Built-in Vector Database for .NET Applications
Turn High‑Dimensional Data into Fast, Reliable Insights with LM‑Kit’s Vector Storage Engine
Store and Retrieve Embeddings at Any Scale
LM-Kit simplifies embedding storage in .NET by providing a built-in vector database engine that supports everything from in-memory prototyping to production-ready deployments. It also includes a flexible API for connecting to any external vector database, giving developers full control over where and how embeddings are stored. Whether you’re building a desktop application or a distributed AI platform, LM-Kit delivers consistent, modular APIs and plug-and-play vector storage options tailored to your needs.
With four built-in storage strategies and full support for custom backends, LM-Kit gives you complete control over how and where you store your embedding vectors.
Key Storage Patterns for Vector Data
Choose the storage that fits your application’s lifecycle. Switch between them seamlessly.
| 🔧 Method | ✅ Best For | 💾 Persistence | 📈 Scale | 🌐 Infra Required |
|---|---|---|---|---|
| In-Memory | Quick tests, live classification | Temporary (serializable) | Low | None |
| Built-In Vector DB | Local/offline tools, desktop apps | Yes (file-based) | Medium | None |
| Qdrant Vector Store | Cloud or distributed workloads | Yes | High | Qdrant instance |
| Custom via IVectorStore | Proprietary systems | Yes (custom) | Varies | Your infrastructure |
Unified with the DataSource Class
DataSource Class
At the heart of LM-Kit’s vector storage is the DataSource abstraction. Whether you’re embedding text, images, or structured documents, DataSource lets you ingest, organize, store, and retrieve embeddings with full support for metadata and multi-section partitioning.
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DataSource
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Sections
- 📄 TextPartitions (now supports images too)
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📂
Sections
Each Section can include metadata, making your vector collections easier to classify, version, and search.
Key Features
Structured, Hierarchical Storage
Organizes your embedding data into sections and text partitions, with optional metadata at each level. This makes it easy to manage multi-modal inputs like documents, images, or web content within a single collection.
Rich Metadata Support
Attach metadata to both sections and individual data entries. Use this for filtering, tagging, or advanced retrieval scenarios across any vector backend.
Built-in Serialization and Portability
Serialize DataSource instances to disk or stream and reload them anywhere. This enables efficient checkpointing, debugging, and deployment without relying on external storage services.
Incremental Updates for Persistent Stores
Whether you’re using an external vector database (via IVectorStore) or LM-Kit’s built-in vector DB, DataSource supports efficient incremental updates, including insertions, deletions, and metadata edits, without needing to reload or rebuild the entire dataset.
In-Memory Vector Store
Ideal for fast prototyping, local testing, and immediate classification tasks. Embeddings are computed and stored in RAM, with optional serialization to disk for reusability.
| Benefits | Sample Use Cases |
|---|---|
| Zero setup, local-only | Semantic search prototypes |
| Instant feedback, live debugging | LLM prompt enrichment |
| Always uses latest model version | Real-time text classification |
Built-In Vector Database
When your application requires long-term storage without relying on external services, LM-Kit’s built-in vector database offers a powerful and lightweight solution. Think of it as a SQLite for dense vectors: a self-contained, file-based engine optimized for embedding workloads on a single machine.
Key Benefits
Zero Setup - Just Point to a Local File Path
No server installation, no configuration steps, no external dependencies. Creating a persistent vector store is as simple as specifying a file path and letting LM-Kit handle the rest. Ideal for developers who want to get started instantly without provisioning infrastructure..
Durable, Portable, and Easy to Share
The entire embedding collection is stored in a compact binary file, making it easy to version, archive, or move between environments. Whether you're sharing a prototype with a teammate or shipping a portable desktop tool, your vector data travels with you.
No Network or Cloud Dependency Required
Everything runs locally on the user’s machine. This not only eliminates latency and hosting costs but also ensures full control over data security, making it a perfect fit for offline tools, air-gapped environments, or compliance-sensitive applications.
Handles Millions of Vectors with Low-Latency I/O
Optimized for local read/write operations, LM-Kit’s built-in vector DB can scale to millions of embeddings without sacrificing performance. Insertions, updates, and queries remain fast and responsive—even as your collection grows.
Supports Insertions, Deletions, and Searches Entirely on Disk
All operations, adding new vectors, removing outdated ones, updating metadata, and performing similarity search—are executed directly on disk. There’s no need to load the entire dataset into memory, enabling you to manage large collections on standard hardware.
Ideal Use Cases
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Desktop Applications with Embedded AI Features Integrate intelligent functionality into desktop software using a fast, local vector store, ideal for indexing user notes, documents, or interactions with no external services required. |
Offline Tools that Need Local-Only Vector Storage Build reliable tools for use in offline or restricted environments such as secure facilities, field deployments, or air-gapped systems that cannot rely on network connectivity. |
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Prototypes that Must Persist Embeddings Across Sessions Ideal for iterating on experimental ideas, store your vector data locally so each test run continues where the last left off, without rebuilding the entire embedding space. |
Portable AI Modules Distributed in Local Environments Package inference logic and embeddings together for deployment in self-contained applications— suitable for USB delivery, software installers, or embedded systems. |
Qdrant Vector Store Integration
Plug into a high-performance, open-source vector database with our Qdrant connector. Ideal for production workloads that need distributed access and advanced indexing.
The Qdrant connector is open source and built as an implementation of the IVectorStore interface, acting as a bridge between LM-Kit.NET and the official Qdrant SDK for C#. This ensures native compatibility, robust performance, and full feature access, whether you’re running Qdrant locally or in distributed environments.
Key Benefits
HNSW Indexing for High-Speed Search
Qdrant supports Hierarchical Navigable Small World (HNSW) indexing, enabling sub-second similarity searches even across millions of vectors. It’s ideal for use cases like semantic search, recommendation engines, and fast retrieval in real-time applications.
Scalable Storage with Automatic Sharding
As your data grows, Qdrant’s architecture supports horizontal scaling through automatic sharding and replication, ensuring consistent performance across larger workloads.
Deploy Anywhere (Cloud or Local)
Run Qdrant in a distributed cloud setup for scalability, or use a local Docker container for quick dev environments and offline testing. LM-Kit’s integration works seamlessly with both.
Unified with LM-Kit’s DataSource for Plug-and-Play Integration
Easily switch to Qdrant without changing your code. LM-Kit’s DataSource handles embedding, storage, and search through the same unified API.
Custom Vector Store via IVectorStore
Projects requiring integration with proprietary infrastructure or highly specialized vector storage logic can implement the IVectorStore interface. This interface provides a standardized contract for custom vector store backends within the LM-Kit framework.
Key Benefits
Full Backend Integration
Enables seamless integration with proprietary databases, internal APIs, or hybrid storage systems not covered by default LM-Kit options.
Custom Storage Logic
Allows definition of how vectors and metadata are indexed, stored, and retrieved, ideal for advanced use cases requiring precise control over data flows.
Scalability Optimization
Supports tailored scaling strategies, including batching, sharding, or embedding storage close to application logic, depending on system architecture.
Future-Proof Architecture
Provides a decoupled interface that can evolve with system requirements, ensuring long-term adaptability without locking into a specific storage vendor or format.
Use Case Overview
| Aspect | Details |
|---|---|
| Best Suited For | Custom or proprietary backends, full control over vector logic |
| Advantages | Flexible integration, total control over storage and retrieval behavior |
| Tradeoffs | Higher development overhead, responsibility for performance optimization |
Why Choose LM-Kit for Embedding Storage?
Choosing the right storage strategy for your embeddings is critical to performance, scalability, and developer productivity. LM-Kit abstracts away the complexity with a unified interface that supports every stage of development, from quick prototyping to full-scale production. Here’s what sets LM-Kit apart when it comes to managing vector data.
Swap Backends Without Rewriting Code
The same embeddings collection management logic works across all storage types, just change the backend.
Privacy by Design
Use local-only or on-prem solutions to keep data secure and compliant.
Performant, Modular, Scalable
From desktop experiments to high-scale RAG systems, LM-Kit adapts to your needs.
Ready for Production or Prototyping
Switch between in-memory and persistent storage instantly for different stages of development.
Ready to Simplify Your Vector Storage?
LM-Kit’s embedding storage system brings powerful, flexible vector management directly to your .NET applications, without the need for complex infrastructure or external dependencies. From in-memory experiments to durable local databases and scalable remote setups, LM-Kit makes switching storage backends effortless.