Pipelines API reference Innovation Release

This documentation covers the current Innovation Release of EDB Postgres AI. See also:

Hybrid Manager dual release strategy
Documentation for the current Long-term support release

This page covers the full public API for AI pipelines: the types and enums used across the API, the views for inspecting pipeline state, the core CRUD and execution functions, and the family of configuration helper functions for pipeline steps, vector indexes, and model providers.

Types

`aidb.PipelineAutoProcessingMode`

Controls how a pipeline automatically processes new or changed data.

CREATE TYPE PipelineAutoProcessingMode AS ENUM (
    'Live',
    'Background',
    'Disabled'
);

Value	Description
`Live`	Processes new data immediately as it arrives, using Postgres triggers.
`Background`	Continuously processes data in the background using Postgres workers.
`Disabled`	No automated processing. Use `aidb.run_pipeline()` to trigger manually.

`aidb.PipelineDataFormat`

Specifies the format of source data the pipeline processes.

CREATE TYPE PipelineDataFormat AS ENUM (
    'Text',
    'Image',
    'Pdf'
);

Value	Description
`Text`	Plain text data.
`Image`	Image data (bytes).
`Pdf`	PDF documents.

`aidb.PipelineSourceType`

Indicates the type of data source a pipeline reads from.

CREATE TYPE PipelineSourceType AS ENUM (
    'Table',
    'Volume',
    'Empty'
);

Value	Description
`Table`	A Postgres table or view.
`Volume`	A PGFS storage volume.
`Empty`	No source; the pipeline generates its own data.

`aidb.PipelineDestinationType`

Indicates the type of destination a pipeline writes to.

CREATE TYPE PipelineDestinationType AS ENUM (
    'Table',
    'Volume',
    'Empty'
);

Value	Description
`Table`	A Postgres table.
`Volume`	A PGFS storage volume.
`Empty`	No destination; output is discarded.

`aidb.PipelineStepOperation`

Defines the operation performed by a pipeline step.

CREATE TYPE PipelineStepOperation AS ENUM (
    'ChunkText',
    'SummarizeText',
    'ParseHtml',
    'ParsePdf',
    'PerformOcr',
    'KnowledgeBase',
    'PdfToImage',
    'SemanticKB'
);

Value	Description
`ChunkText`	Splits text into smaller chunks.
`SummarizeText`	Summarizes text using a language model.
`ParseHtml`	Extracts text content from HTML.
`ParsePdf`	Extracts text or images from PDFs.
`PerformOcr`	Runs optical character recognition on images.
`KnowledgeBase`	Computes and stores embeddings in a knowledge base.
`PdfToImage`	Converts PDF pages to images.
`SemanticKB`	Indexes schema metadata into a semantic knowledge base.

`aidb.PipelineStatus`

Represents the current processing state of a pipeline.

CREATE TYPE PipelineStatus AS ENUM (
    'Stale',
    'Processing',
    'UpToDate',
    'NoResults',
    'Failed',
    'Unknown',
    'HasErrors'
);

Value	Description
`Stale`	Source data has changed and the pipeline needs to run.
`Processing`	The pipeline is currently executing.
`UpToDate`	All source data has been processed successfully.
`NoResults`	Processing completed but produced no output.
`Failed`	The last execution failed.
`HasErrors`	Some records failed to process; others succeeded.
`Unknown`	Status cannot be determined.

`aidb.DistanceOperator`

Specifies the distance metric used for vector similarity search.

CREATE TYPE DistanceOperator AS ENUM (
    'L2',
    'InnerProduct',
    'Cosine',
    'L1',
    'Hamming',
    'Jaccard'
);

Value	Description
`L2`	Euclidean distance.
`InnerProduct`	Inner product.
`Cosine`	Cosine similarity.
`L1`	L1 (Manhattan) distance.
`Hamming`	Hamming distance.
`Jaccard`	Jaccard distance.

Domains

`aidb.pipeline_name_50`

A TEXT domain enforcing that pipeline names are no longer than 50 characters.

`aidb.background_sync_interval`

An INTERVAL domain enforcing that background sync intervals are between 1 second and 2 days (inclusive).

Views

`aidb.pipelines`

Also accessible as aidb.pipes. Lists all registered pipelines and their configuration, including source, destination, processing mode, and step definitions.

Column	Type	Description
`id`	integer	Internal pipeline identifier.
`name`	text	Name of the pipeline.
`source_type`	aidb.PipelineSourceType	Whether the source is a table or volume.
`source_schema`	text	Schema of the source table.
`source`	text	Name of the source table or volume.
`source_key_column`	text	Column used as the unique key in the source.
`source_data_column`	text	Column containing the data to process.
`destination_type`	aidb.PipelineDestinationType	Whether the destination is a table or volume.
`destination_schema`	text	Schema of the destination table.
`destination`	text	Name of the destination table or volume.
`destination_key_column`	text	Key column in the destination table.
`destination_data_column`	text	Column in the destination where processed data is written.
`steps`	jsonb	Ordered array of pipeline step definitions.
`auto_processing`	aidb.PipelineAutoProcessingMode	Auto-processing mode.
`batch_size`	integer	Number of records processed per batch.
`background_sync_interval`	interval	Interval between executions in background mode.
`owner_role`	text	Postgres role that owns this pipeline.

Example

SELECT name, source, destination, auto_processing FROM aidb.pipelines;

`aidb.pipeline_metrics`

Also accessible as aidb.pipem. Shows current processing statistics for each pipeline.

Column	Type	Description
`pipeline`	text	Name of the pipeline.
`auto processing`	text	Current auto-processing mode.
`table: unprocessed rows`	bigint	For table sources: number of rows not yet processed.
`volume: scans completed`	bigint	For volume sources: number of full scans completed.
`count(source records)`	bigint	Total number of records in the source.
`count(destination records)`	bigint	Total number of records in the destination.
`Status`	text	Current pipeline status.

Example

SELECT * FROM aidb.pipeline_metrics;

Functions

`aidb.create_pipeline`

Creates a new pipeline with a source, up to 10 sequential processing steps, and an optional destination.

Parameters

Parameter	Type	Default	Description
`name`	TEXT	Required	Name of the pipeline. Max 50 characters.
`source`	TEXT	Required	Name of the source table or volume.
`step_1`	aidb.PipelineStepOperation	Required	Operation for the first pipeline step.
`source_key_column`	TEXT	NULL	Unique key column in the source table.
`source_data_column`	TEXT	NULL	Column containing the data to process.
`destination`	TEXT	NULL	Name of the destination table or volume.
`auto_processing`	aidb.PipelineAutoProcessingMode	NULL	Auto-processing mode.
`batch_size`	INT	NULL	Number of records to process per batch.
`background_sync_interval`	INTERVAL	NULL	Interval between background executions. Must be between 1 second and 2 days.
`owner_role`	TEXT	NULL	Role to own and execute this pipeline.
`step_1_options`	JSONB	NULL	Configuration for step 1 (use the appropriate step config helper).
`step_2` … `step_10`	aidb.PipelineStepOperation	NULL	Operation for steps 2–10.
`step_2_options` … `step_10_options`	JSONB	NULL	Configuration for steps 2–10.

Returns

Column	Type	Description
`name`	text	Name of the created pipeline.
`destination_type`	text	Type of the pipeline destination.
`destination_schema`	text	Schema of the destination.
`destination`	text	Name of the destination.
`destination_key_column`	text	Key column in the destination.
`destination_data_column`	text	Data column in the destination.

