enne2
72ebb949c8
- gateway: MemoryIn.project_id required (min_length=1) → POST senza project_id = 422 - estensione: qmem_store project_id Type.String (non più Optional), descrizione con obbligo e rinvio a qmem_meta; qmem_correct resta opzionale (eredita dal superseduto) - README/playbook sez. 11.5 aggiornati
396 lines
15 KiB
Python
396 lines
15 KiB
Python
"""
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Memory Gateway — memoria centralizzata condivisa per agenti AI.
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Stack snello: FastAPI + Qdrant + Ollama (BGE-M3). Nessun LLM in scrittura.
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Accesso: UNA o più API key condivise con accesso COMPLETO in lettura e
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scrittura all'intera conoscenza. Nessun isolamento per agente: qualsiasi
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agente (attuale o futuro) con la chiave può consultare e aggiungere
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informazioni liberamente. L'agent_id è solo metadata di provenienza.
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Endpoints:
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POST /v1/memories → crea un record (con supersedes_id corregge un record esistente)
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POST /v1/memories:search → ricerca semantica con filtri (include_superseded per la lineage)
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GET /v1/memories/{id} → recupera per UUID
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DELETE /v1/memories/{id} → elimina per UUID
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GET /v1/meta/overview → discovery: scope×kind, progetti, agenti (cache 60s)
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GET /v1/status → health + statistiche
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"""
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import asyncio
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import json
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import logging
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import os
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import time
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import uuid
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from collections import Counter
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from datetime import datetime, timezone
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from typing import Any, Literal, Optional
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import httpx
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import uvicorn
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from fastapi import Depends, FastAPI, Header, HTTPException, Request
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from pydantic import BaseModel, Field
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as qm
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# ---------------------------------------------------------------------------
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# Configurazione (env)
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# ---------------------------------------------------------------------------
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QDRANT_URL = os.environ.get("QDRANT_URL", "http://127.0.0.1:6333")
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QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", "")
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OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://127.0.0.1:11434")
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EMBED_MODEL = os.environ.get("EMBED_MODEL", "bge-m3")
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EMBED_DIM = int(os.environ.get("EMBED_DIM", "1024"))
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COLLECTION = os.environ.get("COLLECTION", "memories")
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# Chiavi condivise (separate da virgola): accesso completo in lettura/scrittura
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API_KEYS: set[str] = {k.strip() for k in os.environ.get("API_KEYS", "").split(",") if k.strip()}
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RATE_LIMIT_PER_MIN = int(os.environ.get("RATE_LIMIT_PER_MIN", "120"))
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MAX_TEXT_LEN = int(os.environ.get("MAX_TEXT_LEN", "8000"))
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
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log = logging.getLogger("memory-gateway")
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app = FastAPI(title="Memory Gateway", version="2.4.0")
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qdrant = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
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# Rate limit in-memory: {key: [timestamps]}
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_ratelimit: dict[str, list[float]] = {}
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# Cache overview metadati (TTL 60s, invalidata su scrittura)
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_META_TTL_SECONDS = 60
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_meta_cache: dict[str, Any] = {}
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def _invalidate_meta() -> None:
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_meta_cache.clear()
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# ---------------------------------------------------------------------------
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# Modelli
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# ---------------------------------------------------------------------------
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class MemoryIn(BaseModel):
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text: str = Field(min_length=1, max_length=MAX_TEXT_LEN)
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kind: Literal["decision", "fact", "episode", "preference"] = "fact"
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agent_id: Optional[str] = Field(default=None, max_length=64, description="Solo provenienza, nessun isolamento")
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project_id: str = Field(min_length=1, max_length=64, description="OBBLIGATORIO: progetto/dominio di appartenenza (kebab-case)")
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scope: Literal["agent", "project", "org"] = "agent"
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source: Optional[str] = Field(default=None, max_length=256)
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expires_at: Optional[str] = None # ISO 8601
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supersedes_id: Optional[str] = None
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supersede_reason: Optional[str] = Field(default=None, max_length=512)
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class SearchIn(BaseModel):
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query: str = Field(min_length=1, max_length=512)
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kind: Optional[Literal["decision", "fact", "episode", "preference"]] = None
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project_id: Optional[str] = None
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scope: Optional[Literal["agent", "project", "org"]] = None
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include_superseded: bool = False
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min_score: Optional[float] = Field(default=None, ge=0.0, le=1.0)
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top_k: int = Field(default=5, ge=1, le=20)
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# ---------------------------------------------------------------------------
