What Is Microsoft Extensions VectorData and How Does It Power AI Apps
Microsoft.Extensions.VectorData is a group of .NET libraries. These libraries help developers use vector databases in AI apps.
These libraries make a single C# layer. This layer makes it easy to use different vector stores. Developers can do CRUD jobs, search with vectors or text, and handle embeddings. VectorData helps with semantic search and RAG. It also helps with other AI tasks. It works well with the .NET AI system. This helps people work faster and try new vector database tools quickly.
Key Takeaways
Microsoft.Extensions.VectorData gives one C# layer to use many vector databases. This makes building AI apps easier and faster for everyone.
It helps with important AI features like semantic search and RAG. It does this by handling vector data and embeddings well.
The library has simple interfaces for CRUD jobs and vector searches. Developers can use the same code for different vector stores.
VectorData works well with .NET AI tools and top AI models. This helps developers make smart and scalable AI apps.
Developers can start fast by installing the right packages. They set up data models and use help from the community and official docs.
VectorData Overview
Unified Abstraction
VectorData gives .NET developers one way to use many vector databases. This means you can use the same C# code for different vector stores. Some examples are PostgreSQL with pgvector, Qdrant, Azure AI Search, Elasticsearch, Redis, and Pinecone. The system uses modular libraries. You can switch vector stores without changing your main app logic.
The main parts are embedding generators, vector store collections, and search tools. These tools help with similarity checks, hybrid searches, and filtering. Data models use special attributes like VectorStoreKey, VectorStoreData, and VectorStoreVector. These attributes help connect data to the vector store. They also make embedding and searching easier. The package works well with .NET dependency injection. This helps developers set up and manage services fast.
Note: Developers can use LINQ-like syntax for filtering and searching. This makes it easy and familiar to use.
This single way of working lets teams build AI features faster. They do not need to learn each vector database in detail.
Purpose in AI Apps
VectorData is important for AI apps. It helps developers add features like semantic search and retrieval-augmented generation (RAG). For example, when someone asks a question, the app makes an embedding from it. The app compares this embedding to saved document embeddings. It uses vector similarity to find the best matches. This finds the most useful documents, even if the words are not the same.
In RAG, the app gets the best matching documents. It sends them to a large language model. The model uses these documents to give good answers. VectorData also helps chatbots search different tables or sources. Everything is managed in the same workflow.
Azure AI Search works with VectorData. It supports many search types. These include similarity search, hybrid search, multimodal search, and multilingual search. This lets developers build smarter AI apps.
VectorData enables:
Fast and accurate semantic search
Reliable RAG workflows
Easy integration with .NET AI tools and services
VectorData gives a simple and flexible way to use vector data. It helps developers build advanced AI features with less work.
Features
Vector Store Interface
Microsoft.Extensions.VectorData gives one way to use vector databases in .NET apps. This interface uses main classes that help manage and search vector data. The important parts are:
VectorStore: This is the main vector store. It can have many groups of records.
VectorStoreCollection<TKey, TRecord>: This manages named groups in the store. It helps search and manage records.
IVectorSearchable: This shows how to do vector searches in groups.
VectorStoreExtensions: These are extra tools for working with vector stores.
Metadata classes: These give details about stores and groups.
The interface lets you do many jobs at once. It handles errors in a flexible way. You can choose to include or skip vectors when searching. Developers can use general data models if they do not know the database setup. The design suggests using sealed types and decorators for special needs.
Tip: The interface works right away with popular databases like Azure AI Search, CosmosDB, MongoDB, Pinecone, Qdrant, Redis, and Weaviate.
CRUD Operations
VectorData lets developers do CRUD (Create, Read, Update, Delete) jobs on vector data. These jobs work in all vector stores with the same C# code. Developers can add, read, change, or remove records as needed. The library lets you work with one or many records at once. This makes it easy to handle lots of data. If a record is missing, the method gives back null or just some results. It does not cause errors. This keeps apps steady and easy to use.
The system works with many data types, like text and vector embeddings. Developers can use these tools to make apps that store, search, and manage vector data well.
Integration with AI Models
Microsoft.Extensions.VectorData works well with top AI models and tools in .NET. It is part of the Microsoft.Extensions.AI library. This library gives main tools for using AI. Developers can connect to AI services like Azure OpenAI, GitHub Models, or their own large language models. They use the same API each time. The library supports dependency injection, telemetry, caching, and rate limits.
For example, developers can use an embedding generator to make vector versions of text. These embeddings help with jobs like semantic search and sorting. Here is some code that shows how to make embeddings with rate limits and controls for how many can run at once:
IEmbeddingGenerator<string, Embedding<float>> generator =
new RateLimitingEmbeddingGenerator(
new OllamaApiClient(new Uri("http://localhost:11434/"), "phi3:mini"),
new ConcurrencyLimiter(new() { PermitLimit = 1, QueueLimit = int.MaxValue }));
foreach (Embedding<float> embedding in await generator.GenerateAsync(new[] { "What is AI?", "What is .NET?" }))
{
Console.WriteLine(string.Join(", ", embedding.Vector.ToArray()));
}
This setup makes it simple to build AI apps that use vector data for smart search and study.
Use Cases
Semantic Search
Semantic search helps computers find things by meaning. It does not just look for keywords. VectorData lets developers make apps that understand what people want. This works even if people use different words. For example, someone might ask, "How do I reset my password?" The app can find help articles about "account recovery" or "password change." It does this by comparing the meaning of the question and the documents.
