Vector Search in SQL Server 2025: Examples & Best Practices

Introduction

Imagine a support engineer searching a knowledge base for:

“My SQL Server is slow because CPU usage is very high.”

Your knowledge base contains articles about CPU-intensive queries, execution plans, expensive queries, and performance troubleshooting. But the articles use different words from the question.

A traditional keyword search may miss relevant content. Vector search helps find information based on similarity in meaning, rather than relying only on matching words.

SQL Server 2025 introduces a native VECTOR data type and vector functions. These features help developers build semantic search and AI applications using data stored in SQL Server. Vector indexes and VECTOR_SEARCH, however, remain preview features in SQL Server 2025.

Let’s build a small DBA knowledge base and learn how to store vectors, search them, improve performance, and decide when vector search is appropriate.

1. What Is Vector Search?

A vector is an ordered collection of numbers. An embedding is a vector generated by a machine-learning model to represent information such as a document, question, image, or product.

For example, these two questions have different wording but similar meanings:

• “Why is my SQL Server CPU so high?”
• “How can I troubleshoot processor overload on my database server?”

An embedding model converts each question into a numerical representation. Vector search compares those representations and finds the closest matches.

The numbers below are illustrative only.

User question

“Why is my database CPU so high?”

Embedding model

Converts the question into a vector

[0.82, 0.14, 0.21, …]

SQL Server finds similar vectors

SQL Server High CPU Troubleshooting

Potentially relevant result

SQL Server Backup and Restore

Potentially less relevant

The embedding model generates the vector. SQL Server stores and compares vectors. These are separate responsibilities; creating a VECTOR column does not automatically generate embeddings from your text.

2. Keyword Search vs. Vector Search

Requirement	Keyword search	Vector search
Find SQL Server Error 9002	Excellent fit	Usually unnecessary
Find high-CPU articles using different wording	Depends on search configuration	Useful when embeddings capture the meaning
Find similar products or documents	Requires additional logic	A natural use case
Filter by customer ID, date or status	Excellent fit	Combine with relational filters
Find exact phrases	Excellent fit	Not designed to guarantee exact matches

Vector search does not replace normal SQL queries or full-text search. Combining these techniques can deliver better results than relying on only one.

3. Create a Sample Knowledge Base

Let’s create a table to store troubleshooting articles and their embeddings.

Step 1: Create the table

CREATE TABLE dbo.KnowledgeArticles
(
    ArticleID   INT PRIMARY KEY,
    Title       NVARCHAR(200) NOT NULL,
    Category    NVARCHAR(100) NOT NULL,
    ArticleText NVARCHAR(MAX) NOT NULL,
    Embedding   VECTOR(5) NULL
);
GO

VECTOR(5) defines a vector with five dimensions. We are using five dimensions only to keep the example easy to follow. Real embedding models commonly generate much larger vectors.

SQL Server 2025 supports up to 1,998 dimensions for the default float32 vector type. Half-precision float16 supports up to 3,996 dimensions, but float16 remains a preview feature in SQL Server 2025.

Step 2: Insert sample records

INSERT INTO dbo.KnowledgeArticles
    (ArticleID, Title, Category, ArticleText, Embedding)
VALUES
(1, N'SQL Server High CPU Troubleshooting',
    N'Performance',
    N'Investigate CPU-intensive queries, execution plans and waits.',
    '[0.90, 0.10, 0.20, 0.10, 0.10]'),

(2, N'SQL Server Blocking and Locking',
    N'Performance',
    N'Investigate blocking sessions, locks and long transactions.',
    '[0.75, 0.20, 0.25, 0.10, 0.15]'),

(3, N'SQL Server Transaction Log Full',
    N'Recovery',
    N'Investigate log backups, active transactions and log reuse.',
    '[0.10, 0.90, 0.10, 0.20, 0.10]'),

(4, N'SQL Server Backup and Restore',
    N'Recovery',
    N'Plan backups, test restores and validate recovery objectives.',
    '[0.15, 0.80, 0.15, 0.20, 0.10]');
GO

Please Notes: These are mock vectors, manually supplied to demonstrate the SQL syntax. They were not generated from the article text and do not represent real semantic similarity. In production, generate embeddings using a suitable embedding model.

4. Perform Your First Vector Search

Suppose a support engineer asks:

Why is my SQL Server CPU so high?

