In this article, we will explore another well-known vector store called ChromaDB. Chroma DB is a vector store that is open-source.

Learn about a simple method for combining vector stores, lexical search and prompt engineering to perform accurate RAG on commodity hardware.

Harness the power of word vectors and similarity searches to help discover data correlations with enterprise data to assist with a marketing campaign.

Prepare for complexities of deploying vector search in production with insights on indexing, metadata filtering, query language, and vector lifecycle management

A deep dive into 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications.

Learn the value of Retrieval Augmented Generation (RAG) in AI, revolutionizing customer interactions & enhancing dialogue systems with advanced NLP capabilities

A dive into multi-modal embedding and frame search, two advanced video search techniques

There are three main ways to incorporate domain knowledge in an LLM: prompt engineering, fine tuning, and retrieval augmentation.

We show how content-centric knowledge graphs – a vector-store allowing links between chunks – are an easy to use and efficient approach to improve RAG results.

Build a Graph-RAG agent using Neo4j and Milvus to combine graph and vector search, delivering accurate, context-rich answers to complex queries.

Rerankers are ML models that take a set of search results and reorder them to improve relevance. We tested 6 of them.

Learn how vector search helped build a powerful way to identify recurring payments in the financial sector.

Transform your search in 5 minutes! Implement AI-powered hybrid search with OpenSearch and cloud tools for faster, more relevant results.