This series implements rescoring algorithm.
Index options allowing to enable this functionality were introduced in earlier PR https://github.com/scylladb/scylladb/pull/28165.
When Vector Index has enabled quantization, Vector Store uses reduced vector representation to save memory, but it may degrade correctness of ANN queries. For quantized index we can enable rescoring algorithm, which recalculates similarity score from full vector representation stored in Scylla and reorder returned result set.
It works also with oversampling - we fetch more candidates from Vector Store, rescore them at Scylla and return only requested number of results.
Example:
Creating a Vector Index with Rescoring
```sql
-- Create a table with a vector column
CREATE TABLE ks.products (
id int PRIMARY KEY,
embedding vector<float, 128>
);
-- Create a vector index with rescoring enabled
CREATE INDEX products_embedding_idx ON ks.products (embedding)
USING 'vector_index'
WITH OPTIONS = {
'similarity_function': 'cosine',
'quantization': 'i8',
'oversampling': '2.0',
'rescoring': 'true'
};
```
1. **Quantization** (`i8`) compresses vectors in the index, reducing memory usage but introducing precision loss in distance calculations
2. **Oversampling** (`2.0`) retrieves 2× more candidates than requested from the vector store (e.g., `LIMIT 10` fetches 20 candidates)
3. **Rescoring** (`true`) recalculates similarity scores using full-precision (`f32`) vectors from the base table and re-ranks results
Query example:
```sql
-- Find 10 most similar products
SELECT id, similarity_cosine(embedding, [0.1, 0.2, ...]) AS score
FROM ks.products
ORDER BY embedding ANN OF [0.1, 0.2, ...]
LIMIT 10;
```
With rescoring enabled, the query:
1. Fetches 20 candidates from the quantized index (due to oversampling=2.0)
2. Reads full-precision embeddings from the base table
3. Recalculates similarity scores with full precision
4. Re-ranks and returns the top 10 results
In this implementation we use CQL similarity function implementation to calculate new score values and use them in post query ordering. We add that column manually to selection, but it has to be removed from the final response.
Follow-up https://github.com/scylladb/scylladb/pull/28165
Fixes https://scylladb.atlassian.net/browse/SCYLLADB-83
New feature - doesn't need backport.
Closesscylladb/scylladb#27769
* github.com:scylladb/scylladb:
vector_index: rescoring: Fetch oversampled rows
vector_index: rescoring: Sort by similarity column
select_statement: Modify `needs_post_query_ordering` condition
vector_index: rescoring: Add hidden similarity score column
vector_index: Refactor extracting ANN query information
3ec4f67407