What is BM25 Retriever in LlamaIndex ?

 The BM25 Retriever in LlamaIndex uses the BM25 (Best Matching 25) algorithm to retrieve relevant documents or text chunks (called "nodes" in LlamaIndex) in response to a query. It's a classic information retrieval algorithm that's effective for keyword-based search and often serves as a strong baseline. Here's how it works:   

Core Idea: BM25 goes beyond simple keyword matching. It considers not only whether a term appears in a document, but also how frequently it appears and how rare it is across the entire collection of documents. This helps to give higher scores to documents that contain important query terms more often, but also to down-weight documents that contain common words that don't contribute much to relevance.   

How it Works (Step-by-Step):

Indexing: The BM25 Retriever first creates an index of your documents or nodes. This index stores information about the terms that appear in each document, their frequencies, and some statistics about the overall corpus.   

Query Processing: When you provide a query, the retriever tokenizes it (breaks it down into individual words) and may perform some additional preprocessing (like stemming or stop word removal).   

Scoring: For each document in the collection, BM25 calculates a score that represents how relevant that document is to the query. This score is based on:   

Term Frequency (TF): How often each query term appears in the document. BM25 considers term frequency saturation, meaning that the score doesn't increase linearly with term frequency. After a certain point, additional occurrences of a term have diminishing returns.   

Inverse Document Frequency (IDF): How rare each query term is across the entire collection of documents. Rare terms are given more weight because they are more discriminative.

Document Length: BM25 normalizes for document length. Longer documents are more likely to contain more query terms simply because they are longer, not necessarily because they are more relevant.   

Ranking: The retriever ranks the documents based on their BM25 scores. Documents with higher scores are considered more relevant.   

Retrieval: The retriever returns the top-k documents (or nodes) with the highest BM25 scores as the retrieved context for your LLM query.

Key Parameters in LlamaIndex:

k1 and b: These are two important parameters in the BM25 algorithm that control term frequency saturation and length normalization, respectively. You can tune these parameters to optimize retrieval performance for your specific data.   

similarity_top_k: This parameter determines how many top-ranked documents or nodes are returned by the retriever.   

Benefits of BM25 Retriever:

Effective Keyword Search: BM25 is very good at finding documents that contain the query terms, even if the phrasing is slightly different.

Considers Term Importance: It takes into account both term frequency and inverse document frequency, giving higher scores to documents with important and rare terms.   

Fast Retrieval: BM25 is relatively fast, making it suitable for retrieving from large collections of documents.   

Strong Baseline: It often serves as a good baseline for comparison with more complex retrieval methods.

When to use it:

Keyword-based search: When you want to retrieve documents based on the presence of specific keywords.

Large document collections: When you need efficient retrieval from a large number of documents.

Hybrid retrieval: BM25 can be combined with other retrieval methods (like vector search) to improve overall retrieval performance.   

In LlamaIndex, you would use the BM25Retriever class to create a retriever that uses the BM25 algorithm. You can then use this retriever to fetch relevant context for your LLM queries.