RAG with Document Chunks, Embeddings, and GPT-4: A Comprehensive Guide #AIresearch

Retrieval-Augmented Generation (RAG) is a technique that combines document retrieval with text generation to improve the performance of language models. This article explains the implementation of RAG, which involves document chunking, using embeddings models, calculating cosine similarity, and leveraging GPT-4 for generating responses to queries.

Document chunking involves dividing large documents into smaller chunks for better retrieval efficiency. Embeddings models like Sentence-BERT are used to convert text chunks into high-dimensional vectors. Cosine similarity is then calculated between query embeddings and document chunk embeddings to identify the most relevant chunks. Finally, the relevant chunks are fed into GPT-4 to generate accurate responses to queries.

The implementation of RAG involves functions for chunking documents, creating embeddings, finding similar chunks, and generating responses using GPT-4. By following this process, users can efficiently handle and query large collections of documents, providing contextually relevant responses. RAG is valuable in applications such as customer support, research, and information retrieval, enabling users to extract meaningful information quickly and accurately in the age of information overload.

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