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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
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