refactor(memory): integrate unified memory service into agent controller
- Replace direct memory agent service calls with unified MemoryService in read endpoint - Update query preprocessor to use new prompt format and return structured queries - Enhance MemorySearchResult model with filtering, merging, and ID tracking capabilities - Add intermediate outputs display for problem split, perceptual retrieval, and search results - Fix parameter alignment and remove unused history parameter in memory agent service
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Eternity
@@ -12,6 +12,8 @@ from app.core.language_utils import get_language_from_header
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from app.core.logging_config import get_api_logger
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from app.core.memory.agent.utils.redis_tool import store
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from app.core.memory.agent.utils.session_tools import SessionService
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from app.core.memory.enums import SearchStrategy, Neo4jNodeType
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from app.core.memory.memory_service import MemoryService
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from app.core.rag.llm.cv_model import QWenCV
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from app.core.response_utils import fail, success
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from app.db import get_db
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@@ -23,6 +25,7 @@ from app.schemas.memory_agent_schema import UserInput, Write_UserInput
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from app.schemas.response_schema import ApiResponse
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from app.services import task_service, workspace_service
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from app.services.memory_agent_service import MemoryAgentService
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from app.services.memory_agent_service import get_end_user_connected_config as get_config
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from app.services.model_service import ModelConfigService
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load_dotenv()
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@@ -300,33 +303,90 @@ async def read_server(
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api_logger.info(
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f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
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try:
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result = await memory_agent_service.read_memory(
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user_input.end_user_id,
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user_input.message,
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user_input.history,
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user_input.search_switch,
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config_id,
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# result = await memory_agent_service.read_memory(
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# user_input.end_user_id,
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# user_input.message,
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# user_input.history,
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# user_input.search_switch,
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# config_id,
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# db,
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# storage_type,
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# user_rag_memory_id
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# )
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# if str(user_input.search_switch) == "2":
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# retrieve_info = result['answer']
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# history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id,
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# user_input.end_user_id)
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# query = user_input.message
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#
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# # 调用 memory_agent_service 的方法生成最终答案
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# result['answer'] = await memory_agent_service.generate_summary_from_retrieve(
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# end_user_id=user_input.end_user_id,
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# retrieve_info=retrieve_info,
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# history=history,
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# query=query,
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# config_id=config_id,
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# db=db
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# )
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# if "信息不足，无法回答" in result['answer']:
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# result['answer'] = retrieve_info
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memory_config = get_config(user_input.end_user_id, db)
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service = MemoryService(
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db,
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storage_type,
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user_rag_memory_id
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memory_config["memory_config_id"],
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end_user_id=user_input.end_user_id
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)
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if str(user_input.search_switch) == "2":
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retrieve_info = result['answer']
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history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id,
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user_input.end_user_id)
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query = user_input.message
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search_result = await service.read(
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user_input.message,
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SearchStrategy(user_input.search_switch)
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)
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intermediate_outputs = []
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sub_queries = set()
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for memory in search_result.memories:
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sub_queries.add(str(memory.query))
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if user_input.search_switch in [SearchStrategy.DEEP, SearchStrategy.NORMAL]:
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intermediate_outputs.append({
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"type": "problem_split",
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"title": "问题拆分",
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"data": [
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{
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"id": f"Q{idx+1}",
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"question": question
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}
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for idx, question in enumerate(sub_queries)
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]
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})
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perceptual_data = [
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memory.data
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for memory in search_result.memories
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if memory.source == Neo4jNodeType.PERCEPTUAL
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]
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# 调用 memory_agent_service 的方法生成最终答案
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result['answer'] = await memory_agent_service.generate_summary_from_retrieve(
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intermediate_outputs.append({
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"type": "perceptual_retrieve",
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"title": "感知记忆检索",
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"data": perceptual_data,
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"total": len(perceptual_data),
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})
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intermediate_outputs.append({
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"type": "search_result",
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"title": f"合并检索结果 (共{len(sub_queries)}个查询,{len(search_result.memories)}条结果)",
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"result": search_result.content,
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"raw_result": search_result.memories,
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"total": len(search_result.memories),
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})
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result = {
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'answer': await memory_agent_service.generate_summary_from_retrieve(
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end_user_id=user_input.end_user_id,
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retrieve_info=retrieve_info,
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history=history,
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query=query,
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retrieve_info=search_result.content,
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history=[],
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query=user_input.message,
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config_id=config_id,
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db=db
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)
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if "信息不足，无法回答" in result['answer']:
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result['answer'] = retrieve_info
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),
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"intermediate_outputs": intermediate_outputs
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}
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return success(data=result, msg="回复对话消息成功")
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except BaseException as e:
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# Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
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@@ -801,9 +861,6 @@ async def get_end_user_connected_config(
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Returns:
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包含 memory_config_id 和相关信息的响应
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"""
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from app.services.memory_agent_service import (
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get_end_user_connected_config as get_config,
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)
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api_logger.info(f"Getting connected config for end_user: {end_user_id}")
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