Example

-- Single-step pipeline: chunk text from a table into a destination table
SELECT aidb.create_pipeline(
    name                => 'my_chunker',
    source              => 'source_docs',
    source_key_column   => 'id',
    source_data_column  => 'body',
    destination         => 'chunked_docs',
    step_1              => 'ChunkText',
    step_1_options      => aidb.chunk_text_config(200, 250, 25),
    auto_processing     => 'Live'
);

-- Multi-step pipeline: parse PDF, then embed into a knowledge base
SELECT aidb.create_pipeline(
    name                => 'pdf_to_kb',
    source              => 'pdf_volume',
    destination         => 'my_kb',
    step_1              => 'ParsePdf',
    step_1_options      => aidb.pdf_parse_config(),
    step_2              => 'KnowledgeBase',
    step_2_options      => aidb.knowledge_base_config('my_model', 'Text'),
    auto_processing     => 'Background',
    background_sync_interval => '60 seconds'
);

`aidb.update_pipeline`

Updates the auto-processing settings for an existing pipeline.

Parameters

Parameter	Type	Default	Description
`name`	TEXT	Required	Name of the pipeline to update.
`auto_processing`	aidb.PipelineAutoProcessingMode	NULL	New auto-processing mode.
`batch_size`	INT	NULL	New batch size.
`background_sync_interval`	INTERVAL	NULL	New background sync interval.

Example

SELECT aidb.update_pipeline('my_chunker', auto_processing => 'Background', background_sync_interval => '5 minutes');

`aidb.delete_pipeline`

Deletes a pipeline and its configuration. Doesn't delete the source or destination tables.

Parameters

Parameter	Type	Default	Description
`name`	TEXT	Required	Name of the pipeline to delete.

Example

SELECT aidb.delete_pipeline('my_chunker');

`aidb.run_pipeline`

Manually triggers a pipeline to execute immediately, regardless of its auto_processing mode.

Parameters

Parameter	Type	Default	Description
`pipeline_name`	TEXT	Required	Name of the pipeline to run.

Example

SELECT aidb.run_pipeline('my_chunker');

`aidb.get_pipeline_metrics`

Returns current processing statistics for a pipeline.

Parameters

Parameter	Type	Default	Description
`name`	TEXT	Required	Name of the pipeline.

Returns

Column	Type	Description
`name`	text	Name of the pipeline.
`auto_processing`	text	Current auto-processing mode.
`count_events`	bigint	For table sources: number of unprocessed change events.
`count_source`	bigint	Total records in the source (for table sources).
`count_known_objects`	bigint	Total known objects (for volume sources).
`last_full_run_id`	bigint	ID of the last completed full scan (for volume sources).
`count_destination`	bigint	Total records in the destination.
`status`	text	Current pipeline status.

Example

SELECT * FROM aidb.get_pipeline_metrics('my_chunker');

Pipeline step config helpers

These functions return a JSONB configuration object for use in step_N_options parameters of aidb.create_pipeline.

`aidb.chunk_text_config`

Configures a ChunkText step to split text into smaller segments.

Parameters

Parameter	Type	Default	Description
`desired_length`	INTEGER	Required	Target chunk size.
`max_length`	INTEGER	NULL	Maximum allowed chunk size.
`overlap_length`	INTEGER	NULL	Number of units to overlap between consecutive chunks.
`strategy`	TEXT	NULL	Chunking unit: `'chars'` (default) or `'words'`.

Example

-- Chunk into ~200 character segments, max 250, with 25-character overlap
SELECT aidb.chunk_text_config(200, 250, 25, 'chars');

`aidb.summarize_text_config`

Configures a SummarizeText step to summarize text using a language model.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Name of the model to use for summarization.
`chunk_config`	JSONB	NULL	Optional chunking config (from `aidb.chunk_text_config`) applied before summarizing.
`prompt`	TEXT	NULL	Custom prompt to guide the summarization.
`strategy`	TEXT	NULL	`'append'` (default) or `'reduce'`.
`reduction_factor`	INTEGER	NULL	With `'reduce'` strategy: aggressiveness of each reduction pass (default: 3).
`inference_config`	JSONB	NULL	Optional inference settings (from `aidb.inference_config`).

Example

SELECT aidb.summarize_text_config(
    'my_llm',
    chunk_config => aidb.chunk_text_config(100, 100, 10, 'words'),
    prompt       => 'Summarize concisely',
    strategy     => 'reduce',
    reduction_factor => 4
);

`aidb.ocr_config`

Configures a PerformOcr step to extract text from images using a model.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Name of the OCR model to use.

