update

app.py

@@ -1,18 +1,18 @@
 import chainlit as cl
+from langchain_core.document_loaders import PyMuPDFLoader
 from chainlit.playground.providers import ChatOpenAI
 from dotenv import load_dotenv
-# from langchain_community.document_loaders import PyMuPDFLoader
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# import tiktoken
-# from langchain.prompts import ChatPromptTemplate
-# from operator import  itemgetter
-# from langchain_core.runnables import RunnablePassthrough
-# from langchain import ChatOpenAI, OpenAIEmbeddings
-# from langchain_community.vectorstores import FAISS
-# from langchain.retrievers import MultiQueryRetriever
-
-# from langchain.prompts import ChatPromptTemplate
-# from langchain.retrievers import MultiQueryRetriever
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import tiktoken
+from langchain.prompts import ChatPromptTemplate
+from operator import  itemgetter
+from langchain_core.runnables import RunnablePassthrough
+from langchain import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.retrievers import MultiQueryRetriever
+
+from langchain.prompts import ChatPromptTemplate
+from langchain.retrievers import MultiQueryRetriever
 
 template = """
 you can only answer questions related to what's in the context. If it's not in the context, then you would reply with 
@@ -34,43 +34,43 @@ init_settings = {
     "presence_penalty": 0,
 }
 
-# embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
 
 load_dotenv()
 
-# def tiktoken_len(text):
-#     tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
-#         text,
-#     )
-#     return len(tokens)
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
+        text,
+    )
+    return len(tokens)
 
 
 @cl.on_chat_start
 async def main():
     model = ChatOpenAI(streaming=True)
     
-    # prompt = ChatPromptTemplate.from_template(template)
+    prompt = ChatPromptTemplate.from_template(template)
 
-    # nvida_doc = PyMuPDFLoader('../docs/nvidia-document.pdf')
-    # data = nvida_doc.load()
+    nvida_doc = PyMuPDFLoader('../docs/nvidia-document.pdf')
+    data = nvida_doc.load()
 
-    # text_splitter = RecursiveCharacterTextSplitter(
-    # chunk_size = 1700,
-    # chunk_overlap = 0,
-    # length_function = tiktoken_len)
+    text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 1700,
+    chunk_overlap = 0,
+    length_function = tiktoken_len)
     
-    # nvidia_doc_chunks = text_splitter.split_documents(data)
+    nvidia_doc_chunks = text_splitter.split_documents(data)
 
-    # vector_store = FAISS.from_documents(nvidia_doc_chunks, embedding=embeddings)
+    vector_store = FAISS.from_documents(nvidia_doc_chunks, embedding=embeddings)
     
-    # retriever = vector_store.as_retriever()
-    # advanced_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model)
+    retriever = vector_store.as_retriever()
+    advanced_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model)
 
-    # runnable = (
-    # {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    # | RunnablePassthrough.assign(context=itemgetter("context"))
-    # | {"response": prompt | model, "context": itemgetter("context")})
+    runnable = (
+    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    | {"response": prompt | model, "context": itemgetter("context")})
 
     # retrieval_qa_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
     # document_chain = create_stuff_documents_chain(model, retrieval_qa_prompt)
@@ -79,7 +79,7 @@ async def main():
     # cl.user_session.set("settings", init_settings)
     # cl.user_session.set("nvidia_doc", data)
 
-    # cl.user_session.set("runnable", runnable)
+    cl.user_session.set("runnable", runnable)
 
 
 
@@ -87,9 +87,9 @@ async def main():
 async def on_message(message: cl.Message):
     # settings = cl.user_session.get("settings")
     # nvida_doc = cl.user_session.get("nvidia_doc")
-    # runnable = cl.user_session.get("runnable")
+    runnable = cl.user_session.get("runnable")
 
-    # msg = cl.Message(content="")
+    msg = cl.Message(content="")
 
     
     # async for chunk in runnable.astream(
@@ -100,10 +100,10 @@ async def on_message(message: cl.Message):
 
     # await msg.send()
 
-    # inputs = {"question": message.content}
-    # result = await runnable.ainvoke(inputs)
-    # msg = cl.Message(content=result["response"].content)
-    # await msg.send()
+    inputs = {"question": message.content}
+    result = await runnable.ainvoke(inputs)
+    msg = cl.Message(content=result["response"].content)
+    await msg.send()
 
 
  

requirements.txt

@@ -5,4 +5,4 @@ tiktoken==0.5.1
 python-dotenv==1.0.0
 langchain
 langchain_openai
-# langchain-community
+langchain-community==0.0.28