Updating prompt and RAG pipeline for default virtual student behavior

utils.py

@@ -30,40 +30,40 @@ def chunk_documents(docs, tiktoken_len):
 
 def create_raqa_chain_from_docs():
 
-    # Load the documents from a PDF file using PyMuPDFLoader
-    docs = PyMuPDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001326801/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load() # TODO: Update this to enable user to upload PDF
-    print("Loaded", len(docs), "documents")
-    print(docs[0])
+    # # Load the documents from a PDF file using PyMuPDFLoader
+    # docs = PyMuPDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001326801/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load() # TODO: Update this to enable user to upload PDF
+    # print("Loaded", len(docs), "documents")
+    # print(docs[0])
 
-    # Create a Qdrant vector store from the split chunks and embedding model, and obtain its retriever
-    split_chunks = chunk_documents(docs, tiktoken_len)
-    embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
-    qdrant_vectorstore = Qdrant.from_documents(
-        split_chunks,
-        embedding_model,
-        location=":memory:",
-        collection_name="LoadedPDF",
-    )
-    qdrant_retriever = qdrant_vectorstore.as_retriever()
+    # # Create a Qdrant vector store from the split chunks and embedding model, and obtain its retriever
+    # split_chunks = chunk_documents(docs, tiktoken_len)
+    # embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+    # qdrant_vectorstore = Qdrant.from_documents(
+    #     split_chunks,
+    #     embedding_model,
+    #     location=":memory:",
+    #     collection_name="LoadedPDF",
+    # )
+    # qdrant_retriever = qdrant_vectorstore.as_retriever()
 
     # Define the RAG prompt template
     RAG_PROMPT = """
-    CONTEXT:
-    {context}
+    Assume you are a virtual student being taught by the user. You can ask clarifying questions to better understand the user's explanation. Your goal is to ensure that the user understands the concept they are explaining. You can also ask questions to help the user elaborate on their explanation. You can ask questions like "Can you explain that in simpler terms?" or "Can you provide an example?".
 
-    QUERY:
+    USER MESSAGE:
     {question}
-
-    Use the provided context to answer the provided user query. Only use the provided context to answer the query. If you do not know the answer, respond with "I don't know".
     """
     rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
     # Create the retrieval augmented QA chain using the Qdrant retriever, RAG prompt, and OpenAI chat model
     openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
+    # retrieval_augmented_qa_chain = (
+    #     {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
+    #     | RunnablePassthrough.assign(context=itemgetter("context"))
+    #     | {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
+    # )
     retrieval_augmented_qa_chain = (
-        {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
-        | RunnablePassthrough.assign(context=itemgetter("context"))
-        | {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
+        {"response": rag_prompt | openai_chat_model}
     )
 
     return retrieval_augmented_qa_chain