Example

SELECT aidb.ocr_config('my_ocr_model');

`aidb.html_parse_config`

Configures a ParseHtml step to extract text from HTML content.

Parameters

Parameter	Type	Default	Description
`method`	TEXT	NULL	Parsing method to use. If `NULL`, uses the default method.

Example

SELECT aidb.html_parse_config();

`aidb.pdf_parse_config`

Configures a ParsePdf step to extract content from PDF documents.

Parameters

Parameter	Type	Default	Description
`method`	TEXT	NULL	Parsing method to use. If `NULL`, uses the default method.
`allow_partial_parsing`	BOOLEAN	NULL	When `true`, returns partial results if some pages cannot be parsed.

Example

SELECT aidb.pdf_parse_config(allow_partial_parsing => true);

`aidb.knowledge_base_config`

Configures a KnowledgeBase step to compute and store embeddings.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Name of the embedding model to use.
`data_format`	aidb.PipelineDataFormat	Required	Format of the data being embedded.
`distance_operator`	aidb.DistanceOperator	NULL	Distance function for similarity search. Defaults to `L2`.
`vector_index`	JSONB	NULL	Vector index configuration (from a vector index config helper).

Example

SELECT aidb.knowledge_base_config(
    'my_embedding_model',
    'Text',
    distance_operator => 'Cosine',
    vector_index      => aidb.vector_index_hnsw_config(m => 16, ef_construction => 64)
);

`aidb.knowledge_base_config_from_kb`

Configures a KnowledgeBase step to attach a pipeline to an existing knowledge base rather than creating a new one.

Parameters

Parameter	Type	Default	Description
`data_format`	aidb.PipelineDataFormat	Required	Format of the data being embedded.

Example

SELECT aidb.knowledge_base_config_from_kb('Text');

`aidb.inference_config`

Builds an inference configuration object for use with language model steps such as SummarizeText. All parameters are optional.

Parameters

Parameter	Type	Default	Description
`system_prompt`	TEXT	NULL	System prompt prepended to each request.
`temperature`	DOUBLE PRECISION	NULL	Sampling temperature (higher = more random).
`max_tokens`	INTEGER	NULL	Maximum number of tokens to generate.
`top_p`	DOUBLE PRECISION	NULL	Nucleus sampling threshold.
`seed`	BIGINT	NULL	Random seed for reproducible outputs.
`repeat_penalty`	REAL	NULL	Penalty for repeated tokens.
`repeat_last_n`	INTEGER	NULL	Number of recent tokens to apply repeat penalty over.
`thinking`	BOOLEAN	NULL	Enable extended reasoning (supported models only).
`extra_args`	JSONB	NULL	Additional provider-specific inference arguments.

Example

SELECT aidb.inference_config(
    system_prompt => 'You are a technical summarizer.',
    temperature   => 0.3,
    max_tokens    => 512
);

Vector index config helpers

These functions return a JSONB configuration for the vector_index parameter of aidb.knowledge_base_config.

`aidb.vector_index_hnsw_config`

Configures an HNSW index (pgvector).

Parameters

Parameter	Type	Default	Description
`vector_data_type`	TEXT	NULL	Vector storage type.
`m`	INTEGER	NULL	Maximum number of connections per node (default: 16).
`ef_construction`	INTEGER	NULL	Build-time search depth (default: 64).
`ef_search`	INTEGER	NULL	Query-time search depth.

Note

HNSW supports a maximum of 2000 dimensions. For higher-dimensional vectors, use `aidb.vector_index_disabled_config()`.

The following table shows how each distance_operator value maps to a pgvector ops class:

`distance_operator`	Index ops class
`L2`	`vector_l2_ops`
`InnerProduct`	`vector_ip_ops`
`Cosine`	`vector_cosine_ops`
`L1`	`vector_l1_ops`

Example

SELECT aidb.vector_index_hnsw_config(m => 16, ef_construction => 64);

`aidb.vector_index_ivfflat_config`

Configures a pgvector IVFFlat index.

