llama-index-retrievers-bm25
llama-index retrievers bm25 integration
Decision gist · record as of 2026-08-14
Yes, if you're building a LlamaIndex application and need straightforward keyword-based retrieval. The low install friction, active maintenance, MIT license, and zero known vulnerabilities make it a safe choice. Install only if BM25 ranking fits your retrieval needs; for semantic or dense vector search, you'd want a different retriever.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports current versions up to <4.0).
- Low install friction with a pure-Python wheel and only three runtime dependencies.
- Actively maintained as of March 2026, with no known security vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects, with only attribution required.
last release 2026-03-13 (154 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 326,294 downloads/mo, #7,578 on PyPI
Alternatives
Verify before relying
pip install llama-index-retrievers-bm25
from llama_index.retrievers.bm25 import BM25Retriever
retriever = BM25Retriever.from_documents(documents)
results = retriever.retrieve(query_str)- Whether BM25Retriever integrates seamlessly with other LlamaIndex components beyond core retrieval.
- Performance characteristics when indexing or querying large document collections.
- How pystemmer is used internally and whether stemming behavior is configurable.
What it is and what it does
This package provides a BM25-based retriever for LlamaIndex, a framework for building retrieval-augmented generation (RAG) applications. BM25 is a probabilistic ranking function that scores documents based on keyword relevance, making it useful for traditional full-text search within LlamaIndex pipelines. The retriever accepts a collection of documents, builds an index using the bm25s library, and returns ranked results for text queries.
It sits between your document collection and LlamaIndex's query pipeline, handling the retrieval step without requiring external search infrastructure. The package depends on bm25s for the core ranking algorithm, llama-index-core for integration hooks, and pystemmer for linguistic preprocessing. It's designed for developers who want keyword-based retrieval as part of a larger LlamaIndex application, particularly when semantic or hybrid search isn't the primary need.
Use it for
- Build a RAG pipeline where keyword matching is the primary retrieval strategy before passing results to an LLM.
- Add full-text search to a document Q&A system without setting up a separate search engine.
- Combine BM25 retrieval with other LlamaIndex retrievers in a hybrid or ensemble approach.
- Index and retrieve from domain-specific documents where exact keyword matches are important.
- Prototype or test retrieval logic quickly without infrastructure overhead.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you're building a LlamaIndex application and need straightforward keyword-based retrieval.
The low install friction, active maintenance, MIT license, and zero known vulnerabilities make it a safe choice. Install only if BM25 ranking fits your retrieval needs; for semantic or dense vector search, you'd want a different retriever.
Install
llama-index-retrievers-bm25 on PyPI
Before you install
Low install friction with a pure-Python wheel and only three runtime dependencies. Actively maintained as of March 2026, with no known security vulnerabilities.
Requires Python 3.10 or later (supports current versions up to <4.0).
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects, with only attribution required.
Quickstart
pip install llama-index-retrievers-bm25
from llama_index.retrievers.bm25 import BM25Retriever
retriever = BM25Retriever.from_documents(documents)
results = retriever.retrieve(query_str)Verify before relying
- Whether BM25Retriever integrates seamlessly with other LlamaIndex components beyond core retrieval.
- Performance characteristics when indexing or querying large document collections.
- How pystemmer is used internally and whether stemming behavior is configurable.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesbm25sllama-index-corepystemmer |
| Maintenance | Actively maintained 154 days since the last release |
| First released | |
| Downloads | 326,294 / month, #7,578 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_retrievers_bm25-0.7.1-py3-none-any.whl
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See also llama-index-vector-stores-qdrant · llama-index-vector-stores-chroma · llama-index-vector-stores-faiss · llama-index-embeddings-openai · rank-bm25 · llama-index-embeddings-langchain · bm25s · llama-index-vector-stores-postgres · llama-index-vector-stores-redis · llama-index-indices-managed-llama-cloud