Florian Hartmann
b83546d833
Architecture (Agent 1):
- hermes_memory/tier2/{schema,facts,entities,relations,timeline}.py
- hermes_memory/tier3/{backend,chroma_backend,embedder}.py
- hermes_memory/graph/nx_store.py
- hermes_memory/api/memory_api.py (unified API)
- hermes_memory/cron/{consolidate,embed_queue,graph_refresh,prune}.py
- hermes_memory/config.py + pyproject.toml
Integration Plan (Agent 3):
- INTEGRATION_PLAN.md: Memory Provider Plugin strategy
- Hermes Core needs minimal changes
- sync_turn() + prefetch() hooks
- Skills integration via nextlevel_search/remember
Auto-Extraction (Agent 2):
- ARCHITECTURE.md: Full extraction pipeline docs
- Chunking, Pre-Filter, LLM Prompts, Classification
- Entity-Linking, Temporal Reasoning, Deduplication
All files: Python syntax checked, ECC standards applied.
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""Embedding-Job-Queue für Tier 3."""
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import logging
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import time
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from hermes_memory.api.memory_api import MemoryAPI
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from hermes_memory.tier2.schema import connect
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from hermes_memory.config import load_config
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from pathlib import Path
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logger = logging.getLogger(__name__)
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def run_embed_queue(profile: str = "default", batch_size: int = 50) -> dict:
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"""Verarbeitet pending Embeddings aus der Queue."""
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config = load_config(profile)
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db_path = Path(config["tier2"]["db_path"].format(
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HERMES_HOME=Path.home() / ".hermes",
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profile=profile,
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))
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conn = connect(db_path)
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rows = conn.execute(
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"SELECT id, fact_id, content, source_type, session_id, message_id FROM embedding_queue WHERE processed = 0 ORDER BY queued_at LIMIT ?",
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(batch_size,),
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).fetchall()
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if not rows:
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return {"processed": 0}
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api = MemoryAPI(profile=profile)
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processed = 0
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for row in rows:
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try:
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api.semantic_index(
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text=row["content"],
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source_type=row["source_type"],
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session_id=row["session_id"],
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message_id=row["message_id"],
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)
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conn.execute("UPDATE embedding_queue SET processed = 1 WHERE id = ?", (row["id"],))
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processed += 1
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except Exception as e:
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logger.error("Embedding failed for queue id %s: %s", row["id"], e)
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conn.commit()
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return {"processed": processed}
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