Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)

Step-by-Step Tutorial: Understanding Retrieval Augmented Generation (RAG)

Introduction to Language Models

1. What is a Language Model?

   • A language model is a probabilistic model that assigns probabilities to sequences of words.
   • It helps in predicting the probability of the next token given a prompt.

2. Training Language Models

   • Large language models are trained on vast corpora of text, such as Wikipedia or web pages, using Transformer-based architectures like BERT and GPT.
   • Training with more parameters enables capturing more knowledge.

Fine-Tuning vs. Prompt Engineering

3. Fine-Tuning Language Models

   • Fine-tuning involves training a model on specific data but has limitations in capturing diverse knowledge.
   • It can be computationally expensive and may not be additive in knowledge acquisition.

4. Prompt Engineering

   • Involves structuring prompts for language models to perform specific tasks they were not explicitly trained on.
   • Enables models like GPT to provide accurate responses based on context and examples provided.

Retrieval Augmented Generation (RAG) Pipeline

5. Concept of RAG

   • RAG combines prompt engineering with a vector database to enhance knowledge retrieval and question-answering capabilities.
   • It leverages embeddings, Sentence BERT, and a Vector Database (such as HNSW) for efficient information retrieval.

6. Components of RAG Pipeline

   • Splitting documents into sentences, converting them into embeddings using Sentence BERT, and storing them in a Vector Database.
   • Converting queries into embeddings and searching for best-matching embeddings in the Vector Database.

7. Using Vector Databases

   • Vector Databases store fixed-size embeddings and utilize algorithms like Hierarchical Navigable Small Worlds (HNSW) for efficient similarity searches.
   • They enable quick retrieval of relevant embeddings based on query similarity scores.

Implementing RAG with Gradient AI Platform

8. Building RAG Pipelines with Gradient

   • Utilize Gradient's AI Cloud platform for fine-tuning models, generating embeddings, and running inferences seamlessly.
   • Implement RAG pipelines using simple APIs for model customization and knowledge retrieval.

9. Training Models with Gradient

   • Fine-tune models on custom datasets and generate embeddings for efficient information retrieval.
   • Achieve total ownership of data and model weights for enhanced control and customization.

Conclusion and Future Directions

10. Understanding RAG Algorithms

    • Explore the concepts of embeddings, Sentence BERT, and Vector Databases to enhance language model capabilities.
    • Learn how algorithms like HNSW optimize similarity searches for efficient knowledge retrieval.

11. Enhancing Knowledge Retrieval

    • Combine prompt engineering with vector databases to create powerful RAG pipelines for accurate question-answering.
    • Stay updated with advancements in AI technology and leverage tools like Gradient for seamless model development.

12. Engage and Share

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    • Share the knowledge with peers and contribute to the AI community for continuous learning and growth.

By following these steps, you can gain a comprehensive understanding of Retrieval Augmented Generation and its components for efficient knowledge retrieval and question-answering tasks.