Streamlit + langchain 实现RAG问答机器人

import os

os.environ["OPENAI_API_KEY"] = ''
os.environ["OPENAI_API_BASE"] = ''

import streamlit as st
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model = 'text-embedding-ada-002'
)
llm = OpenAI(
    model_name = 'gpt-3.5-turbo'
)

st.set_page_config(page_title="Chat", page_icon="", layout="centered", initial_sidebar_state="auto", menu_items=None)
# openai.api_key = st.secrets.openai_key
st.title("Chat with AI")

# function for writing uploaded file in temp
def write_text_file(content, file_path):
    try:
        with open(file_path, 'w') as file:
            file.write(content)
        return True
    except Exception as e:
        print(f"Error occurred while writing the file: {e}")
        return False
    

uploaded_file = st.file_uploader("Upload an article", type="txt")
if uploaded_file is not None:
    content = uploaded_file.read().decode('utf-8')
    # st.write(content)
    file_path = "temp/file.txt"
    write_text_file(content, file_path)   
    
    loader = TextLoader(file_path)
    docs = loader.load()    
    text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
    texts = text_splitter.split_documents(docs)
    db = Chroma.from_documents(texts, embeddings)    
    st.success("File Loaded Successfully!!")
        
if "messages" not in st.session_state.keys(): # Initialize the chat messages history
    st.session_state.messages = [
        {"role": "assistant", "content": "Ask me anything!"}
    ]


if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
        st.session_state.chat_engine = None

if question := st.chat_input("Your question"): # Prompt for user input and save to chat history
    st.session_state.messages.append({"role": "user", "content": question})

for message in st.session_state.messages: # Display the prior chat messages
    with st.chat_message(message["role"]):
        st.write(message["content"])

# If last message is not from assistant, generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            # response = st.session_state.chat_engine.chat(prompt)
            similar_doc = db.similarity_search(question, k=1)
            context = similar_doc[0].page_content

            # set prompt template
            prompt_template = """
Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

{context}

Question: {question}
Answer:
"""
            prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
            query_llm = LLMChain(llm=llm, prompt=prompt)
            response = query_llm.run({"context": context, "question": question})
            st.write(response)
            message = {"role": "assistant", "content": response}
            st.session_state.messages.append(message) # Add response to message history
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