AIDB v7

AIDB is an EDB-maintained PostgreSQL extension that brings AI data workflows directly into your Postgres database. Define pipelines in SQL to parse, chunk, embed, and index your data — then query it with semantic search, hybrid search, and LLM-powered retrieval. Common use cases include Retrieval-Augmented Generation (RAG), semantic search over documents and images, and automated document ingestion pipelines from S3 or local storage.

Where to start

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

1. Overview — Learn the core concepts: what pipelines and knowledge bases are, and how they fit together.

2. Install — 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 built-in local model (no setup needed) or an external API model that you register with aidb.create_model(). Do this before creating pipelines.

4. 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. 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.

6. Knowledge bases — 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.

Documentation map