Sharpvector
Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application
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What is Sharpvector?
Build5Nines.SharpVector is a lightweight, in-memory semantic search text vector database built specifically for .NET applications. It enables fast and flexible vector-based similarity search for text data, making it ideal for building search engines, recommendation systems, semantic analysis tools, and AI-enhanced features. The library allows developers to store, search, and manage text data using vector representations entirely within their .NET application without requiring external database servers.
SharpVector makes it easy to build RAG-based (Retrieval-Augmented Generation) workflows in .NET by enabling fast, in-memory vector search of documents, notes, or knowledge bases. It supports multiple embedding options including built-in local vectorization, OpenAI embeddings, Azure OpenAI embeddings, and Ollama embeddings. The library uses vector similarity metrics like cosine similarity (default) and Euclidean distance to find semantically similar text, even when keywords don't match exactly.
This tool is designed for .NET developers who want to add semantic search capabilities to their applications without the overhead of spinning up heavyweight vector database servers like Azure CosmosDB, PostgreSQL with pgvector, Azure AI Search, or Elasticsearch. It is particularly useful for desktop applications, offline/edge computing scenarios, development and testing environments, and any .NET application that needs fast, context-aware search with low latency.
Key features include text embedding and search with pluggable embeddings support, in-memory performance optimized for local/embedded applications, custom metadata storage with each text entry, async/await support for scalable non-blocking operations, and various vector comparison methods. The library has zero dependencies and is released under an MIT license, making it easy to integrate into any .NET 8.0 or later project via NuGet.
Sharpvector pricing
Pricing model: Freemium
Free and open-source under MIT license. The Build5Nines.SharpVector library is available as a free NuGet package with no paid tiers or subscription required. Additional packages for OpenAI embeddings (Build5Nines.SharpVector.OpenAI) are also free. Users only pay for external services they choose to use like OpenAI/Azure OpenAI API calls if they opt not to use the built-in local vectorization.
Sharpvector pros
- Lightweight in-memory design with no external server required
- Zero dependencies and MIT license for easy integration
- Works with .NET 8.0 or later applications
- Built-in local vectorization without external LLM server dependencies
- Supports OpenAI and Azure OpenAI embeddings integration
- Supports Ollama embeddings for local LLM vectorization
- Multiple vector comparison methods including cosine similarity and Euclidean distance
- Custom metadata storage with each text entry
- Async/await support for non-blocking database operations
- Fast performance with no serialization or network hops
- Perfect for development and testing without server installation
- Ideal for offline or edge computing with limited internet connectivity
- Customizable and extensible with pluggable vectorization strategies
- Simple API that is easy to learn and use
- Enables RAG-based workflows for generative AI applications
- Supported by Microsoft MVP Chris Pietschmann and Build5Nines community
Sharpvector cons
- In-memory only - data not persisted after application restart
- Requires .NET 8.0 or later (not compatible with older .NET versions)
- Built-in local vectorization may be less accurate than OpenAI embeddings
- No built-in persistence layer - must implement your own if needed
- Limited to text data only (not images, audio, or other modalities)
- Memory usage scales with dataset size (not ideal for massive datasets)
- Single-process only - no distributed/clustering support
- OpenAI and Ollama embeddings require API keys or local server setup
Frequently asked questions about Sharpvector
What is SharpVector?
Build5Nines.SharpVector is a lightweight, in-memory semantic search text vector database built for .NET applications. It enables fast and flexible vector-based similarity search for text data, allowing you to store, search, and manage text using vector representations entirely within your .NET application without needing external database servers.
Do I need an external vector database server?
No, SharpVector is an in-memory vector database that runs entirely within your .NET application. You don't need to install or manage external servers like Azure CosmosDB, PostgreSQL with pgvector, Azure AI Search, or Elasticsearch. This makes it perfect for desktop applications, edge computing, and development/testing scenarios.
What .NET versions does SharpVector support?
SharpVector requires .NET 8.0 or later. You cannot use it with older versions of .NET Framework or .NET Core earlier than 8.0.
How do I install SharpVector?
Install SharpVector via NuGet package manager using the command: dotnet add package Build5Nines.SharpVector. The library is also available through PowerShell with Install-Package Build5Nines.SharpVector.
What embedding options does SharpVector support?
SharpVector supports three embedding options: (1) Built-in local vectorization that generates embeddings automatically without any external dependencies, (2) OpenAI and Azure OpenAI embeddings like text-embedding-ada-002 for higher quality vectors, and (3) Ollama embeddings like nomic-embed-text for local LLM vectorization.
What vector comparison methods are available?
SharpVector supports multiple vector comparison methods including cosine similarity (which is the default), Euclidean distance, and the ability to write your own custom vector comparison algorithm. This flexibility allows you to choose the method that best fits your use case.
Can I use SharpVector for RAG applications?
Yes, SharpVector makes it easy to build RAG-based (Retrieval-Augmented Generation) workflows in .NET. It enables fast, in-memory vector search of documents, notes, or knowledge bases that can be combined with OpenAI, Azure OpenAI, or local LLM embeddings to power intelligent AI apps with context-aware responses.
Is SharpVector free to use?
Yes, SharpVector is completely free and open-source under the MIT license. The NuGet package has no cost and no paid tiers. You only pay for external services if you choose to use OpenAI/Azure OpenAI API calls instead of the built-in local vectorization.
What are the main use cases for SharpVector?
SharpVector is great for: building semantic search over documents or notes, embedding search within desktop or server .NET applications, powering recommendation features based on text similarity, integrating with OpenAI or local embeddings for intelligent querying, offline or edge computing with limited internet connectivity, and development/testing without the overhead of installing a server.
Who maintains SharpVector?
The Build5Nines SharpVector project is maintained by Chris Pietschmann, founder of Build5Nines, Microsoft MVP, HashiCorp Ambassador, and Microsoft Certified Trainer (MCT). The project is open-source on GitHub and welcomes contributions, feedback, and new ideas from the community.