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Teradata MCP Server

Official
by Teradata

rag_executeWorkflow

Process user questions by executing the complete RAG workflow: configures settings, generates query embeddings, performs semantic search, and retrieves relevant context chunks for answer generation.

Instructions

Execute complete RAG workflow to answer user questions based on document context.

This function handles the entire RAG pipeline:

  1. Configuration setup (using configurable values from rag_config.yml)

  2. Store user query (with /rag prefix stripping)

  3. Generate query embeddings (tokenization + embedding)

  4. Perform semantic search against chunk embeddings

  5. Return retrieved context chunks for answer generation

The function uses configuration values from rag_config.yml with fallback defaults.

Arguments: question - user question to process k - number of top-k results to return (optional, uses config default if not provided)

Returns: Returns the top-k most relevant chunks with metadata for context-grounded answer generation.

Input Schema

NameRequiredDescriptionDefault
kNo
questionYes

Input Schema (JSON Schema)

{ "properties": { "k": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "K" }, "question": { "title": "Question", "type": "string" } }, "required": [ "question" ], "title": "handle_rag_executeWorkflowArguments", "type": "object" }

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