Upload 2 files

main.py

@@ -36,7 +36,7 @@ async def section(request: Request):
 
     data = await request.json()
 
-    from qa_v2 import answer
+    from qa_v3 import answer
     answer = answer(data["Question"])
 
     return {"Answer": answer}

qa_v3.py

@@ -0,0 +1,45 @@
+#### Installing required module
+# API Token key
+import os 
+# Getting the API_KEY.
+os.environ['OPENAI_API_KEY'] = os.environ["openai"]
+ 
+# imports
+import os 
+import openai
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains.question_answering import load_qa_chain
+
+
+
+#### Embeddings
+# select which embeddings we want to use
+embeddings = OpenAIEmbeddings()
+
+#### Creating a vector store
+# Loading
+db = Chroma(persist_directory='./database', embedding_function=embeddings)
+
+# expose this index in a retriever interface
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
+
+# Loading the OpenAI model 
+llm_model = ChatOpenAI(
+    temperature=0, 
+    openai_api_key=openai.api_key, 
+    model="gpt-3.5-turbo")
+
+# Create a chain to answer questions
+chain = load_qa_chain(llm_model, chain_type = "stuff")
+
+def answer(question, chat_history=[]):
+
+    qa = ConversationalRetrievalChain.from_llm(llm_model, retriever, chain_type = "stuff")
+    result = qa({"question": question, "chat_history": chat_history})
+
+    return result["answer"]
+