# Neon's PGrag Extension for Postgres RAG Pipelines

**Published:** 2026-05-30T05:02:02.000Z  
**Topic:** Google  
**Sentiment:** neutral  
**Publisher:** TrendWatcher — https://www.trendwatcher.in/article/33f0ba9b-c1af-4356-b942-8b796ff277d9

PGrag is a Neon integration for building retrieval-augmented generation pipelines directly in Postgres, utilizing specific preloaded libraries for text

PGrag is an integration developed by Neon designed to facilitate end-to-end retrieval-augmented generation (RAG) pipelines directly within the Postgres environment [2]. This tool bridges vector databases and text embedding, allowing users to perform complex AI-driven tasks without leaving the database system [2]. To function, the extension relies on specific preloaded libraries that must be configured during the project setup or update process [1].

**Key takeaways**
*   PGrag enables the construction of RAG pipelines directly inside Postgres, connecting vector databases and text embedding [2].
*   The extension requires specific preloaded libraries, including `rag_bge_small_en_v15` and `rag_jina_reranker_v1_tiny_en` [1].
*   Users can exclude these libraries via API calls if they do not utilize the PGrag extension [1].
*   The provided sources do not contain specific details regarding version 1.1.0.

## Configuration and Library Requirements

Neon's implementation of PGrag necessitates the use of shared preload libraries, which are loaded into memory when the Postgres server starts [1]. Specifically, the extension utilizes the libraries `rag_bge_small_en_v15` and `rag_jina_reranker_v1_tiny_en` [1]. These libraries are not enabled by default for all users; instead, they must be explicitly included in the `preloaded_libraries` object during a Create or Update project API call [1].

When configuring these libraries, users must specify all desired libraries in a single, comma-separated string. If a library is omitted from the API call, it will not be enabled [1]. Neon notes that the `neon` and `pg_stat_statements` libraries will remain enabled regardless of user configuration because they are utilized by the system-managed database [1].

## Managing Extension Dependencies

Database administrators have the option to manage these libraries based on their project needs. If a project does not use the PGrag extension, the associated libraries can be excluded from the API call to configure the project [1]. It is important to note that Neon does not enable libraries for all extensions that have them, and available libraries may differ depending on the Postgres version being used [1].

Furthermore, Neon does not actively maintain third-party extension code, meaning bugs or issues should be reported to the upstream maintainers rather than Neon support [1]. While Neon supports the extension for installation and use, the underlying code is maintained externally.

## Why it matters

Integrating RAG capabilities directly into Postgres via tools like

## Sources
1. Neon — [Supported Postgres extensions - Neon Docs](https://neon.com/docs/extensions/pg-extensions)
2. Dataroots — [Podcast Ep: #67 The AI Race: ChatGPT's New Web Search,](https://dataroots.io/blog/2367-the-ai-race-3a-chatgpt-27s-new-web-search-2c-meta-e2-80-99s-llama-ai-scaling-efforts-26-python-3-13-27s-upgrades)
3. News — [Ask HN: Local RAG with private knowledge base | Hacker News](https://news.ycombinator.com/item?id=41968366)

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Cite as: TrendWatcher, "Neon's PGrag Extension for Postgres RAG Pipelines", https://www.trendwatcher.in/article/33f0ba9b-c1af-4356-b942-8b796ff277d9