Ragify.
A RAG evaluation platform for testing retrieval strategies, assessing answer quality, and analyzing retrieval performance.
See the experience.
One product. Every screen.
Illustrative visual previews. Replace with real product screenshots.
Behind the build.
The Problem
Building a RAG application is only the first step. Developers also need to understand whether retrieved documents are relevant, generated answers are faithful to their sources, and retrieval configurations deliver reliable results. Ragify focuses on making these evaluations easier to perform and understand.
The Approach
I worked on the RAG evaluation architecture, retrieval workflows, vector database integration, backend services, database management, and evaluation functionality. I also worked with containerized infrastructure and database migrations.
Decisions that matter.
Qdrant serves as the vector database for semantic search and document retrieval. The evaluation workflow is designed to assess retrieval behavior and answer quality.
PostgreSQL supports structured application data, while Alembic manages database schema migrations. Separating relational storage from vector search keeps their responsibilities clear.
Docker provides containerized database services for consistent local development. The architecture separates retrieval, evaluation logic, and persistent storage.
Evaluation focuses on measures such as faithfulness, context precision, and answer relevance to help compare RAG configurations.
Built with purpose.
Project Record
Ragify combines retrieval evaluation, vector search infrastructure, and evaluation reporting in one project. It provides a foundation for comparing RAG configurations and understanding answer quality. Independently verified benchmark results are not yet published.
The evaluation workflow and infrastructure are under development. Public benchmark results, production-scale testing, and independently verified performance measurements are not currently available.
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