Your application would normally send this question to an embedding model and receive a query vector. For this exercise, we’ll provide a mock vector directly.

DECLARE @QueryVector VECTOR(5) =
    '[0.85, 0.15, 0.20, 0.10, 0.10]';

SELECT TOP (3)
    ArticleID,
    Title,
    Category,
    VECTOR_DISTANCE(
        'cosine',
        @QueryVector,
        Embedding
    ) AS Distance
FROM dbo.KnowledgeArticles
WHERE Embedding IS NOT NULL
ORDER BY Distance ASC;

How does it work?

1. @QueryVector represents the user’s question.
2. VECTOR_DISTANCE calculates the distance between the query and each stored vector.
3. WHERE Embedding IS NOT NULL excludes records without embeddings.
4. ORDER BY Distance ASC puts the closest vectors first.

VECTOR_DISTANCE performs an exact distance calculation. It does not use a vector index, even if one exists.

How should you interpret the results?

A smaller distance means the vectors are closer according to the selected metric. It does not guarantee that the article answers the question correctly.

Because we are using mock vectors, this example teaches the SQL syntax and ordering not actual semantic retrieval quality.

5. Understand Vector Distance Metrics

SQL Server supports three distance metrics through VECTOR_DISTANCE.

Metric	What it measures	Practical note
cosine	Angular difference between vectors	Commonly used for text embeddings
euclidean	Straight-line distance	Useful when geometric distance is appropriate
dot	Negative dot product	Smaller values indicate greater similarity

For example, you can use Euclidean distance instead of cosine distance:

SELECT
    Title,
    VECTOR_DISTANCE(
        'euclidean',
        @QueryVector,
        Embedding
    ) AS Distance
FROM dbo.KnowledgeArticles
WHERE Embedding IS NOT NULL
ORDER BY Distance ASC;

Do not assume that a distance of 0.2 always means “highly relevant.” The interpretation depends on the metric and embedding model. Follow the model’s recommendations and evaluate results against known examples.

6. Improve Performance with Vector Indexes

An exact search calculates distances against every eligible vector. That can become expensive as your knowledge base grows.

SQL Server 2025 supports vector indexes based on DiskANN for approximate nearest-neighbor search. Approximate search can reduce the work required to find similar vectors, but it may not return exactly the same neighbors as an exhaustive search.

Step 1: Enable preview features

Vector indexes and VECTOR_SEARCH are preview features in SQL Server 2025. Enable preview features in the target database before using them:

ALTER DATABASE SCOPED CONFIGURATION
SET PREVIEW_FEATURES = ON;
GO

Review your organization’s change-control and production-readiness requirements before enabling preview features.

Step 2: Create a vector index

CREATE VECTOR INDEX IX_KnowledgeArticles_Embedding
ON dbo.KnowledgeArticles (Embedding)
WITH
(
    METRIC = 'cosine',
    TYPE = 'DiskANN'
);

The latest vector-index version requires at least 100 rows before index creation. Our four-row example is intentionally small for learning exact search, so it does not meet this requirement. Use a larger, representative test dataset before trying the indexed search.

Step 3: Search with VECTOR_SEARCH

With the latest vector-index version, use SELECT TOP (N) WITH APPROXIMATE:

DECLARE @QueryVector VECTOR(5) =
    '[0.85, 0.15, 0.20, 0.10, 0.10]';

SELECT TOP (5) WITH APPROXIMATE
    a.ArticleID,
    a.Title,
    a.Category,
    a.distance
FROM VECTOR_SEARCH
(
    TABLE = dbo.KnowledgeArticles AS a,
    COLUMN = Embedding,
    SIMILAR_TO = @QueryVector,
    METRIC = 'cosine'
) AS s
ORDER BY s.distance ASC;

This example assumes a compatible vector index exists, the table meets the row requirement, and preview features are enabled. The alias a refers to the table supplied to VECTOR_SEARCH; the function returns the table columns plus a distance column.

For the latest index version, use SELECT TOP (N) WITH APPROXIMATE rather than the older TOP_N argument, which is retained for backward compatibility with earlier vector-index versions.

