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feat!: better api for manual hybrid queries (#1575)

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Currently, the only documented way of performing hybrid search is by
using embedding API and passing string queries that get automatically
embedded. There are use cases where users might like to pass vectors and
text manually instead.
This ticket contains more information and historical context -
https://github.com/lancedb/lancedb/issues/937

This breaks a undocumented pathway that allowed passing (vector, text)
tuple queries which was intended to be temporary, so this is marked as a
breaking change. For all practical purposes, this should not really
impact most users

### usage
```
results = table.search(query_type="hybrid")
                .vector(vector_query)
                .text(text_query)
                .limit(5)
                .to_pandas()
```
This commit is contained in:
Ayush Chaurasia
2024-08-30 17:37:58 +05:30
committed by GitHub
parent 1521435193
commit dc72ece847
3 changed files with 131 additions and 116 deletions
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13
docs/src/hybrid_search/hybrid_search.md
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@@ -43,6 +43,19 @@ table.create_fts_index("text")
# hybrid search with default re-ranker
results = table.search("flower moon", query_type="hybrid").to_pandas()
```
!!! Note
You can also pass the vector and text query manually. This is useful if you're not using the embedding API or if you're using a separate embedder service.
### Explicitly passing the vector and text query
```python
vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
text_query = "flower moon"
results = table.search(query_type="hybrid")
.vector(vector_query)
.text(text_query)
.limit(5)
.to_pandas()
```
By default, LanceDB uses `LinearCombinationReranker(weight=0.7)` to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers: