Add Kiln to integrations section

content/docs/integrations/_index.md

@@ -11,6 +11,6 @@ hide_toc: true
 |:----------------|:-------------|
 | [Embedding Models](/docs/integrations/embedding/) | Connect with popular embedding models including OpenAI, Cohere, Hugging Face, and more |
 | [Reranking Models](/docs/integrations/reranking/) | Enhance search results with advanced reranking models and techniques |
-| [AI Frameworks](/docs/integrations/frameworks/) | Integrate with LangChain, LlamaIndex, Haystack, and other AI development frameworks |
+| [AI Frameworks](/docs/integrations/frameworks/) | Integrate with LangChain, LlamaIndex, Kiln, and other AI development frameworks |
 | [Data Platforms](/docs/integrations/platforms/) | Deploy LanceDB on AWS, Google Cloud, Azure, and other cloud platforms |
 

content/docs/integrations/frameworks/_index.md

@@ -11,6 +11,7 @@ sidebar_collapsed: true
 |:----------|:------------|
 | [LangChain](/docs/integrations/frameworks/langchain) | Build LLM applications with memory, agents, and chat models using LanceDB as a vector store |
 | [LlamaIndex](/docs/integrations/frameworks/llamaindex) | Create data-aware LLM applications with structured data retrieval and RAG pipelines |
+| [Kiln AI](/docs/integrations/frameworks/kiln) | Build and evaluate RAG pipelines with a drag-and-drop UI or Python library, deploy to LanceDB |
 | [PromptTools](/docs/integrations/frameworks/prompttools) | Evaluate and optimize LLM prompts with LanceDB for storing and retrieving evaluation results |
 | [Pydantic](/docs/integrations/frameworks/pydantic) | Define structured data models and schemas for LanceDB tables with type safety |
 | [GenKit](/docs/integrations/frameworks/genkit) | Build AI applications with Google's GenKit framework using LanceDB for vector storage |

content/docs/integrations/frameworks/kiln.md

@@ -0,0 +1,50 @@
+---
+title: "Kiln AI"
+sidebar_title: "Kiln"
+weight: 1
+---
+
+[**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. 
+
+## Quick Start: Build a RAG Pipeline in 5 Minutes with Kiln & LanceDB
+
+{{< vimeo 1119945690 >}}
+
+<br>
+
+Kiln's [app](https://kiln.tech/download) makes it easy to:
+
+ - Build a RAG pipeline with a simple drag-and-drop interface
+ - [Compare](#find-the-best-rag-pipeline-for-your-use-case) search index options (powered by LanceDB), document extractors, embedding models, and chunking strategies
+ - Create end-to-end [evaluations](https://docs.kiln.tech/docs/evaluations) to determine which search configuration works best for your use case
+ - Load your data from Kiln into LanceDB Cloud for production use
+ - Iterate with confidence by evaluating new content, prompts, models, and embeddings in minutes instead of weeks
+
+## Find the Best RAG Pipeline for Your Use Case
+
+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.|
+
+## Get Started
+
+To get started, download the [Kiln App](https://kiln.tech/download), create a project, and navigate to "Docs & Search".
+
+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.
+
+## More Information
+
+ - [Kiln Homepage](https://kiln.tech)
+ - [Download the Kiln App](https://kiln.tech/download)
+ - [Kiln GitHub Repository](https://github.com/Kiln-AI/Kiln)
+ - [Building RAG Systems - Kiln Documentation](https://docs.kiln.tech/docs/documents-and-search-rag)
+ - [Python Library](https://pypi.org/project/kiln-ai/) or `pip install kiln_ai`
+

content/docs/overview/_index.md

@@ -114,7 +114,7 @@ LanceDB integrates seamlessly with the modern AI ecosystem, providing connectors
 
 | Category | Integrations | Documentation |
 |:---|:---|:---|
-| **[AI Frameworks](/docs/integrations/frameworks/)** | LangChain, LlamaIndex, Haystack | [AI Frameworks](/docs/integrations/frameworks/) |
+| **[AI Frameworks](/docs/integrations/frameworks/)** | LangChain, LlamaIndex, Kiln | [AI Frameworks](/docs/integrations/frameworks/) |
 | **[Embedding Models](/docs/integrations/embedding/)** | OpenAI, Cohere, Hugging Face, Custom Models | [Embedding Models](/docs/integrations/embedding/) |
 | **[Reranking Models](/docs/integrations/reranking/)** | BGE-reranker, Cohere Rerank, Custom Models | [Reranking Models](/docs/integrations/reranking/) |
 | **[Data Platforms](/docs/integrations/platforms/)** | DuckDB, Pandas, Polars | [Data Platforms](/docs/integrations/platforms/) |