This repository demonstrates how to integrate Llama-Index and a knowledge graph into the RAG (Retrieval-Augmented Generation) architecture. By doing so, it enables the retrieval of information from a large-scale knowledge base and improves the performance of natural language generation tasks.
The RAG architecture is a powerful approach to natural language generation that leverages retrieved information to improve the quality and relevance of generated text. By integrating a knowledge graph into the RAG architecture, we can tap into a vast source of structured and interconnected information, enabling the model to generate more accurate and contextually relevant responses.
This project utilizes the Llama-Index library to efficiently index and query the knowledge graph, allowing for fast and accurate retrieval of relevant information during the generation process. The library provides a simple and intuitive interface for working with knowledge graphs and integrating them into the RAG pipeline.
For a detailed explanation of the concepts and techniques used in this project, please refer to the following article: Knowledge Graphs for RAG (WIP),
This project is based on the concepts and techniques outlined in the article "Implement RAG with Knowledge Graph and Llama-Index", which serves as a foundation for integrating knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture using the Llama-Index library.
- Integration of a knowledge graph into the RAG architecture
- Efficient indexing and querying of the knowledge graph using Llama-Index
- Improved quality and relevance of generated text
- Support for various natural language generation tasks, such as question answering, content creation, and conversational AI
- Easy-to-use and modular codebase for customization and extension