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| import streamlit as st | |
| import openai | |
| from llama_index.llms.openai import OpenAI | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings | |
| st.set_page_config(page_title="Chat with the Streamlit docs, powered by LlamaIndex", page_icon="🦙", layout="centered", initial_sidebar_state="auto", menu_items=None) | |
| openai.api_key = st.secrets.openai_key | |
| st.title("Chat with the Streamlit docs, powered by LlamaIndex 💬🦙") | |
| st.info("Check out the full tutorial to build this app in our [blog post](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/)", icon="📃") | |
| if "messages" not in st.session_state.keys(): # Initialize the chat messages history | |
| st.session_state.messages = [ | |
| { | |
| "role": "assistant", | |
| "content": "Ask me a question about Streamlit's open-source Python library!", | |
| } | |
| ] | |
| def load_data(): | |
| reader = SimpleDirectoryReader(input_dir="./data", recursive=True) | |
| docs = reader.load_data() | |
| Settings.llm = OpenAI( | |
| model="gpt-3.5-turbo", | |
| temperature=0.2, | |
| system_prompt="""You are an expert on | |
| the Streamlit Python library and your | |
| job is to answer technical questions. | |
| Assume that all questions are related | |
| to the Streamlit Python library. Keep | |
| your answers technical and based on | |
| facts – do not hallucinate features.""", | |
| ) | |
| index = VectorStoreIndex.from_documents(docs) | |
| return index | |
| index = load_data() | |
| if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine | |
| st.session_state.chat_engine = index.as_chat_engine( | |
| chat_mode="condense_question", verbose=True, streaming=True | |
| ) | |
| if prompt := st.chat_input( | |
| "Ask a question" | |
| ): # Prompt for user input and save to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| for message in st.session_state.messages: # Write message history to UI | |
| 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"): | |
| response_stream = st.session_state.chat_engine.stream_chat(prompt) | |
| st.write_stream(response_stream.response_gen) | |
| message = {"role": "assistant", "content": response_stream.response} | |
| # Add response to message history | |
| st.session_state.messages.append(message) |