RAG MCP Server (Lambda + OpenSearch Serverless)
@0x00000002
基本情報
RAG MCP Server について
概要はまだありません
RAG MCP Server の設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"rag-mcp-server": {
"command": "python",
"args": [
"example.py"
]
}
}
}RAG MCP Server のツール
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is RAG MCP Server (Lambda + OpenSearch Serverless)?
It is an MCP (Model Context Protocol) server implementing a RAG (Retrieval-Augmented Generation) system using a serverless AWS architecture. The server integrates AWS Lambda, API Gateway, OpenSearch Serverless, OpenAI, and S3, and is intended for developers building AI agents that need a document retrieval and generation backend.
How to use RAG MCP Server (Lambda + OpenSearch Serverless)?
Deploy the server to your AWS account using the provided Makefile. The typical workflow is: install dependencies (make deps), bootstrap CDK (make bootstrap), create required secrets in AWS Secrets Manager (an OpenAI API key and an application API key), then deploy (make deploy). After deployment, interact with the API using the endpoint URL and the application API key in the X-API-Key header; the server exposes a /mcp endpoint for MCP discovery and execution.
Key features of RAG MCP Server (Lambda + OpenSearch Serverless)
- Serverless RAG server on AWS
- MCP (Model Context Protocol) compatible
- Vector search via OpenSearch Serverless
- Embeddings and generation with OpenAI
- Persistent document storage on S3
- Infrastructure defined with AWS CDK
Use cases of RAG MCP Server (Lambda + OpenSearch Serverless)
- Add documents to a knowledge base for retrieval
- Query the knowledge base with RAG retrieval and generation
- List all stored documents via the API
- Integrate with AI agents using the MCP protocol
- Deploy a production
「メモリとナレッジ」の他のコンテンツ
Codebase Memory
DeusDataHigh-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

UmmahAPI
Yousuf KazmiIslamic source data for assistants: Quran, hadith, tafsir, duas, prayer times and qibla. Answers come from real sources with citations instead of recalled text, so verses and hadith are not hallucinated. 36,000+ hadiths

Sooveryn
PANDAPROGSooveryn gives Claude Code a team of AI personas that remembers your project. Soov, the partner and team lead, keeps your decisions and how you work in a lasting memory, and finds them again by words and by meaning, from

Sensefold
SenseFold LabsPersonal context for every AI: search, read, and write back to your private Markdown library of articles, threads, PDFs, notes, and captured ChatGPT/Claude/Gemini/Grok conversations. Remote server, OAuth 2.1 paste-and-au

agentleFS
agentleFSOne permissioned folder your team and your AI agents both log into.
コメント