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:
- ๐ป Develop a retrieval augmented generation (RAG) based LLM application from scratch.
- ๐ Scale the major components (load, chunk, embed, index, serve, etc.) in our application.
- โ Evaluate different configurations of our application to optimize for both per-component (ex. retrieval_score) and overall performance (quality_score).
- ๐ Implement LLM hybrid routing approach to bridge the gap b/w OSS and closed LLMs.
- ๐ฆ Serve the application in a highly scalable and available manner.
- ๐ฅ Share the 1st order and 2nd order impacts LLM applications have had on our products.
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.
GitHub Codespaces
Used for initial development and configuration.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.).
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 .
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 PATH=$(python -m site --user-site)/bin:$PATH
pre-commit install
pre-commit autoupdate
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!
- Forked from https://github.com/ray-project/llm-applications.git
- Registered for Anyscale Endpoints which gave $10 free for usage