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# Auth: chiave condivisa → accesso completo (nessun isolamento)
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# ---------------------------------------------------------------------------
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def require_auth(x_api_key: str = Header(...)) -> str:
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if x_api_key not in API_KEYS:
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raise HTTPException(status_code=401, detail="API key non valida")
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# rate limit per chiave
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now = time.monotonic()
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window = _ratelimit.setdefault(x_api_key, [])
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window[:] = [t for t in window if now - t < 60]
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if len(window) >= RATE_LIMIT_PER_MIN:
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raise HTTPException(status_code=429, detail="Rate limit superato")
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window.append(now)
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return x_api_key
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def _audit(key: str, action: str, **extra: Any) -> None:
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"""Audit log in JSON lines (catturato da docker logs)."""
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entry = {
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"ts": datetime.now(timezone.utc).isoformat(),
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"key": key[:8] + "...",
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"action": action,
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**extra,
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}
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log.info(json.dumps(entry, default=str))
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def _now_iso() -> str:
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return datetime.now(timezone.utc).isoformat()
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def _parse_ts(value: Optional[str]) -> Optional[float]:
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"""Converte ISO 8601 in timestamp Unix (per i range query Qdrant)."""
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if not value:
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return None
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try:
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return datetime.fromisoformat(value.replace("Z", "+00:00")).timestamp()
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except ValueError:
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return None
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# ---------------------------------------------------------------------------
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# Embedding via Ollama (BGE-M3)
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# ---------------------------------------------------------------------------
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async def embed(text: str) -> list[float]:
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async with httpx.AsyncClient(timeout=30) as client:
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r = await client.post(
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f"{OLLAMA_URL}/api/embed",
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json={"model": EMBED_MODEL, "input": text},
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)
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r.raise_for_status()
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return r.json()["embeddings"][0]
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# ---------------------------------------------------------------------------
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# Startup: crea collection e indici se non esistono
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# ---------------------------------------------------------------------------
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@app.on_event("startup")
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def startup() -> None:
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collections = qdrant.get_collections().collections
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if not any(c.name == COLLECTION for c in collections):
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qdrant.create_collection(
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collection_name=COLLECTION,
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vectors_config=qm.VectorParams(size=EMBED_DIM, distance=qm.Distance.COSINE),
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)
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for field in ("agent_id", "project_id", "scope", "kind", "supersedes_id", "superseded_by"):
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qdrant.create_payload_index(
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collection_name=COLLECTION,
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field_name=field,
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field_schema=qm.PayloadSchemaType.KEYWORD,
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)
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log.info("collection %s creata con indici", COLLECTION)
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else:
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log.info("collection %s già esistente", COLLECTION)
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# ---------------------------------------------------------------------------
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# Endpoints
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# ---------------------------------------------------------------------------
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@app.post("/v1/memories")
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async def add_memory(body: MemoryIn, key: str = Depends(require_auth)) -> dict:
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memory_id = str(uuid.uuid4())
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# Supersede: il nuovo record corregge uno esistente, che resta in archivio marcato
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superseded_id: Optional[str] = None
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if body.supersedes_id:
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old = qdrant.retrieve(collection_name=COLLECTION, ids=[body.supersedes_id], with_payload=True)
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if not old:
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raise HTTPException(status_code=404, detail="Memoria da supersedere non trovata")
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if old[0].payload.get("superseded_by"):
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raise HTTPException(status_code=409, detail="La memoria è già stata superseduta: correggi la versione attiva")
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superseded_id = body.supersedes_id
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vector = await embed(body.text)
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payload: dict[str, Any] = {
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"text": body.text,
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"kind": body.kind,
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"agent_id": body.agent_id or "shared",
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"project_id": body.project_id,
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"scope": body.scope,
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"source": body.source,
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"created_at": _now_iso(),