Exact vs. approximate search

Consideration	VECTOR_DISTANCE	VECTOR_SEARCH with index
Search type	Exact distance calculation	Approximate nearest-neighbor search
Index used	No	Yes, with a compatible vector index
Best starting point	Small datasets and correctness baselines	Workloads where measured performance justifies it
Trade-off	Can be expensive for large candidate sets	Can miss some exact nearest neighbors

DBA decision: Don’t create a vector index just because the feature is available. Measure exact-search performance first, then compare latency, resource consumption and retrieval quality.

7. Combine Vector Search with SQL Filters

A practical application often needs more than similarity. For example, a support engineer might want relevant articles from the Performance category only.

For exact search, you can combine vector distance with normal SQL predicates:

SELECT TOP (3)
    ArticleID,
    Title,
    Category,
    VECTOR_DISTANCE(
        'cosine',
        @QueryVector,
        Embedding
    ) AS Distance
FROM dbo.KnowledgeArticles
WHERE Category = N'Performance'
  AND Embedding IS NOT NULL
ORDER BY Distance ASC;

This limits the candidates to Performance articles before ordering them by distance.

With approximate search, how filters work depends on the vector index version. Older versions may apply filters after finding the nearest vectors, so you might get fewer matching rows than expected. Before using filters with VECTOR_SEARCH, check the latest documentation to understand how they work.

For sensitive data, don’t rely only on similarity filters. Always use database permissions and application security rules to control who can access the data.

8. Practical Use Case: A DBA Knowledge Assistant

Vector search can be one part of a Retrieval-Augmented Generation (RAG) application.

Imagine an internal DBA assistant that answers questions about SQL Server incidents.

1. User asks a question

“Why is my transaction log full?”

2. Embedding model

Converts the question into a vector

3. SQL Server vector search

Retrieves related troubleshooting articles

4. Generative AI model

Uses retrieved articles as context

5. Answer with references

Provides relevant steps and links to the source material

The search might retrieve articles about:

• SQL Server Error 9002: Transaction Log Full
• How to check log_reuse_wait_desc
• How to identify long-running transactions
• How to verify log-backup schedules

The generative AI model can use those documents to prepare a response. SQL Server handles vector storage and retrieval, while the embedding and generative models perform separate tasks through the application or supported integrations.

Vector search alone does not create a chatbot or guarantee a correct answer. The quality of the retrieved content, embedding model and application design all matter.

9. Vector Search Best Practices

• Start small. Validate your data model and search quality before scaling up.
• Generate real embeddings. Do not use manually invented vectors in production.
• Use compatible embeddings. Keep the model and dimensions consistent between stored content and search queries.
• Measure before indexing. Compare exact and approximate search using representative questions.
• Validate relevance. Check whether the correct articles appear near the top, not just whether a query runs successfully.
• Keep embeddings current. When source documents change, determine whether their embeddings need regeneration.
• Protect retrieved data. Apply the same authorization rules to search results as to the underlying documents.
• Review storage needs. A float32 vector with 1,536 dimensions contains 6,144 bytes of raw element data, before additional storage overhead. Plan for the size of the dataset.
• Test after deployment. Recheck latency, resource use, result quality, data changes and recovery procedures.
• Review preview limitations. Vector indexes and VECTOR_SEARCH can change before general availability in SQL Server 2025.

10. Version and Permission Notes

Item	SQL Server 2025 status or requirement
VECTOR data type	Generally available
VECTOR_DISTANCE	Generally available
Vector indexes	Preview
VECTOR_SEARCH	Preview
float16 vectors	Preview
Creating the sample table	Appropriate table-creation permission
Inserting and searching	Appropriate INSERT and SELECT permissions
Creating a vector index	Appropriate ALTER permission on the table, subject to documented requirements
Enabling preview features	Sufficient permission to change database-scoped configuration

Feature availability differs between SQL Server 2025, Azure SQL Database and Azure SQL Managed Instance. Check the current Microsoft documentation for your platform and installed build before production deployment.

Summary

Vector search gives SQL Server developers and DBAs a way to retrieve information based on similarity in meaning. It can support semantic search, internal knowledge assistants, recommendations and RAG applications without necessarily introducing a separate search platform.

The practical approach is to start with a small knowledge base, generate real embeddings, test exact search with VECTOR_DISTANCE, and evaluate vector indexes only when measured performance requirements justify them.

Remember: Storing vectors is the first step. A useful vector-search solution also needs appropriate embeddings, relevant content, secure filtering, performance testing and ongoing maintenance.