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What is a vector database?
A vector database stores and searches vector representations of data. An embedding model creates those representations from items such as text, images, or other content. Each vector captures features learned by the model, allowing a search system to find items that are close to one another in vector space.
For text search, this can surface content that expresses an idea in different words from the query. A conventional keyword search looks for matching terms; vector search compares representations of meaning. Many applications combine the two approaches or apply filters to narrow results.
A vector database is one way to store vectors and perform this search. Vector search is also offered within broader database and managed cloud platforms, so the term does not necessarily mean a separate, standalone product.
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How do embeddings and vector search work?
- Prepare the source material. An application collects content to make searchable. For a RAG system, this may include documents divided into chunks so relevant passages can be retrieved individually.
- Create embeddings. An embedding model converts each item into a vector, a sequence of numbers representing features of that item.
- Index the vectors. The application stores the vectors with links to the underlying content and any useful metadata. The index can be built or updated as source material changes; Google Cloud documents an architecture for generating embeddings and building or updating a vector index (Google Cloud’s RAG-capable generative AI architecture).
- Embed the query. At search time, the application converts the user’s question or other input into a vector using an appropriate embedding model.
- Retrieve similar items. Vector search compares the query vector with indexed vectors and returns likely matches. Similarity metrics vary by task: Cloudflare, for example, describes cosine distance for text or sentence similarity and document search, and Euclidean distance for certain image or speech use cases (Cloudflare’s distance-metrics guidance).
- Use the results. The application can display retrieved content, use it to inform recommendations, or pass it to a generative model as context.
How does RAG use a vector database?
RAG connects retrieval with text generation. The application searches its source material for content relevant to a user’s question, then supplies selected results to a generative model along with the prompt. The model generates language; the retrieval system helps locate external information for it to use.
A vector database can support the retrieval step, but it is not the whole RAG system. Ingestion, embedding generation, index updates, query handling, access controls, context selection, and generation also matter. AWS describes RAG workflows that use knowledge sources and vector database retrieval (AWS documentation on knowledge bases for Amazon Bedrock).
Retrieval can give an application access to domain-specific or updated material, provided that material has been ingested and the index is maintained. It does not guarantee that the retrieved passages are relevant, that the model interprets them correctly, or that the final answer is correct. Results depend on the source data, embedding and indexing choices, retrieval behavior, and how the application uses the returned context.
What can vector search be used for?
- Semantic search: Find documents or passages relevant to a query even when the wording differs from the source. AWS describes semantic search as a vector search use case (AWS overview of vector databases).
- RAG: Retrieve relevant material to provide context for an AI-generated response.
- Recommendations: Find items similar to a user’s interests, a product, or another item.
- Combined application search: Use vector retrieval alongside ordinary records, operational data, or interaction data. Microsoft documents vector search and RAG with operational data (Microsoft’s Azure Cosmos DB vector-search documentation); MongoDB documents vector search within its database platform (MongoDB Atlas Vector Search).
Do you need a dedicated vector database?
Not necessarily. The right choice depends on whether semantic retrieval is the workload’s central requirement or one feature in a larger application, and on what platform already stores the data. Dedicated vector databases are an option; vector search within a managed cloud service or an existing database may fit better when it reduces integration and operational complexity.
Compare the approaches against the application’s real requirements rather than assuming one architecture is best for every team:
- Workload: Is semantic retrieval central, or one capability alongside other database queries?
- Platform fit: Where does the source data live, and how well does the search option integrate with the application and its operational systems?
- Data lifecycle: How will content be ingested, embeddings generated, and indexes updated when the source material changes?
- Retrieval controls: Does the system support the metadata filters, permissions, and governance the application requires?
- Freshness: How quickly must new or changed content become searchable?
- Evaluation: How will the team measure relevance and latency using representative queries and the actual workload?
These questions matter because vendor documentation describes supported architectures and features, not a neutral performance ranking. Gartner’s 2025 forecast that 80% of GenAI business applications will be developed on existing data management platforms is a forecast, not a measured adoption rate (Gartner’s 2025 forecast).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What determines whether vector retrieval works well?
Vector search is a retrieval method, not a guarantee of relevance. Performance for a particular application depends on choices across the full pipeline:
- Source quality and coverage: Missing, outdated, or poorly prepared content cannot provide useful results. For document-based RAG, chunk size and boundaries affect what the system can retrieve as a unit.
- Embedding fit: The embedding model must represent the content and queries in a way that supports the task. Changing the model can require regenerating embeddings and rebuilding or updating the index.
- Index maintenance: Stale vectors can fail to reflect current source material. The application needs a process for updates and removals.
- Search configuration: Similarity metrics and any metadata filters should match the workload. As Cloudflare’s guidance illustrates, metric choice can vary across text, images, and speech rather than being universal (Cloudflare’s distance-metrics guidance).
- Context use: In RAG, retrieved passages must be relevant and presented appropriately to the generative model. Retrieval alone does not ensure a grounded or accurate response.
Measure retrieval quality and response latency on representative queries before committing to an approach. A configuration that performs well on a small demonstration may not suit the application’s data, access rules, or freshness needs.
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