Kiln is a free tool for building production-ready AI systems, combining an intuitive desktop application and an open-source Python library. It supports RAG pipelines, evaluations, agents, MCP tool-calling, synthetic data generation, and fine-tuning. Kiln provides deep integration with LanceDB for vector search, full-text search (BM25), and hybrid search.
Kiln’s app makes it easy to:
There is no universal best RAG solution—only the best solution for your specific use case. Kiln makes it easy to compare state-of-the-art configurations and find which works best for you.
Start with pre-configured templates for state-of-the-art RAG at various performance/quality/cost levels, or experiment with any combination of options:
| Area | Technologies | Description |
|---|---|---|
| Search Index | LanceDB | Compare LanceDB’s vector search, full-text search (BM25), and hybrid search to find the best approach for your use case. |
| Content | Kiln Document Library | Collaborate on a document library with your team to find the best content for your RAG. Track every revision and tag document sets. |
| Document Extraction | Gemini, OpenAI GPT, Qwen VL, and more | Find the most accurate document extraction models for converting PDFs, images, audio, video, and other formats into textual data for RAG. |
| Embeddings | Embedding models from Gemini, OpenAI, Nomic, Qwen, and more | Find the embedding model best suited to your use case. |
| Chunking | LlamaIndex | Find the ideal chunk size and method. |
To get started, download the Kiln App , create a project, and navigate to “Docs & Search”.
See the Kiln documentation for creating a RAG system for details on each step of the process.