--- title: '🚀 Streamlit' description: 'Integrate with Streamlit to plug and play with any LLM' --- In this example, we will learn how to use `mistralai/Mixtral-8x7B-Instruct-v0.1` and Embedchain together with Streamlit to build a simple RAG chatbot. ![Streamlit + Embedchain Demo](https://github.com/embedchain/embedchain/assets/73601258/052f7378-797c-41cf-ac81-f004d0d44dd1) ## Setup Install Embedchain and Streamlit. ```bash pip install embedchain streamlit ``` ```python import os from embedchain import App import streamlit as st with st.sidebar: huggingface_access_token = st.text_input("Hugging face Token", key="chatbot_api_key", type="password") "[Get Hugging Face Access Token](https://huggingface.co/settings/tokens)" "[View the source code](https://github.com/embedchain/examples/mistral-streamlit)" st.title("💬 Chatbot") st.caption("🚀 An Embedchain app powered by Mistral!") if "messages" not in st.session_state: st.session_state.messages = [ { "role": "assistant", "content": """ Hi! I'm a chatbot. I can answer questions and learn new things!\n Ask me anything and if you want me to learn something do `/add `.\n I can learn mostly everything. :) """, } ] for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input("Ask me anything!"): if not st.session_state.chatbot_api_key: st.error("Please enter your Hugging Face Access Token") st.stop() os.environ["HUGGINGFACE_ACCESS_TOKEN"] = st.session_state.chatbot_api_key app = App.from_config(config_path="config.yaml") if prompt.startswith("/add"): with st.chat_message("user"): st.markdown(prompt) st.session_state.messages.append({"role": "user", "content": prompt}) prompt = prompt.replace("/add", "").strip() with st.chat_message("assistant"): message_placeholder = st.empty() message_placeholder.markdown("Adding to knowledge base...") app.add(prompt) message_placeholder.markdown(f"Added {prompt} to knowledge base!") st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"}) st.stop() with st.chat_message("user"): st.markdown(prompt) st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("assistant"): msg_placeholder = st.empty() msg_placeholder.markdown("Thinking...") full_response = "" for response in app.chat(prompt): msg_placeholder.empty() full_response += response msg_placeholder.markdown(full_response) st.session_state.messages.append({"role": "assistant", "content": full_response}) ``` ```yaml app: config: name: 'mistral-streamlit-app' llm: provider: huggingface config: model: 'mistralai/Mixtral-8x7B-Instruct-v0.1' temperature: 0.1 max_tokens: 250 top_p: 0.1 stream: true embedder: provider: huggingface config: model: 'sentence-transformers/all-mpnet-base-v2' ``` ## To run it locally, ```bash streamlit run app.py ```