How to Implement AI-Powered Search On-Prem with Vector Support in SQL Server

You can now use ai-powered search with sql server 2025 in your own building. This keeps your data safe and you do not need more database systems. Many groups say they get these good things:

• Better search results and more correct answers

• Quicker ways to find information

• More personal choices for users

• Rules for data and following laws

For example, Bookshop.org had happier users and faster searches after they changed to this way. You will learn easy steps to set up analytics and use sql server for smart search.

Key Takeaways

• AI-powered search in SQL Server 2025 makes searches faster and more correct. It helps users find better results that matter to them.

• Native vector search lets you ask smarter questions. It understands what you mean, so it makes searching easier and faster.

• Setting up vector search is easy to do. You need the right Azure resources. Just follow the steps to install it.

• SQL Server 2025 has security tools like Row-Level Security. These tools keep important data safe when you use AI features.

• You can add AI models to SQL Server without trouble. This gives you strong search tools. You do not need extra systems.

AI-Powered Search and Native Vector Search

What Is Native Vector Search

Native vector search in SQL Server 2025 helps you find things in a smarter way. You can do vector operations right inside SQL. You do not need other tools. Native vector search looks for meaning, not just exact matches. It uses AI and semantic search to understand what you want. You get results that fit your needs, not just your words. You can use this for recommendations and analytics. It works for other AI tasks too. With vector search, everything stays in SQL Server.

Tip: Native vector search puts AI features in your database. This makes search and analytics stronger and safer.

Here is a table that shows how native vector search is different from keyword search in SQL Server:

Why AI-Powered Search Matters

AI-powered search changes how you look for things in SQL Server. It uses machine learning and natural language processing to know what you mean. Your searches become more correct and useful. You do not have to worry about spelling or exact words. The system learns from you and gets smarter. You can ask hard questions, and AI will find good answers.

Some main benefits are:

• Gives results that match the context, not just words.

• Learns and improves to give better answers.

• Can handle tough, chat-like questions.

You save time too. AI-powered search gives you direct answers, not long lists. It connects to all your work systems, like CRMs and file stores. This helps everyone work together and get more done. Using analytics and vector search lets your team focus on what matters most.

Use Cases in SQL Server

Vector search in SQL Server works for many jobs. Here are some examples from different fields:

You can also use vector search for product tips, document checks, and smart searches in your database. AI in SQL Server uses vector search for faster and safer analytics. With SQL Server, you keep your data in your building and use strong AI tools without needing more systems.

SQL Server Setup for Vector Search

Installation Steps

Setting up vector search in SQL Server 2025 is simple. First, make sure you have everything you need. Here is what you should get:

1. You need an Azure subscription. You can make one for free.

2. You need Azure SQL Database. This is also free to use.

3. You need an Azure OpenAI resource in your Azure account.

4. Download Azure Data Studio. It is free to download.

5. Get the git-lfs extension if you want to copy code from repositories.

6. Install SQL Server 2025 to try out vector search features.

After you have these things, follow these steps to set up your system:

1. Look at your virtual machine settings and check the cost.

2. Make the virtual machine.

3. Use Remote Desktop Protocol (RDP) to connect to your virtual machine.

4. Open SQL Server Management Studio (SSMS) and log in to SQL Server 2025. Set encryption to optional.

5. Copy the setup files by running:

git clone https://github.com/nocentino/ollama-sql-faststart.git
cd ollama-sql-faststart

1. Build and start the services by running:

docker compose up --detach

1. Make sure all services are working by running:

docker ps

1. Connect to SQL Server using SSMS or VSCode at localhost, 1433. Use the username sa and password S0methingS@Str0ng!

2. Restore the AdventureWorks2025 database from a backup file.

3. Add a VECTOR(768) column to the Product table to store embeddings.

4. Use the AI_GENERATE_EMBEDDINGS function for each product record.

5. Turn on needed trace flags and make a vector index with DiskANN.

Tip: These steps help you get ready for analytics and AI-powered search.

Enabling Vector Features

SQL Server 2025 lets you use vector data right away. You can turn on these features with a quick setting. The table below shows the main steps:

After you turn on these features, you can use vector search and analytics in SQL Server. This setup lets you run AI models and do semantic searches without other tools.

Note: Always look at the latest documentation for updates about vector features in SQL Server 2025.

Security and Compliance

SQL Server 2025 has strong security to keep your vector data safe. You can use Microsoft Entra managed identities for secure access. Better credential management helps you control who can see your data. The system also gives you better compliance and audit tools. This is important for groups that must follow strict data rules.

You get new locking tools, like TID Locking and LAQ, to lower resource problems. These features help your analytics and AI work run well and safely.

SQL Server 2025 is ready for big companies as a vector database. It uses a built-in vector store and index with DiskANN. This helps you find similar data fast, even in big datasets. With these tools, you can follow data rules and do advanced analytics with confidence.

Security Tip: Use Row-Level Security (RLS) to control who can see sensitive vector data. This keeps your AI and analytics work safe and follows the rules.

Managing Embeddings and Indexes

Creating Embeddings with AI Models

Embeddings help vector search work in SQL Server. They change words, pictures, or other data into numbers. AI can use these numbers. You make embeddings using special methods. Text embeddings help you find meaning in words. They do not just match words.

Here are some tips for good embeddings:

1. Change your AI model if your company uses special words.

2. Check how well your searches work. If they stop getting better, change your model.

3. Use lots of company data for better results.

4. Watch how people search and what documents they use most.

"Old search methods and signals are still very important. Every good search system should use them."

You can try different ways to make embeddings. You can use RAG methods to fit your needs. Embedding metrics now check many tasks, not just one.

