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import chainlit as cl |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.document_loaders import BSHTMLLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import FAISS |
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from langchain.chains import RetrievalQA |
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from langchain.chat_models import ChatOpenAI |
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from langchain.prompts import ChatPromptTemplate |
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import chainlit as cl |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) |
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@cl.author_rename |
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def rename(orig_author: str): |
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rename_dict = {"RetrievalQA": "Coho Blogs", "Chatbot": "Coho Assistant"} |
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return rename_dict.get(orig_author, orig_author) |
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@cl.on_chat_start |
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async def init(): |
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msg = cl.Message(content=f"Building Index...") |
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await msg.send() |
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core_embeddings_model = OpenAIEmbeddings() |
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new_db = FAISS.load_local("faiss_index", core_embeddings_model) |
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chain = RetrievalQA.from_chain_type( |
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ChatOpenAI(model="gpt-3.5-turbo", temperature=0, streaming=True), |
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chain_type="stuff", |
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return_source_documents=True, |
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retriever=new_db.as_retriever(), |
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) |
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msg.content = f"Index built!" |
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await msg.send() |
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cl.user_session.set("chain", chain) |
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@cl.on_message |
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async def main(message): |
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chain = cl.user_session.get("chain") |
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cb = cl.AsyncLangchainCallbackHandler( |
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stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"] |
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) |
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cb.answer_reached = True |
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res = await chain.acall(message, callbacks=[cb]) |
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answer = res["result"] |
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if cb.has_streamed_final_answer: |
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await cb.final_stream.update() |
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else: |
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await cl.Message(content=answer).send() |
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