For a collection of advanced Retrieval-Augmented Generation (RAG) techniques this is a very resourceful repo. Many topics are covered like - Metadata Filtering: Apply filters based on attributes like date, source, author, or document type. - Similarity... See more

Anthropic just reduced the error rate of RAGs by 67% using a ridiculously simple method. They add important context to small text chunks before storing them, which improves accuracy later. Instead of just saying “the company grew by 3%,” it includes details like which company and when,... See more

1. Synthetic Data for Baseline Metrics¶

Synthetic data can be used to establish baseline precision and recall metrics for your reverse search. The simplest kind of synthetic data is to take existing text chunks, generate synthetic questions, and verify that when we query our synthetic questions, the sourced text chunk is retrieved correctly.

Benefi... See more

RAG, which stands for "Retrieval Augmented Generation," is a strategy in artificial intelligence where a large language model (LLM) retrieves relevant information from an external knowledge base (like a database or document collection) before generating a response to a user query, ensuring the response is more accurate and contextually relevant by... See more