Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a cutting-edge technique in machine learning that combines retrieval-based and generation-based approaches to improve the quality and coherence of text generation. This short read provides a comprehensive overview of RAG, its components, and its applications.

Components of RAG

RAG consists of two main components:

• Retrieval Module: This module retrieves relevant documents from a large corpus based on the input query. It leverages information retrieval techniques to identify documents that contain information related to the input.
• Generation Module: This module generates text using the retrieved documents as additional context. It utilizes language models, such as Transformers, to produce coherent and informative text that is consistent with the retrieved information.

How RAG Works

RAG operates in the following steps:

1. Query Formulation: The input query is used to formulate a search query.
2. Document Retrieval: The retrieval module retrieves a set of relevant documents from the corpus.
3. Document Encoding: The retrieved documents are encoded into a suitable format, such as embeddings or sequences.
4. Context Augmentation: The encoded documents are concatenated with the input query to provide additional context for the generation module.
5. Text Generation: The generation module utilizes the augmented context to generate text.

Advantages of RAG

RAG offers several advantages over traditional text generation methods:

• Improved Coherence: By incorporating retrieved documents, RAG ensures that the generated text is coherent and consistent with the input query.
• Enhanced Factuality: The retrieved documents provide factual information that can be incorporated into the generated text, improving its accuracy and reliability.
• Reduced Hallucination: Unlike pure generation models, RAG is less prone to generating hallucinated or inaccurate information.
• Increased Efficiency: RAG can leverage pre-trained retrieval models and document encoders, reducing the computational cost of text generation.

Applications of RAG

RAG has a wide range of applications, including:

• Question Answering: RAG can be used to generate comprehensive answers to complex questions by retrieving relevant documents and incorporating their information.
• Document Summarization: RAG can summarize long documents by extracting key information from retrieved documents and generating a coherent summary.
• Conversational AI: RAG can enhance conversational AI systems by providing relevant information and generating coherent responses based on retrieved documents.
• Text Generation with Constraints: RAG can generate text that meets specific constraints, such as tone, style, or factual accuracy, by incorporating retrieved documents that satisfy those constraints.

Below is a link to the code for an example RAG implementation using Open Source stack:

• Llama2-7B as the base model
• Langchain & Llama CPP frameworks for orchestration.

https://github.com/chaba-victor/LLama2-chatbot

Conclusion

RAG is a powerful technique in machine learning that combines retrieval and generation to improve the quality and coherence of text generation. Its advantages, such as enhanced factuality, reduced hallucination, and increased efficiency, make it a promising approach for various applications, including question answering, document summarization, conversational AI, and text generation with constraints. As research in RAG continues, we can expect further advancements and broader adoption of this innovative technique.