Parameters

Parameter	Type	Default	Description
`vector_data_type`	TEXT	NULL	Vector storage type.
`lists`	INTEGER	NULL	Number of clusters (inverted lists).
`probes`	INTEGER	NULL	Number of clusters to search at query time.

Example

SELECT aidb.vector_index_ivfflat_config(lists => 100);

`aidb.vector_index_chord_hnsw_config`

Configures a VectorChord HNSW index.

Parameters

Parameter	Type	Default	Description
`vector_data_type`	TEXT	NULL	Vector storage type.
`m`	INTEGER	NULL	Maximum connections per node.
`ef_construction`	INTEGER	NULL	Build-time search depth.
`max_connections`	INTEGER	NULL	Maximum connections in the graph.
`ml`	DOUBLE PRECISION	NULL	Level multiplier controlling graph layer structure.

Example

SELECT aidb.vector_index_chord_hnsw_config(m => 16, ef_construction => 64);

`aidb.vector_index_chord_vchordq_config`

Configures a VectorChord Vchordq index.

Parameters

Parameter	Type	Default	Description
`vector_data_type`	TEXT	NULL	Vector storage type.
`lists`	TEXT	NULL	Number of clusters.
`spherical_centroids`	BOOLEAN	NULL	Use spherical (normalized) centroids when clustering.

Example

SELECT aidb.vector_index_chord_vchordq_config(lists => '100', spherical_centroids => true);

`aidb.vector_index_hsphere_optimized_config`

Configures an HSphere Optimized index.

Parameters

Parameter	Type	Default	Description
`clusters`	INTEGER	Required	Number of clusters.
`precision_val`	DOUBLE PRECISION	Required	Indexing precision value.
`vector_data_type`	TEXT	NULL	Vector storage type.

Example

SELECT aidb.vector_index_hsphere_optimized_config(clusters => 256, precision_val => 0.95);

`aidb.vector_index_disabled_config`

Disables automatic vector index creation. Use this when your embedding dimensions exceed 2000 or when you want to manage indexes manually.

Example

SELECT aidb.vector_index_disabled_config();

Model config helpers

These functions return a JSONB configuration object for the config parameter of aidb.create_model. Each helper corresponds to a specific model provider.

`aidb.embeddings_config`

Configuration for generic OpenAI-compatible embeddings endpoints.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model identifier as expected by the API.
`api_key`	TEXT	NULL	API key for authentication.
`basic_auth`	TEXT	NULL	Basic auth credentials (`user:password`).
`url`	TEXT	NULL	API endpoint URL.
`max_concurrent_requests`	INTEGER	NULL	Maximum concurrent requests to the model endpoint.
`max_batch_size`	INTEGER	NULL	Maximum number of inputs per batch request.
`input_type`	TEXT	NULL	Input type hint for encoding (provider-specific).
`input_type_query`	TEXT	NULL	Input type hint for query encoding (provider-specific).
`tls_config`	JSONB	NULL	TLS configuration (see `aidb.create_model`).
`is_hcp_model`	BOOLEAN	NULL	Set to `true` if referencing a model running on HCP.

Example

SELECT aidb.create_model(
    'my_embedder',
    'openai_embeddings',
    config => aidb.embeddings_config(
        model   => 'text-embedding-3-small',
        api_key => 'sk-...',
        url     => 'https://api.openai.com/v1'
    )
);

`aidb.completions_config`

Configuration for generic OpenAI-compatible chat/completions endpoints.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model identifier.
`api_key`	TEXT	NULL	API key for authentication.
`basic_auth`	TEXT	NULL	Basic auth credentials (`user:password`).
`url`	TEXT	NULL	API endpoint URL.
`max_concurrent_requests`	INTEGER	NULL	Maximum concurrent requests to the endpoint.
`max_tokens`	JSONB	NULL	Max tokens config (from `aidb.max_tokens_config`).
`is_hcp_model`	BOOLEAN	NULL	Set to `true` if referencing a model running on HCP.
`system_prompt`	TEXT	NULL	Default system prompt.
`temperature`	DOUBLE PRECISION	NULL	Sampling temperature.
`top_p`	DOUBLE PRECISION	NULL	Nucleus sampling threshold.
`seed`	BIGINT	NULL	Random seed for reproducible outputs.
`thinking`	BOOLEAN	NULL	Enable extended reasoning (supported models only).
`extra_args`	JSONB	NULL	Additional provider-specific arguments.

