ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/gaia-experience/AWorld/aworld/memory/mem0/mem0_memory.py

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+import json
+import os
+import traceback
+from typing import Optional
+
+from pydantic import BaseModel
+
+from aworld.config import ConfigDict
+from aworld.core.memory import MemoryStore, MemoryConfig, MemoryItem, AgentMemoryConfig
+from aworld.logs.util import logger
+from aworld.memory.main import Memory
+from aworld.models.llm import get_llm_model
+
+
+class Mem0Memory(Memory):
+    def __init__(self, memory_store: MemoryStore, config: MemoryConfig | None = None, **kwargs):
+        super().__init__(memory_store, config, **kwargs)
+        self.config = config
+
+        conf = ConfigDict(
+            llm_provider=config.llm_provider,
+            llm_model_name=os.getenv("MEM_LLM_MODEL_NAME") if os.getenv("MEM_LLM_MODEL_NAME") else os.getenv(
+                'LLM_MODEL_NAME'),
+            llm_temperature=os.getenv("MEM_LLM_TEMPERATURE") if os.getenv("MEM_LLM_TEMPERATURE") else 1.0,
+            llm_base_url=os.getenv("MEM_LLM_BASE_URL") if os.getenv("MEM_LLM_BASE_URL") else os.getenv('LLM_BASE_URL'),
+            llm_api_key=os.getenv("MEM_LLM_API_KEY") if os.getenv("MEM_LLM_API_KEY") else os.getenv('LLM_API_KEY')
+        )
+        self.config.llm_instance = get_llm_model(conf=conf, streaming=False)
+
+        # Check for required packages
+        try:
+            # also disable mem0's telemetry when ANONYMIZED_TELEMETRY=False
+            if os.getenv('ANONYMIZED_TELEMETRY', 'true').lower()[0] in 'fn0':
+                os.environ['MEM_TELEMETRY'] = 'False'
+            from mem0 import Memory as Mem0
+        except ImportError:
+            raise ImportError('mem0 is required when enable_memory=True. Please install it with `pip install mem0`.')
+
+        # Initialize Mem0 with the configuration
+        config_dict = self.config.full_config_dict
+        self.mem0 = Mem0.from_config(config_dict=self.config.full_config_dict)
+        self.memory_store = memory_store
+
+    def _add(self, memory_item: MemoryItem, filters: dict = None, agent_memory_config: AgentMemoryConfig = None):
+        # generate summary memory if needed
+        message_filters = {
+            "memory_type": "message"
+        }
+        if filters:
+            message_filters = {
+                "memory_type": "message",
+                "agent_id": memory_item.metadata.get("agent_id"),
+                "task_id": memory_item.metadata.get("task_id"),
+                "user_id": memory_item.metadata.get("user_id"),
+                "session_id": memory_item.metadata.get("session_id"),
+            }
+        if self._need_summary(memory_item, message_filters):
+            self.create_summary_memory(
+                agent_id=memory_item.metadata.get("agent_id"),
+                task_id=memory_item.metadata.get("task_id"),
+                user_id=memory_item.metadata.get("user_id"),
+                session_id=memory_item.metadata.get("session_id"),
+                filters=message_filters
+            )
+        self.memory_store.add(memory_item)
+
+    def _need_summary(self, memory_item, message_filters):
+        """
+        Check if a summary is needed based on the current step.
+        1. If the number of messages is greater than the summary rounds.
+        2. If the message is a message and the content is greater than the summary single context length.
+        """
+        return self.memory_store.total_rounds(message_filters) > self.config.summary_rounds or (
+                memory_item.memory_type == 'message' and len(
+            memory_item.content) >= self.config.summary_single_context_length)
+
+    def create_summary_memory(self, agent_id, task_id, user_id, session_id, filters: dict) -> None:
+        """
+        Create a summary memory if needed based on the current step.
