moneo

moneo / laravel-rag

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A complete, driver based RAG pipeline for Laravel with pgvector & sqlite-vec, streaming, agentic retrieval, hybrid search, evals, MCP server, Filament admin.

ai laravel pg-vector php rag
15
0
100% credibility
Found Mar 30, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
PHP
AI Summary

A Laravel package that enables building AI-powered search and conversational features by processing documents into a searchable knowledge base.

How It Works

1
🔍 Discover the tool

You find a handy addition for your web app that lets it answer questions using your own files and documents.

2
🚀 Add to your app

You bring the tool into your existing project with a quick and easy step.

3
💾 Ready your storage

You prepare a simple space in your app to hold all the smart knowledge from your files.

4
📚 Feed in your files

You upload documents or add text, and it automatically turns them into searchable pieces.

5
🧠 Link smart helper

You connect a thinking service so your app can understand questions and pull the right info.

6
💬 Chat and query

You type questions into a chat box or test area and get instant, accurate answers with sources shown.

✨ Smart app alive!

Your app now chats intelligently about your exact content, delighting users with spot-on responses.

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Star Growth

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AI-Generated Review

What is laravel-rag?

Laravel-rag delivers a full RAG pipeline for Laravel apps, letting you ingest docs or DB content, chunk it smartly, embed with OpenAI or Ollama, retrieve via pgvector or sqlite-vec, and generate answers—all via simple facades like `Rag::from(Document::class)->ask('query')`. It solves the hassle of wiring AI search into PHP apps without third-party services, handling everything from hybrid search to streaming responses. Built for PHP/Laravel 11+, it includes Artisan commands like `rag:index` and `rag:eval` for quick setup.

Why is it gaining traction?

Unlike scattered RAG libs, this offers driver-based swaps (pgvector for prod, sqlite-vec for dev) with Laravel-native traits for models, plus agentic retrieval, LLM re-ranking, and evals for faithfulness/relevancy. Devs love the drop-in Livewire chat component, Filament admin for testing embeddings, and MCP server for tools like Cursor/Claude. It's a complete driver pack that cuts API costs 60-80% via caching, with fluent pipelines beating manual OpenAI/Laravel Ollama rag setups.

Who should use this?

Laravel backend devs building internal knowledge bases, customer support chatbots, or doc search over Eloquent models. Ideal for teams doing complete driver training on vector DBs without ops overhead, or prototyping RAG evals like context recall on company data. Skip if you're not in Laravel or need non-PHP stacks.

Verdict

Grab it for Laravel AI prototypes—insane feature density with 99% test coverage and PHPStan level 9—but at 15 stars and 1.0% credibility, treat as early alpha despite polished docs and CI. Production? Vectorize a small index first, run evals, then scale.

(198 words)

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