Llama 3 RAG Demo with DSPy Optimization, Ollama, and Weaviate!

Step-by-Step Tutorial: Building a RAG System with Llama 3, DSPy Optimization, Ollama, and Weaviate

Introduction:

1. The video discusses the release of Llama 3, a large language model, and its capabilities.
2. It introduces the concept of building a Retrieval Augmented Generation (RAG) system using Llama 3 with DSPy optimization, Ollama, and Weaviate.

Setting Up the Environment:

1. Connect to DSPy locally and pass in the Llama 3 model.
2. Connect to Weaviate and set the defaults in DSPy to use LM and the retrieval model.

Data Preparation:

1. Prepare a dataset for the RAG system, such as questions and answers synthesized from a specific dataset.
2. Separate the dataset into training, development, and test sets for optimization and evaluation purposes.

Building the RAG Program:

1. Define the RAG program structure with modules like the retrieval engine and generate answer prompt.
2. In the forward path, use the question to retrieve context and generate an answer based on the context and question.
3. Create multiple prompts and integrate tools like retrieval engines or calculators into the RAG program.

Optimizing the Prompt:

1. Use DSPy tools like bootstrap few-shot to experiment with different prompts and paraphrasings.
2. Assess the performance of each prompt and paraphrasing to propose an optimized prompt for the RAG system.

Testing the Optimized Prompt:

1. Implement the optimized prompt, such as "Given the provided context, your task is to understand the content and accurately answer the question..."
2. Evaluate the performance of the optimized prompt in the RAG system.

Conclusion:

1. Understand the importance of task communication in optimizing prompts for large language models like Llama 3.
2. Stay updated with AI developments and participate in events like the Ai and Cohere featuring Omar Kab, the lead author of DSPy in San Francisco.

By following these steps, you can build a RAG system using Llama 3 with DSPy optimization, Ollama, and Weaviate. Experiment with different prompts and paraphrasings to enhance the performance of your language model.