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

langchain-streamlit-demo

This project shows how to build a simple chatbot UI with Streamlit and LangChain.

This README was originally written by Claude 2, an LLM from Anthropic.

Features

• Chat interface for talking to AI assistant
• Supports models from
  • OpenAI
    • gpt-3.5-turbo
    • gpt-4
  • Anthropic
    • claude-instant-v1
    • claude-2
  • Anyscale Endpoints
    • meta-llama/Llama-2-7b-chat-hf
    • meta-llama/Llama-2-13b-chat-hf
    • meta-llama/Llama-2-70b-chat-hf
    • codellama/CodeLlama-34b-Instruct-hf
    • mistralai/Mistral-7B-Instruct-v0.1
  • Azure OpenAI Service
    • [configurable]
• Streaming output of assistant responses
• Leverages LangChain for dialogue and memory management
• Integrates with LangSmith 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
• Includes various forms of document chat
  • Question/Answer Pair Generation
  • Summarization
  • Standard retrieval chains

Deployment

langchain-streamlit-demo is deployed as a Docker image based on the python:3.11-slim-bookworm 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

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

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
• LangChain
• LangSmith
• OpenAI
• Anthropic
• Anyscale Endpoints
• Azure OpenAI Service