landing_page/qdrant-landing/content/documentation/edge/edge-bm25.md

title, short_description, description, weight, partition

title	short_description	description	weight	partition
BM25	Generate BM25 sparse embeddings for keyword search with Qdrant Edge, compatible with server-side BM25 collections.	Use the built-in BM25 embedder in Qdrant Edge to generate sparse text embeddings on-device for keyword search, without an internet connection or external model server.	16	develop

BM25 with Qdrant Edge

BM25 (Best Matching 25) is a popular sparse-vector ranking algorithm for full-text search. Qdrant Edge includes a built-in BM25 embedder, so you can run keyword search without an internet connection or external embedding service.

The BM25 embedder is compatible with server-side BM25: vectors produced by the Qdrant Edge embedder use the same token IDs and scoring formula as Qdrant Server's text search pipeline. You can initialize an Edge Shard from a server snapshot and query it with locally produced BM25 vectors without re-indexing.

In Python, use the Bm25 and Bm25Config classes. In Rust, use EdgeBm25 and EdgeBm25Config from the qdrant_edge::bm25_embed module.

Configure a Sparse Vector

To get started with BM25, create an Edge Shard with a sparse vector field and Modifier.Idf. The IDF modifier enables inverse document frequency weighting, which is required for BM25 scoring:

{{< code-snippet path="/documentation/headless/snippets/edge/bm25/" block="configure-bm25-shard" >}}

Create a BM25 Embedder

Instantiate a BM25 embedder with a language setting. The embedder applies stemming and stopword filtering for the specified language:

{{< code-snippet path="/documentation/headless/snippets/edge/bm25/" block="create-bm25" >}}

Bm25Config accepts the following parameters:

Parameter	Description
language	Language for stemming and stopwords (for example, "english", "german"). Defaults to None, which falls back to English stemming and stopwords.
k	Term frequency saturation parameter. Default: 1.2.
b	Document length normalization factor. Default: 0.75.
avg_len	Expected average document length in tokens. Default: 256.
lowercase	Convert tokens to lowercase before embedding. Default: true.
ascii_folding	Normalize accented characters to ASCII equivalents. Default: false.
stemmer	Override the stemming algorithm.
stopwords	Override the stopword list.
tokenizer	Tokenizer used to break down text into individual tokens (words). Can be "prefix", "whitespace", "word", or "multilingual". Default: "word".
min_token_len	Minimum token length to include.
max_token_len	Maximum token length to include.

For a full description of each parameter, see Configuring BM25 Parameters.

Embed and Upsert Documents

Use embed_document to generate a sparse vector for each document, then upsert the points. Call optimize after bulk inserts to build the sparse index:

{{< code-snippet path="/documentation/headless/snippets/edge/bm25/" block="embed-and-upsert" >}}

Query

Use embed_query to generate a sparse vector for the query text, then query the shard:

{{< code-snippet path="/documentation/headless/snippets/edge/bm25/" block="query-with-bm25" >}}

Always use embed_query for query text and embed_document for document text. Using the wrong function produces incorrect results, since BM25 applies different term weighting depending on the input type.