AI inference functions

AI inference functions#

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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