Many vector databases have this feature. Chroma and Qdrant are good for hard searches. Pgvector lets PostgreSQL do vector similarity search. These tools help apps give fast and correct results. Developers can use these databases with VectorData to make smarter searches.
RAG Scenarios
Retrieval-augmented generation (RAG) mixes search with language models. In RAG, the app finds the best documents. It sends them to a big language model like GPT-4. The model uses these documents to answer questions or make summaries.
VectorData helps RAG by making it easy to store, search, and get embeddings. Developers can use Azure OpenAI to make embeddings. They store them in a vector database and run searches. The app sends the best matches to the language model. This helps chatbots, helpers, and knowledge bases give better answers.
RAG works better with fast vector jobs. New .NET drivers give up to 50x faster reads and better write speeds. This makes these jobs quick.
Other AI Workloads
VectorData helps with many other AI jobs. Recommendation engines use vector search to suggest things. Real-time personalization changes things for each user. Machine learning models train and work faster with direct vector jobs.
Some databases that work are Deep Lake for messy data, Faiss for big similarity searches, and Vespa for real-time suggestions. These tools help apps use video, audio, and text data. Semantic Kernel connectors make it easy to use these databases without learning each SDK.
Example AI jobs:
Making and saving embeddings with OpenAI models
Doing similarity searches for suggestions
Handling big datasets for machine learning
These features help developers build smart AI apps that work fast and grow easily.
Getting Started with VectorData
Installation Steps
Developers need to set up .NET for VectorData.
Next, make a new console app with
dotnet new console -o VectorDataAI.Go into the project folder by typing
cd VectorDataAI.Add NuGet packages using
dotnet add package. These include Azure.Identity, Azure.AI.OpenAI, Microsoft.Extensions.AI.OpenAI, Microsoft.Extensions.VectorData.Abstractions, Microsoft.SemanticKernel.Connectors.InMemory, Microsoft.Extensions.Configuration, Microsoft.Extensions.Configuration.UserSecrets, and System.Linq.AsyncEnumerable.Open your project in Visual Studio Code or another editor.
Set up Azure OpenAI service and model by following the official guide.
Use user secrets to set the Azure OpenAI endpoint and model name.
Write code that defines vector store entities with VectorData attributes.
Tip: If you use SQLite, make sure the vector search extension is loaded. Registering the connection by hand helps stop errors from missing modules.
Sample Code
Here is an example that shows how to connect a .NET app to a vector store with VectorData.
// Define embedding generator
EmbeddingGenerator<string, Embedding<float>> embeddingGenerator = ...;
// Create vector store collection
VectorStoreCollection<int, Product> collection = new SqliteCollection<int, Product>("Data Source=products.db", "products", new SqliteCollectionOptions {
EmbeddingGenerator = embeddingGenerator,
});
// Upsert a product with embedding text
await collection.UpsertAsync(new Product
{
Id = 1,
Name = "Kettle",
TenantId = 5,
Embedding = "This kettle is great for making tea, it heats up quickly and has a large capacity."
});
// Product data model with vector store attributes
record Product
{
[VectorStoreKey]
public int Id { get; set; }
[VectorStoreData]
public required string Name { get; set; }
[VectorStoreData]
public int TenantId { get; set; }
[VectorStoreVector(Dimensions: 1536)]
public string? Embedding { get; set; }
}
// Example search query with filtering
var query = "Find me kettles that can hold a lot of water";
await foreach (var result in collection.SearchAsync(query, top: 5, new() { Filter = r => r.TenantId == 8 }))
{
yield return result.Record;
}
This code shows how to make a product record, add data, and search with filters.
Resources
Developers can find official VectorData docs on Microsoft Learn. The API reference pages explain namespaces, classes, and properties. These pages link to GitHub for source code and issues.
The .NET Aspire Community Toolkit has open-source extensions for VectorData.
The Azure SQL DB Vector Search GitHub repo has samples for embedding generation, vector search, and retrieval augmented generation.
The .NET AI Chat template uses VectorData and helps you start building AI chat apps.
The .NET Data Community Standups have live sessions with the .NET Data team. They talk about VectorData and vector databases.
Developers can check these resources to learn more and see real examples.
Microsoft.Extensions.VectorData lets developers make AI apps faster and easier.
The shared .NET libraries help teams use many vector stores and AI models with the same code.
This way, they do not need to write special code. It is easy to change providers.
Developers can grow and take care of their apps more easily.
To begin, developers should add the right NuGet packages. They should set up data models and use Semantic Kernel tools. Looking at the official docs and community resources helps teams learn more and make better AI apps.
FAQ
What is Microsoft.Extensions.VectorData used for?
Microsoft.Extensions.VectorData lets developers link .NET apps to many vector databases. It makes it easy to store, search, and manage vector data. This tool helps add AI features like semantic search and retrieval-augmented generation.
What vector databases work with VectorData?
VectorData works with many well-known vector databases. These are Azure AI Search, PostgreSQL with pgvector, Qdrant, Pinecone, Redis, and Elasticsearch. Developers can use any of these without changing their main code.
What makes VectorData different from other libraries?
VectorData gives one C# interface for many vector stores. Developers do not need to learn each database’s special commands. The library also works with .NET AI tools and is quick to set up.
What types of AI workloads can use VectorData?
Developers use VectorData for jobs like semantic search, retrieval-augmented generation, recommendations, and personalization. The library helps apps find answers by meaning, not just by matching words.
What resources help with learning VectorData?
Microsoft Learn has official guides and docs. Developers can also find code samples on GitHub and join .NET community events. These resources help teams start using VectorData fast.