dev新增短期记忆功能 (#47)
* dev新增短期记忆功能 * dev新增短期记忆功能 * dev新增短期记忆功能 * dev新增短期记忆功能 * dev新增短期记忆功能 * dev新增短期记忆功能 * dev新增短期记忆功能
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lixinyue11
@@ -4,6 +4,7 @@ Memory Agent Service
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Handles business logic for memory agent operations including read/write services,
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health checks, and message type classification.
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"""
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import datetime
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import json
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import os
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import re
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@@ -24,6 +25,7 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
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from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
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from app.db import get_db_context
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from app.models.knowledge_model import Knowledge, KnowledgeType
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from app.repositories.memory_short_repository import ShortTermMemoryRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.schemas.memory_config_schema import ConfigurationError, MemoryConfig
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from app.services.memory_config_service import MemoryConfigService
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@@ -393,7 +395,7 @@ class MemoryAgentService:
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import time
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start_time = time.time()
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ori_message=message
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# Resolve config_id if None using end_user's connected config
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if config_id is None:
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try:
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@@ -406,15 +408,15 @@ class MemoryAgentService:
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raise # Re-raise our specific error
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logger.error(f"Failed to get connected config for end_user {group_id}: {e}")
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raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
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logger.info(f"Read operation for group {group_id} with config_id {config_id}")
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# 导入审计日志记录器
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try:
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from app.core.memory.utils.log.audit_logger import audit_logger
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except ImportError:
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audit_logger = None
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# Get group lock to prevent concurrent processing
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group_lock = self.get_group_lock(group_id)
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@@ -430,7 +432,7 @@ class MemoryAgentService:
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except ConfigurationError as e:
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error_msg = f"Failed to load configuration for config_id: {config_id}: {e}"
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logger.error(error_msg)
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# Log failed operation
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if audit_logger:
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duration = time.time() - start_time
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@@ -442,9 +444,9 @@ class MemoryAgentService:
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duration=duration,
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error=error_msg
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)
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raise ValueError(error_msg)
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# Step 2: Prepare history
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history.append({"role": "user", "content": message})
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logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}")
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@@ -452,7 +454,7 @@ class MemoryAgentService:
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# Step 3: Initialize MCP client and execute read workflow
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mcp_config = get_mcp_server_config()
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client = MultiServerMCPClient(mcp_config)
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async with client.session('data_flow') as session:
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logger.debug("Connected to MCP Server: data_flow")
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tools = await load_mcp_tools(session)
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@@ -475,7 +477,7 @@ class MemoryAgentService:
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# Capture any errors from the state
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if event.get('errors'):
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workflow_errors.extend(event.get('errors', []))
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for msg in messages:
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msg_content = msg.content
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msg_role = msg.__class__.__name__.lower().replace("message", "")
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@@ -483,7 +485,7 @@ class MemoryAgentService:
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"role": msg_role,
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"content": msg_content
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})
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# Extract intermediate outputs
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if hasattr(msg, 'content'):
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try:
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@@ -496,7 +498,7 @@ class MemoryAgentService:
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break
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else:
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continue # No text block found
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# Try to parse content as JSON
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if isinstance(content_to_parse, str):
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try:
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@@ -506,16 +508,16 @@ class MemoryAgentService:
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if '_intermediate' in parsed:
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intermediate_data = parsed['_intermediate']
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output_key = self._create_intermediate_key(intermediate_data)
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if output_key not in seen_intermediates:
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seen_intermediates.add(output_key)
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intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
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# Check for multiple intermediate outputs (from Retrieve)
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if '_intermediates' in parsed:
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for intermediate_data in parsed['_intermediates']:
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output_key = self._create_intermediate_key(intermediate_data)
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if output_key not in seen_intermediates:
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seen_intermediates.add(output_key)
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intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
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@@ -523,7 +525,7 @@ class MemoryAgentService:
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pass
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except Exception as e:
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logger.debug(f"Failed to extract intermediate output: {e}")
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workflow_duration = time.time() - start
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logger.info(f"Read graph workflow completed in {workflow_duration}s")
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@@ -532,7 +534,7 @@ class MemoryAgentService:
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for messages in outputs:
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if messages['role'] == 'tool':
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message = messages['content']
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# Handle MCP content format: [{'type': 'text', 'text': '...'}]
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if isinstance(message, list):
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# Extract text from MCP content blocks
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@@ -542,7 +544,7 @@ class MemoryAgentService:
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break
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else:
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continue # No text block found
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try:
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parsed = json.loads(message) if isinstance(message, str) else message
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if isinstance(parsed, dict):
