AI inference functions v7

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AIDB provides a set of SQL functions for performing AI inference directly in queries. These functions let you encode data into vector representations, generate text with language models, and control inference behavior — without setting up a pipeline.

aidb.encode_text()

Encodes a single text string into a vector using a registered embedding model.

SELECT aidb.encode_text(
    input   => 'What is pgvector?',
    options => '{"model": "my_embedder"}'
);
Output
                         encode_text
--------------------------------------------------------------
 [0.021,-0.043,0.117, ...]
(1 row)

For the full parameter reference, see Models reference.

aidb.encode_text_batch()

Encodes multiple text strings into vectors in a single batched request. More efficient than calling encode_text() in a loop when processing many inputs.

SELECT * FROM aidb.encode_text_batch(
    'my_model_name',
    ARRAY['What is pgvector?', 'How does RAG work?', 'Explain embeddings.']
);
Output
  encode_text_batch
-------------------------
 [0.021,-0.043,0.117,...]
 [0.008, 0.091,-0.034,...]
 [0.055,-0.012, 0.076,...]
(3 rows)

For the full parameter reference, see Models reference.

aidb.encode_image()

Encodes a single image into a vector using a registered multimodal embedding model (for example, a CLIP model).

SELECT aidb.encode_image(
    'my_model_name',
    pg_read_binary_file('/path/to/image.png')::BYTEA
);
Output
                         encode_image
--------------------------------------------------------------
 [-0.012, 0.084, 0.031, ...]
(1 row)

For the full parameter reference, see Models reference.

aidb.decode_text()

Generates text from a language model (LLM) given a prompt. This is the core function for LLM inference in AIDB.

SELECT aidb.decode_text(
    model_name => 'my_model_name',
    input   => 'Explain what a knowledge base is in two sentences.',
    inference_config => NULL
);
Output
                                        decode_text
--------------------------------------------------------------------------------------------
 A knowledge base is a structured repository of information used to answer queries or ...
(1 row)

To control generation behavior, pass an inference_config object in options:

SELECT aidb.decode_text(
               model_name => 'my_model_name',
               input   => 'Summarize the following text: ...',
               inference_config => aidb.inference_config(
                               system_prompt => 'You are a concise technical writer.',
                               temperature   => 0.2,
                               max_tokens    => 256
                                           )
                          
       );

For the full parameter reference, see Models reference.

aidb.decode_text_batch()

Generates text responses for multiple prompts in a single batched request.

SELECT * FROM aidb.decode_text_batch(
    model_name => 'my_model_name',
    input  => ARRAY[
        'What is a vector database?',
        'What is retrieval-augmented generation?'
    ]
);
Output
 id |                          value
----+----------------------------------------------------------
  0 | A vector database stores high-dimensional vectors ...
  1 | Retrieval-augmented generation (RAG) combines a ...
(2 rows)

For the full parameter reference, see Models reference.

aidb.inference_config()

Builds an inference configuration object to control language model generation behavior. Pass the result as the "inference_config" key inside the options JSONB of decode_text(), decode_text_batch(), or summarize_text().

SELECT aidb.decode_text(
    input   => 'Write a haiku about PostgreSQL.',
    options => jsonb_build_object(
        'model',            'my_llm',
        'inference_config', aidb.inference_config(
            temperature => 0.9,
            max_tokens  => 64,
            seed        => 42
        )
    )
);

For the full parameter reference, see Models reference.

Note

To use these functions as steps inside a pipeline, see Pipeline steps.

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