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Readme

@sf-bot/rag-core

Core RAG (Retrieval-Augmented Generation) schema with pgvector support for document embeddings.

Overview

This package provides the foundational database schema for storing and managing document embeddings using PostgreSQL and pgvector. It supports:

Multiple embedding models per collection
Semantic chunking strategies
JSON/CSV import and export for migrations

Schema

Tables

Table	Description
`rag.embedding_model`	Embedding model configurations (OpenAI, Cohere, local, etc.)
`rag.collection`	Document collections with chunking configuration
`rag.collection_model`	Links collections to embedding models (many-to-many)
`rag.document`	Source documents with content and metadata
`rag.chunk`	Document segments/chunks for embedding
`rag.embedding`	Vector embeddings (pgvector) linked to chunks

Chunking Configuration

The chunk_config JSONB column on rag.collection supports:

{
  "strategy": "semantic",
  "max_tokens": 512,
  "overlap_tokens": 50
}

Supported strategies: semantic, fixed, sentence, paragraph

Installation

pgpm deploy @sf-bot/rag-core

Requirements

PostgreSQL 17+
pgvector extension

License

SF License