""" Mem0 OSS wrapper for long-term CVE query memory. Self-hosted — reuses existing Qdrant + HuggingFace embeddings. Zero new infrastructure, zero API keys. Gracefully degrades to no-op if mem0ai is not installed. """ from __future__ import annotations import logging from typing import Any, Dict, List, Optional from src.agents.agent_config import ( MEM0_ENABLED, MEM0_MAX_MEMORIES, MEM0_COLLECTION_NAME, ) from src.generators.rag_config import ( QDRANT_URL, QDRANT_API_KEY, EMBEDDING_MODEL_NAME, LLM_BASE_URL, LLM_API_KEY, LLM_MODEL_NAME, ) logger = logging.getLogger(__name__) _MEM0_AVAILABLE = False try: from mem0 import Memory _MEM0_AVAILABLE = True except ImportError: logger.info("mem0ai not installed — memory is disabled. Install with: pip install mem0ai") def _build_mem0_config() -> dict: return { "vector_store": { "provider": "qdrant", "config": { "url": QDRANT_URL, "api_key": QDRANT_API_KEY, "collection_name": MEM0_COLLECTION_NAME, }, }, "llm": { "provider": "openai", "config": { "model": LLM_MODEL_NAME, "base_url": LLM_BASE_URL, "api_key": LLM_API_KEY if LLM_API_KEY and LLM_API_KEY != "not-needed" else "not-needed", }, }, "embedder": { "provider": "huggingface", "config": {"model": EMBEDDING_MODEL_NAME}, }, "history_db_path": "~/.mem0/cve_kgrag_history.db", } class CVEKGMemory: """Long-term memory via Mem0. Stores/recalls past CVE queries and answers.""" def __init__(self) -> None: self._client: Any = None if not MEM0_ENABLED: logger.info("Mem0 disabled via MEM0_ENABLED=false") return if not _MEM0_AVAILABLE: logger.info("Mem0 unavailable — pip install mem0ai to enable long-term memory") return try: config = _build_mem0_config() self._client = Memory.from_config(config) logger.info("Mem0 memory ready (Qdrant collection=%s)", MEM0_COLLECTION_NAME) except Exception as e: logger.warning("Mem0 init failed: %s — memory disabled", e) self._client = None def recall(self, query: str, user_id: str = "default_session") -> str: """Search past memories for relevant context. Returns empty string on failure.""" if self._client is None: return "" try: results = self._client.search( query, filters={"user_id": user_id}, limit=MEM0_MAX_MEMORIES, ) memories = results.get("results", []) if not memories: return "" lines = ["[Past context from memory:]"] for m in memories: lines.append(f"- {m.get('memory', '')}") return "\n".join(lines) except Exception as e: logger.warning("Mem0 recall failed: %s", e) return "" def save(self, query: str, answer: str, user_id: str = "default_session") -> None: """Store an interaction. Mem0 auto-extracts key facts.""" if self._client is None: return try: self._client.add( [ {"role": "user", "content": query}, {"role": "assistant", "content": answer}, ], user_id=user_id, ) except Exception as e: logger.warning("Mem0 save failed: %s", e) @property def enabled(self) -> bool: return self._client is not None