What Is a Vector Database?

Quick Definition#

A vector database is a specialized data store designed to store, index, and retrieve high-dimensional numerical vectors — specifically, the embedding representations that language models and embedding models produce from text. The key capability that distinguishes a vector database from a traditional database is similarity search: rather than finding exact matches, a vector database finds the items in storage that are mathematically most similar to a query vector.

For agents, vector databases are the infrastructure that makes Retrieval-Augmented Generation (RAG) and persistent AI Agent Memory possible. To understand what vectors and embeddings are before diving into databases, read What Are Embeddings in AI?. Browse the full AI Agents Glossary for more related terms.

Why Vector Databases Matter for AI Agents#

Language model context windows have limits. You cannot give an agent access to your entire knowledge base, all customer records, or the full history of past interactions by putting everything in the prompt. Vector databases solve this by enabling selective retrieval: the agent generates an embedding of the current question, searches the vector database for semantically similar content, and retrieves only the most relevant pieces into its context.

This makes two critical capabilities possible:

RAG (Retrieval-Augmented Generation): The agent retrieves relevant documents from a knowledge base before reasoning, grounding its response in actual information rather than model memory. This reduces hallucinations on knowledge-intensive tasks.

Persistent memory: The agent stores summaries of past interactions, completed tasks, and accumulated knowledge as vectors. In future sessions, it can retrieve relevant memories without replaying the entire history.

For deployment-level platform considerations, see Best AI Agent Platforms in 2026.

How Vector Databases Work#

Storing vectors#

When a document, chunk of text, or memory record is added to a vector database, it is first converted to a vector embedding using an embedding model. This vector — typically a list of 384 to 3072 floating-point numbers depending on the model — is stored alongside the original content and any associated metadata.

Indexing#

Vector databases use specialized indexing algorithms (such as HNSW — Hierarchical Navigable Small World — or FAISS-based approaches) to organize vectors for efficient approximate nearest neighbor (ANN) search. Without indexing, querying a large vector database would require computing similarity against every stored vector sequentially, which becomes prohibitively slow at scale.

Querying with similarity search#

To retrieve relevant content, the agent:

1. Generates an embedding of the current query using the same embedding model used during ingestion
2. Sends the query vector to the vector database
3. The database returns the top-k vectors with the highest similarity scores (typically measured by cosine similarity or dot product)
4. The original content associated with those vectors is returned to the agent

The result is a ranked list of the most semantically relevant content — documents, memories, or records — for the agent's current task.

Major Vector Database Options#

Pinecone#

Pinecone is a fully managed cloud vector database that handles infrastructure, scaling, and index management automatically. It supports namespace-based separation of data, metadata filtering alongside vector search, and integrates with major LLM frameworks.

Best for: Teams that want production-scale vector search without managing infrastructure. Considerations: Managed service with associated costs; less control over data residency.

Chroma#

Chroma is an open-source, lightweight vector store designed for local development and smaller-scale applications. It can run in-memory or persist to disk, and integrates easily with LangChain and other frameworks.

Best for: Local development, prototyping, smaller-scale applications. Considerations: Horizontal scaling requires more effort than managed services.

Weaviate#

Weaviate is a feature-rich open-source vector database with GraphQL and REST APIs, built-in vectorization (it can call embedding models directly), and support for multi-modal content.

Best for: Teams that need flexible querying, self-hosted deployment, or multi-modal support. Considerations: More complex to configure and maintain than simpler alternatives.

pgvector#

pgvector is a PostgreSQL extension that adds vector storage and similarity search to a standard Postgres database. If your team already uses Postgres, pgvector is often the lowest-complexity path to adding vector capabilities.

Best for: Teams with existing Postgres infrastructure who want to avoid adding a separate service. Considerations: Performance at very large scale may be lower than dedicated vector databases; requires managing index tuning.

Using Vector Databases with Agents#

RAG pipeline integration#

In a RAG pipeline, the vector database sits between the agent and the knowledge base:

1. Documents are chunked and embedded at ingestion time
2. At query time, the agent embeds the user's question
3. The vector database returns the top-k most relevant chunks
4. Those chunks are inserted into the agent's prompt as context
5. The agent responds based on the retrieved context

For a full tutorial, see Introduction to RAG for AI Agents.

Long-term memory#

For persistent agent memory, the pattern is:

1. After each interaction or task completion, generate a summary and embed it
2. Store the embedding and summary in the vector database with session metadata
3. At the start of each new interaction, retrieve the most relevant past memories
4. Include retrieved memories in the agent's context to inform its behavior

This gives agents the ability to recall relevant past context without storing full conversation histories.

Practical Considerations#

Chunking strategy matters: How you split documents before embedding significantly affects retrieval quality. Chunks that are too large return too much noise. Chunks that are too small lose context. A 500-1000 token overlap chunking strategy is a reasonable starting point.

Metadata filtering: Most vector databases support filtering results by metadata fields (date, category, author, etc.) before or after vector search. Use metadata filtering to reduce the search space and improve result relevance.

Embedding model consistency: Always use the same embedding model for both ingestion and query. Using different models breaks similarity comparisons.

Index maintenance: For databases with frequent updates, plan for index rebuilds and monitor query performance over time.

Implementation Checklist#

1. Choose a vector database based on your scale requirements and infrastructure preferences.
2. Select an embedding model and document it — this model must be used consistently.
3. Define your chunking strategy before beginning ingestion.
4. Add metadata to stored vectors to enable filtered retrieval.
5. Test retrieval quality with representative queries before integrating with agents.
6. Monitor retrieval latency and result quality in production.

Related Terms and Further Reading#

• Embeddings
• Retrieval-Augmented Generation (RAG)
• AI Agent Memory
• AI Agents
• Introduction to RAG for AI Agents
• Build an AI Agent with LangChain
• AI Agent Examples in Business

Frequently Asked Questions#

What is a vector database?#

A vector database stores high-dimensional embeddings and enables similarity search — finding content that is semantically similar to a query rather than matching exact keywords. It is the infrastructure behind RAG and agent long-term memory.

How do AI agents use vector databases?#

Agents use vector databases for RAG (retrieving relevant documents at inference time) and for long-term memory (storing and retrieving past interactions and accumulated knowledge across sessions).

What is the difference between Pinecone, Chroma, Weaviate, and pgvector?#

Pinecone is a managed cloud service for production scale. Chroma is lightweight and open-source, good for development. Weaviate is feature-rich and self-hostable. pgvector adds vector search to existing Postgres databases.