Rag Grounding

Reference

The public surface is RagCodeGenWorkflow, re-exported from attune.workflows.

RagCodeGenWorkflow — attune.workflows.rag_code_gen

Symbol	Purpose
RagCodeGenWorkflow(**kwargs)	Construct the workflow (pipeline is lazily initialized on first execute).
RagCodeGenWorkflow.execute(**kwargs)	Async. Retrieve + generate. Honors query (required), k, depth, feedback, model, path (and deprecated cwd). Returns a WorkflowResult.
RagCodeGenWorkflow.name	The registered CLI slug, "rag-code-gen".
RagCodeGenWorkflow.stages	["retrieve", "generate"] — retrieve at CHEAP (zero-LLM), generate at CAPABLE.

Depth → turns and budget

Depth	Max turns	Budget cap	Notes
quick	6	$2	Narrowest, cheapest.
standard	12	$10	Default.
deep	24	$25	Enables extended thinking.

WorkflowResult fields read after a run

Field	Type	Meaning
success	bool	Whether the run completed.
final_output	Any	Generated answer followed by a ## Sources citations block.
summary	str \| None	Short overview.
metadata	dict	query, depth, max_turns, citation (structured provenance), fallback_used, confidence, retrieval_ms, feedback_recorded.
error	str \| None	Failure reason (e.g. missing query, bad k, unknown model, RAG retrieval failure).

Entry points

Surface	Invocation
Python	await RagCodeGenWorkflow().execute(query=<q>, k=<n>, depth=<d>).
CLI	attune workflow run rag-code-gen --input '{"query": "<q>"}' [--depth ...] [--json].
Skill	/rag-code-gen in a Claude Code conversation.

There is no dedicated MCP tool for this workflow.