Turbine

What is Turbine?

Turbine automates data pipelines for AI applications. It connects to your databases, pulls your data, and organizes it for vector searches, keeping data in sync without requiring you to manage the underlying infrastructure. This makes it easier to build AI bots backed by searchable databases.

Key features:

• Database connections: Works with popular databases such as PostgreSQL, MongoDB, and MySQL, as well as sources like S3.
• Real-time updates: Syncs database changes as they happen, eliminating the need for batch jobs.
• Embedding storage: Supports storing embeddings in vector databases like Pinecone and Milvus.
• Model flexibility: Use a range of embedding models, from MiniLM-L6-V2 to OpenAI models, or bring your own.
• Customization: Configure embedding models, data filters, and which fields to include.

You can get started with Turbine’s SDKs for Python and TypeScript, its HTTP API, or the Turbine Console. LangChain AI bots can be integrated with just a few lines of code. Turbine is built for scalability, using distributed stream-processing platforms to handle data efficiently.

Who created Turbine?

Turbine launched with the goal of simplifying and automating the data pipelines that AI applications rely on. The founders’ identities are not disclosed.

What is Turbine used for?

• Data synchronization: Syncs data from virtually any source to any vector database you choose.
• Semantic search: Enables fast semantic searches over your database, with results that stay current through real-time syncing.
• AI application support: Prepares data for vector searches so AI bots can use language models effectively.
• LangChain integration: Connects to LangChain AI bots with minimal code.

Turbine suits teams building context-rich AI applications who want automated, real-time data pipelines instead of managing sync and embedding infrastructure themselves.