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

# langchain-streamlit-demo

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[![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

# Usage
## 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`

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

## Docker Compose
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. Run in terminal: `docker compose up`
5. 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 in terminal: `cd kubernetes && kubectl apply -f resources.yaml`
4. Get the IP address for your new service: `kubectl get service langchain-streamlit-demo`

# Configuration
- Select a model from the dropdown
- _Optional_: Create a `.env` file based on `.env-example`, or
  - Enter an API key for the relevant provider
  - Optionally enter a LangSmith API key to enable conversation tracing
- Customize the assistant prompt and temperature

# 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
The app is packaged as a Docker image for easy deployment. It is published to Docker Hub and Hugging Face Spaces:

- [DockerHub](https://hub.docker.com/r/joshuasundance/langchain-streamlit-demo)
- [HuggingFace Spaces](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)

CI/CD workflows in `.github/workflows` handle building and publishing the image.

# 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/)

# TODO
1. More customization / parameterization in sidebar