduler.yml`.

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#!/usr/bin/env bash
# nightly-pipeline-scheduler.sh — Auto-start batch pipelines when inference is available.
#
# Checks provider health, pipeline progress, token budget, and interactive load.
# Starts the highest-priority incomplete pipeline that can run.
#
# Usage:
# ./scripts/nightly-pipeline-scheduler.sh # Normal run
# ./scripts/nightly-pipeline-scheduler.sh --dry-run # Show what would start
# ./scripts/nightly-pipeline-scheduler.sh --status # Pipeline status report
set -euo pipefail
# --- Configuration ---
HERMES_HOME="${HERMES_HOME:-$HOME/.hermes}"
BUDGET_FILE="${HERMES_HOME}/pipeline_budget.json"
STATE_FILE="${HERMES_HOME}/pipeline_state.json"
LOG_FILE="${HERMES_HOME}/logs/pipeline-scheduler.log"
TOKEN_DAILY_LIMIT="${PIPELINE_TOKEN_LIMIT:-500000}"
PEAK_HOURS_START="${PIPELINE_PEAK_START:-9}"
PEAK_HOURS_END="${PIPELINE_PEAK_END:-18}"
# Pipeline definitions (priority order)
# Each pipeline: name, script, max_tokens, dependencies
PIPELINES=(
"playground-factory|scripts/pipeline_playground_factory.sh|100000|none"
"training-factory|scripts/pipeline_training_factory.sh|150000|none"
"knowledge-mine|scripts/pipeline_knowledge_mine.sh|80000|training-factory"
"adversary|scripts/pipeline_adversary.sh|50000|knowledge-mine"
"codebase-genome|scripts/pipeline_codebase_genome.sh|120000|none"
)
# --- Colors ---
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
CYAN='\033[0;36m'
NC='\033[0m'
# --- Helpers ---
now_hour() { date +%-H; }
is_peak_hours() {
local h=$(now_hour)
[[ $h -ge $PEAK_HOURS_START && $h -lt $PEAK_HOURS_END ]]
}
ensure_dirs() {
mkdir -p "$(dirname "$LOG_FILE")" "$(dirname "$BUDGET_FILE")" "$(dirname "$STATE_FILE")"
}
log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG_FILE"; }
get_budget_used_today() {
if [[ -f "$BUDGET_FILE" ]]; then
local today=$(date +%Y-%m-%d)
python3 -c "
import json, sys
with open('$BUDGET_FILE') as f:
d = json.load(f)
print(d.get('daily', {}).get('$today', {}).get('tokens_used', 0))
" 2>/dev/null || echo 0
else
echo 0
fi
}
get_budget_remaining() {
local used=$(get_budget_used_today)
echo $((TOKEN_DAILY_LIMIT - used))
}
update_budget() {
local pipeline="$1"
local tokens="$2"
local today=$(date +%Y-%m-%d)
python3 -c "
import json, os
path = '$BUDGET_FILE'
d = {}
if os.path.exists(path):
with open(path) as f:
d = json.load(f)
daily = d.setdefault('daily', {})
day = daily.setdefault('$today', {'tokens_used': 0, 'pipelines': {}})
day['tokens_used'] = day.get('tokens_used', 0) + $tokens
day['pipelines']['$pipeline'] = day['pipelines'].get('$pipeline', 0) + $tokens
with open(path, 'w') as f:
json.dump(d, f, indent=2)
"
}
get_pipeline_state() {
if [[ -f "$STATE_FILE" ]]; then
cat "$STATE_FILE"
else
echo "{}"
fi
}
set_pipeline_state() {
local pipeline="$1"
local state="$2" # running, complete, failed, skipped
python3 -c "
import json, os
path = '$STATE_FILE'
d = {}
if os.path.exists(path):
with open(path) as f:
d = json.load(f)
d['$pipeline'] = {'state': '$state', 'updated': '$(date -Iseconds)'}
with open(path, 'w') as f:
json.dump(d, f, indent=2)
"
}
is_pipeline_complete() {
local pipeline="$1"
python3 -c "
import json, os
path = '$STATE_FILE'
if not os.path.exists(path):
print('false')
else:
with open(path) as f:
d = json.load(f)
state = d.get('$pipeline', {}).get('state', 'not_started')
print('true' if state == 'complete' else 'false')
" 2>/dev/null || echo false
}
is_pipeline_running() {
local pipeline="$1"
python3 -c "
import json, os
path = '$STATE_FILE'
if not os.path.exists(path):
print('false')
else:
with open(path) as f:
d = json.load(f)
state = d.get('$pipeline', {}).get('state', 'not_started')
print('true' if state == 'running' else 'false')
" 2>/dev/null || echo false
}
check_dependency() {
local dep="$1"
if [[ "$dep" == "none" ]]; then
return 0
fi
# For knowledge-mine: training-factory must be running or complete
if [[ "$dep" == "training-factory" ]]; then
local state=$(python3 -c "
import json, os
path = '$STATE_FILE'
if not os.path.exists(path):
print('not_started')
else:
with open(path) as f:
d = json.load(f)
print(d.get('training-factory', {}).get('state', 'not_started'))
" 2>/dev/null || echo "not_started")
[[ "$state" == "running" || "$state" == "complete" ]]
return $?
fi
