Update app.py

app.py

@@ -14,97 +14,6 @@ from langchain.prompts.chat import (
 )
 import chainlit as cl
 
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+import build_langchain_vector_store
 
-system_template = """
-Use the following pieces of context to answer the user's question.
-Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-You can make inferences based on the context as long as it still faithfully represents the feedback.
-
-Example of your response should be:
-
-```
-The answer is foo
-```
-
-Begin!
-----------------
-{context}"""
-
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-prompt = ChatPromptTemplate(messages=messages)
-chain_type_kwargs = {"prompt": prompt}
-
-@cl.author_rename
-def rename(orig_author: str):
-    rename_dict = {"RetrievalQA": "Consulting The Kens"}
-    return rename_dict.get(orig_author, orig_author)
-
-@cl.on_chat_start
-async def init():
-    msg = cl.Message(content=f"Building Index...")
-    await msg.send()
-
-    # build FAISS index from csv
-    loader = CSVLoader(file_path="./data/barbie.csv", source_column="Review_Url")
-    data = loader.load()
-    documents = text_splitter.transform_documents(data)
-    store = LocalFileStore("./cache/")
-    core_embeddings_model = OpenAIEmbeddings()
-    embedder = CacheBackedEmbeddings.from_bytes_store(
-        core_embeddings_model, store, namespace=core_embeddings_model.model
-    )
-    # make async docsearch
-    docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
-
-    chain = RetrievalQA.from_chain_type(
-        ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
-        chain_type="stuff",
-        return_source_documents=True,
-        retriever=docsearch.as_retriever(),
-        chain_type_kwargs = {"prompt": prompt}
-    )
-
-    msg.content = f"Index built!"
-    await msg.send()
-
-    cl.user_session.set("chain", chain)
-
-
-@cl.on_message
-async def main(message):
-    chain = cl.user_session.get("chain")
-    cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
-    )
-    cb.answer_reached = True
-    res = await chain.acall(message, callbacks=[cb], )
-
-    answer = res["result"]
-    source_elements = []
-    visited_sources = set()
-
-    # Get the documents from the user session
-    docs = res["source_documents"]
-    metadatas = [doc.metadata for doc in docs]
-    all_sources = [m["source"] for m in metadatas]
-
-    for source in all_sources:
-        if source in visited_sources:
-            continue
-        visited_sources.add(source)
-        # Create the text element referenced in the message
-        source_elements.append(
-            cl.Text(content="https://www.imdb.com" + source, name="Review URL")
-        )
-
-    if source_elements:
-        answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
-    else:
-        answer += "\nNo sources found"
-
-    await cl.Message(content=answer, elements=source_elements).send()
+build_langchain_vector_store.py