pip install -qU langchain openai tiktoken pyPDF2 chainlitconda install -c conda-forge chromadb

#import required librariesfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.vectorstores  import Chromafrom langchain.chains import RetrievalQAWithSourcesChainfrom langchain.chat_models import ChatOpenAIfrom langchain.prompts.chat import (ChatPromptTemplate,                                    SystemMessagePromptTemplate,                                    HumanMessagePromptTemplate)#import chainlit as climport PyPDF2from io import BytesIOfrom getpass import getpass#import osfrom configparser import ConfigParserenv_config =  ConfigParser()
# Retrieve the openai key from the environmental variablesdef read_config(parser: ConfigParser, location: str) -> None:    assert parser.read(location), f"Could not read config {location}"#CONFIG_FILE = os.path.join("./env", "env.conf")read_config(env_config, CONFIG_FILE)api_key = env_config.get("openai", "api_key").strip()
#os.environ["OPENAI_API_KEY"] = api_key
# Chunking the texttext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=100)##system templatesystem_template = """Use the following pieces of context to answer the user's question.If you don't know the answer, just say that you don't know, don't try to make up an answer.ALWAYS return a "SOURCES" part in your answer.The "SOURCES" part should be a reference to the source of the document from which you got your answer.


Begin!----------------{summaries}"""messages = [SystemMessagePromptTemplate.from_template(system_template),HumanMessagePromptTemplate.from_template("{question}"),]prompt = ChatPromptTemplate.from_messages(messages)chain_type_kwargs = {"prompt": prompt}
#Decorator to react to the user websocket connection event. @cl.on_chat_startasync def init():    files = None
    # Wait for the user to upload a PDF file    while files is None:        files = await cl.AskFileMessage(            content="Please upload a PDF file to begin!",            accept=["application/pdf"],        ).send()
    file = files[0]
    msg = cl.Message(content=f"Processing `{file.name}`...")    await msg.send()
    # Read the PDF file    pdf_stream = BytesIO(file.content)    pdf = PyPDF2.PdfReader(pdf_stream)    pdf_text = ""    for page in pdf.pages:        pdf_text += page.extract_text()
    # Split the text into chunks    texts = text_splitter.split_text(pdf_text)
    # Create metadata for each chunk    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
    # Create a Chroma vector store    embeddings = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"))    docsearch = await cl.make_async(Chroma.from_texts)(        texts, embeddings, metadatas=metadatas    )
    # Create a chain that uses the Chroma vector store    chain = RetrievalQAWithSourcesChain.from_chain_type(        ChatOpenAI(temperature=0,                    openai_api_key=os.environ["OPENAI_API_KEY"]),        chain_type="stuff",        retriever=docsearch.as_retriever(),    )
    # Save the metadata and texts in the user session    cl.user_session.set("metadatas", metadatas)    cl.user_session.set("texts", texts)
    # Let the user know that the system is ready    msg.content = f"`{file.name}` processed. You can now ask questions!"    await msg.update()
    cl.user_session.set("chain", chain)
# react to messages coming from the UI@cl.on_messageasync def process_response(res):    chain = cl.user_session.get("chain")  # type: RetrievalQAWithSourcesChain    cb = cl.AsyncLangchainCallbackHandler(        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"])    cb.answer_reached = True    res = await chain.acall(res, callbacks=[cb])    print(f"response: {res}")    answer = res["answer"]    sources = res["sources"].strip()    source_elements = []
    # Get the metadata and texts from the user session    metadatas = cl.user_session.get("metadatas")    all_sources = [m["source"] for m in metadatas]    texts = cl.user_session.get("texts")
    if sources:        found_sources = []
        # Add the sources to the message        for source in sources.split(","):            source_name = source.strip().replace(".", "")            # Get the index of the source            try:                index = all_sources.index(source_name)            except ValueError:                continue            text = texts[index]            found_sources.append(source_name)            # Create the text element referenced in the message            source_elements.append(cl.Text(content=text, name=source_name))
        if found_sources:            answer += f"\nSources: {', '.join(found_sources)}"        else:            answer += "\nNo sources found"
    if cb.has_streamed_final_answer:        cb.final_stream.elements = source_elements        await cb.final_stream.update()    else:        await cl.Message(content=answer, elements=source_elements).send()

chainlit run of the python script>