RAG Pipeline Builder
advanced
Retrieval-augmented generation pipeline combining vector search with document ingestion.
Architecture
Qdrant MCP
Vector database for storing and querying document embeddings
84/100
LlamaCloud MCP
Document ingestion, parsing, and chunking pipeline
78/100
Pinecone MCP
Managed vector index for production-scale retrieval
78/100
Expected Output
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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