Q/A component added

Author: rakshita-arora

backend/src/QA_integration.py

@@ -0,0 +1,87 @@
+from langchain_community.vectorstores.neo4j_vector import Neo4jVector
+from langchain.chains import GraphCypherQAChain
+from langchain.graphs import Neo4jGraph
+import os
+from dotenv import load_dotenv
+from langchain.chains import RetrievalQA
+from langchain_openai import ChatOpenAI
+from langchain_openai import OpenAIEmbeddings
+load_dotenv()
+
+openai_api_key = os.environ.get('OPENAI_API_KEY')
+
+def vector_embed_results(qa,question):
+    # question ="What do you know about machine learning"
+    result = qa({"query": question})
+    vector_res={}
+    vector_res['result']=result["result"]
+    list_source_docs=[]
+    for i in result["source_documents"]:
+        list_source_docs.append(i.metadata['source'])
+        vector_res['source']=list_source_docs
+    return vector_res
+
+def cypher_results(graph,question,model_version):
+    graph.refresh_schema()
+    cypher_chain = GraphCypherQAChain.from_llm(
+        graph=graph,
+        # cypher_llm=ChatOpenAI(temperature=0, model="gpt-4"),
+        cypher_llm=ChatOpenAI(temperature=0, model=model_version),
+        qa_llm=ChatOpenAI(temperature=0, model=model_version),
+        validate_cypher=True, # Validate relationship directions
+        verbose=True,
+        top_k=2
+    )
+
+    cypher_res=cypher_chain.invoke({"query": question})
+    return cypher_res
+
+
+def QA_RAG(uri,userName,password,model_version,question):
+    if model_version=='OpenAI GPT 3.5':
+        model_version='gpt-3.5-turbo'
+    elif model_version=='OpenAI GPT 4':
+        model_version='gpt-4-0125-preview'
+    retrieval_query="""
+    MATCH (node)-[:PART_OF]->(d:Document)
+    WITH d, apoc.text.join(collect(node.text),"\n----\n") as text, avg(score) as score
+    RETURN text, score, {source: COALESCE(CASE WHEN d.url CONTAINS "None" THEN d.fileName ELSE d.url END, d.fileName)} as metadata
+    """
+
+    neo_db=Neo4jVector.from_existing_index(
+            embedding=OpenAIEmbeddings(),
+            url=uri,
+            username=userName,
+            password=password,
+            database="neo4j",
+            index_name="vector",
+            retrieval_query=retrieval_query,
+        )
+    llm = ChatOpenAI(model= model_version, temperature=0)
+
+    qa = RetrievalQA.from_chain_type(
+        llm=llm, chain_type="stuff", retriever=neo_db.as_retriever(search_kwargs={"score_threshold": 0.5}), return_source_documents=True
+    )
+
+    graph = Neo4jGraph(
+        url=uri,
+        username=userName,
+        password=password
+    )
+    vector_res=vector_embed_results(qa,question)
+    print(vector_res)
+    cypher_res= cypher_results(graph,question,model_version)
+    print(cypher_res)
+    final_prompt = f"""You are a helpful question-answering agent. Your task is to analyze
+    and synthesize information from two sources: the top result from a similarity search
+    (unstructured information) and relevant data from a graph database (structured information).
+    Given the user's query: {question}, provide a meaningful and efficient answer based
+    on the insights derived from the following data:
+    Structured information: {cypher_res['result']}.
+    Unstructured information: {vector_res['result']}.
+
+    If structured information fails to find an answer then use the answer from unstructured information and vice versa. I only want a straightforward answer without mentioning from which source you got the answer.
+    """
+    response = llm.predict(final_prompt)
+    res={"message":response,"user":"chatbot"}
+    return res