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https://github.com/ray-project/llm-applications

A comprehensive guide to building RAG-based LLM applications for production.
https://github.com/ray-project/llm-applications

anyscale fine-tuning llama2 llms machine-learning openai ray serving

Last synced: 6 days ago
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A comprehensive guide to building RAG-based LLM applications for production.

Host: GitHub
URL: https://github.com/ray-project/llm-applications
Owner: ray-project
License: cc-by-4.0
Created: 2023-08-16T04:41:26.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-08-02T00:27:10.000Z (6 months ago)
Last Synced: 2025-01-02T09:07:05.082Z (13 days ago)
Topics: anyscale, fine-tuning, llama2, llms, machine-learning, openai, ray, serving
Language: Jupyter Notebook
Homepage:
Size: 29.7 MB
Stars: 1,741
Watchers: 18
Forks: 235
Open Issues: 14
Metadata Files:
- Readme: README.md
- License: LICENSE

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jimsghstars - ray-project/llm-applications - A comprehensive guide to building RAG-based LLM applications for production. (Jupyter Notebook)
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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](https://platform.openai.com/docs/models/) to access ChatGPT models like `gpt-3.5-turbo`, `gpt-4`, etc. and [Anyscale Endpoints](https://endpoints.anyscale.com/) 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 g3.8xlarge head 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_py39 cluster environment.

Use the us-west-2 if you'd like to use the artifacts in our shared storage (source docs, vector DB dumps, etc.).

### Repository
```bash
git clone https://github.com/ray-project/llm-applications.git .
git config --global user.name
git config --global user.email
```

### 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)
```bash
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:

```bash
pip install --user -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$PWD
pre-commit install
pre-commit autoupdate
```

### Credentials
```bash
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](notebooks/rag.ipynb) interactive notebook to develop and serve our LLM application!

### Learn more
- If your team is investing heavily in developing LLM applications, [reach out](mailto:[email protected]) to us to learn more about how [Ray](https://github.com/ray-project/ray) and [Anyscale](http://anyscale.com/) can help you scale and productionize everything.
- Start serving (+fine-tuning) OSS LLMs with [Anyscale Endpoints](https://endpoints.anyscale.com/) ($1/M tokens for `Llama-3-70b`) and private endpoints available upon request (1M free tokens trial).
- Learn more about how companies like OpenAI, Netflix, Pinterest, Verizon, Instacart and others leverage Ray and Anyscale for their AI workloads at the [Ray Summit 2024](https://raysummit.anyscale.com/) this Sept 18-20 in San Francisco.