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A vector database stores numerical representations called embeddings and retrieves records whose vectors are similar to a query vector. An embedding model creates the vectors; the database indexes and searches them; an application may then use retrieved records as context for an AI model. Similarity can help find useful material, but it does not mean the database understands a question or guarantees a correct answer.
What is a vector database?
An embedding is a list of numbers generated by a model to represent an object, such as a passage of text, an image, audio, or video. A vector database stores those vectors alongside the original records or metadata and provides ways to search them.
Weaviate’s documentation describes an embedding as capturing an object’s semantic meaning in a vector space. In practice, what a vector represents depends on the embedding model and the data used to create it. The database searches the resulting numerical representations; it does not create their meaning on its own. See Weaviate’s vector-search documentation.
How does AI search embeddings?
- Prepare the records. An application splits or otherwise prepares source material, such as support documents, into records suitable for retrieval.
- Create embeddings. An embedding model turns each record into a vector. The query must be embedded in a compatible representation so it can be compared with the stored vectors.
- Store and index. The database stores each vector with its associated record or metadata and organizes vectors for search.
- Search by similarity. The database compares the query vector with stored vectors using a distance or similarity measure, then returns nearby matches.
- Use the results. The application can show the matching records or pass selected passages to a language model as context.
For example, a support application can embed passages from help documents, save each vector with its text and attributes, and embed a customer’s question when it arrives. The database returns similar passages; the application can provide them to a language model to help formulate a response. This retrieval step can surface relevant material, but it does not establish that the passages are complete, current, or sufficient to support the final answer. OpenAI describes retrieval as part of the broader process of building applications with its models; see OpenAI’s retrieval guide.
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What the embedding model, index, and AI application each do
| Component | Role |
|---|---|
| Embedding model | Converts an object or query into a numerical vector. |
| Vector index | Organizes vectors so the system can find likely neighbors without necessarily comparing every stored vector. |
| Vector database | Stores vectors and related records or metadata, and performs vector retrieval. |
| Application or language model | Chooses how to prepare data and queries, uses retrieved records, and generates or presents a response. |
These roles may be implemented in separate services or combined through integrations. For example, vector creation may occur in an application or through a database integration. Regardless of where it happens, the embedding model and the retrieval index are different parts of the system.
How vector indexes and similarity measures affect search
An index is a data structure for finding likely nearby vectors efficiently. A straightforward flat search can suit smaller collections or situations where exhaustive comparison is acceptable. Approximate-nearest-neighbor methods, including HNSW, aim to reduce search work by balancing retrieval behavior against resources and speed. The right approach depends on the collection and workload; no index is best for every application.
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Common comparison measures include cosine distance, dot product, and Euclidean distance. The chosen metric should fit the embedding model and retrieval task, and stored and query vectors must have compatible dimensions. Some index configurations return approximate rather than exact nearest neighbors. Weaviate documents these search concepts and metrics in its similarity-search reference and index options in its vector-index documentation.
When evaluating a system, test it on representative data and queries. Compare retrieval quality or recall, latency, throughput, memory and storage use, ingestion and update behavior, filter selectivity, and operational complexity. A result that is fast but regularly misses useful records may be a poor fit; a highly accurate configuration may also carry resource or latency costs. Available documentation does not establish an independent, apples-to-apples performance ranking across products.
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Vector search versus keyword and hybrid search
Vector search ranks records by proximity between vectors. It can retrieve passages whose wording differs from a query while their representations are nearby, but it is not exact keyword matching and may miss a precise name, code, or phrase that matters.
Keyword search is useful when exact terms and lexical matches are important. Hybrid search combines lexical and vector retrieval, which can be helpful when a query needs both conceptual relevance and exact-term coverage. The appropriate mix depends on the application’s queries and should be evaluated with examples that reflect real use. See Weaviate’s hybrid-search documentation.
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How metadata filters narrow vector results
An application can constrain a similarity search with metadata conditions—for example, retrieve only documents tagged for a particular product or access group. Whether filtering happens before or during vector retrieval, and how that affects results and performance, depends on the database and its configuration.
As one implementation-specific example, Weaviate documents pre-filtering and says ACORN became its default filter strategy starting with Weaviate v1.34. That release detail applies to Weaviate, not to vector databases generally. Consult Weaviate’s filtering documentation for its behavior and version details.
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Is a vector database the same as a RAG system?
No. Retrieval-augmented generation (RAG) is an application pattern in which retrieved information is supplied to a generative model as context. A vector database can provide the retrieval layer, but a working RAG system also depends on choices around document preparation, embedding generation, access controls, query handling, prompt construction, and evaluation.
Vector proximity is evidence that two representations are near under a particular model and metric; it is not proof that a passage answers a question. The application still needs to select relevant context, respect permissions, and assess whether the resulting response is supported.
Do you need a dedicated vector database?
Not necessarily. One option is a dedicated vector service such as Pinecone; another is to add vector search to PostgreSQL with pgvector. These are different implementation paths, not a universal ranking. Existing architecture and measured workload should guide the choice.
| Decision factor | Questions to evaluate |
|---|---|
| Existing data architecture | Does application data already live in PostgreSQL or another operational database, and would keeping retrieval there simplify the system? |
| Workload | How large is the collection, how frequently are queries made, what latency is acceptable, and how often do records change? |
| Filtering and retrieval | Which metadata filters are required, when must they apply, and is hybrid keyword-plus-vector search needed? |
| Quality and cost | How do recall, latency, throughput, infrastructure use, and operational effort compare on representative queries? |
| Operations | Who will manage hosting, scaling, backups, access controls, and ongoing maintenance? |
Measure candidate approaches with the same representative data and queries before choosing. Product capabilities, hosted options, release behavior, and pricing can change, so confirm details for the specific deployment and date rather than relying on a general product label.
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Quick Recap
What a vector database does not do
- It does not necessarily generate embeddings; that is the embedding model’s job, though integrations may combine steps.
- It does not make an exact keyword search equivalent to a similarity search; the two methods find matches differently.
- It does not guarantee that retrieved material is relevant, complete, authorized for the user, or correct.
- It is not, by itself, a complete RAG application or a guarantee that an AI model’s answer is true.
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