mem0/docs/integration/streamlit-mistral.mdx

---
title: '🚀 Streamlit'
description: 'Integrate with Streamlit to plug and play with any LLM'
---

In this example, we will learn how to use `mistralai/Mistral-7B-v0.1` and Embedchain together with Streamlit to build a simple RAG chatbot.

## Setup

<Accordion title="Customize using code.">
    1. Install Embedchain and Streamlit
    ```bash
    pip install embedchain
    pip install streamlit
    ```
    <Tabs>
        <Tab title="app.py">
        ```python
            import os
            from embedchain import Pipeline as 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 <source>`.\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})
            ```
        </Tab>
        <Tab title="config.yaml">
        ```yaml
            app:
            config:
                name: 'mistral-streamlit-app'

            llm:
            provider: huggingface
            config:
                model: 'mistralai/Mistral-7B-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'
        ```
        </Tab>
    </Tabs>
</Accordion>

## To run it locally,

```bash
streamlit run app.py
```