Update query_data.py

query_data.py

@@ -1,11 +1,28 @@
 from langchain.prompts.prompt import PromptTemplate
-from langchain.llms import OpenAI, OpenAIChat
-from langchain.chains import ChatVectorDBChain, ConversationalRetrievalChain
+from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
+# from langchain.llms import OpenAI
 
-_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a
-standalone question.
+# from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT,QA_PROMPT
 
+# _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a
+# standalone question.
+#
+#
+# Chat History:
+# {chat_history}
+# Follow Up Input: {question}
+# Standalone question:"""
+# CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+#
+# template = """You are given an context and a question find an answer from the given
+# context and provide an answer. If the answer is not in the context then
+# simply say "No information found". Don't try to make an answer. And also don't use "According to the information
+# provided in the given links," Question: {question} ========= {context} ========= Answer in Markdown: """
+# QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
+
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a 
+standalone question. 
 
 Chat History:
 {chat_history}
@@ -13,25 +30,39 @@ Follow Up Input: {question}
 Standalone question:"""
 CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
 
-template = """Give an answer to the question based on the context below, and if you don't find the answer in 
-the context, then simply say "Sorry no information is present in twimbit's data", But do give an answer that fits 
-right, the answer can be outside of this context and at the end of the answer, do mention that this answer is not 
-from twimbit's data and also include sources like article links etc. And also don't use "According to the information 
-provided in the given links, " while giving an answer. 
+prompt_template = """Give an answer to the question based on the context below and try to explain in detail and do 
+add source links if exist in context, and if you don't find the answer in the context, then simply say "That's a great 
+question, and I want to make sure I provide you with the most accurate and helpful information possible. 
+Unfortunately, our current pool of insights does not have an answer to this.", don't try to make up an answer And 
+also don't use "According to the information provided in the given links, " while giving an answer. 
 
 {context}
 
 Question: {question}
 Helpful Answer:"""
-QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
+QA_PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
 
 
 def get_chain(vectorstore):
+    from langchain.chains.llm import LLMChain
+    # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+    from langchain.chains.question_answering import load_qa_chain
     llm = ChatOpenAI(temperature=0)
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        vectorstore.as_retriever(search_kwargs={"k": 8, "include_metadata": True})
-        # qa_prompt=QA_PROMPT,
-        # condense_question_prompt=CONDENSE_QUESTION_PROMPT,
-    )
+    streaming_llm = ChatOpenAI(streaming=True, temperature=0, max_tokens=300)
+    question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
+    doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)
+
+    # qa_chain = ConversationalRetrievalChain.from_llm(
+    #     llm,
+    #     vectorstore.as_retriever(search_kwargs={"k": 8, "include_metadata": True}),
+    #     condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+    #     qa_prompt=QA_PROMPT
+    # )
+
+    qa_chain = ConversationalRetrievalChain(return_source_documents=False,
+                                            retriever=vectorstore.as_retriever(
+                                                search_kwargs={"k": 8, "include_metadata": True}),
+                                            combine_docs_chain=doc_chain, question_generator=question_generator)
     return qa_chain