| """ |
| 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 |
|
|
