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added retrieve_docs endpoint for external RAG agent integration #1053

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added retrieve_docs endpoint for external RAG agent integration #1053

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26 changes: 24 additions & 2 deletions backend/score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import asyncio
import base64
from langserve import add_routes
from langchain_core.messages import HumanMessage, AIMessage
from langchain_google_vertexai import ChatVertexAI
from src.api_response import create_api_response
from src.graphDB_dataAccess import graphDBdataAccess
Expand Down Expand Up @@ -375,7 +376,28 @@ async def post_processing(uri=Form(), userName=Form(), password=Form(), database

finally:
gc.collect()


@app.post('/retrieve_docs')
async def retrieve_docs(uri=Form(),model=Form(None),userName=Form(), password=Form(), database=Form(), document_names=Form(None), mode=Form(None), question=Form(None)):
if mode == "graph":
graph = Neo4jGraph( url=uri,username=userName,password=password,database=database,sanitize = True, refresh_schema=True)
else:
graph = create_graph_database_connection(uri, userName, password, database)

chat_mode_settings = get_chat_mode_settings(mode=mode)

messages = [HumanMessage(question)]

try:
llm, doc_retriever, model_version = setup_chat(model, graph, document_names, chat_mode_settings)
docs, transformed_question = retrieve_documents(doc_retriever, messages)
return {
'docs': docs,
'transformed_question': transformed_question
}
except Exception as e:
return {'docs': [], 'transformed_question': None}

@app.post("/chat_bot")
async def chat_bot(uri=Form(),model=Form(None),userName=Form(), password=Form(), database=Form(),question=Form(None), document_names=Form(None),session_id=Form(None),mode=Form(None),email=Form()):
logging.info(f"QA_RAG called at {datetime.now()}")
Expand Down Expand Up @@ -1053,4 +1075,4 @@ async def get_schema_visualization(uri=Form(), userName=Form(), password=Form(),
gc.collect()

if __name__ == "__main__":
uvicorn.run(app)
uvicorn.run(app)