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BACH-AI-Tools

BachStudio Teradata MCP Server

rag_Execute_Workflow

Executes a complete RAG pipeline to answer user questions by retrieving relevant document chunks and returning grounded answers without external knowledge.

Instructions

Execute complete RAG workflow to answer user questions based on document context. This tool handles the entire RAG pipeline in a single step when a user query is tagged with /rag.

WORKFLOW STEPS (executed automatically):

  1. Configuration setup using configurable values from rag_config.yml

  2. Store user query with '/rag ' prefix stripping

  3. Generate query embeddings using either BYOM (ONNXEmbeddings) or IVSM functions based on config

  4. Perform semantic search against precomputed chunk embeddings

  5. Return context chunks for answer generation

CONFIGURATION VALUES (from rag_config.yml):

  • version: 'ivsm' or 'byom' to select embedding approach

  • All database names, table names, and model settings are configurable

  • Vector store metadata fields are dynamically detected

  • Embedding parameters are configurable

  • Default chunk retrieval count is configurable

  • Default values are provided as fallback

TECHNICAL DETAILS:

  • Strips the '/rag ' prefix if present from user questions

  • Creates query table if it does not exist (columns: id, txt, created_ts)

  • BYOM approach: Uses mldb.ONNXEmbeddings UDF for tokenization and embedding

  • IVSM approach: Uses ivsm.tokenizer_encode and ivsm.IVSM_score functions

  • Both approaches store embeddings in configured output table

  • Uses cosine similarity via TD_VECTORDISTANCE for semantic search

  • Returns the top-k matching chunks from the configured vector store

  • Each result includes chunk text, similarity score, and metadata fields

CRITICAL ANSWERING RULES:

  • Answer ONLY using retrieved chunks - no external knowledge, speculation, or inference

  • Quote source content directly without paraphrasing, summarizing, or rewriting

  • If no relevant context found: "Not enough information found in the provided context"

  • If partial context: "The available context does not fully answer the question"

  • Include document/page references when available (e.g., "On page 2 of 'demo_policy.pdf'...")

  • Execute entire workflow silently without showing function calls to user - only show final answer

LANGUAGE RESTRICTIONS:

  • Do not say "According to the context" or "The context says" - just quote directly

  • Do not say "It can be inferred that..." - no inference allowed

  • Use exact or near-verbatim quotes only

  • No paraphrasing, summarizing, or adding transitions between quotes

  • Copy content precisely - no expansion or interpretation

BEHAVIORAL REQUIREMENTS:

  • Execute all RAG workflow steps silently without showing tool calls to users

  • Only display final answers based strictly on retrieved context chunks

  • Do not use external knowledge or speculation - only quote retrieved content directly

  • RAG mode only triggers with explicit /rag prefix - do not enter RAG mode implicitly

  • Hide all tool execution steps from user

  • Provide clean, direct response with only the answer based on retrieved chunks

  • This is a complete end-to-end RAG workflow - no additional tools needed

EXECUTION: Run completely silently - user only sees their question and the final context-grounded answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
kNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully covers behavioral traits: workflow steps, silent execution, answering rules, language restrictions, and how it handles configuration. It discloses that it strips /rag prefix, uses cosine similarity, and executes automatically without showing tool calls.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-organized into sections (purpose, workflow, configuration, technical details, rules). It front-loads the core purpose and usage. While each section adds value, it could be slightly more concise without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of output schema or annotations, the description thoroughly covers workflow, configuration, behavioral rules, error handling (e.g., partial context responses), and usage constraints. It provides all necessary context for correct tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains the 'question' parameter (user query with /rag prefix) but does not explicitly describe the optional 'k' parameter, which controls chunk retrieval count. Schema description coverage is 0%, so the description should compensate but only partially does.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes a complete RAG workflow to answer user questions based on document context. It distinguishes from siblings which are primarily database utility tools, as this is an end-to-end RAG pipeline tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: when a user query is tagged with /rag. Also provides not-to-use guidance: 'RAG mode only triggers with explicit /rag prefix - do not enter RAG mode implicitly.' This clearly differentiates from other scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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