runtime/node/agent/memory/mem0_memory.py - main - githubmirror/ChatDev

runtime/node/agent/memory/mem0_memory.py
219 строк7 KB
kartik-mem0
refactor: update Mem0Memory to use independent user/agent scoping and exclude assistant output
06 апр 2026, 10:46
06 апр 2026, 10:465317415
"""Mem0 managed memory store implementation."""

import logging
import re
import time
import uuid
from typing import Any, Dict, List

from entity.configs import MemoryStoreConfig
from entity.configs.node.memory import Mem0MemoryConfig
from runtime.node.agent.memory.memory_base import (
    MemoryBase,
    MemoryContentSnapshot,
    MemoryItem,
    MemoryWritePayload,
)

logger = logging.getLogger(__name__)


def _get_mem0_client(config: Mem0MemoryConfig):
    """Lazy-import mem0ai and create a MemoryClient."""
    try:
        from mem0 import MemoryClient
    except ImportError:
        raise ImportError(
            "mem0ai is required for Mem0Memory. Install it with: pip install mem0ai"
        )

    client_kwargs: Dict[str, Any] = {}
    if config.api_key:
        client_kwargs["api_key"] = config.api_key
    if config.org_id:
        client_kwargs["org_id"] = config.org_id
    if config.project_id:
        client_kwargs["project_id"] = config.project_id

    return MemoryClient(**client_kwargs)


class Mem0Memory(MemoryBase):
    """Memory store backed by Mem0's managed cloud service.

    Mem0 handles embeddings, storage, and semantic search server-side.
    No local persistence or embedding computation is needed.

    Important API constraints:
    - Agent memories use role="assistant" + agent_id
    - user_id and agent_id are independent scoping dimensions and can be
      combined in both add() and search() calls.
    - search() uses filters dict; add() uses top-level kwargs.
    - SDK returns {"memories": [...]} from search.
    """

    def __init__(self, store: MemoryStoreConfig):
        config = store.as_config(Mem0MemoryConfig)
        if not config:
            raise ValueError("Mem0Memory requires a Mem0 memory store configuration")
        super().__init__(store)
        self.config = config
        self.client = _get_mem0_client(config)
        self.user_id = config.user_id
        self.agent_id = config.agent_id

    # -------- Persistence (no-ops for cloud-managed store) --------

    def load(self) -> None:
        """No-op: Mem0 manages persistence server-side."""
        pass

    def save(self) -> None:
        """No-op: Mem0 manages persistence server-side."""
        pass

    # -------- Retrieval --------

    def _build_search_filters(self, agent_role: str) -> Dict[str, Any]:
        """Build the filters dict for Mem0 search.

        Mem0 search requires a filters dict for entity scoping.
        user_id and agent_id are stored as separate records, so
        when both are configured we use an OR filter to match either.
        """
        if self.user_id and self.agent_id:
            return {
                "OR": [
                    {"user_id": self.user_id},
                    {"agent_id": self.agent_id},
                ]
            }
        elif self.user_id:
            return {"user_id": self.user_id}
        elif self.agent_id:
            return {"agent_id": self.agent_id}
        else:
            # Fallback: use agent_role as agent_id
            return {"agent_id": agent_role}

    def retrieve(
        self,
        agent_role: str,
        query: MemoryContentSnapshot,
        top_k: int,
        similarity_threshold: float,
    ) -> List[MemoryItem]:
        """Search Mem0 for relevant memories.

        Uses the filters dict to scope by user_id, agent_id, or both
        (via OR filter). The SDK returns {"memories": [...]}.
        """
        if not query.text.strip():
            return []

        try:
            filters = self._build_search_filters(agent_role)
            search_kwargs: Dict[str, Any] = {
                "query": query.text,
                "top_k": top_k,
                "filters": filters,
            }
            if similarity_threshold >= 0:
                search_kwargs["threshold"] = similarity_threshold

            response = self.client.search(**search_kwargs)

            # SDK returns {"memories": [...]} — extract the list
            if isinstance(response, dict):
                raw_results = response.get("memories", response.get("results", []))
            else:
                raw_results = response
        except Exception as e:
            logger.error("Mem0 search failed: %s", e)
            return []

        items: List[MemoryItem] = []
        for entry in raw_results:
            item = MemoryItem(
                id=entry.get("id", f"mem0_{uuid.uuid4().hex}"),
                content_summary=entry.get("memory", ""),
                metadata={
                    "agent_role": agent_role,
                    "score": entry.get("score"),
                    "categories": entry.get("categories", []),
                    "source": "mem0",
                },
                timestamp=time.time(),
            )
            items.append(item)

        return items

    # -------- Update --------

    def update(self, payload: MemoryWritePayload) -> None:
        """Store user input as a memory in Mem0.

        Only user input is sent for extraction. Assistant output is excluded
        to prevent noise memories from the LLM's responses.
        """
        raw_input = payload.inputs_text or ""
        if not raw_input.strip():
            return

        messages = self._build_messages(payload)
        if not messages:
            return

        add_kwargs: Dict[str, Any] = {
            "messages": messages,
            "infer": True,
        }

        # Include both user_id and agent_id when available — they are
        # independent scoping dimensions in Mem0, not mutually exclusive.
        if self.agent_id:
            add_kwargs["agent_id"] = self.agent_id
        if self.user_id:
            add_kwargs["user_id"] = self.user_id

        # Fallback when neither is configured
        if "agent_id" not in add_kwargs and "user_id" not in add_kwargs:
            add_kwargs["agent_id"] = payload.agent_role

        try:
            result = self.client.add(**add_kwargs)
            logger.info("Mem0 add result: %s", result)
        except Exception as e:
            logger.error("Mem0 add failed: %s", e)

    @staticmethod
    def _clean_pipeline_text(text: str) -> str:
        """Strip ChatDev pipeline headers so Mem0 sees clean conversational text.

        The executor wraps each input with '=== INPUT FROM <source> (<role>) ==='
        headers. Mem0's extraction LLM treats these as system metadata and skips
        them, resulting in zero memories extracted.
        """
        cleaned = re.sub(r"===\s*INPUT FROM\s+\S+\s*\(\w+\)\s*===\s*", "", text)
        return cleaned.strip()

    def _build_messages(self, payload: MemoryWritePayload) -> List[Dict[str, str]]:
        """Build Mem0-compatible message list from write payload.

        Only sends user input to Mem0. Assistant output is excluded because
        Mem0's extraction LLM processes ALL messages and extracts facts from
        assistant responses too, creating noise memories like "Assistant says
        Python is fascinating" instead of actual user facts.
        """
        messages: List[Dict[str, str]] = []

        raw_input = payload.inputs_text or ""
        clean_input = self._clean_pipeline_text(raw_input)
        if clean_input:
            messages.append({
                "role": "user",
                "content": clean_input,
            })

        return messages