llm-applications
Comprehensive guide to building RAG-based LLM applications for production
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Decision brief
The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray.
Good fit when
- You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability.
- Your team has experience or interest in working with Jupyter Notebooks as the primary format for tutorials and documentation.
Avoid when
- If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations.
- When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
git clone https://github.com/ray-project/llm-applicationsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Offers instructions and resources focussed on developing large language model applications using Ray for production environments.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Aug 24, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 24, 2026)
OPENAI_API_BASE="https://api.openai.com/v1"Source link
Source: README excerpt (regex_v1, Aug 24, 2026)
pip install --user -r requirements.txtSource link
Source: README excerpt (regex_v1, Aug 24, 2026)
We'll be using [OpenAI](https://platform.openai.com/docs/models/) to access ChatGPT models like `gpt-3.5-turbo`, `gpt-4`, etc. and [Anyscale Endpoints](https://endSource link
Tags
README
LLM Applications
A comprehensive guide to building RAG-based LLM applications for production.
- Blog post: https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
- GitHub repository: https://github.com/ray-project/llm-applications
- Interactive notebook: https://github.com/ray-project/llm-applications/blob/main/notebooks/rag.ipynb
- Anyscale Endpoints: https://endpoints.anyscale.com/
- Ray documentation: https://docs.ray.io/
In this guide, we will learn how to:
- 💻 Develop a retrieval augmented generation (RAG) based LLM application from scratch.
- 🚀 Scale the major components (load, chunk, embed, index, serve, etc.) in our application.
- ✅ Evaluate different configurations of our application to optimize for both per-component (ex. retrieval_score) and overall performance (quality_score).
- 🔀 Implement LLM hybrid routing approach to bridge the gap b/w OSS and closed LLMs.
- 📦 Serve the application in a highly scalable and available manner.
- 💥 Share the 1st order and 2nd order impacts LLM applications have had on our products.
Setup
API keys
We'll be using OpenAI to access ChatGPT models like gpt-3.5-turbo, gpt-4, etc. and Anyscale Endpoints to access OSS LLMs like Llama-2-70b. Be sure to create your accounts for both and have your credentials ready.
Compute
Local
You could run this on your local laptop but a we highly recommend using a setup with access to GPUs. You can set this up on your own or on [Anyscale](http://anyscale.com/).Anyscale
- Start a new Anyscale workspace on staging using an
g3.8xlargehead node, which has 2 GPUs and 32 CPUs. We can also add GPU worker nodes to run the workloads faster. If you're not on Anyscale, you can configure a similar instance on your cloud. - Use the
default_cluster_env_2.6.2_py39cluster environment. - Use the
us-west-2if you'd like to use the artifacts in our shared storage (source docs, vector DB dumps, etc.).
Repository
git clone https://github.com/ray-project/llm-applications.git .
git config --global user.name <GITHUB-USERNAME>
git config --global user.email <EMAIL-ADDRESS>
Data
Our data is already ready at /efs/shared_storage/goku/docs.ray.io/en/master/ (on Staging, us-east-1) but if you wanted to load it yourself, run this bash command (change /desired/output/directory, but make sure it's on the shared storage,
so that it's accessible to the workers)
git clone https://github.com/ray-project/llm-applications.git .
Environment
Then set up the environment correctly by specifying the values in your .env file,
and installing the dependencies:
pip install --user -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$PWD
pre-commit install
pre-commit autoupdate
Credentials
touch .env
# Add environment variables to .env
OPENAI_API_BASE="https://api.openai.com/v1"
OPENAI_API_KEY="" # https://platform.openai.com/account/api-keys
ANYSCALE_API_BASE="https://api.endpoints.anyscale.com/v1"
ANYSCALE_API_KEY="" # https://app.endpoints.anyscale.com/credentials
DB_CONNECTION_STRING="dbname=postgres user=postgres host=localhost password=postgres"
source .env
Now we're ready to go through the rag.ipynb interactive notebook to develop and serve our LLM application!
Learn more
- If y
For agents
This page has a .md twin and JSON over the API.