Update app.py

app.py

@@ -8,6 +8,9 @@ from langchain.embeddings import OpenAIEmbeddings # for creating embeddings
 from langchain.vectorstores import Chroma # for the vectorization part
 from langchain.chains import RetrievalQA # for conversing with chatGPT
 from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT)
+from langchain import PromptTemplate
+
+
 
 
 def load_doc(pdf_doc, open_ai_key):
@@ -25,7 +28,17 @@ def load_doc(pdf_doc, open_ai_key):
         
         #Finally, we create the bot using the RetrievalQAChain class
         global pdf_qa
-        pdf_qa = RetrievalQA.from_chain_type(ChatOpenAI(temperature=0, model_name="gpt-4"), vectordb.as_retriever(), return_source_documents=False)
+
+        prompt_template = """Use the following pieces of context to answer the question at the end. If you do not know the answer, just return the question followed by N/A. If you encounter a date, return it in mm/dd/yyyy format.
+
+        {context}
+
+        Question: {question}
+        Return the key fields from the question followed by the answer :"""
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+        chain_type_kwargs = {"prompt": PROMPT}
+        pdf_qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=0, model_name="gpt-4"),chain_type="stuff", retriever=vectordb.as_retriever(), chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
+        
         
         return "Ready"
     else: