Introduction

Introduction#

Where to start#

If you’re new to AIDB, follow this end-to-end workflow:

  1. ** Knowledge base overview ** — Learn the core concepts: what pipelines and knowledge bases are, and how they fit together.

  2. ** Installing AIDB ** — Install the aidb package, configure shared_preload_libraries , and enable the extension in your database.

  3. ** 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 default local model (no setup needed) or an external API model that you register with aidb.create_model() . Do this before creating pipelines.

  4. ** 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.

  5. ** Agents reference ** (optional) — Build an in-database agent that reasons over multiple steps and calls Tools reference — AIDB’s own operations, your own SQL queries, or tools imported from an external MCP server.

  6. ** AI 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.

  7. ** Knowledge bases reference ** — Query the vector embeddings your pipeline produced. Use aidb.retrieve_text() or aidb.retrieve_key() for semantic search, or combine vector and keyword search with hybrid search.

  8. ** Semantic knowledge bases ** (optional) — Index your schema — tables, views, columns, and comments — for natural-language schema search and text-to-SQL, so questions map to the right relations and to reusable, parameterized queries.

Documentation map#

Section

What it covers

AIDB 7.2.0 release notes

What changed in each version

Compatibility

Supported platforms and Postgres versions for AIDB and PGFS

Installing AIDB

How to install and configure the AIDB extension

Integrating models

Local models, external APIs (OpenAI, NIM, Gemini, OpenRouter), and supported model variants

Configuring external storage

Using S3-compatible object stores and local file systems as pipeline data sources

Agents reference

Building in-database agents, invoking them, and delegation

Tools reference

The unified tool catalog: native tools, custom SQL tools, and MCP tools

AI pipelines

Defining pipelines, pipeline steps, orchestration, and examples

Knowledge bases reference

Semantic search, hybrid search, and vector index options

Semantic knowledge bases

Schema-metadata search and text-to-SQL: semantic KBs, semantic aliases, and agent tools

SQL functions

Standalone SQL functions for chunking, parsing, OCR, summarization, and embedding

AI pipelines

Full API reference for all AIDB functions and views
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