Vector Database
A specialized database designed to store, index, and query high-dimensional vector embeddings at scale.
Vector databases (such as Pinecone, Weaviate, Qdrant, and pgvector) are purpose-built to handle the approximate nearest-neighbor search operations required for semantic retrieval. Unlike traditional relational databases that query on exact values, vector databases find the most semantically similar items to a query vector using algorithms like HNSW (Hierarchical Navigable Small World) that balance speed and recall.
The performance characteristics of vector databases — query latency, indexing throughput, scalability, filtering capabilities — directly affect the user experience of document intelligence applications. A system with a 3-second retrieval step will feel slow for interactive Q&A. Vector databases also support metadata filtering, allowing retrieval to be restricted to specific documents, date ranges, or user-defined tags — essential for multi-tenant applications where users should only access their own documents.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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