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BM25 Retriever - LlamaIndex 🦙 0.8.49

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Node ID: d95537b4-b398-4b47-94ff-da86f05a27f7 Similarity: 5.171801938898801 Text: I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get… Node ID: 6f84e2a5-1ab1-4389-8799-b7713e085931 Similarity: 4.838241203957084 Text: All you had to do was teach SHRDLU more words. There weren’t any classes in AI at Cornell then, … Node ID: a4fd0b29-4138-4741-9e27-9f65d6968eb4 Similarity: 8.090884087344435 Text: Not so much because it was badly written as because the problem is so convoluted. When you’re wor… Node ID: d95537b4-b398-4b47-94ff-da86f05a27f7 Similarity: 5.830874349482576 Text: I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get… Now we will combine bm25 retriever with vector index retriever. Node ID: fcd399c1-3544-4df3-80a9-0a7d3fd41f1f Similarity: 0.7942753162501964 Text: [10] Wow, I thought, there’s an audience. If I write something and put it on the web, anyone can… Node ID: b203e140-d549-4284-99f4

BM25 Retriever | Developer Documentation Skip to content LlamaIndex Framework Integrations Retrievers BM25 Retriever Copy Markdown Open in Claude Open in ChatGPT Open in Cursor Copy Markdown View as Markdown BM25 Retriever In this guide, we define a bm25 retriever that search documents using the bm25 method. BM25 (Best Matching 25) is a ranking function that extends TF-IDF by considering term frequency saturation and document length. BM25 effectively ranks documents based on query term occurrence and rarity across the corpus. This notebook is very similar to the RouterQueryEngine notebook. Set

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