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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
LLM + MCP + RAG = Magic
| Date | Stars |
|---|---|
| 2026-07-31 | 563 |
| 2026-08-03 | 566 |
| 2026-08-06 | 566 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# LLM + MCP + RAG
## 目标
- **Augmented LLM** (Chat + MCP + RAG)
- 不依赖框架
- LangChain, LlamaIndex, CrewAI, AutoGen
- **MCP**
- 支持配置多个MCP Serves
- **RAG** 极度简化板
- 从知识中检索出有关信息，注入到上下文
- **任务**
- 阅读网页 → 整理一份总结 → 保存到文件
- 本地文档 → 查询相关资料 → 注入上下文
## **The augmented LLM**
- [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents)
```mermaid
classDiagram
class Agent {
+init()
+close()
+invoke(prompt: string)
-mcpClients: MCPClient[]
-llm: ChatOpenAI
-model: string
-systemPrompt: string
-context: string
}
class ChatOpenAI {
+chat(prompt?: string)
+appendToolResult(toolCallId: string, toolOutput: string)
-llm: OpenAI
-model: string
-messages: OpenAI.Chat.ChatCompletionMessageParam[]
-tools: Tool[]
}
class EmbeddingRetriever {
+embedDocument(document: string)
+embedQuery(query: string)
+retrieve(query: string, topK: number)
-embeddingModel: string
-vectorStore: VectorStore
}
class MCPClient {
+init()
+close()
+getTools()
+callTool(name: string, params: Record<string, any>)
-mcp: Client
-command: string
-args: string[]
-transport: StdioClientTransport
-tools: Tool[]
}
class VectorStore {
+addEmbedding(embedding: number[], document: string)
+search(queryEmbedding: number[], topK: number)
-vectorStore: VectorStoreItem[]
}
class VectorStoreItem {
-embedding: number[]
-document: string
}
Agent --> MCPClient : uses
Agent --> ChatOpenAI : interacts with
ChatOpenAI --> ToolCall : manages
EmbeddingRetriever --> VectorStore : uses
VectorStore --> VectorStoreItem : contains
```
## **依赖**
```bash
git clone [email protected]:KelvinQiu802/ts-node-esm-template.git
pnpm install
pnpm add dotenv openai @modelcontextprotocol/sdk chalk**
```
## LLM
- [OpenAI API](https://platform.openai.com/docs/api-reference/chat)
## MCP
- [MCP 架构](https://modelcontextprotocol.io/docs/concepts/architecture)
- [MCP Client](https://modelcontextprotocol.io/quickstart/client)
- [Fetch MCP](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch)
- [Filesystem MCP](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem)
## RAG
- [Retrieval Augmented Generation](https://scriv.ai/guides/retrieval-augmented-generation-overview/)
- 译文: https://www.yuque.com/serviceup/misc/cn-retrieval-augmented-generation-overview
- 各种Loaders: https://python.langchain.com/docs/integrations/document_loaders/
- [硅基流动](https://cloud.siliconflow.cn/models)
- 邀请码： **x771DtAF**
- [json数据](https://jsonplaceholder.typicode.com/)
## 向量
- 维度
- 模长
- 点乘 Dot Product
- 对应位置元素的积，求和
- 余弦相似度 cos
- 1 → 方向完全一致
- 0 → 垂直
- -1 → 完全想法
Excerpt of 3,106 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e5e6225620ec4348, llm:Repository name and description: 'llm-mcp-rag' and 'LLM + MCP + RAG = Magic' (TypeScript). Implies integration of LLMs with Retrieval-Augmented Generation (RAG) and potentially 'MCP' (likely model control/management/connector) in a TypeScript project for building RAG systems.
matched fp:e5e6225620ec4348, llm:Repository name and description: 'llm-mcp-rag' and 'LLM + MCP + RAG = Magic' (TypeScript). Implies integration of LLMs with Retrieval-Augmented Generation (RAG) and potentially 'MCP' (likely model control/management/connector) in a TypeScript project for building RAG systems.
matched fp:e5e6225620ec4348, llm:Repository name and description: 'llm-mcp-rag' and 'LLM + MCP + RAG = Magic' (TypeScript). Implies integration of LLMs with Retrieval-Augmented Generation (RAG) and potentially 'MCP' (likely model control/management/connector) in a TypeScript project for building RAG systems.