The AI Agent Framework in .NET
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Updated
Jul 3, 2024 - C#
The AI Agent Framework in .NET
RAG architecture: index and query any data using LLM and natural language, track sources, show citations, asynchronous memory patterns.
A versatile multi-modal chat application that enables users to develop custom agents, create images, leverage visual recognition, and engage in voice interactions. It integrates seamlessly with local LLMs and commercial models like OpenAI, Gemini, Perplexity, and Claude, and allows to converse with uploaded documents and websites.
SQL Server connector for Semantic Kernel plugin and Kernel Memory
Semantic search in Unity!
A lightweight implementation of Kernel Memory as a Service
SQL Server as a vector database, SQL Server Extenstion for RAG
Lightweight In-memory Vector Database to embed in any .NET Applications
Implements a framework to build Generative AI applications.
This example shows how a multitenant service can distribute requests evenly among multiple Azure OpenAI Service instances and manage tokens per minute (TPM) for multiple tenants.
ChatGPT-like Application using RAG pattern that allows to ask question to my own documents - I Used Semantic Kernel to integrate a LLM (OpenAI) using C# to orchestrate AI pluggins (Azure Cognitive Services). For the document embeddings I used Qdrant for the vector database and Pdfpig to extract the content from the pdfs
RAG implementation using Microsoft Semantic Kernel and .NET
A service for automating document ingestion for Semantic Kernel's KernelMemory service
Some unofficial extensions to Kernel Memory to do some advanced RAG
Explore AI Capabilities for Your .NET Projects with OpenAI's API: Unlock the power of AI in your applications
A sample showing how vector comparison can be used to detect plagiarismm using a simple in-memory vector store.
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