Build5Nines/SharpVector
Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application
This is an in-memory vector database library that helps .NET developers integrate semantic search and AI context retrieval directly into their applications. It takes text data, converts it into vector representations, and allows for efficient searching based on meaning rather than just keywords. .NET developers building applications with advanced text search or generative AI capabilities would use this.
121 stars.
Use this if you are a .NET developer needing to add local, in-memory semantic search or Retrieval-Augmented Generation (RAG) context to your applications without relying on external vector database services.
Not ideal if you need a persistent, large-scale, or distributed vector database solution that operates outside of a single .NET application's memory.
Stars
121
Forks
9
Language
C#
License
MIT
Category
Last pushed
Jan 10, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/Build5Nines/SharpVector"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
alibaba/zvec
A lightweight, lightning-fast, in-process vector database
devflowinc/trieve
All-in-one platform for search, recommendations, RAG, and analytics offered via API
matte1782/edgevec
High-performance vector search for Browser, Node, and Edge
rryam/VecturaKit
Swift-based vector database for on-device RAG using MLTensor and MLX Embedders
KyroDB/KyroDB
Autonomous Vector database for AI agents and RAG. Hybrid Semantic Cache eliminates cold-cache...