sales-assistant-py-new/app/chat_qa_query/query_graph.py

59 lines
2.7 KiB
Python

import asyncio
from datetime import datetime, timedelta
from typing import List, Dict, Any
from langgraph.constants import START, END
from langgraph.graph import StateGraph
from app.chat_qa.nodes.finalize_node import finalize
from app.chat_qa.nodes.generate_qa_node import generate_qa
from app.chat_qa.nodes.quality_check_node import quality_check
from app.chat_qa.nodes.query_message_node import query_message
from app.chat_qa.nodes.route_by_quality_node import route_by_quality
from app.chat_qa_query.query_context import QueryQAContext
from app.chat_qa_query.query_nodes.node_answer_output import answer_output
from app.chat_qa_query.query_nodes.node_rewrite import rewrite_query
from app.chat_qa_query.query_nodes.node_search_embedding import search_embed
from app.chat_qa_query.query_qa_state import QueryQAState
from app.client.embedding_client_manager import embedding_client
from app.client.milvus_client_manager import milvus_client
from app.client.mysql_client_manager import db_assistant_mysql_client_manager
from app.repository.archive_messages_repository import ArchiveMessagesRepository
from app.repository.milvus.message_qa_repository import QARepository
from app.repository.milvus.summary_repository import SummaryRepository
graph_builder = StateGraph(state_schema=QueryQAState, context_schema=QueryQAContext)
graph_builder.add_node("rewrite_query", rewrite_query)
graph_builder.add_node("search_embed", search_embed)
graph_builder.add_node("answer_output", answer_output)
graph_builder.add_edge(START, "rewrite_query")
graph_builder.add_edge("rewrite_query", "search_embed")
graph_builder.add_edge("search_embed", "answer_output")
graph_builder.add_edge("answer_output", END)
qa_graph = graph_builder.compile()
if __name__ == '__main__':
async def test():
db_assistant_mysql_client_manager.init()
embedding_client.init()
milvus_client.init()
async with db_assistant_mysql_client_manager.session_factory() as db_session:
archive_messages_mysql_repository = ArchiveMessagesRepository(db_session)
qa_milvus_repository = QARepository()
state: QueryQAState = QueryQAState(original_query="老师,我想问一下,是不是学费涨了", history=[])
context = QueryQAContext(meta_mysql_repository=archive_messages_mysql_repository,
qa_milvus_repository=qa_milvus_repository
)
async for chunk in qa_graph.astream(input=state, context=context, stream_mode="custom"):
for item in chunk:
print("=================="+item)
milvus_client.close()
await embedding_client.close()
asyncio.run(test())