Delete week2.py

week2.py

@@ -1,105 +0,0 @@
-import asyncio
-from dotenv import load_dotenv
-from pathlib import Path
-from ingest_data import download_data_and_create_embedding
-
-from langchain_community.vectorstores import FAISS
-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.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-
-import chainlit as cl
-
-# load env variables
-load_dotenv()
-
-# Specify the path to the file you want to check
-file_path = Path('./faiss_index/index.faiss')
-
-# 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}"),
-        ]
-    )
-    runnable = prompt | model | StrOutputParser()
-    cl.user_session.set("runnable", runnable)
-
-
-# @cl.on_message
-# async def on_message(message: cl.Message):
-#     runnable = cl.user_session.get("runnable")  # type: Runnable
-
-#     msg = cl.Message(content="")
-
-#     async for chunk in runnable.astream(
-#         {"question": message.content},
-#         config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
-#     ):
-#         await msg.stream_token(chunk)
-
-#     await msg.send() 
-
-@cl.on_message
-async def main(question):
-    response = await call_chain_async(question.content)
-    await cl.Message(content=response).send()