Upload app.py

app.py

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+import chainlit as cl 
+from langchain_together import ChatTogether, TogetherEmbeddings
+from langchain_core.runnables import RunnableSequence, RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.document_loaders import YoutubeLoader
+from typing import List
+import langchain_core
+from langchain_community.vectorstores import FAISS
+from langchain.retrievers.ensemble import EnsembleRetriever
+from langchain_community.retrievers import BM25Retriever
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+def create_youtube_transcription(youtube_url: str):
+    loader = YoutubeLoader.from_youtube_url(
+    youtube_url, add_video_info=False
+    )
+    youtube_docs = loader.load()
+    return youtube_docs
+
+def create_text_splitter(docs: List[langchain_core.documents.Document]):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    docs = text_splitter.split_documents(docs)
+    return docs
+
+def create_vector_store(docs: List[langchain_core.documents.Document]):
+    embedding = cl.user_session.get("embedding")
+    vector_db = FAISS.from_documents(docs, embedding)
+    return vector_db
+
+def create_bm25_vector_store(docs: List[langchain_core.documents.Document]):
+    bm25 = BM25Retriever.from_documents(docs)
+    return bm25
+
+def create_ensemble_retriever(vector_db:FAISS, bm25:BM25Retriever):
+    ensemble_retreiver = EnsembleRetriever(retrievers=[vector_db.as_retriever(), bm25], weights=[.3, .7])
+    return ensemble_retreiver
+
+@cl.on_chat_start
+async def start():
+    await cl.Message(content="my name is josh!").send()
+    try: 
+        llm = ChatTogether(model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo")
+        await cl.Message(content=f"model is successfully loaded").send()
+        cl.user_session.set("llm", llm)
+        embedding = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
+        cl.user_session.set("embedding", embedding)
+        await cl.Message(content="embedding model loaded").send()
+        youtube_link = await cl.AskUserMessage("Please provide the YouTube video link").send()
+        youtube_docs = create_youtube_transcription(youtube_link['output'])
+        split_docs = create_text_splitter(youtube_docs)
+        vector_db = create_vector_store(split_docs)
+        bm25 = create_bm25_vector_store(split_docs)
+        ensemble_retriever = create_ensemble_retriever(vector_db, bm25)
+        cl.user_session.set("ensemble_retriever", ensemble_retriever)
+        transcription = youtube_docs[0].page_content
+        await cl.Message(content=f"youtube docs: {transcription}").send()
+        cl.user_session.set("transcription", transcription)
+    except Exception as e:
+        await cl.Message(content=f"failed to load model: {e}").send()
+
+
+@cl.on_message
+async def message(message: cl.Message):
+    prompt_template = ChatPromptTemplate.from_template(template="""
+    You are a helpful assistant that can answer questions about the following video. Here is the appropriate chunks of context: {context}.
+    Answer the question: {question} but do not use any information outside of the video. Site the source or information you used to answer the question
+    """)
+    llm = cl.user_session.get("llm")
+    vector_db = cl.user_session.get("vector_db")
+    transcription = cl.user_session.get("transcription")
+    ensemble_retriever = cl.user_session.get("ensemble_retriever")
+    rag_chain = RunnableSequence({"context": ensemble_retriever, "question": RunnablePassthrough()}, prompt_template | llm)
+    response = rag_chain.invoke(message.content)
+    await cl.Message(content=response.content).send()
+