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"expires_at": _parse_ts(body.expires_at),
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"supersedes_id": superseded_id,
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"supersede_reason": body.supersede_reason,
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"embedding_model": EMBED_MODEL,
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}
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qdrant.upsert(
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collection_name=COLLECTION,
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points=[qm.PointStruct(id=memory_id, vector=vector, payload=payload)],
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)
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_invalidate_meta()
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if superseded_id:
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qdrant.set_payload(
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collection_name=COLLECTION,
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payload={
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"superseded_by": memory_id,
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"superseded_at": _now_iso(),
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"supersede_reason": body.supersede_reason,
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},
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points=[superseded_id],
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)
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_audit(key, "supersede", old_id=superseded_id, new_id=memory_id, kind=body.kind, agent_id=payload["agent_id"])
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else:
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_audit(key, "create", memory_id=memory_id, kind=body.kind, agent_id=payload["agent_id"])
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return {"memory_id": memory_id, "created_at": payload["created_at"], "supersedes_id": superseded_id}
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@app.post("/v1/memories:search")
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async def search_memories(body: SearchIn, key: str = Depends(require_auth)) -> dict:
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vector = await embed(body.query)
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must: list[Any] = []
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if body.kind:
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must.append(qm.FieldCondition(key="kind", match=qm.MatchValue(value=body.kind)))
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if body.project_id:
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must.append(qm.FieldCondition(key="project_id", match=qm.MatchValue(value=body.project_id)))
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if body.scope:
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must.append(qm.FieldCondition(key="scope", match=qm.MatchValue(value=body.scope)))
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if not body.include_superseded:
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# default: esclude i record già corretti (superseded_by presente)
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must.append(qm.IsEmptyCondition(is_empty=qm.PayloadField(key="superseded_by")))
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hits = qdrant.search(
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collection_name=COLLECTION,
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query_vector=vector,
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query_filter=qm.Filter(must=must) if must else None,
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limit=body.top_k,
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score_threshold=body.min_score, # filtra a livello motore: sotto soglia = rumore
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with_payload=True,
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)
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results = [
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{
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"memory_id": h.id,
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"score": round(h.score, 4),
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"text": h.payload.get("text"),
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"kind": h.payload.get("kind"),
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"agent_id": h.payload.get("agent_id"),
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"scope": h.payload.get("scope"),
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"project_id": h.payload.get("project_id"),
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"created_at": h.payload.get("created_at"),
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"source": h.payload.get("source"),
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"supersedes_id": h.payload.get("supersedes_id"),
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"superseded_by": h.payload.get("superseded_by"),
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"supersede_reason": h.payload.get("supersede_reason"),
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}
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for h in hits
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]
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_audit(key, "search", query=body.query[:80], top_k=body.top_k, min_score=body.min_score, hits=len(results))
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return {"results": results, "min_score": body.min_score, "total_hits": len(results)}
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@app.get("/v1/memories/{memory_id}")
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async def get_memory(memory_id: str, key: str = Depends(require_auth)) -> dict:
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point = qdrant.retrieve(
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collection_name=COLLECTION, ids=[memory_id], with_payload=True
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)
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if not point:
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raise HTTPException(status_code=404, detail="Memoria non trovata")
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_audit(key, "get", memory_id=memory_id)
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return {"memory_id": memory_id, **point[0].payload}
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@app.delete("/v1/memories/{memory_id}")
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async def delete_memory(memory_id: str, key: str = Depends(require_auth)) -> dict:
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point = qdrant.retrieve(
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collection_name=COLLECTION, ids=[memory_id], with_payload=True
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)
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if not point:
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raise HTTPException(status_code=404, detail="Memoria non trovata")
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qdrant.delete(collection_name=COLLECTION, points_selector=[memory_id])
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_invalidate_meta()
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_audit(key, "delete", memory_id=memory_id)
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return {"deleted": memory_id}
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@app.get("/v1/meta/overview")
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async def meta_overview(key: str = Depends(require_auth)) -> dict:
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"""Panoramica della memoria: scope×kind con conteggi, progetti, agenti, superseduti.
