Turbine

Enhanced chatbot with semantic search.

apps development data platform

Tool Information

Primary Task	Apps
Category	specialized-technologies
Sub Categories	low-code-development api-and-development-tools
Pricing	From $49/mo
Country	India

Turbine is an automated data pipeline tool designed to support AI applications. It functions as a vector search engine that facilitates the synchronization of data from various databases and prepares it for vector searches. The tool allows users to leverage the latest language models to power their AI bots without needing to concern themselves with infrastructure management. Turbine offers several key features. It seamlessly integrates with existing databases such as PostgreSQL, MongoDB, and MySQL, with additional integrations planned for the future. The pipeline synchronizes database changes in real-time, eliminating the need for batch jobs. The tool enables blazing-fast and always up-to-date semantic searches over the database, eliminating the need for engineers to manually modify the data. Turbine supports storage of embeddings using leading vector databases Pinecone and Milvus. It also provides support for various embedding models, from smaller ones like MiniLM-L6-V2 to the latest OpenAI models. Getting started with Turbine is straightforward, thanks to its SDKs for Python and TypeScript. Users can also utilize the HTTP API if preferred. The tool offers extensive configurability, allowing users to optimize various aspects such as the choice of embedding model, data filters, and included fields. Integration with LangChain AI bots requires just a few lines of code. Turbine is designed with scalability in mind and utilizes modern distributed stream-processing platforms to handle data effectively. Its efficient design and functionality enable users to create AI applications that deliver accurate and context-rich results by leveraging the power of language models and searchable databases.

Turbine offers real-time semantic search over any database. It fetches changes from your database, creates vector embeddings, and stores them for efficient vector search.