+        """
+        logger.info(f'Creating summary memory, {filters}')
+
+        # Get all messages
+        all_messages = self.memory_store.get_all(filters=filters)
+
+        # Separate messages into those to keep as-is and those to process for memory
+        summary_messages = []
+        messages_to_process = []
+
+        for msg in all_messages:
+            if isinstance(msg, MemoryItem) and msg.memory_type in {'summary'}:
+                # Keep system and memory messages as they are
+                summary_messages.append(msg)
+            elif msg.memory_type in {'init'}:
+                messages_to_process.append(msg)
+            else:
+                if len(msg.content) > 0:
+                    messages_to_process.append(msg)
+        if messages_to_process[-1].metadata.get("tool_calls"):
+            messages_to_process = messages_to_process[:-1]
+        # Need at least 1 message to create a meaningful summary
+        if len(messages_to_process) < 1:
+            logger.info('Not enough non-memory messages to summarize')
+            return
+        # Create a procedural memory
+
+        memory_content = self._create_summary_memory(messages_to_process)
+
+        if not memory_content:
+            logger.warning('Failed to create procedural memory')
+            return
+
+        # Add the summary message
+        summary_message = MemoryItem(content=memory_content, memory_type='summary', metadata={
+            "role": "user",
+            "agent_id": agent_id,
+            "session_id": session_id,
+            "task_id": task_id,
+            "user_id": user_id,
+        })
+        summary_messages.append(summary_message)
+
+        # Update the history
+        [self.memory_store.delete(m.id) for m in messages_to_process]
+        self.memory_store.add(summary_message)
+
+        logger.info(f'Messages consolidated: {len(messages_to_process)} messages converted to procedural memory')
+
+    def _create_summary_memory(self, messages: list[MemoryItem]) -> str | None:
+
+        parsed_messages = [{'role': message.metadata['role'], 'content': message.content if not message.metadata.get(
+            'tool_calls') else message.content + "\n\n" + self.__format_tool_call(message.metadata.get('tool_calls'))}
+                           for message in
+                           messages]  # TODO add tool_call from metadata['tool_calls']  such as [{"id": "fc-7b66b01a-f125-44d5-9f32-5e3723384d8e", "type": "function", "function": {"name": "mcp__amap-amap-sse__maps_geo", "arguments": "{\"address\": \"\u676d\u5dde\", \"city\": \"\u676d\u5dde\"}"}}] append to content
+        try:
+            results = self.mem0.add(
+                messages=parsed_messages,
+                agent_id=messages[-1].metadata.get('agent_id'),
+                memory_type='procedural_memory'
+            )
+            if len(results.get('results', [])):
+                logger.info(f'creating summary memory result: {results}')
+                return results.get('results', [])[0].get('memory')
+            return None
+        except Exception as e:
+            logger.error(f'Error creating summary memory: {e}')
+            traceback.print_exc()
+            return None
+
+    def __format_tool_call(self, tool_calls):
+        return json.dumps(tool_calls, default=lambda o: o.model_dump_json() if isinstance(o, BaseModel) else str(o))
+
+    def update(self, memory_item: MemoryItem):
+        self.memory_store.update(memory_item)
+
+    def delete(self, memory_id):
+        self.memory_store.delete(memory_id)
+
+    def get(self, memory_id) -> Optional[MemoryItem]:
+        # self.memory_store.get(memory_id)
+        return self.memory_store.get(
+            memory_id,
+        )
+
+    def get_all(self, filters: dict = None) -> list[MemoryItem]:
+        return self.memory_store.get_all(
+            filters=filters,
+        )
+
+    def get_last_n(self, last_rounds, add_first_message=True, filters: dict = None, memory_config: MemoryConfig = None) -> list[MemoryItem]:
+        """
+        Get last n memories.
+
+        Args:
+            last_rounds (int): Number of memories to retrieve.
+            add_first_message (bool):
+
+        Returns:
+            list[MemoryItem]: List of latest memories.
+        """
+        return self.memory_store.get_last_n(
+            last_rounds=last_rounds,
+            filters=filters,
+        )