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Vendor rubyat
Package laravel-rag
Short Description Add semantic search and Retrieval-Augmented Generation (RAG) to any Laravel app with PostgreSQL + pgvector.
License MIT

Keywords laravel openai embeddings rag pgvector semantic-search vector-search

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rubyat/laravel-rag

A production-ready Retrieval-Augmented Generation (RAG) toolkit for Laravel with PostgreSQL + pgvector. Ingest documents, store embeddings, and answer questions grounded only in your own content — with source citations.

Features

🧩 Document ingestion — text is chunked (configurable size/overlap), embedded, and stored.
🔎 pgvector vector search — cosine similarity over an HNSW index for high recall at any scale.
💬 RAG pipeline — retrieves the most relevant chunks and asks an LLM to answer from them.
📎 Source citations — every answer returns the chunks it used, with similarity scores.
🔌 Driver-based — swap embedding/chat providers; OpenAI built-in, plus deterministic fake drivers for offline dev and tests (no API key, no network).
⚙️ Sync or queued ingestion — inline by default, or dispatch a job for large documents.
🌐 HTTP API — ready-to-use POST /api/rag/ingest and POST /api/rag/ask endpoints.
✅ Tested — ships with a Pest suite that runs against pgvector using the fake drivers.

Requirements

PHP 8.3+
Laravel 11, 12, or 13
PostgreSQL with the pgvector extension (0.5+ for HNSW)

Installation

Publish the config and run the migration:

The migration runs CREATE EXTENSION IF NOT EXISTS vector, which needs a database role allowed to create extensions. The pgvector Docker image (pgvector/pgvector:pg16) is the quickest way to get it locally.

Configure a provider (or keep the offline fake drivers — no network needed):

Quick Start

In code

HTTP API

Ingest:

Ask:

Configuration

Published to config/rag.php. Every option is environment-driven:

Option	Env	Default	Description
`embedding_driver`	`RAG_EMBEDDING_DRIVER`	`openai`	`openai` or `fake`
`chat_driver`	`RAG_CHAT_DRIVER`	`openai`	`openai` or `fake`
`dimensions`	`RAG_DIMENSIONS`	`1536`	Vector size; must match the embedding model
`chunk_size`	`RAG_CHUNK_SIZE`	`1000`	Chunk length in characters
`chunk_overlap`	`RAG_CHUNK_OVERLAP`	`200`	Overlap between chunks
`top_k`	`RAG_TOP_K`	`4`	Chunks retrieved per question
`queue_ingestion`	`RAG_QUEUE_INGESTION`	`false`	Dispatch ingestion to the queue (needs a worker)
`register_routes`	`RAG_REGISTER_ROUTES`	`true`	Auto-register the HTTP routes
`route_prefix`	`RAG_ROUTE_PREFIX`	`api/rag`	Prefix for the package routes

The fake drivers are deterministic and require no network access — they power the test suite and let you try the full flow without API keys.

Swappable drivers

Embedding and chat providers are resolved from config and implement small contracts, so you can plug in your own:

Built in: OpenAiEmbeddingDriver / OpenAiChatDriver (HTTP) and FakeEmbeddingDriver / FakeChatDriver (offline, deterministic).

How it works

Documents are stored one row per chunk in the documents table (source, chunk_index, content, metadata, embedding vector(n)), indexed with HNSW (vector_cosine_ops). Retrieval orders by cosine distance (embedding <=> :query) and returns a similarity score in [0, 1].

Testing

The suite runs against PostgreSQL + pgvector using the deterministic fake drivers, so no API keys or network are required:

License

The MIT License (MIT). See LICENSE.

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Version v0.1.1 Release 02. Jun 2026
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Requires php Version ^8.3
illuminate/contracts Version ^11.0|^12.0|^13.0
illuminate/database Version ^11.0|^12.0|^13.0
illuminate/support Version ^11.0|^12.0|^13.0

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