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KDB.AI Documentation

Get started with KDB.AI, explore the functionality and customize to your needs. Use the following quick links below, or explore the menu to find out more.

Getting Started Data Reference
Quickstart Supported Data Types Python Client
Starter Edition Setup Query Release Notes
Self Managed Setup Search Glossary

What is KDB.AI?

KDB.AI is a powerful knowledge-based vector database and search engine that allows developers to build scalable, reliable and real-time applications by providing advanced search, recommendation and personalization for AI applications, using real-time data.

What can KDB.AI do?

KDB.AI allows you to set-up a knowledge-based vector database and search engine in a few simple steps. With KDB.AI you can:

  • Create an index of vectors (Flat, IVF, IVFPQ, or HNSW).
  • Append vectors to an index.
  • Perform fast vector similarity search with optional metadata filtering.
  • Persist an index to disk.
  • Load an index from disk.
Vector database overview A vector database is a specialized architecture that stores vector embeddings (or lists of numeric values) to represent data points. In contrast, traditional databases typically rely on structured tables with rows and columns, where each row represents a single data entry, and each column corresponds to a specific attribute or field of the data. Head to our [Learning Hub](https://kdb.ai/learning-hub/) to read our Vector Database 101 article.
Understanding indexes Vector databases utilize a crucial element known as the “Vector Index” to process data. The creation of this index involves applying an algorithm to the vector embeddings stored within the database. This algorithm functions to map these vectors to a specialized data structure, facilitating rapid searches. Searches are more efficient this way due to the index's condensed representation of the original vector data. This compactness reduces memory requirements and enhances accessibility when contrasted with processing searches via raw embeddings. You don't need to know the details of creating indices themselves with KDB.AI; they are easily created through simple commands that you choose. However, grasping the fundamental workings of vector indices and their various forms can be helpful in knowing which one to choose and when. Head to our [Learning Hub](https://kdb.ai/learning-hub/) to learn more about indexes.
Understanding LLMS Large Language Models (LLMs) and AI Chatbots are one of the key uses of vector databases. Vector databases facilitate the fast retrieval of embedded contextual data, and combined with other components are the backbone for conversational AI. Head to our [Learning Hub](https://kdb.ai/learning-hub/) to learn more about LLMs.

Support

Slack Channel

For assistance, post a question on our community Slack channel. Our support team will look into your query and get back to you.

Email

Email (mailto:support@cloud.kdb.ai) in ?? instances.