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@@ -552,15 +554,15 @@ class MemoryAgentService:
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final_answer = summary_result
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except (json.JSONDecodeError, ValueError):
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pass
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# 记录成功的操作
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total_duration = time.time() - start_time
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# Check for workflow errors
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if workflow_errors:
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error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors])
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logger.warning(f"Read workflow completed with errors: {error_details}")
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if audit_logger:
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audit_logger.log_operation(
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operation="READ",
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@@ -577,11 +579,11 @@ class MemoryAgentService:
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"errors": workflow_errors
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}
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)
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# Raise error if no answer was produced
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if not final_answer:
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raise ValueError(f"Read workflow failed: {error_details}")
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if audit_logger and not workflow_errors:
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audit_logger.log_operation(
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operation="READ",
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@@ -596,7 +598,31 @@ class MemoryAgentService:
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"has_answer": bool(final_answer)
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}
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)
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retrieved_content=[]
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repo = ShortTermMemoryRepository(db)
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if str(search_switch)!="2":
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for intermediate in intermediate_outputs:
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intermediate_type=intermediate['type']
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if intermediate_type=="search_result":
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query=intermediate['query']
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raw_results=intermediate['raw_results']
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reranked_results=raw_results.get('reranked_results',[])
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statements=[statement['statement'] for statement in reranked_results.get('statements', [])]
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statements=list(set(statements))
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retrieved_content.append({query:statements})
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if '信息不足，无法回答' in str(final_answer) or retrieved_content!=[]:
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# 使用 upsert 方法
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repo.upsert(
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end_user_id=group_id, # 确保这个变量在作用域内
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messages=ori_message,
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aimessages=final_answer,
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retrieved_content=retrieved_content,
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search_switch=str(search_switch)
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)
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print("写入成功")
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return {
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"answer": final_answer,
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"intermediate_outputs": intermediate_outputs
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56
api/app/services/memory_short_service.py
Normal file
56
api/app/services/memory_short_service.py
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@@ -0,0 +1,56 @@
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from app.core.logging_config import get_api_logger
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from app.db import get_db
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from app.repositories.memory_short_repository import LongTermMemoryRepository
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from app.repositories.memory_short_repository import ShortTermMemoryRepository
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api_logger = get_api_logger()
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db=next(get_db())
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class ShortService:
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def __init__(self, end_user_id):
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self.short_repo = ShortTermMemoryRepository(db)
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self.end_user_id = end_user_id
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def get_short_databasets(self):
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short_memories = self.short_repo.get_latest_by_user_id(self.end_user_id, 3)
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short_result = []
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for memory in short_memories:
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deep_expanded = {} # Create a new dictionary for each memory
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messages = memory.messages
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aimessages = memory.aimessages
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retrieved_content = memory.retrieved_content or []
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api_logger.debug(f"Retrieved content: {retrieved_content}")
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retrieval_source = []
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for item in retrieved_content:
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if isinstance(item, dict):
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for key, values in item.items():
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retrieval_source.append({"query": key, "retrieval": values})
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deep_expanded['retrieval'] = retrieval_source
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deep_expanded['message'] = messages # 修正拼写错误
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deep_expanded['answer'] = aimessages
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short_result.append(deep_expanded)
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return short_result
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def get_short_count(self):
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short_count = self.short_repo.count_by_user_id(self.end_user_id)
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return short_count
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class LongService:
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def __init__(self, end_user_id):
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self.long_repo = LongTermMemoryRepository(db)
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self.end_user_id = end_user_id
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def get_long_databasets(self):
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# 获取长期记忆数据
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long_memories = self.long_repo.get_by_user_id(self.end_user_id, 1)
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long_result = []
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for long_memory in long_memories:
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if long_memory.retrieved_content:
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for memory_item in long_memory.retrieved_content:
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if isinstance(memory_item, dict):
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for key, values in memory_item.items():
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long_result.append({"query": key, "retrieval": values})
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return long_result
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@@ -1496,8 +1496,8 @@ def _extract_node_properties(label: str, properties: Dict[str, Any]) -> Dict[str
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field_whitelist = {
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"Dialogue": ["content", "created_at"],
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"Chunk": ["content", "created_at"],
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"Statement": ["temporal_info", "stmt_type", "statement", "valid_at", "created_at", "caption"],
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"ExtractedEntity": ["description", "name", "entity_type", "created_at", "caption"],
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"Statement": ["temporal_info", "stmt_type", "statement", "valid_at", "created_at", "caption","emotion_keywords","emotion_type","emotion_subject"],
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"ExtractedEntity": ["description", "name", "entity_type", "created_at", "caption","aliases","connect_strength"],
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"MemorySummary": ["summary", "content", "created_at", "caption"] # 添加 content 字段
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}
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