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docs: add Keboola platform integration (#1634)
* Create keboola.md * Add Keboola documentation and integration details
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| [Bubble](/documentation/platforms/bubble/) | Development platform for application development with a no-code interface |
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| [BuildShip](/documentation/platforms/buildship/) | Low-code visual builder to create APIs, scheduled jobs, and backend workflows. |
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| [DocsGPT](/documentation/platforms/docsgpt/) | Tool for ingesting documentation sources and enabling conversations and queries. |
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| [Keboola](/documentation/platforms/keboola/) | Data operations platform that unifies data sources, transformations, and ML deployments. |
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| [Kotaemon](/documentation/platforms/kotaemon/) | Open-source & customizable RAG UI for chatting with your documents. |
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| [Make](/documentation/platforms/make/) | Cloud platform to build low-code workflows by integrating various software applications. |
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| [Mulesoft Anypoint](/documentation/platforms/mulesoft/) | Integration platform to connect applications, data, and devices across environments. |
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---
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title: Keboola
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---
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# Keboola
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[Keboola](https://www.keboola.com/) is a data operations platform that integrates data engineering, analytics, and machine learning tools into a single environment. It helps businesses unify their data sources, transform data, and deploy ML models to production.
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## Prerequisites
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1. A Qdrant instance to connect to. You can get a free cloud instance at [cloud.qdrant.io](https://cloud.qdrant.io/).
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2. A [Keboola](https://www.keboola.com/) account to develop your data workflows.
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## Setting Up
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- In your Keboola platform, navigate to the Components section.
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- Find and add the Qdrant component from the component marketplace.
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- Configure the connection to your Qdrant instance using your URL and API key.
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## Using Qdrant in Keboola
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With Keboola's Qdrant integration, you can:
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- **Data Pipeline Integration**: Extract data from any source in Keboola, transform it, and load vector embeddings into Qdrant for semantic search capabilities.
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- **Vector Database Management**: Create, manage, and update collections in Qdrant directly from your Keboola workflows.
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- **Orchestration**: Schedule and automate your vector database operations as part of your data pipeline.
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- **ML Operations**: Combine your machine learning models with vector search capabilities for advanced AI applications.
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## Example Use Case
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A common use case is to build a RAG (Retrieval Augmented Generation) system where:
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1. Data is extracted from multiple sources in Keboola
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2. Text is processed and transformed in Keboola's transformation engine
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3. Embeddings are generated and stored in Qdrant
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4. Applications query the Qdrant vectors for semantic search capabilities
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## Further Reading
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- [Keboola Documentation](https://help.keboola.com/)
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- [Keboola Academy](https://academy.keboola.com/)
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- [Data Operations with Keboola](https://www.keboola.com/blog/data-operations)
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