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