Example

SELECT aidb.create_model(
    'my_llm',
    'openai_completions',
    config => aidb.completions_config(
        model       => 'gpt-4o',
        api_key     => 'sk-...',
        temperature => 0.2
    )
);

`aidb.max_tokens_config`

Builds the max_tokens object for use with aidb.completions_config.

Parameters

Parameter	Type	Default	Description
`size`	INTEGER	Required	Maximum number of tokens to generate.
`format`	TEXT	NULL	Token format hint (provider-specific, optional).

Example

SELECT aidb.max_tokens_config(1024);

`aidb.bert_config`

Configuration for local BERT-based embedding models.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model name or path (HuggingFace identifier).
`revision`	TEXT	NULL	Model revision or branch.
`cache_dir`	TEXT	NULL	Local directory for caching model files.

Example

SELECT aidb.create_model('my_bert', 'bert_local', config => aidb.bert_config('sentence-transformers/all-MiniLM-L6-v2'));

`aidb.clip_config`

Configuration for local CLIP multimodal models (text and image embeddings).

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model name or path (HuggingFace identifier).
`revision`	TEXT	NULL	Model revision or branch.
`cache_dir`	TEXT	NULL	Local directory for caching model files.
`image_size`	INTEGER	NULL	Input image size in pixels (e.g., 224).

Example

SELECT aidb.create_model('my_clip', 'clip_local', config => aidb.clip_config('openai/clip-vit-base-patch32'));

`aidb.llama_config`

Configuration for local Llama-family language models.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model name or path.
`revision`	TEXT	NULL	Model revision or branch.
`cache_dir`	TEXT	NULL	Local directory for caching model files.
`system_prompt`	TEXT	NULL	Default system prompt.
`use_flash_attention`	BOOLEAN	NULL	Enable Flash Attention for faster inference.
`model_path`	TEXT	NULL	Explicit local path to model weights (overrides `model`).
`seed`	BIGINT	NULL	Random seed for reproducible outputs.
`temperature`	DOUBLE PRECISION	NULL	Sampling temperature.
`top_p`	DOUBLE PRECISION	NULL	Nucleus sampling threshold.
`sample_len`	INTEGER	NULL	Number of tokens to generate per sample.
`use_kv_cache`	BOOLEAN	NULL	Enable key-value cache for faster inference.
`repeat_penalty`	REAL	NULL	Penalty applied to repeated tokens.
`repeat_last_n`	INTEGER	NULL	Number of recent tokens to consider for repeat penalty.

Example

SELECT aidb.create_model('my_llama', 'llama_local', config => aidb.llama_config('meta-llama/Llama-3.2-3B-Instruct', temperature => 0.5));

`aidb.t5_config`

Configuration for local T5 text-to-text models.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	Model name or path.
`revision`	TEXT	NULL	Model revision or branch.
`temperature`	DOUBLE PRECISION	NULL	Sampling temperature.
`seed`	BIGINT	NULL	Random seed.
`max_tokens`	INTEGER	NULL	Maximum output tokens.
`repeat_penalty`	REAL	NULL	Penalty for repeated tokens.
`repeat_last_n`	INTEGER	NULL	Recent token window for repeat penalty.
`model_path`	TEXT	NULL	Explicit local path to model weights.
`cache_dir`	TEXT	NULL	Local directory for caching model files.
`top_p`	DOUBLE PRECISION	NULL	Nucleus sampling threshold.

Example

SELECT aidb.create_model('my_t5', 't5_local', config => aidb.t5_config('google/flan-t5-base'));

`aidb.gemini_config`

Configuration for Google Gemini models.

