Spaces:
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Joshua Sundance Bailey
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Parent(s):
9578b22
python:3.11-slim-bookworm
Browse files- Dockerfile +1 -1
- README.md +16 -30
Dockerfile
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FROM python:3.11-slim-
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RUN adduser --uid 1000 --disabled-password --gecos '' appuser
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USER 1000
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FROM python:3.11-slim-bookworm
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RUN adduser --uid 1000 --disabled-password --gecos '' appuser
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USER 1000
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README.md
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@@ -46,12 +46,24 @@ This `README` was written by [Claude 2](https://www.anthropic.com/index/claude-2
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- Integrates with [LangSmith](https://smith.langchain.com) for tracing conversations
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- Allows giving feedback on assistant's responses
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- Tries reading API keys and default values from environment variables
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# Usage
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## Run on HuggingFace Spaces
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[](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)
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## With Docker (pull from Docker Hub)
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1. _Optional_: Create a `.env` file based on `.env-example`
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2. Run in terminal:
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`docker run -p 7860:7860 --env-file .env joshuasundance/langchain-streamlit-demo:latest`
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## Docker Compose
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1. Clone the repo. Navigate to cloned repo directory
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2. _Optional_: Create a `.env` file based on `.env-example`
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3. Run in terminal:
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`docker compose up --env-file env`
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4.
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5. Open http://localhost:7860 in your browser
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## Kubernetes
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1. Clone the repo. Navigate to cloned repo directory
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3. Run in terminal: `cd kubernetes && kubectl apply -f resources.yaml`
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4. Get the IP address for your new service: `kubectl get service langchain-streamlit-demo`
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# Configuration
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- Select a model from the dropdown
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- _Optional_: Create a `.env` file based on `.env-example`, or
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- Enter an API key for the relevant provider
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- Optionally enter a LangSmith API key to enable conversation tracing
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- Customize the assistant prompt and temperature
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# Code Overview
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- `langchain-streamlit-demo/app.py` - Main Streamlit app definition
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- `langchain-streamlit-demo/llm_stuff.py` - LangChain helper functions
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- `Dockerfile`, `docker-compose.yml`: Docker deployment
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- `kubernetes/`: Kubernetes deployment files
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- `.github/workflows/`: CI/CD workflows
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# Deployment
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The app is packaged as a Docker image for easy deployment. It is published to Docker Hub and Hugging Face Spaces:
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- [DockerHub](https://hub.docker.com/r/joshuasundance/langchain-streamlit-demo)
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- [HuggingFace Spaces](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)
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CI/CD workflows in `.github/workflows` handle building and publishing the image.
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# Links
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- [Streamlit](https://streamlit.io)
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- [LangChain](https://langchain.com)
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- [OpenAI](https://openai.com/)
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- [Anthropic](https://www.anthropic.com/)
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- [Anyscale Endpoints](https://endpoints.anyscale.com/)
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# TODO
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1. More customization / parameterization in sidebar
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- Integrates with [LangSmith](https://smith.langchain.com) for tracing conversations
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- Allows giving feedback on assistant's responses
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- Tries reading API keys and default values from environment variables
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- Parameters in sidebar can be customized
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# Code Overview
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- `langchain-streamlit-demo/app.py` - Main Streamlit app definition
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- `langchain-streamlit-demo/llm_stuff.py` - LangChain helper functions
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- `Dockerfile`, `docker-compose.yml`: Docker deployment
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- `kubernetes/`: Kubernetes deployment files
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- `.github/workflows/`: CI/CD workflows
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# Deployment
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`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.
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CI/CD workflows in `.github/workflows` handle building and publishing the image as well as pushing it to Hugging Face.
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## Run on HuggingFace Spaces
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[](https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo)
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## With Docker (pull from Docker Hub)
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1. _Optional_: Create a `.env` file based on `.env-example`
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2. Run in terminal:
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`docker run -p 7860:7860 --env-file .env joshuasundance/langchain-streamlit-demo:latest`
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3. Open http://localhost:7860 in your browser
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## Docker Compose (build locally)
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1. Clone the repo. Navigate to cloned repo directory
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2. _Optional_: Create a `.env` file based on `.env-example`
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3. Run in terminal:
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`docker compose up --env-file env`
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4. Open http://localhost:7860 in your browser
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## Kubernetes
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1. Clone the repo. Navigate to cloned repo directory
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3. Run in terminal: `cd kubernetes && kubectl apply -f resources.yaml`
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4. Get the IP address for your new service: `kubectl get service langchain-streamlit-demo`
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# Links
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- [Streamlit](https://streamlit.io)
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- [LangChain](https://langchain.com)
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- [OpenAI](https://openai.com/)
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- [Anthropic](https://www.anthropic.com/)
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- [Anyscale Endpoints](https://endpoints.anyscale.com/)
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