Create app.py

app.py

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+
+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.storage import LocalFileStore
+
+import chainlit as cl  
+from chainlit.playground.providers import ChatOpenAI  
+from dotenv import load_dotenv
+load_dotenv()
+
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.storage import LocalFileStore
+from langchain.vectorstores import Pinecone
+
+from operator import itemgetter
+import pinecone
+
+# =============================================================================
+# Retrieval Chain
+# =============================================================================
+def load_llm():
+  llm = ChatOpenAI(
+        model='gpt-3.5-turbo',
+        temperature=0.0,
+        openai_api_key='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'
+    )
+  return llm
+
+
+def load_vectorstore():
+
+    pinecone.init(
+        api_key='1ac2dff1-ef00-4f27-9b83-b1e56bfa461d',
+        environment='gcp-starter'
+    )
+
+    index = pinecone.Index("youtube-index")
+    store = LocalFileStore("./cache/")
+    core_embeddings_model = OpenAIEmbeddings(openai_api_key='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')
+
+    embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings_model,
+        store,
+        namespace=core_embeddings_model.model
+    )
+
+    text_field = "text"
+
+    vectorstore = Pinecone(
+        index,
+        embedder,  
+        text_field
+    )
+
+    return vectorstore
+
+
+def qa_chain():
+
+    vectorstore = load_vectorstore()
+    llm = load_llm()
+    retriever = vectorstore.as_retriever()
+
+    template = """You are a helpful assistant that answers questions on the provided context, if its not answered within the context respond with "This query is not directly mentioned by David Bradford in his videos" then respond to the best of your ability. 
+                  Additionally, the context includes a specific integer formatted as <int>, representing a timestamp. 
+                  In your response, include this integer as a citation, formatted as a YouTube video link: "https://www.youtube.com/watch?v=[video_id]&t=<int>s" and have the hyperlink text be the title of video.
+
+
+    ### CONTEXT
+    {context}
+
+    ### QUESTION
+    {question}
+    """
+
+    prompt = ChatPromptTemplate.from_template(template)
+
+    retrieval_augmented_qa_chain = (
+        {"context": itemgetter("question") | retriever,
+        "question": itemgetter("question")
+        }
+        | RunnablePassthrough.assign(
+            context=itemgetter("context")
+        )
+        | {
+            "response": prompt  | llm,
+            "context": itemgetter("context"),
+        }
+    )
+
+    return retrieval_augmented_qa_chain
+
+# =============================================================================
+# Chainlit
+# =============================================================================
+@cl.on_chat_start
+async def on_chat_start():
+    chain = qa_chain()
+    cl.user_session.set("chain", chain)
+    msg=cl.Message(content="What is your question for David Bradford?")
+    await msg.send()
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    chain=cl.user_session.get("chain")
+    res = chain.invoke({"question" : message.content})
+
+    answer = res['response'].content
+    await cl.Message(content=answer).send()
+    
+    #Use to show all source documents used
+    '''
+    source_documents = set()
+
+    for document in res['context']:
+        source_url = document.metadata['source_document']
+        source_documents.add(source_url)
+
+    combined_message = answer + "\n\nSource Documents:\n" + "\n".join(source_documents)
+
+    await cl.Message(content=combined_message).send()
+    '''