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Fix/develop memory rag (#419)

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* fix_rag/fast summary

* fix_rag/fast summary
This commit is contained in:
lixinyue11
2026-02-28 18:47:08 +08:00
committed by GitHub
parent 79dc6f3f69
commit 6718553bf4
1 changed files with 43 additions and 6 deletions
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49
api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
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@@ -17,6 +17,8 @@ from app.core.memory.agent.utils.llm_tools import (
from app.core.memory.agent.utils.redis_tool import store
from app.core.memory.agent.utils.session_tools import SessionService
from app.core.memory.agent.utils.template_tools import TemplateService
from app.core.rag.nlp.search import knowledge_retrieval
from app.db import get_db
template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
@@ -32,6 +34,41 @@ class SummaryNodeService(LLMServiceMixin):
# 创建全局服务实例
summary_service = SummaryNodeService()
async def rag_config(state):
user_rag_memory_id = state.get('user_rag_memory_id', '')
kb_config = {
"knowledge_bases": [
{
"kb_id": user_rag_memory_id,
"similarity_threshold": 0.7,
"vector_similarity_weight": 0.5,
"top_k": 10,
"retrieve_type": "participle"
}
],
"merge_strategy": "weight",
"reranker_id": os.getenv('reranker_id'),
"reranker_top_k": 10
}
return kb_config
async def rag_knowledge(state,question):
kb_config = await rag_config(state)
end_user_id = state.get('end_user_id', '')
user_rag_memory_id=state.get("user_rag_memory_id",'')
retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)])
try:
retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
clean_content = '\n\n'.join(retrieval_knowledge)
cleaned_query = question
raw_results = clean_content
logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
except Exception :
retrieval_knowledge=[]
clean_content = ''
raw_results = ''
cleaned_query = question
logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
return retrieval_knowledge,clean_content,cleaned_query,raw_results
async def summary_history(state: ReadState) -> ReadState:
end_user_id = state.get("end_user_id", '')
@@ -71,7 +108,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
)
# 验证结构化响应
if structured is None:
logger.warning(f"LLM返回None，使用默认回答")
logger.warning("LLM返回None，使用默认回答")
return "信息不足，无法回答"
# 根据操作类型提取答案
@@ -82,7 +119,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
if hasattr(structured, 'data') and structured.data:
aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答"
else:
logger.warning(f"结构化响应缺少data字段")
logger.warning("结构化响应缺少data字段")
aimessages = "信息不足，无法回答"
# 验证答案不为空
@@ -186,12 +223,13 @@ async def Input_Summary(state: ReadState) -> ReadState:
}
try:
retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
if storage_type!="rag":
retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
else:
retrieval_knowledge, retrieve_info, question, raw_results = await rag_knowledge(state, data)
except Exception as e:
logger.error( f"Input_Summary: hybrid_search failed, using empty results: {e}", exc_info=True )
retrieve_info, question, raw_results = "", data, []
try:
# aimessages=await summary_llm(state,history,retrieve_info,'Retrieve_Summary_prompt.jinja2',
# 'input_summary',RetrieveSummaryResponse)
@@ -290,7 +328,6 @@ async def Summary(state: ReadState)-> ReadState:
summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
summary = summary_result[1]
return {"summary":summary}
async def Summary_fails(state: ReadState)-> ReadState:
storage_type=state.get("storage_type", '')
user_rag_memory_id=state.get("user_rag_memory_id", '')