Overview of RAG Approaches with Vector Databases

Step-by-Step Tutorial:

1. Understand the Purpose of RAG (Retrieval Augmented Generation):

   • RAG allows you to use the power of large language models (LLMs) on your own data by extracting relevant information and passing it to the LLM for answering questions.

2. Explore Different RAG Approaches:

   • Learn about structured retrieval, document summaries, time-based retrieval, and more to optimize your RAG process.

3. Consider Metadata for Retrieval:

   • Utilize metadata such as genres, ratings, dates, and times to filter and retrieve relevant chunks of data efficiently.

4. Implement Guardrails and Rankers:

   • Set up guardrails to steer conversations towards specific topics and use rankers to ensure the most relevant chunks are retrieved.

5. Enhance Retrieval with Recursive Methods:

   • Implement recursive retrievers to handle hierarchical data structures and improve the relevance of retrieved chunks.

6. Optimize Chunking and Embedding:

   • Chunk documents at a sentence level, summarize documents, and link summaries to original documents for efficient retrieval and generation.

7. Improve Query Generation:

   • Use multiquery retrievers to generate multiple similar queries for better retrieval results.

8. Evaluate RAG Performance:

   • Understand how to evaluate the performance of your RAG system using metrics like Rogue and Blue Frameworks.

9. Engage in Live Sessions and Resources:

   • Attend live sessions on chunking methods, agents with LLMs, and explore resources on YouTube for further learning and assistance.

10. Engage and Ask Questions:

• Reach out to the experts for questions on KX, Llama Index, and Lang Chain subreddits or follow them on LinkedIn for more insights and guidance.

11. Stay Updated and Engaged:

• Stay tuned for upcoming live sessions, recordings, and resources to enhance your understanding and implementation of RAG approaches.

By following these steps and staying engaged with the resources and live sessions provided, you can enhance your knowledge and skills in utilizing RAG approaches with vector databases effectively.