Hands on exercises to get you started

  • KDB.AI Hugging Face Integration  

    The KDB.AI Hugging Face integration guide helps you execute a wide variety of tasks with your KDB.AI vector database.

  • LlamaIndex Advanced RAG

    Utilize LlamaIndex and KDB.AI for a RAG pipeline to semantically search and analyze financial publications over time and make better trading decisions.

  • Transformed Temporal Similarity Search

    Our model compresses time series data by over 99% while preserving its shape, enabling high-speed searches on large temporal datasets with minimal memory use.

  • Non-Transformed Temporal Similarity Search

    Efficiently analyze rapid time series data with direct similarity searches on columns, bypassing embedding and indexing—perfect for identifying trends and anomalies in financial markets.

  • Multimodal Retrieval Augmented Generation

    Simultaneously manage and search multiple data types in KDB.AI. Create multimodal embeddings, retrieve diverse data, and integrate it into LLM-driven response generation.

  • Metadata Filtering 

    Improve search performance in vector databases by incorporating metadata filtering, enhancing both the speed and precision of searches.

  • Hybrid Search 

    Increase the relevancy of search results by combining keyword-based sparse vector search with the semantic understanding of dense vector search.

  • Document Search

    Streamline semantic searches on unstructured texts with KDB.AI. From document ingestion to running similarity searches, discover how to effectively utilize our vector database.

  • Sentiment Analysis

    Extract insights from reviews using sentiment analysis combined with vector search, helping you understand customer experiences and identify areas for improvement.

  • Pattern Matching and Outliers

    Apply similarity search for pattern recognition in manufacturing data, supporting quality control, process optimization, and predictive maintenance.

  • Recommendation Systems

    Develop a music recommendation engine using vector embeddings from categorical and numerical music data, quickly identifying songs that match user inputs.

  • Retrieval Augmented Generation with LangChain

    Construct a comprehensive RAG pipeline with LangChain, from data ingestion to leveraging retrieved information for LLM response generation.

  • Image Search

    Utilize KDB.AI to quickly find similar brain scan images stored as vector embeddings. Learn the process from image embedding generation to executing image-based queries.

  • Quickstart Guide

    Hello, KDB.AI! Learn to get started with the KDB.AI vector database in 10 minutes.