(2026 Updated) Database Backup Automation for Laravel Projects Using Docker Compose

danielpetrica.com - submitted by Andrei-Daniel Petrica

I updated My popular guide on automated database backups with Docker Compose, now using the renamed nfrastack/container-db-backup image with multi-job support. Now it Includes a handy interactive configurator tool that generates docker-compose and .env files on the fly. Built for devs running multiple Laravel projects who want painless, scheduled backups to S3 or local storage.

Read more [danielpetrica.com]

Wiring Tools to a Laravel AI Agent Is the Easy Part

mujahidabbas.dev - submitted by Muhammad Mujahid Abbas

Wiring a tool onto a Laravel AI agent takes one line, and every tutorial stops there. The hard part is composing several into one loop: scraped web text becomes part of your prompt, tool output quietly balloons your token bill, and async tools never finish in one turn. One real research agent on the Laravel AI SDK toolkit, with the app on GitHub.

Read more [mujahidabbas.dev]

Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval

mujahidabbas.dev - submitted by Muhammad Mujahid Abbas

Every Laravel RAG tutorial builds the same ingestion pipeline (chunk, embed, store) and stops the moment the agent answers on screen. None of them check whether retrieval is any good. But retrieval quality is decided at ingestion, before the model runs once, and four decisions there fail with no error, no exception, no failed test: - **Chunking** that severs the answer mid-sentence, so `answer@1` falls while `source hit@1` still looks healthy. - An **HNSW index** built with `vector_l2_ops` while you query with cosine `<=>`. Postgres silently ignores the index and scans every row. Laravel 13's native `whereVectorSimilarTo()` hardcodes `<=>`, so it's easier to hit than ever. Shown with `EXPLAIN`. - The **embedding dimension** baked into the `vector(1536)` column type, so "shrink it to save storage" is a migration plus a full re-embed that quietly drops retrieval to 47%. - **Ingesting and querying with different models**, which turns every distance into noise. Each bug is real code from a working repo, proven against an eval suite. It's the prequel to my earlier "Evaluating RAG in Laravel" post: build it, prove it, tune it. Every example verified against `laravel/ai` v0.7.2 and pgvector, with the full repo to clone.

Read more [mujahidabbas.dev]