Search & Retrieval

The retrieval half of RAG: classical ranking models, indexing strategies for unstructured data, and the readability measures used to judge what comes back.

Pages in this Section

• BM25 Probabilistic Information Retrieval Model — BM25 (Best Matching 25) is a ranking function used by search engines to rank documents based on their relevance to a given query. It is one of the most well-known algorithms within the…
• Gunning Fog Index — Gunning Fog Index The Gunning Fog Index is a readability test that estimates the years of formal education needed to understand a text on the first reading. It takes into account the number…
• Retrieval-Augmented Generation (RAG) Workflow
• RAG (Retrieval Augmented Generation) — RAG enhances the response generation process by retrieving relevant documents from an external knowledge base (e.g., a vector database) and using these documents to inform the generated…
• TF-IDF — TF-IDF Value Breakdown Higher TF-IDF values: The higher the TF-IDF score for a term in a document, the more relevant or important that term is to that specific document.
• Unstructured Data Indexing — This process results in a structured, indexed data store that enables efficient search, retrieval, and analysis of previously unstructured information. By summarizing content and embedding…
• Python Whoosh

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