vector database

A vector database is a specialized data system for storing, indexing, and querying high-dimensional embedding vectors (numerical arrays). It enables items with similar meaning or structure to be close together in vector space.

It supports fast nearest-neighbor or similarity searches using different techniques, such as graph-based indices, inverted-file clustering, product quantization or hybrid indexes—often in conjunction with metadata filters and keyword plus vector hybrid ranking.

Core capabilities of a vector database include inserting, updating, deleting (CRUD/upsert) vectors and associated metadata, batch ingestion, support for similarity metrics like cosine similarity or inner product, and trade-offs between recall and latency.

Related Resources

Tutorial

Embeddings and Vector Databases With ChromaDB

Vector databases are a crucial component of many NLP applications. This tutorial will give you hands-on experience with ChromaDB, an open-source vector database that's quickly gaining traction. Along the way, you'll learn what's needed to understand vector databases with practical examples.

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For additional information on related topics, take a look at the following resources:

• Build an LLM RAG Chatbot With LangChain (Tutorial)
• LangGraph: Build Stateful AI Agents in Python (Tutorial)
• First Steps With LangChain (Course)
• Build an LLM RAG Chatbot With LangChain (Quiz)
• LangGraph: Build Stateful AI Agents in Python (Quiz)