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ホーム>Recipes>RAG Pipeline Builder

RAG Pipeline Builder

advanced

Retrieval-augmented generation pipeline combining vector search with document ingestion.

アーキテクチャ

Qdrant MCP
Vector database for storing and querying document embeddings
84/100
uvx mcp-server-qdrant
LlamaCloud MCP
Document ingestion, parsing, and chunking pipeline
78/100
uvx llamacloud-mcp@latest
Pinecone MCP
Managed vector index for production-scale retrieval
78/100
npx -y @pinecone-database/mcp

期待される出力

RAG Pipeline Status Documents ingested: 156 Chunks created: 1,240 Vectors stored: 1,240 Query: "How do I configure auth?" Retrieved: 5 relevant chunks (similarity > 0.82) -> Answer generated with source citations

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すべてインストール

uvx mcp-server-qdrant
uvx llamacloud-mcp@latest
npx -y @pinecone-database/mcp

難易度

advanced

Components

3

作成日

Mar 25, 2026

シーン

data
ai