Anto01
1a2ee5e07f
scripts/auto_triage.py: fetches candidate memories, asks a triage model (claude -p, default sonnet) to classify each as promote / reject / needs_human, and executes the verdict via the API. Trust model: - Auto-promote: model says promote AND confidence >= 0.8 AND dedup-checked against existing active memories for the project - Auto-reject: model says reject - needs_human: everything else stays in queue for manual review The triage model receives both the candidate content AND a summary of existing active memories for the same project, so it can detect duplicates and near-duplicates. The system prompt explicitly lists the rejection categories learned from the first two manual triage passes (stale snapshots, impl details, planned-not-implemented, process rules that belong in ledger not memory). deploy/dalidou/batch-extract.sh now runs extraction (Step A) then auto-triage (Step B) in sequence. The nightly cron at 03:00 UTC will run the full pipeline: backup → cleanup → rsync → extract → triage. Only needs_human candidates reach the human. Supports --dry-run for preview without executing. Supports --model override for multi-model triage (e.g. opus for higher-quality review, or a future Gemini/Ollama backend). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
55 lines
1.7 KiB
Bash
55 lines
1.7 KiB
Bash
#!/usr/bin/env bash
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#
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# deploy/dalidou/batch-extract.sh
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# --------------------------------
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# Host-side LLM batch extraction for Dalidou.
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#
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# The claude CLI is available on the Dalidou HOST but NOT inside the
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# Docker container. This script runs on the host, fetches recent
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# interactions from the AtoCore API, runs the LLM extractor locally
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# (claude -p sonnet), and posts candidates back to the API.
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#
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# Intended to be called from cron-backup.sh after backup/cleanup/rsync,
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# or manually via:
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#
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# bash /srv/storage/atocore/app/deploy/dalidou/batch-extract.sh
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#
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# Environment variables:
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# ATOCORE_URL default http://127.0.0.1:8100
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# ATOCORE_EXTRACT_LIMIT default 50
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set -euo pipefail
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ATOCORE_URL="${ATOCORE_URL:-http://127.0.0.1:8100}"
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LIMIT="${ATOCORE_EXTRACT_LIMIT:-50}"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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APP_DIR="$(cd "$SCRIPT_DIR/../.." && pwd)"
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TIMESTAMP="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
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log() { printf '[%s] %s\n' "$TIMESTAMP" "$*"; }
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# The Python script needs the atocore source on PYTHONPATH
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export PYTHONPATH="$APP_DIR/src:${PYTHONPATH:-}"
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log "=== AtoCore batch extraction + triage starting ==="
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log "URL=$ATOCORE_URL LIMIT=$LIMIT"
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# Step A: Extract candidates from recent interactions
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log "Step A: LLM extraction"
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python3 "$APP_DIR/scripts/batch_llm_extract_live.py" \
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--base-url "$ATOCORE_URL" \
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--limit "$LIMIT" \
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2>&1 || {
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log "WARN: batch extraction failed (non-blocking)"
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}
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# Step B: Auto-triage candidates in the queue
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log "Step B: auto-triage"
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python3 "$APP_DIR/scripts/auto_triage.py" \
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--base-url "$ATOCORE_URL" \
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2>&1 || {
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log "WARN: auto-triage failed (non-blocking)"
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}
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log "=== AtoCore batch extraction + triage complete ==="
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