AI Engineering Academy: 2.13 RAG-Fusion: Enhanced Retrieval Enhanced Generation

present (sb for a job etc)

RAG-Fusion is an advanced information retrieval and text generation methodology built on Retrieval Augmented Generation (RAG). This project implements RAG-Fusion to provide more accurate, contextually relevant and comprehensive responses to user queries.

https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/RAG/08_RAG_Fusion

locomotive

traditional RAG While effective, systems often face limitations in capturing the full intent of the user and retrieving the most relevant information.RAG-Fusion solves these problems in the following ways:

1. Generate multiple queries to capture different aspects of user intent.
2. Improve search accuracy using advanced reordering techniques.
3. Provide more detailed context for language models to generate more relevant responses.

Methodological details

Document preprocessing and vector store creation

1. Text Splitting: Divide documents into manageable chunks.
2. Embedding Vector Generation: Convert each chunk into a vector representation using a pre-trained embedding model.
3. indexing: Store the embedded vectors in a vector database for efficient retrieval.

Retrieval Enhancement Generation Workflow

1. Query Extension: Expand the original user query into multiple related queries using a language model.
2. Multi-query embedding: Convert all queries (original and generated) into embedding vectors.
3. vector search: Use each query embedding to retrieve relevant document blocks from the vector database.
4. Reciprocal Ranking Fusion (RRF): Combine and reorder the results of multiple queries using the RRF algorithm.
5. contextualization: The original query, the generated query and the reordering result together form the context.
6. Response Generation: Generate final responses based on rich context, large language models.

Key features of RAG-Fusion

• Multi-query generation to fully capture user intent.
• Reciprocal Ranking Fusion (RRF) improves result relevance.
• Integration of multiple information retrieval technologies.
• Flexible architecture with support for various embedded models and language models.

Benefits of this method

1. Enhanced query comprehension: By generating multiple queries, RAG-Fusion captures a broad dimension of user intent.
2. Improved search accuracy: Use RRF to improve the correlation of multiple query results.
3. Reduction of hallucinations: Reduce the chances of the model producing an incorrect response by providing a more comprehensive and accurate context.
4. Multi-disciplinary applicability: The system can be applied to a variety of fields and types of queries.
5. scalability: The architecture is designed to support efficient processing of large document collections.

reach a verdict

RAG-Fusion represents a significant technological advancement in the field of information retrieval and text generation. By addressing the limitations of traditional RAG systems, it provides a more robust, accurate and flexible information retrieval solution for a wide range of scenarios, from question and answer systems to document summarization tasks.