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49 lines
3.1 KiB
Plaintext
---
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title: "Kiln AI"
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sidebarTitle: "Kiln"
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---
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[**Kiln**](https://kiln.tech) 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.
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## Quick Start: Build a RAG Pipeline in 5 Minutes with Kiln and LanceDB {#quick-start-build-a-rag-pipeline-in-5-minutes-with-kiln-and-lancedb}
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Watch the [quick start overview on Vimeo](https://vimeo.com/1119945690).
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Kiln's [app](https://kiln.tech/download) makes it easy to:
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- Build a RAG pipeline with a simple drag-and-drop interface
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- [Compare](#find-the-best-rag-pipeline-for-your-use-case) search index options (powered by LanceDB), document extractors, embedding models, and chunking strategies
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- Create end-to-end [evaluations](https://docs.kiln.tech/docs/evaluations) to determine which search configuration works best for your use case
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- Load your data from Kiln into [LanceDB Enterprise](/enterprise) for production use
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- Iterate with confidence by evaluating new content, prompts, models, and embeddings in minutes instead of weeks
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## Find the Best RAG Pipeline for Your Use Case {#find-the-best-rag-pipeline-for-your-use-case}
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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.
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Start with pre-configured templates for state-of-the-art RAG at various performance/quality/cost levels, or experiment with any combination of options:
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|Area|Technologies|Description|
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|Search Index|LanceDB|Compare LanceDB's vector search, full-text search (BM25), and hybrid search to find the best approach for your use case.|
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|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.|
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|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.|
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|Embeddings|Embedding models from Gemini, OpenAI, Nomic, Qwen, and more|Find the embedding model best suited to your use case.|
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|Chunking|LlamaIndex|Find the ideal chunk size and method.|
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## Get Started {#get-started}
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To get started, download the [Kiln App](https://kiln.tech/download), create a project, and navigate to "Docs & Search".
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See the [Kiln documentation for creating a RAG system](https://docs.kiln.tech/docs/documents-and-search-rag) for details on each step of the process.
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## More Information {#more-information}
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- [Kiln Homepage](https://kiln.tech)
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- [Download the Kiln App](https://kiln.tech/download)
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- [Kiln GitHub Repository](https://github.com/Kiln-AI/Kiln)
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- [Building RAG Systems - Kiln Documentation](https://docs.kiln.tech/docs/documents-and-search-rag)
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- [Python Library](https://pypi.org/project/kiln-ai/) or `pip install kiln_ai`
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