AIDB#
Where to start#
If you’re new to AIDB, follow this end-to-end workflow:
** Pipelines overview ** — Learn the core concepts: what pipelines and knowledge bases are, and how they fit together.
** Installing AIDB ** — Install the
aidbpackage, configureshared_preload_libraries, and enable the extension in your database.** Integrating models ** — Register the AI model that will power your pipeline. Every pipeline step that generates embeddings or processes text requires a model — either a built-in local model (no setup needed) or an external API model that you register with
aidb.create_model(). Do this before creating pipelines.** Configuring external storage ** (optional) — If your source data lives in S3 or a local file system rather than a Postgres table, configure a volume using PGFS so pipelines can read from it.
** Data pipelines ** — Define a pipeline that reads from your data source, transforms it (chunk, parse, embed), and writes the output to a knowledge base. This is where the model you registered in step 3 is put to work.
** Knowledge bases reference ** — Query the vector embeddings your pipeline produced. Use
aidb.retrieve_text()oraidb.retrieve_key()for semantic search, or combine vector and keyword search with hybrid search.
Documentation map#
Section |
What it covers |
|---|---|
What changed in each version |
|
Supported platforms and Postgres versions for AIDB and PGFS |
|
How to install and configure the AIDB extension |
|
Local models, external APIs (OpenAI, NIM, Gemini, OpenRouter), and supported model variants |
|
Using S3-compatible object stores and local file systems as pipeline data sources |
|
Defining pipelines, pipeline steps, orchestration, and examples |
|
Semantic search, hybrid search, and vector index options |
|
Standalone SQL functions for chunking, parsing, OCR, summarization, and embedding |
|
Full API reference for all AIDB functions and views |