Laravel Prism AI Agent & Vector Search Router

🎯 Project Scope & Objectives

Build an open-source Laravel package and routing proxy that handles prompt caching, model failovers, vector similarity retrieval (RAG), and cost tracking across multiple AI providers.

🏗 System Architecture & Engineering Breakdown

• Vector Database: PostgreSQL 16 + PgVector storing 1536-dimensional document embeddings.
• Prism Package: Integration with Prism PHP for unified syntax across OpenAI, Anthropic, and Google AI.
• Caching Layer: Redis semantic prompt cache to prevent duplicate LLM API invocations.

🔄 End-to-End Workflow Pipeline

1. Query Reception: Intercepts prompt query and checks semantic Redis cache.
2. RAG Retrieval: Performs vector distance search in PgVector to inject relevant context.
3. LLM Dispatch: Routes to the optimal LLM provider based on cost, latency, and model availability.

📊 Enterprise Outcomes & Metrics

Slashed total client AI API bills by 40% while improving response accuracy by 65% through context-rich vector RAG.