Knowledge bases#
A knowledge base (KB) stores embeddings in a vector table, along with its configuration — the model, distance operator, and vector index. Pipelines write to it, and you query it by name using aidb.retrieve_text() or aidb.retrieve_key() .
A knowledge base starts with a single pipeline and a 지식 기반 . You can attach additional pipelines to the same knowledge base — letting different sources or processing chains all contribute embeddings to one queryable vector table.
Page |
What it covers |
|---|---|
Attach multiple pipelines to one KB; compatibility rules and deletion semantics |
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Combining semantic search with relational filters and full-text search |
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End-to-end worked examples for table and volume sources |
Retrieval functions#
Once a pipeline has run, query the knowledge base using
aidb.retrieve_text() or aidb.retrieve_key() . Both use vector
similarity to find results based on meaning rather than exact keywords,
and support both TEXT and BYTEA (image) as the query input.
Flow of retrieval functions#
When a retrieval function is called, the system performs the following steps internally:
Embedding: The input query (text or image) is converted into a vector using the specific embedding model configured for that knowledge base.
Similarity search: A vector similarity search is performed against the knowledge base’s internal vector table to find the Top K nearest neighbors.
Source lookup (text only): For
retrieve_text, the system identifies the source table and retrieves the raw content corresponding to the matched keys.
aidb.retrieve_text()#
Use this function when you need to retrieve the actual source text associated with the closest vector matches.
Process: The function embeds your query, performs a similarity search, and then executes a second phase to look up the source text from the original table using the
pipeline_id.Returns: A set of columns including:
key: The identifier from the source table.
value: The actual source text.
distance: The similarity score. A lower usually indicates a closer match.
part_ids: An array of IDs indicating which specific chunks or parts were matched.
pipeline_name: The name of the pipeline that supplied the data.
intermediate_steps: A JSONB column containing data from steps occurring before the knowledge base. For example, ChunkText.
aidb.retrieve_key()#
Use this function for high-performance searches where you only need the unique identifiers of the matches, rather than the full source content.
Returns: A set of columns including:
key: The identifier from the source table.
distance: The similarity score. A lower value usually indicates a closer match.
part_ids: An array of IDs indicating which specific chunks or parts were matched.
pipeline_name: The name of the pipeline that supplied the data.
Advanced querying: Joining intermediate steps#
For pipelines that include intermediate transformations such as
ChunkText or ParseHtml, you can access specific transformed segments by
joining retrieval results with intermediate pipeline tables using the
part_ids column.
Example syntax:#
The following query joins the retrieval results with an intermediate step table to access specific chunked values:
SELECT
r.key,
r.value,
r.distance,
r.part_ids,
int_step.value AS chunked_content
FROM aidb.retrieve_text(my_kb, search query, 5) AS r
JOIN pipeline_my_pipeline_step_1 AS int_step
ON int_step.source_id = r.key
AND int_step.part_ids = (r.part_ids)[:1];