Hybrid search reference

AIDB provides helper functions and a metadata view for constructing hybrid search queries. Use these to encode queries against a specific knowledge base, rerank retrieved results, and inspect knowledge base configuration without hardcoding model names or table paths.

aidb.kb_query_encode()

Encodes a text query using the embedding model configured for a specific knowledge base, without needing to look up the model name:

SELECT aidb.kb_query_encode(public.my_kb_vector, test query);

Use this in place of aidb.encode_text_query('<model>', '<query>') when constructing custom vector queries.

aidb.rerank_text()

After retrieving candidates, pass them to a reranking model for a final relevance-ordered result:

SELECT aidb.rerank_text(my_reranking_model, query text, ARRAY[result1, result2, ...]);

Reranking models such as nim_reranking and hf_tei_reranking are supported as a separate post-processing step after retrieval.

aidb.knowledge_bases view

The aidb.knowledge_bases view (also accessible as aidb.kbs ) exposes all the metadata needed to construct hybrid queries:

Column	Type	Description
id	integer	Internal identifier
name	text	Knowledge base name
vector_schema	text	Schema of the embeddings table
vector_table	text	Embeddings storage table
model_name	text	Embedding model used by this KB
distance_operator	aidb.distanceoperator	Distance metric enum value
distance_operator_sql	text	The distance operator in SQL-operator syntax
vector_data_column	text	Column storing the embeddings
vector_key_column	text	Column linking embeddings to source rows
vector_index	jsonb	Vector index configuration
pipeline_ids	integer[]	IDs of all pipelines attached to this knowledge base
pipeline_names	text[]	Names of all pipelines attached to this knowledge base

SELECT name, vector_schema, vector_table, distance_operator_sql
FROM aidb.knowledge_bases;