Hitonabi
05bef8642d
Paket A des Plans. Ersetzt die flache SQLite-Fakten-DB durch Mem0 (LLM-Auto-
Extraktion + Vektor/Chroma-Suche). Architektur erzwungen durch Python-Split:
MC2-Backend laeuft auf 3.14 (kann mem0 nicht importieren), mem0+chromadb nur
auf 3.12 (~/.mem0/venv) -> Mem0-Sidecar (FastAPI, localhost:8765), MC2 spricht
ihn per HTTP. /api/memory-Form bleibt unveraendert (UI + MCP kompatibel).
- mem0_service/: Sidecar (app.py), Migration (migrate.py), deps.
- Embeddings: neue llama-swap embed-Rolle (Qwen3-Embedding-0.6B, 1024 Dim,
pooling last) ueber /v1/embeddings.
- LLM-Extraktion: lokales fast-Hirn; NoThinkLLM schaltet Qwen3-Thinking ab
(sonst bricht json_object-Extraktion ab), custom_instructions halten Deutsch.
- backend/services/memory.py: duenner HTTP-Client auf den Sidecar (semantische
Suche mit score, verbatim add, learn()). Router: /api/memory/learn.
- mcp/mcp_memory.py: neues learn-Tool (Auto-Lernen aus Gespraechs-Turns),
search jetzt semantisch.
- Frontend: Relevanz-Score + Auto/Manuell-Herkunft im Gedaechtnis-Tab.
- deploy/: mem0-service.service + deploy.sh (uv-Install, Migration, Restart).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
"""Memory-Endpoints (geteiltes Gedächtnis). LAN-only, kein Token in 2.0-Phase 3."""
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from services import memory
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router = APIRouter(prefix="/api")
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class MemIn(BaseModel):
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content: str
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category: str = "stable"
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source: str = "manual"
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class MemUp(BaseModel):
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content: str | None = None
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category: str | None = None
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class DedupeIn(BaseModel):
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apply: bool = False
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threshold: float = 0.85
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class LearnIn(BaseModel):
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text: str | None = None
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messages: list[dict] | None = None
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source: str = "auto"
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category: str = "stable"
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@router.get("/memory/export")
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def export() -> dict:
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return memory.export_text()
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@router.post("/memory/learn", status_code=201)
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def learn(body: LearnIn) -> dict:
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"""Auto-Lernen: Gesprächs-Turns/Text durchreichen → Mem0 extrahiert Fakten selbst."""
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return memory.learn(text=body.text, messages=body.messages,
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source=body.source, category=body.category)
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@router.post("/memory/dedupe")
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def dedupe(body: DedupeIn) -> dict:
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return memory.dedupe(apply=body.apply, threshold=body.threshold)
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@router.get("/memory")
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def list_mem(q: str = "", category: str = "") -> list[dict]:
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return memory.list_memories(q=q, category=category)
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@router.post("/memory", status_code=201)
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def add(body: MemIn) -> dict:
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if body.category not in memory.CATEGORIES:
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raise HTTPException(400, f"Kategorie '{body.category}' unbekannt.")
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return memory.add_memory(body.content, body.category, body.source)
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@router.put("/memory/{mid}")
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def update(mid: str, body: MemUp) -> dict:
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res = memory.update_memory(mid, content=body.content, category=body.category)
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if not res:
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raise HTTPException(404, "Eintrag nicht gefunden")
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return res
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@router.delete("/memory/{mid}")
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def delete(mid: str) -> dict:
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if not memory.delete_memory(mid):
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raise HTTPException(404, "Eintrag nicht gefunden")
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return {"ok": True}
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