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title, weight
| title | weight |
|---|---|
| BM25 | 20 |
Server-side Inference: BM25
BM25 (Best Matching 25) is a ranking function for text search. BM25 uses sparse vectors that represent documents, where each dimension corresponds to a word. Qdrant can generate these sparse embeddings from input text directly on the server.
While upserting points, provide the text and the qdrant/bm25 embedding model:
{{< code-snippet path="/documentation/headless/snippets/inference/ingest/" >}}
Qdrant uses the model to generate the embeddings and stores the point with the resulting vector. Retrieving the point shows the embeddings that were generated:
....
"my-bm25-vector": {
"indices": [
112174620,
177304315,
662344706,
771857363,
1617337648
],
"values": [
1.6697302,
1.6697302,
1.6697302,
1.6697302,
1.6697302
]
}
....
]
Similarly, use the BM25 model at query time by providing the query string and the qdrant/bm25 embedding model:
{{< code-snippet path="/documentation/headless/snippets/inference/query/" >}}
Read more about full-text search with BM25 in the text search guide.