README.md

---
title: langchain-streamlit-demo
emoji: 🦜
colorFrom: green
colorTo: red
sdk: docker
app_port: 7860
pinned: true
tags: [langchain, streamlit, docker]
---

# langchain-streamlit-demo

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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[![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?&logo=docker&logoColor=white)](https://hub.docker.com/r/joshuasundance/langchain-streamlit-demo)
[![Docker Image Size (tag)](https://img.shields.io/docker/image-size/joshuasundance/langchain-streamlit-demo/latest)](https://hub.docker.com/r/joshuasundance/langchain-streamlit-demo)
[![Open HuggingFace Space](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)


This project shows how to build a simple chatbot UI with [Streamlit](https://streamlit.io) and [LangChain](https://langchain.com).

This `README` was written by [Claude 2](https://www.anthropic.com/index/claude-2), an LLM from [Anthropic](https://www.anthropic.com/).

# Features
- Chat interface for talking to AI assistant
- Supports models from
  - [OpenAI](https://openai.com/)
    - `gpt-3.5-turbo`
    - `gpt-4`
  - [Anthropic](https://www.anthropic.com/)
    - `claude-instant-v1`
    - `claude-2`
  - [Anyscale Endpoints](https://endpoints.anyscale.com/)
    - `meta-llama/Llama-2-7b-chat-hf`
    - `meta-llama/Llama-2-13b-chat-hf`
    - `meta-llama/Llama-2-70b-chat-hf`
- Streaming output of assistant responses
- Leverages LangChain for dialogue and memory management
- Integrates with [LangSmith](https://smith.langchain.com) for tracing conversations
- Allows giving feedback on assistant's responses
- Tries reading API keys and default values from environment variables
- Parameters in sidebar can be customized

# Code Overview
- `langchain-streamlit-demo/app.py` - Main Streamlit app definition
- `langchain-streamlit-demo/llm_stuff.py` - LangChain helper functions
- `Dockerfile`, `docker-compose.yml`: Docker deployment
- `kubernetes/`: Kubernetes deployment files
- `.github/workflows/`: CI/CD workflows

# Deployment
`langchain-streamlit-demo` is deployed as a [Docker image](https://hub.docker.com/r/joshuasundance/langchain-streamlit-demo) based on the [`python:3.11-slim-bookworm`](https://github.com/docker-library/python/blob/81b6e5f0643965618d633cd6b811bf0879dee360/3.11/slim-bookworm/Dockerfile) image.
CI/CD workflows in `.github/workflows` handle building and publishing the image as well as pushing it to Hugging Face.

## Run on HuggingFace Spaces
[![Open HuggingFace Space](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)

## With Docker (pull from Docker Hub)

1. _Optional_: Create a `.env` file based on `.env-example`
2. Run in terminal:

`docker run -p 7860:7860 joshuasundance/langchain-streamlit-demo:latest`

or

`docker run -p 7860:7860  --env-file .env joshuasundance/langchain-streamlit-demo:latest`

3. Open http://localhost:7860 in your browser

## Docker Compose (build locally)
1. Clone the repo. Navigate to cloned repo directory
2. _Optional_: Create a `.env` file based on `.env-example`
3. Run in terminal:

`docker compose up`

or

`docker compose up --env-file .env`

4. Open http://localhost:7860 in your browser

## Kubernetes
1. Clone the repo. Navigate to cloned repo directory
2. Create a `.env` file based on `.env-example`
3. Run bash script: `/bin/bash ./kubernetes/deploy.sh`
4. Get the IP address for your new service: `kubectl get service langchain-streamlit-demo`

# Links
- [Streamlit](https://streamlit.io)
- [LangChain](https://langchain.com)
- [LangSmith](https://smith.langchain.com)
- [OpenAI](https://openai.com/)
- [Anthropic](https://www.anthropic.com/)
- [Anyscale Endpoints](https://endpoints.anyscale.com/)