Parameters

Parameter	Type	Default	Description
`api_key`	TEXT	Required	Google API key.
`model`	TEXT	NULL	Gemini model identifier (e.g., `gemini-2.0-flash`).
`url`	TEXT	NULL	API endpoint URL override.
`max_concurrent_requests`	INTEGER	NULL	Maximum concurrent requests to the API.
`thinking_budget`	INTEGER	NULL	Extended thinking token budget (Gemini 2.x models).

Example

SELECT aidb.create_model('my_gemini', 'gemini', config => aidb.gemini_config('AIza...', model => 'gemini-2.0-flash'));

`aidb.nim_clip_config`

Configuration for NVIDIA NIM CLIP multimodal embedding models.

Parameters

Parameter	Type	Default	Description
`api_key`	TEXT	NULL	API key for NIM authentication.
`basic_auth`	TEXT	NULL	Basic auth credentials.
`model`	TEXT	NULL	NIM CLIP model identifier.
`url`	TEXT	NULL	NIM endpoint URL.
`is_hcp_model`	BOOLEAN	NULL	Set to `true` if the model is running on HCP.

Example

SELECT aidb.create_model('my_nim_clip', 'nim_clip', config => aidb.nim_clip_config(api_key => 'nvapi-...', model => 'nvidia/nvclip'));

`aidb.nim_ocr_config`

Configuration for NVIDIA NIM OCR models.

Parameters

Parameter	Type	Default	Description
`api_key`	TEXT	NULL	API key for NIM authentication.
`basic_auth`	TEXT	NULL	Basic auth credentials.
`url`	TEXT	NULL	NIM endpoint URL.
`model`	TEXT	NULL	NIM OCR model identifier.
`is_hcp_model`	BOOLEAN	NULL	Set to `true` if the model is running on HCP.

Example

SELECT aidb.create_model('my_nim_ocr', 'nim_ocr', config => aidb.nim_ocr_config(api_key => 'nvapi-...'));

`aidb.nim_reranking_config`

Configuration for NVIDIA NIM reranking models.

Parameters

Parameter	Type	Default	Description
`api_key`	TEXT	NULL	API key for NIM authentication.
`basic_auth`	TEXT	NULL	Basic auth credentials.
`model`	TEXT	NULL	NIM reranking model identifier.
`url`	TEXT	NULL	NIM endpoint URL.
`is_hcp_model`	BOOLEAN	NULL	Set to `true` if the model is running on HCP.

Example

SELECT aidb.create_model('my_reranker', 'nim_reranking', config => aidb.nim_reranking_config(api_key => 'nvapi-...', model => 'nvidia/nv-rerankqa-mistral-4b-v3'));

`aidb.openrouter_chat_config`

Configuration for chat/completions models accessed via OpenRouter.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	OpenRouter model identifier.
`api_key`	TEXT	NULL	OpenRouter API key.
`url`	TEXT	NULL	API endpoint URL override.
`max_concurrent_requests`	INTEGER	NULL	Maximum concurrent requests.
`max_tokens`	JSONB	NULL	Max tokens config (from `aidb.max_tokens_config`).

Example

SELECT aidb.create_model('my_or_chat', 'openrouter_chat', config => aidb.openrouter_chat_config('anthropic/claude-3-5-haiku', api_key => 'sk-or-...'));

`aidb.openrouter_embeddings_config`

Configuration for embedding models accessed via OpenRouter.

Parameters

Parameter	Type	Default	Description
`model`	TEXT	Required	OpenRouter embeddings model identifier.
`api_key`	TEXT	NULL	OpenRouter API key.
`url`	TEXT	NULL	API endpoint URL override.
`max_concurrent_requests`	INTEGER	NULL	Maximum concurrent requests.
`max_batch_size`	INTEGER	NULL	Maximum inputs per batch request.

Example

SELECT aidb.create_model('my_or_embedder', 'openrouter_embeddings', config => aidb.openrouter_embeddings_config('mistral/mistral-embed', api_key => 'sk-or-...'));

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