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Usata dalla discovery per la ricerca settorializzata. Cache TTL 60s invalidata su write."""
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now = time.time()
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cached = _meta_cache.get("overview")
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if cached and now - cached["ts"] < _META_TTL_SECONDS:
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_audit(key, "meta", cached=True)
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return {**cached["data"], "cached": True}
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scope_kinds: dict[str, Counter] = {}
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projects: Counter = Counter()
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agents: Counter = Counter()
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total = 0
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superseded = 0
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offset: Any = None
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while True:
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points, next_offset = qdrant.scroll(
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collection_name=COLLECTION,
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limit=1000,
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with_payload=["scope", "kind", "project_id", "agent_id", "superseded_by"],
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with_vectors=False,
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offset=offset,
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)
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for p in points:
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pl = p.payload
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total += 1
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s = pl.get("scope", "agent")
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k = pl.get("kind", "fact")
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scope_kinds.setdefault(s, Counter())[k] += 1
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if pl.get("project_id"):
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projects[pl["project_id"]] += 1
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agents[pl.get("agent_id", "shared")] += 1
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if pl.get("superseded_by"):
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superseded += 1
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if not next_offset:
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break
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offset = next_offset
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data = {
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"scopes": [
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{"scope": s, "count": sum(c.values()), "kinds": [{"kind": k, "count": v} for k, v in sorted(c.items())]}
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for s, c in sorted(scope_kinds.items())
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],
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"projects": [{"project_id": pid, "count": c} for pid, c in projects.most_common()],
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"agents": [{"agent_id": aid, "count": c} for aid, c in agents.most_common()],
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"superseded": superseded,
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"total": total,
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}
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_meta_cache["overview"] = {"ts": now, "data": data}
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_audit(key, "meta", cached=False, total=total)
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return {**data, "cached": False}
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@app.get("/v1/status")
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async def status() -> dict:
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info = qdrant.get_collection(COLLECTION)
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return {
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"status": "ok",
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"collection": COLLECTION,
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"points": info.points_count,
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"embedding_model": EMBED_MODEL,
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"embedding_dim": EMBED_DIM,
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"access": "shared",
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"api_keys": len(API_KEYS),
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}
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# ---------------------------------------------------------------------------
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# Cleanup periodico: rimuove record scaduti (expires_at < now)
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# ---------------------------------------------------------------------------
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async def _cleanup_loop() -> None:
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while True:
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try:
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now = time.time()
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scroll = qdrant.scroll(
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collection_name=COLLECTION,
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scroll_filter=qm.Filter(
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must=[
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qm.FieldCondition(
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key="expires_at",
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range=qm.Range(lt=now),
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)
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]
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),
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limit=100,
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with_payload=False,
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)
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ids = [p.id for p in scroll[0]]
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if ids:
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qdrant.delete(collection_name=COLLECTION, points_selector=ids)
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_invalidate_meta()
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log.info("cleanup: rimossi %d record scaduti", len(ids))
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except Exception as e: # noqa: BLE001
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log.warning("cleanup error: %s", e)
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await asyncio.sleep(3600)
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@app.on_event("startup")
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async def start_cleanup() -> None:
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asyncio.create_task(_cleanup_loop())
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8080)
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