feat: add Nemotron Super Text-to-SQL and Search Agent recipes
Add two new recipes derived from the Nemotron Super post-training pipelines:
Nemotron Super Text-to-SQL:
- Five-stage pipeline: seeding, prompt generation, schema with distractors,
dialect-specific SQL, validation + quality scoring
- 14 conditional samplers (10 industries, 50 topics, complexity-gated task
types, data quality concepts, knowledge dependencies, 100 style combos)
- Dialect-specific prompts for SQLite, MySQL, and PostgreSQL
- 5 LLM judges (prompt, SQL, context, data quality, knowledge) with 15
scoring dimensions and flat score extraction columns
- Per-dialect syntax validation via CodeValidatorParams
Nemotron Super Search Agent:
- Four-stage pipeline: Wikidata KG seed paths, two-stage riddle generation
(draft + BrowseComp-style obfuscation), Tavily web search trajectories
via MCP, structured JSON formatting
- Tavily hosted MCP endpoint (streamable_http) -- no local server or extra
dependencies beyond data-designer
- Full tool-call trace capture (with_trace=ALL_MESSAGES) for SFT data
- Built-in demo seeds (3 Wikidata paths) for quick testing
Both recipes include ASCII pipeline diagrams, Nemotron Super context in
docstrings, dev note links in the markdown pages, and follow existing
recipe conventions (PEP 723 metadata, --model-alias/--num-records/
--artifact-path CLI args).
* add tool usage statistics tracking
- Add ToolUsageStats class with metrics for tool calls, turns, and
statistical aggregates (mean/stddev per generation)
- Extend ModelUsageStats to include tool_usage tracking
- Update ModelFacade.generate() to track total tool calls and turns
- Update tests with tool_call_count method and new assertions
* silence noisy mcp logs
* log message updates
* add tools enabled info message
* exclude empty tool_usage from usage stats output
* add tool usage summary logging after column generation
- Track tool usage snapshots before/after column processing
- Log mean tool calls per generation for columns with tools enabled
- Add get_tool_usage_snapshot/get_tool_usage_delta methods to ModelRegistry
- Remove unused extra_info parameter from progress_tracker.log_start()
- Add comprehensive tests for ToolUsageStats
* pretty format model usage logs
* reuse stubs and fixtures
* add merge method to ToolUsageStats for accurate stats aggregation
The previous implementation used extend() to combine tool usage stats,
but extend() is designed for single generation data. This caused
incorrect stddev calculations when merging stats from multiple sources.
- Add ToolUsageStats.merge() that properly combines sum-of-squares
- Update ModelUsageStats.extend() to use merge() for tool usage
- Add tests verifying stddev accuracy after merging
* fix tool usage stats missing generations_with_tools count
When tracking tool usage after generation, the ToolUsageStats was
created without setting generations_with_tools, causing the usage
summary to report zeros for calls/gen and turns/gen metrics.
* fix tool usage delta objects returning incorrect stddev values
- Simplify facade API to use tool_usage.extend() directly
- Return NaN for stddev when sum of squares wasn't tracked
- Add docstring to get_tool_usage_delta explaining NaN behavior
- Add comprehensive tests for stddev variance calculation
* fix tool usage delta stddev by including sum of squares in deltas
Convert sum_of_squares_turns and sum_of_squares_calls from private
attributes to public fields, enabling them to be included in delta
calculations. This allows get_tool_usage_delta to return objects that
compute accurate stddev values instead of NaN.
* fix test to use get_tool_usage_snapshot for accurate stddev tracking
The test was manually constructing a ToolUsageStats snapshot without
sum_of_squares fields, causing stddev to be NaN. Now uses the proper
snapshot method that includes all fields needed for delta calculations.
* use nvidia-reasoning by default
* mean -> average in log message
* refactor log indentation to use centralized LOG_INDENT constant
- Add LOG_INDENT constant to logging.py for consistent indentation
- Replace hardcoded " |-- " strings across all log statements
- Add tool alias and MCP provider info to pre-generation logs
- Improve model usage log format for better consistency
- Update tests to match new log formats
* simplify usage stats dict access in model registry
Remove defensive .get() calls and unnecessary type casts since
the usage statistics dictionary structure is now guaranteed.
* walrus baby
* simplify tool usage tracking and reduce log verbosity
- Remove mean/stddev calculations from ToolUsageStats in favor of simple
counts and generation ratios
- Add total_generations field to track all tool-enabled generations
- Simplify registry log output to show generations ratio (with_tools/total)
- Remove per-column tool usage snapshot/delta logging from column builder
- Track tool usage for all tool-enabled generations, not just those with calls
* format inference parameters as multi-line log output
- Add get_formatted_params() method to BaseInferenceParams
- Add LOG_DOUBLE_INDENT constant for nested indentation
- Update log_pre_generation() to display each parameter on its own line
* update tests to use LOG_INDENT constants
Align test assertions with the centralized log indentation
constants introduced in the logging module refactor.
* two-space consistency