# For adversary: knowledge-mine must be at least 50% done
# Simplified: check if it's running (we'd need progress tracking for 50%)
if [[ "$dep" == "knowledge-mine" ]]; then
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('knowledge-mine', {}).get('state', 'not_started'))
" 2>/dev/null || echo "not_started")
[[ "$state" == "running" || "$state" == "complete" ]]
return $?
fi
return 0
}
check_inference_available() {
# Check if any inference provider is responding
# 1. Check OpenRouter
local or_ok=$(curl -s -o /dev/null -w "%{http_code}" \
--connect-timeout 5 "https://openrouter.ai/api/v1/models" 2>/dev/null || echo "000")
# 2. Check local Ollama
local ollama_ok=$(curl -s -o /dev/null -w "%{http_code}" \
--connect-timeout 5 "http://localhost:11434/api/tags" 2>/dev/null || echo "000")
# 3. Check RunPod (if configured)
local runpod_ok="000"
if [[ -n "${RUNPOD_ENDPOINT:-}" ]]; then
runpod_ok=$(curl -s -o /dev/null -w "%{http_code}" \
--connect-timeout 5 "$RUNPOD_ENDPOINT/health" 2>/dev/null || echo "000")
fi
if [[ "$or_ok" == "200" || "$ollama_ok" == "200" || "$runpod_ok" == "200" ]]; then
return 0
fi
return 1
}
check_interactive_load() {
# Check if there are active interactive sessions (don't fight with live users)
# Look for tmux panes with active hermes sessions
local active=$(tmux list-panes -a -F '#{pane_pid} #{pane_current_command}' 2>/dev/null \
| grep -c "hermes\|python3" || echo 0)
# If more than 3 interactive sessions, skip pipeline start
if [[ $active -gt 3 ]]; then
return 1
fi
return 0
}
start_pipeline() {
local name="$1"
local script="$2"
local max_tokens="$3"
local budget_remaining="$4"
local mode="${5:-run}"
if [[ "$budget_remaining" -lt "$max_tokens" ]]; then
log "SKIP $name: insufficient budget ($budget_remaining < $max_tokens tokens)"
return 1
fi
if [[ ! -f "$script" ]]; then
log "SKIP $name: script not found ($script)"
return 1
fi
if [[ "$mode" == "dry-run" ]]; then
log "DRY-RUN: Would start $name (budget: $budget_remaining, needs: $max_tokens)"
return 0
fi
log "START $name (budget: $budget_remaining, max_tokens: $max_tokens)"
set_pipeline_state "$name" "running"
# Run in background, capture output
local log_path="${HERMES_HOME}/logs/pipeline-${name}.log"
bash "$script" --max-tokens "$max_tokens" >> "$log_path" 2>&1 &
local pid=$!
# Wait a moment to check if it started OK
sleep 2
if kill -0 $pid 2>/dev/null; then
log "RUNNING $name (PID: $pid, log: $log_path)"
# Record the PID
python3 -c "
import json, os
path = '$STATE_FILE'
d = {}
if os.path.exists(path):
with open(path) as f:
d = json.load(f)
d['$name']['pid'] = $pid
with open(path, 'w') as f:
json.dump(d, f, indent=2)
"
return 0
else
log "FAIL $name: script exited immediately"
set_pipeline_state "$name" "failed"
return 1
fi
}
# --- Main ---
main() {
local mode="${1:-run}"
ensure_dirs
log "=== Pipeline Scheduler ($mode) ==="
# Check 1: Is inference available?
if ! check_inference_available; then
log "No inference provider available. Skipping all pipelines."
exit 0
fi
log "Inference: AVAILABLE"
# Check 2: Is it peak hours?
if is_peak_hours && [[ "$mode" != "--force" ]]; then
local h=$(now_hour)
log "Peak hours ($h:00). Skipping pipeline start. Use --force to override."
exit 0
fi
log "Off-peak: OK"
# Check 3: Interactive load
if ! check_interactive_load && [[ "$mode" != "--force" ]]; then
log "High interactive load. Skipping pipeline start."
exit 0
fi
log "Interactive load: OK"
# Check 4: Token budget
local budget=$(get_budget_remaining)
log "Token budget remaining: $budget / $TOKEN_DAILY_LIMIT"
if [[ $budget -le 0 ]]; then
log "Daily token budget exhausted. Stopping."
exit 0
fi
# Check 5: Pipeline status
if [[ "$mode" == "--status" ]]; then
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 [[ $started -eq 0 ]]; then
log "No pipelines to start (all complete, running, or blocked)."
fi
log "=== Pipeline Scheduler done ==="
}
main "$@"

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scripts/token-tracker.py Normal file
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#!/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()

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#!/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()

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@@ -1,4 +1,3 @@
#!/usr/bin/env python3
import json
from hermes_tools import browser_navigate, browser_vision