Samples

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

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.

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

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

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

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

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

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

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.