Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI - Qdrant

This post details the integration of Qdrant with Relari AI for data-driven RAG evaluation. It introduces two key evaluation methods: Top-K parameter optimization and Auto Prompt Optimization. The post provides a practical example using the GitLab legal policies dataset, demonstrating how to build a RAG app with Qdrant, FastEmbed, and LangChain, log retrieval results for different Top-K values, and submit these results to Relari for performance analysis using metrics like Precision/Recall and Rank-Aware methods. It also outlines the setup for Auto Prompt Optimization by analyzing prompts and setting up a base prompt.