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AI Chat Application with RAG and MCP Servers

@garrardkitchen

基本信息

分类

AI 与智能体

传输方式

stdio

作者

garrardkitchen

关于 AI Chat Application with RAG and MCP Servers

An .NET Aspire AI chat application that demonstrates how to chat with custom data (RAG - Event Driven) and context extensions (MCP Server) using an AI language model. .NET Aspire, RAG, Qdrant and MCP Server returning GitLab Groups based on search pattern

#serverless#rag#chat-ai#dotnet-aspire#mcp-server

AI Chat Application with RAG and MCP Servers 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ai-chat-example": {
      "command": "npx",
      "args": [
        "@modelcontextprotocol/inspector",
        "dotnet",
        "run"
      ]
    }
  }
}

AI Chat Application with RAG and MCP Servers 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is AI Chat Application with RAG and MCP Servers?

A .NET Aspire chat application that demonstrates chatting with custom data using event-driven RAG and MCP Server context extensions. It integrates AI language models with Qdrant as the vector store and supports GitHub Models and OpenAI endpoints. Designed for developers exploring AI chat, RAG patterns, and MCP tool integration in .NET.

How to use AI Chat Application with RAG and MCP Servers?

Clone the repository, restore dependencies, and configure user secrets for your chosen AI provider (GitHubModels:Token or ConnectionStrings:openai) and optionally for the GitLab tools (GitLab:Token, GitLab:domain). Run the application from Visual Studio or Visual Studio Code using the provided solution file.

Key features of AI Chat Application with RAG and MCP Servers

  • Chat with custom data via RAG and MCP Server tools.
  • Supports GitHub Models and OpenAI as AI providers.
  • Includes MCP WhoIs and GitLab SearchGroups tools.
  • Upload PDFs to use as context for the AI model.
  • Dynamically create PDFs from markdown content.
  • Uses Qdrant as vector store for event-driven RAG.

Use cases of AI Chat Application with RAG and MCP Servers

  • Query an AI model about uploaded PDF documents

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