VectorRAG.Net: High-Performance Vector Search & RAG Library for .NET

Native Embedded Vector Database for Semantic Search Applications

VectorRAG.Net is a commercial-grade .NET library that provides an in-process vector database and semantic search engine designed for Retrieval-Augmented Generation (RAG) workloads.

Built specifically for the .NET ecosystem, it delivers low-latency vector similarity search with controlled memory allocation, eliminating the need for external vector databases or network-based services.

Core Architecture & Design Philosophy

Embedded Database Engine

Unlike cloud-based vector services or external database systems, VectorRAG.Net runs directly inside your application process.

This architecture eliminates:

  • network round-trips
  • serialization overhead
  • external service dependencies

The result is deterministic, low-latency performance suitable for real-time and high-throughput systems.

RAG-Optimized Design

VectorRAG.Net is purpose-built for RAG pipelines and includes:

  • document chunking
  • metadata filtering
  • context assembly utilities

This reduces the need to combine multiple external components into a single RAG stack.

Key Capabilities & Features

1. High-Performance Vector Search

  • Approximate Nearest Neighbor (ANN)
    Random hyperplane LSH with configurable precision/recall trade-offs
  • Exact reranking
    Dot product and cosine similarity with SIMD optimization
  • Hybrid search
    Combined vector similarity + BM25 text relevance scoring
  • Candidate filtering
    Metadata-based pre-filtering before vector comparisons

2. RAG-Specific Functionality

  • Intelligent chunking
    Fixed-size and boundary-aware strategies with overlap control
  • Parent document grouping
    Automatic aggregation of chunks during retrieval
  • Context assembly
    Utilities for building LLM-ready prompts from search results
  • Embedding abstraction layer
    Works with multiple embedding providers (OpenAI, local models, custom APIs)

3. Operational Features

  • File-based persistence
    Snapshot save/load of full database state
  • Runtime metrics
    Query analytics and performance monitoring
  • Memory efficiency
    ArrayPool-based allocations and configurable caching
  • Atomic updates
    Safe upsert operations with version tracking

4. Enterprise-Grade Performance

  • Predictable latency under varying load conditions
  • High throughput with thread-safe parallel operations
  • Scales to millions of vectors per node
  • Zero-allocation hot paths for core search operations

Performance Characteristics

Search Benchmarks

  • Vector-only search: ~66,000 queries/sec
    (10k documents, 64 dimensions, TopK=5)
  • Hybrid search: ~8,500 queries/sec
    (same dataset with BM25 scoring)
  • Indexing throughput: ~50,000 documents/min
    (including chunking and embedding generation)
  • Memory footprint: ~4 KB per 1,000 vectors (64D) + metadata overhead

Scalability Profile

  • Linear query scaling with candidate set size
  • Optimized storage for high-dimensional vectors
  • Efficient batch ingestion and updates

Quality Metrics

  • Adjustable recall via LSH parameters
  • Tunable vector/text weighting (alpha parameter)
  • Fast metadata filtering via indexed predicates

Technical Specifications

Target Environment

  • .NET 8.0+ runtime
  • Cross-platform: Windows, Linux, macOS
  • Architectures: x64, ARM64
  • Embedding dimensions: 64–2048

Integration Architecture

  • No external dependencies
  • Embedding-provider agnostic
  • File-based persistence (no database server required)
  • Fully in-process execution model

Developer Experience

  • Clean, intuitive API design
  • Full documentation and examples
  • Built-in diagnostics and metrics
  • Extensible chunking and scoring pipeline

Licensing & Commercial Model

Free Usage (Evaluation License)

VectorRAG.Net can be used free of charge for:

  • evaluation
  • testing
  • development
  • research
  • educational purposes
  • proof-of-concept projects

There are:

  • no time limits
  • no feature restrictions

Production Usage

Production use requires an active commercial subscription.

Pricing

  • $1,000 USD per month per organization

Industry Applications

Financial Services

  • AML/KYC compliance search systems
  • Market research semantic search
  • Intelligent customer support systems

E-commerce & Retail

  • Semantic product search
  • Context-aware recommendation engines
  • Catalog enrichment and tagging

Enterprise Systems

  • Internal knowledge bases
  • CRM semantic search
  • Technical support automation

Healthcare & Legal

  • Medical record semantic retrieval
  • Legal precedent search
  • Compliance monitoring systems

Why VectorRAG.Net?

Technical Advantages

  • Fully .NET-native implementation
  • No network latency overhead
  • Predictable performance characteristics
  • Single-component architecture

Operational Benefits

  • No vector database infrastructure required
  • Reduced system complexity
  • Lower latency compared to external services
  • Strong data isolation and privacy

Developer Experience

  • NuGet-based integration
  • Familiar .NET design patterns
  • Production-ready diagnostics
  • Minimal setup overhead

Resources & Distribution

GitHub: https://github.com/likeslines-maker/VectorRAG.Net

Support & Contact

  • Telegram: @vipvodu

Summary

VectorRAG.Net is a native in-process vector database for .NET that enables high-performance semantic search and RAG pipelines without external infrastructure.

It is designed for systems where:

  • latency matters
  • control matters
  • infrastructure simplicity matters