AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Vector databases, vector search, and semantic search
A vector database stores vectors and supports similarity queries with metadata. Vector search is the numerical operation; semantic search is the application goal of finding meaningfully related content. Existing databases can also add vector indexes, so you…
Keyword search and BM25
Keyword search matches words or indexed terms rather than learned meaning. BM25 is a common relevance formula that rewards useful matching terms, accounts for document length, and avoids letting repeated words dominate indefinitely. It is usually implemented…
Hybrid search and metadata filtering
Hybrid search combines lexical and semantic candidate retrieval. Metadata filtering narrows records by explicit attributes such as tenant, policy version, date, or region. Filtering answers “which records are eligible,” while ranking answers “which eligible…
Reranking
Reranking is a second ranking stage that examines a short candidate list more carefully. A reranker often reads the query and each candidate together, allowing richer comparison than separate embeddings. It changes candidate order; it cannot recover a…
Choosing a vector/search store
Pinecone, Qdrant, Weaviate, pgvector, MongoDB Vector Search, Elasticsearch, and OpenSearch provide different ways to store and retrieve vector-backed knowledge. Some are dedicated vector services; others extend databases or search engines you may already…
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.