Storing Vectors in SQL Server

SQL Server 2025 lets you keep vectors in a special way. You use the VECTOR data type for similarity searches and AI jobs. Each part of a vector is a floating-point number. You can have up to 1998 parts in one vector.

You can keep vectors as JSON. This makes it easy to put them in tables with the openjson() function. You can also use the VECTOR data type for faster work. For big databases, use a columnstore index to make things quicker.

• Keep vectors as JSON for easy changes.

• Use VECTOR data type for similarity searches.

• Add a columnstore index for fast analytics.

• Use DiskANN for quick and correct searches.

Vector Indexing

Vector indexing helps you find similar things fast. SQL Server 2025 uses DiskANN for big sets of data. You make a vector index to speed up searches and help AI work better.

• Use DiskANN for quick vector indexing.

• Make a binary search index for fast results.

• Add a columnstore index for analytics.

• Use JSON indexing for data that is not all the same.

Here is how you make a DiskANN index in SQL Server:

CREATE VECTOR INDEX ix_ai_vector_search
ON dbo.ai_search_index(embedding)
WITH
(
TYPE   = 'diskann',    -- Only DiskANN is supported today
METRIC = 'cosine'      -- 'cosine' | 'euclidean' | 'dot'
);

Vector indexing in SQL Server uses the VECTOR data type and DiskANN. This helps you keep and search embeddings easily. You can filter and index vectors when you search. This helps with recommendations and large language models.

Querying for Similarity Search

Similarity search helps you find items close to your query. SQL Server 2025 makes this easy with VECTOR_SEARCH and vector queries. You can use cosine, euclidean, or dot to check how close things are.

Here is a sample query to find the top 5 closest embeddings:

DECLARE @input VECTOR(9) =
'[0.0023, 0.1456, -0.1987, 0.2345, -0.3456, 0.4567, -0.5678, 0.6789, -0.7890]';

SELECT TOP 5
id,
content,
VECTOR_DISTANCE('cosine', @input, embedding) AS cosine_distance
FROM dbo.ai_search_index
ORDER BY cosine_distance;

You can also use VECTOR_SEARCH for more advanced searches:

DECLARE @query_vector VECTOR(9) =
'[0.0023,0.1456,-0.1987,0.2345,-0.3456,0.4567,-0.5678,0.6789,-0.7890]';

SELECT TOP (5)
id,
content,
distance
FROM VECTOR_SEARCH (
TABLE      = ai_search_index,
COLUMN     = embedding,
SIMILAR_TO = @query_vector,
METRIC     = 'cosine',
TOP_N      = 5
)
ORDER BY distance DESC;

To make similarity searches better, use vector indexes, columnstore indexes, and JSON indexing. These tools help you get quick and correct results, even with lots of data.

SQL Server 2025 gives you smart AI tools for similarity searches. You can use these for product tips, document searches, and other AI jobs. Vector queries help you find meaning, not just matches.

Tip: Use DiskANN and columnstore indexes to make your similarity searches quick and able to grow.

Integrating AI Models and Real-World Search

Connecting External or Local AI

You can link AI models to your database in many ways. This lets you use ai-powered search with cloud or local models. Here are your main choices:

• Online Model (Azure OpenAI): You set up cloud integration with REST API calls. You make an external model using Azure OpenAI. This works well for natural language questions and big ai projects.

• Local Model (Ollama): You run a local model by picking the right embedding model. You make an external model inside sql server. This keeps your data in your building and helps keep it safe.

SQL Server 2025 lets you handle AI models with T-SQL. You can add AI to your work using sql commands. This setup works with cloud services and makes things more secure.

End-to-End Search Workflow

You can follow easy steps to use ai-powered search in your database. Here is what you do:

1. Make a product table with a vector column for embeddings.

2. Add product records. Leave the embedding column empty first.

3. Use an external AI model to connect and turn your data into vectors.

4. Make vector embeddings from text with registered AI models. This helps with natural language questions and other ai jobs.

5. Run a semantic search on the vector data. You get results based on meaning, not just words.

SQL Server 2025 makes it easy to connect AI. You use normal sql commands to set up models and run searches. This way, you do not need extra systems and can build strong ai projects.

Overcoming Challenges

You might have some problems when you connect AI. Here are good ways to fix them:

• Start with the most important or used content for ai-powered search.

• Batch and cache embeddings to save money and help connections.

• Use built-in indexing and similarity tools to stop slow searches.

• Change your database step by step for smooth connections.

Tip: Adding AI little by little helps you learn and change your ai-powered search as you go. You keep your server quick and your database safe.

You can use these ideas to make strong ai projects. Connecting with SQL Server 2025 works with cloud and local models. You get smart natural language questions and better search features.

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You can now use AI-powered search with SQL Server 2025 in your own building. This helps keep your data safe and makes searching smarter. Here are some main benefits:

If you want to learn more, read about semantic search and how to use AI models. You can also look up commands like CREATE EXTERNAL MODEL or AI_GENERATE_EMBEDDINGS. Try SQL Server and see how AI can help your group.

FAQ

How do you enable vector search in SQL Server 2025?

You turn on vector search by using the PREVIEW_FEATURES setting. This lets you use new vector data types and indexes. You can start using AI-powered search right away.

Can you use local AI models with SQL Server?

Yes, you can connect local AI models to your server. You set up an external model and use T-SQL commands to create embeddings and run searches. This keeps your data safe.

What is the VECTOR data type in SQL?

The VECTOR data type lets you store numbers that represent words or images. You use this type for similarity searches and AI tasks. It supports up to 1998 dimensions.

How does Row-Level Security help protect vector data?

Row-Level Security controls who can see each row in your database. You set rules so only certain users can view sensitive vector data. This helps you follow company policies.