Update app.py

app.py

@@ -1,115 +1,69 @@
 import asyncio
-import logging
-from dotenv import load_dotenv
-from pathlib import Path
-from ingest_data import download_data_and_create_embedding
+import chainlit as cl
 
 from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
 from langchain_core.runnables.passthrough import RunnablePassthrough
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
-from ingest_data import underlying_embeddings, openai_api_key
-
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-
-import chainlit as cl
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
+from langchain.schema.runnable import Runnable, RunnablePassthrough, RunnableConfig
+from langchain.prompts import PromptTemplate
 
-# load env variables
-load_dotenv()
-
-# Specify the path to the file you want to check
-file_path = Path('./faiss_index/index.faiss')
+@cl.on_chat_start
+async def on_chat_start():
 
-# Check if the file exists
-if file_path.exists():
     print("Embeddings already done, use the saved index")
     # Combine the retrieved data with the output of the LLM
     vector_store = FAISS.load_local(
         "faiss_index", underlying_embeddings, allow_dangerous_deserialization=True
     )
-else:
-    vector_store = download_data_and_create_embedding()
-
-
-# create a prompt template to send to our LLM that will incorporate the documents from our retriever with the
-# question we ask the chat model
-prompt_template = ChatPromptTemplate.from_template(
-    "Answer the {question} based on the following {context}."
-)
-
-# create a retriever for our documents
-retriever = vector_store.as_retriever()
-
-# create a chat model / LLM
-chat_model = ChatOpenAI(
-    model="gpt-4o-2024-05-13", temperature=0, api_key=openai_api_key
-)
-
-# create a parser to parse the output of our LLM
-parser = StrOutputParser()
-
-# 💻 Create the sequence (recipe)
-runnable_chain = (
-    # TODO: How do we chain the output of our retriever, prompt, model and model output parser so that we can get a good answer to our query?
-    {"context": retriever, "question": RunnablePassthrough()}
-    | prompt_template
-    | chat_model
-    | StrOutputParser()
-)
-
-
-# Asynchronous execution (e.g., for a better a chatbot user experience)
-async def call_chain_async(question):
-    output_chunks = await runnable_chain.ainvoke(question)
-    return output_chunks
-
-
-# output_stream =  asyncio.run(call_chain_async("What are some good sci-fi movies from the 1980s?"))
-# print("".join(output_stream))
-
-@cl.on_chat_start
-async def on_chat_start():
-    model = ChatOpenAI(streaming=True)
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                "You're a very knowledgeable historian who provides accurate and eloquent answers to historical questions.",
-            ),
-            ("human", "{question}"),
-        ]
+    
+    # create a prompt template to send to our LLM that will incorporate the documents from our retriever with the
+    # question we ask the chat model
+    prompt_template = ChatPromptTemplate.from_template(
+        "Answer the {question} based on the following {context}."
+    )
+    
+    # create a retriever for our documents
+    retriever = vector_store.as_retriever()
+    
+    # create a chat model / LLM
+    chat_model = ChatOpenAI(
+        model="gpt-4o-2024-05-13", temperature=0, api_key=openai_api_key
+    )
+    
+    # create a parser to parse the output of our LLM
+    parser = StrOutputParser()
+    
+    # 💻 Create the sequence (recipe)
+    runnable_chain = (
+        # TODO: How do we chain the output of our retriever, prompt, model and model output parser so that we can get a good answer to our query?
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt_template
+        | chat_model
+        | StrOutputParser()
     )
-    runnable = prompt | model | StrOutputParser()
+
     cl.user_session.set("runnable", runnable)
 
 
 @cl.on_message
 async def on_message(message: cl.Message):
-    try:
-        logger.info('Starting application')
-        # Your main application logic here
-        runnable = cl.user_session.get("runnable")  # type: Runnable
-    
-        msg = cl.Message(content="")
-    
-        async for chunk in runnable.astream(
-            {"question": message.content},
+    logger.info('Starting application')
+    # Your main application logic here
+    runnable = cl.user_session.get("runnable")  # type: Runnable
+
+    msg = cl.Message(content="")
+
+    async with cl.Step(type="run", name="QA Assistant"):
+        
+        await msg.stream_token("OAI says: ")
+
+        async for chunk in runn.astream(
+            message.content,
             config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
         ):
             await msg.stream_token(chunk)
-    
-        await cl.Message(content=response).send()
-        logger.info('Application finished successfully')
-    except Exception as e:
-        logger.exception("Unhandled exception: %s", e)
-        
 
-# @cl.on_message
-# async def main(question):
-#     response = await call_chain_async(question.content)
-#     await cl.Message(content=response).send()
+    await msg.send()