Text reranking

Text reranking#

Use the aidb.rerank_text() function to reorder a set of candidate text passages by their relevance to a query. Reranking is typically applied as a post-retrieval step — after an initial set of candidates has been retrieved from a knowledge base — to surface the most relevant results at the top.

Step 1: Register a reranking model

- - Remote: NVIDIA NIM
SELECT aidb.create_model(
    my_reranker,
    nim_reranking,
    config => aidb.nim_reranking_config(
        api_key => nvapi-...,  -- or use create_models own credentials_env parameter instead, to avoid storing this
        model   => nvidia/nv-rerankqa-mistral-4b-v3
    )
);

- - Local: llama.cpp (GGUF), no API key required
SELECT aidb.create_model(
    my_reranker,
    llamacpp_reranking,
    config => jsonb_build_object(
        model, gpustack/bge-reranker-v2-m3-GGUF,
        model_file, bge-reranker-v2-m3-Q4_K_M.gguf,
        n_ctx, 2048
    )
);

Step 2: Rerank a set of candidates

SELECT * FROM aidb.rerank_text(
    model_name => my_reranker,
    query      => What is retrieval-augmented generation?,
    input      => ARRAY[
        RAG combines retrieval and generation to produce grounded answers.,
        Vector databases store high-dimensional embeddings for similarity search.,
        RAG systems retrieve relevant documents before generating a response.
    ]
) ORDER BY logit_score DESC;

__OUTPUT__
                              text                              | logit_score | id
- ---------------------------------------------------------------+-------------+----
 RAG systems retrieve relevant documents before generating ...  |        0.97 |  2
 RAG combines retrieval and generation to produce grounded ...  |        0.91 |  0
 Vector databases store high-dimensional embeddings for ...     |        0.43 |  1
(3 rows)

The id column is the zero-based index of each string in the input array, so you can join back to the original data.

For the full parameter reference, see Models reference .

Using reranking after knowledge base retrieval#

A typical pattern is to retrieve an initial candidate set from a knowledge base and then rerank the results:

WITH candidates AS (
    SELECT key, value
    FROM aidb.retrieve_text(my_kb, What is RAG?, topk => 20)
)
SELECT c.key, c.value, r.logit_score
FROM candidates c
JOIN aidb.rerank_text(
    model_name => my_reranker,
    query      => What is RAG?,
    input      => ARRAY(SELECT value FROM candidates ORDER BY key)
) r ON r.text = c.value
ORDER BY r.logit_score DESC
LIMIT 5;

!!!note Reranking models score passages against a query but don’t generate text. To register a reranking model, use the appropriate Model config helpers for your provider. For example, aidb.nim_reranking_config() for NVIDIA NIM reranking endpoints. llamacpp_reranking has no config helper yet; build config with jsonb_build_object() instead, as shown above. See Local models .

!!!note logit_score ’s scale depends on the provider: some return a normalized score in a fixed range (as in the example above), while llamacpp_reranking returns a raw, unbounded model logit. Compare scores within one aidb.rerank_text() call and provider, not across different providers or calls.

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