Delete app.py

app.py

@@ -1,132 +0,0 @@
-import os
-import chainlit as cl
-from dotenv import load_dotenv
-from operator import itemgetter
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain_community.document_loaders import TextLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEndpointEmbeddings
-from langchain_core.prompts import PromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.runnable.config import RunnableConfig
-
-# GLOBAL SCOPE - ENTIRE APPLICATION HAS ACCESS TO VALUES SET IN THIS SCOPE #
-# ---- ENV VARIABLES ---- # 
-"""
-This function will load our environment file (.env) if it is present.
-
-NOTE: Make sure that .env is in your .gitignore file - it is by default, but please ensure it remains there.
-"""
-load_dotenv()
-
-"""
-We will load our environment variables here.
-"""
-HF_LLM_ENDPOINT = os.environ["HF_LLM_ENDPOINT"]
-HF_EMBED_ENDPOINT = os.environ["HF_EMBED_ENDPOINT"]
-HF_TOKEN = os.environ["HF_TOKEN"]
-
-# ---- GLOBAL DECLARATIONS ---- #
-
-# -- RETRIEVAL -- #
-"""
-1. Load Documents from Text File
-2. Split Documents into Chunks
-3. Load HuggingFace Embeddings (remember to use the URL we set above)
-4. Index Files if they do not exist, otherwise load the vectorstore
-"""
-### 1. CREATE TEXT LOADER AND LOAD DOCUMENTS
-### NOTE: PAY ATTENTION TO THE PATH THEY ARE IN. 
-text_loader = 
-documents = 
-
-### 2. CREATE TEXT SPLITTER AND SPLIT DOCUMENTS
-text_splitter = 
-split_documents = 
-
-### 3. LOAD HUGGINGFACE EMBEDDINGS
-hf_embeddings = 
-
-if os.path.exists("./data/vectorstore"):
-    vectorstore = FAISS.load_local(
-        "./data/vectorstore", 
-        hf_embeddings, 
-        allow_dangerous_deserialization=True # this is necessary to load the vectorstore from disk as it's stored as a `.pkl` file.
-    )
-    hf_retriever = vectorstore.as_retriever()
-    print("Loaded Vectorstore")
-else:
-    print("Indexing Files")
-    os.makedirs("./data/vectorstore", exist_ok=True)
-    ### 4. INDEX FILES
-    ### NOTE: REMEMBER TO BATCH THE DOCUMENTS WITH MAXIMUM BATCH SIZE = 32
-
-hf_retriever = vectorstore.as_retriever()
-
-# -- AUGMENTED -- #
-"""
-1. Define a String Template
-2. Create a Prompt Template from the String Template
-"""
-### 1. DEFINE STRING TEMPLATE
-RAG_PROMPT_TEMPLATE = 
-
-### 2. CREATE PROMPT TEMPLATE
-rag_prompt =
-
-# -- GENERATION -- #
-"""
-1. Create a HuggingFaceEndpoint for the LLM
-"""
-### 1. CREATE HUGGINGFACE ENDPOINT FOR LLM
-hf_llm = 
-
-@cl.author_rename
-def rename(original_author: str):
-    """
-    This function can be used to rename the 'author' of a message. 
-
-    In this case, we're overriding the 'Assistant' author to be 'Paul Graham Essay Bot'.
-    """
-    rename_dict = {
-        "Assistant" : "Paul Graham Essay Bot"
-    }
-    return rename_dict.get(original_author, original_author)
-
-@cl.on_chat_start
-async def start_chat():
-    """
-    This function will be called at the start of every user session. 
-
-    We will build our LCEL RAG chain here, and store it in the user session. 
-
-    The user session is a dictionary that is unique to each user session, and is stored in the memory of the server.
-    """
-
-    ### BUILD LCEL RAG CHAIN THAT ONLY RETURNS TEXT
-    lcel_rag_chain = 
-
-    cl.user_session.set("lcel_rag_chain", lcel_rag_chain)
-
-@cl.on_message  
-async def main(message: cl.Message):
-    """
-    This function will be called every time a message is recieved from a session.
-
-    We will use the LCEL RAG chain to generate a response to the user query.
-
-    The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
-    """
-    lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
-
-    msg = cl.Message(content="")
-
-    async for chunk in lcel_rag_chain.astream(
-        {"query": message.content},
-        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
-    ):
-        await msg.stream_token(chunk)
-
-    await msg.send()