Vector Retrieval and RAG · Public

pgai

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot5.8k stars

312 forks · 35 contributors

Doramagic.ai Last verification date: 2026-06-23 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-23

What is pgai?

pgai is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/timescale/pgai, https://github.com/timescale/pgai#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Repositorytimescale/pgai

5.8k stars · 312 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview & System Architecture

Related topics: Vectorizer Pipeline & Worker, Semantic Catalog (Text-to-SQL), Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Vectorizer Pipeline & Worker

Related topics: Overview & System Architecture, Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Semantic Catalog (Text-to-SQL)

Related topics: Overview & System Architecture, Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Embedding Providers, Model Integration & Known Issues

Related topics: Overview & System Architecture, Vectorizer Pipeline & Worker

Source: https://github.com/timescale/pgai / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/timescale/pgai, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars5.8k stars

Forks312 forks

Contributors35 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using pgai with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
[Bug]: semantic catalog text to sql stuck in refinement iterations
github / github_issue
02
[Feature]: Use ReRank models (i.e. qwen-reranker) through Ollama
github / github_issue
03
cannot import name 'Worker' from 'pgai'
github / github_issue
04
[Bug]: ollama_embed error on ollama higher 13.3
github / github_issue
05
[Feature]: RaBitQ
github / github_issue
06
HTTP client connection leak in embedders causes file descriptor exhausti
github / github_issue
07
Outdated dependency versions are blocking adoption
github / github_issue
08
[Feature]: OCR for PDF and DOCX
github / github_issue
09
[Feature]: llama.cpp embedding worker integration
github / github_issue
10
extension-0.11.2
github / github_release
11
pgai: v0.12.1
github / github_release
12
pgai: v0.12.0
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install pgai

Official start command · https://github.com/timescale/pgai#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

pgai Manual

A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL

Open the full manual

https://github.com/timescale/pgai Project Manual
Table of Contents
Overview & System Architecture
Related Pages
Purpose and Scope
High-Level Architecture
Core Components
The Vectorizer Pipeline

Overview & System Architecture

Related topics: Vectorizer Pipeline & Worker, Semantic Catalog (Text-to-SQL), Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Vectorizer Pipeline & Worker

Related topics: Overview & System Architecture, Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Semantic Catalog (Text-to-SQL)

Related topics: Overview & System Architecture, Embedding Providers, Model Integration & Known Issues

Source: https://github.com/timescale/pgai / Human Manual

Embedding Providers, Model Integration & Known Issues

Related topics: Overview & System Architecture, Vectorizer Pipeline & Worker

Source: https://github.com/timescale/pgai / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.