marcominerva/OpenAIEmbeddingSample
An example that shows how to use Semantic Kernel and Kernel Memory to work with embeddings in a .NET application using SQL Server as Vector Database.
This project demonstrates how to integrate large language model (LLM) capabilities into .NET applications using Semantic Kernel and Kernel Memory. It shows how to store and efficiently search document embeddings within a SQL Server database. This is for .NET developers who want to build applications with advanced semantic search and AI functionalities.
No commits in the last 6 months.
Use this if you are a .NET developer looking for a reference implementation to incorporate document embeddings and vector search with SQL Server into your applications.
Not ideal if you are not a .NET developer or are looking for a ready-to-use end-user application rather than a code example.
Stars
31
Forks
5
Language
C#
License
MIT
Category
Last pushed
Feb 12, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/marcominerva/OpenAIEmbeddingSample"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
RediSearch/RediSearch
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector...
redis/redis-vl-python
Redis Vector Library (RedisVL) -- the AI-native Python client for Redis.
redis-developer/redis-ai-resources
✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.
redis-developer/redis-product-search
Visual and semantic vector similarity with Redis Stack, FastAPI, PyTorch and Huggingface.
luyug/GradCache
Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint