Welcome to a transformative journey in building Retrieval Augmented Generation (RAG)-based applications using Language Learning Models (LLMs). In this guide, we will cover everything you need to know about developing your own RAG-based LLM application from scratch, scaling its components, and optimizing its performance for production.
What You’ll Learn
- Developing a RAG-based LLM application from the ground up
- Scaling critical components such as load, chunk, embed, index, and serve
- Evaluating configurations to maximize performance
- Implementing LLM hybrid routing
- Serving your application in a scalable and highly available way
- Understanding the impacts of LLM applications on your products
Setup Requirements
Before diving into development, ensure you have your APIs set up. You will need access to:
- OpenAI for ChatGPT models like gpt-3.5-turbo and gpt-4.
- Anyscale Endpoints for OSS LLMs like Llama-2-70b.
Computing Environment
For optimal performance, while you can run the application on your laptop, a GPU-enabled setup is highly recommended. Here’s how to proceed:
- Create an Anyscale workspace using a
g3.8xlargehead node with 2 GPUs and 32 CPUs. - Optionally, add GPU worker nodes for faster processing.
- If not using Anyscale, replicate a similar cloud instance on your preferred service.
Repository Setup
First, clone the GitHub repository. Open your terminal and run:
bash
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 Preparation
Your data is located at efsshared_storagegokudocs.ray.io/en/master. To load it manually, execute the following command (ensure your output directory is accessible to workers):
bash
git clone https://github.com/ray-project/llm-applications.git .
Environment Configuration
Set up your environment by specifying the necessary values in the .env file and installing dependencies:
bash
pip install --user -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$PWD
pre-commit install
pre-commit autoupdate
Credentials Setup
Create a .env file and populate it with your credentials as follows:
bash
touch .env
# Add environment variables to .env
OPENAI_API_BASE=https://api.openai.com/v1
OPENAI_API_KEY=
ANYSCALE_API_BASE=https://api.endpoints.anyscale.com/v1
ANYSCALE_API_KEY=
DB_CONNECTION_STRING=dbname=postgres user=postgres host=localhost password=postgres
source .env
Getting Started with the Interactive Notebook
You’re all set to move on! Open the rag.ipynb interactive notebook to develop and serve your LLM application.
Troubleshooting
If you encounter any issues while setting up or running your application, consider the following troubleshooting tips:
- Double-check your API keys and ensure they are correctly entered in the .env file.
- Verify that the package dependencies are installed without errors.
- Ensure your computing resources (like GPUs) are correctly allocated and available.
- Consult the Ray documentation for detailed explanations on common issues.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.