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https://github.com/NVIDIA-NeMo/DataDesigner
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1448f9cbda
* docs: add text-to-sql devnote * add diagram, update content * correct inconsistencies * docs: address PR #349 feedback and add BIRD benchmark results PR feedback fixes: - Fix Window Functions contradiction: Key Takeaway #1 now uses "Geospatial SQL" (Advanced) instead of "Window Functions" (Intermediate) - Fix score-0 truthiness bug: use `is not none` instead of truthy check in Jinja2 expression columns (inline example + production pipeline) - Soften Code Sandbox language: "A natural next step would be..." instead of "We are actively implementing..." - Cut Gretel reference per mvansegbroeck: replaced with NVIDIA/Nemotron team description - Replace Qwen model references with Nemotron per mvansegbroeck: MODEL_NAME, ASCII diagram labels, Pipeline Overview prose - Rename sdg_qwen_235b.py -> sdg_ndd_text2sql.py per mvansegbroeck - Fix Try It Yourself: use MODEL_ALIAS = "nvidia-text" with default provider pattern (matches structured-outputs dev note), remove unused explicit ModelConfig - Remove placeholder dataset link (#), add "Dataset: Internal" note New content: - Add BIRD Benchmark Results section with bar chart (JPG), data table, BIRD caveat paragraph, and Jocelyn Huang acknowledgement (Nemotron Super EX: 26.77% -> 41.80%, +15 pts, beats GPT-OSS-120B) - Replace "Looking Ahead: Code Sandbox" with broader "Next Steps": Code Sandbox, RL on BIRD via NeMo Gym, schema representation, Spider 2.0 - Add Project Summary table at end of post * docs: address second round of PR #349 feedback - Fix "EHR Systems" -> "Electronic Health Records" in Key Takeaway #1 to match the exact taxonomy string in the code example (greptile) - Add admonition clarifying code snippets are illustrative, not runnable, with link to Enterprise Text-to-SQL Recipe (nabinchha) - Add context before score extraction snippet referencing the five LLMJudgeColumnConfig columns and linking to full recipe (nabinchha) - Add companion file note and recipe link to production pipeline details block for prompts.py, rubrics.py, text2sql_seed.json (nabinchha) * docs: address round 2 PR #349 feedback, replace production block with recipe - Fix "EHR Systems" -> "Electronic Health Records" in Key Takeaway #1 to match the exact taxonomy string in the code example (greptile) - Add admonition clarifying inline code snippets are illustrative, with link to runnable Enterprise Text-to-SQL Recipe (nabinchha) - Add context before score extraction snippet referencing the five LLMJudgeColumnConfig columns and linking to full recipe (nabinchha) - Replace production pipeline <details> block (230 lines with phantom imports from prompts.py, rubrics.py, text2sql_seed.json) with snippet include of enterprise_text_to_sql.py recipe — self-contained and runnable, consistent with other merged dev notes (nabinchha) * docs: polish Try It Yourself and Summary sections - Wrap minimal inline example in collapsible <details> dropdown - Rename "A Team Effort" section to "Summary" - Remove redundant Scale/Dialects/Dataset line * docs: add missing sql_dialect sampler to Step 1 code snippet The Step 3/4 prompt templates reference {{ sql_dialect }} but the Step 1 seeding code never defined it, leaving an unresolved Jinja2 variable for readers following along. Add the sql_dialect sampler with a comment explaining the pipeline runs once per dialect. * fix ascii diagram * docs: fix BIRD score framing and MySQL dialect wording - Remove specific "60-70%" BIRD claim from intro to avoid contradiction with the 41.80%/38.25% direct-generation results shown later (those higher figures come from specialized systems with schema linking) - Reword MySQL "forbids" to "prompts exclude" -- REGEXP_REPLACE and CONVERT_TZ are valid MySQL functions; the pipeline excluded them for portability, not because the dialect forbids them * docs: move text-to-sql images to assets/ convention and update refs * docs: address text-to-sql devnote review comments - Add devnote to mkdocs nav after Async All the Way Down - Swap Recursive CTEs to Advanced, CASE Expressions to Intermediate (matches recipe) - Fix score extraction truthy check to use 'is not none' (preserves score-0 values) - Drop REPLACE() vs regexp_replace from dialect takeaway (REPLACE is cross-dialect) - Tighten prose: remove 'The key insight:', use actual BIRD number, trim X-not-Y - Fix knowledge dependency count: 8 -> 9 concepts (3x3 in recipe) --------- Signed-off-by: Yev Meyer <ymeyer@nvidia.com> Co-authored-by: Yev Meyer <ymeyer@nvidia.com> |
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| .. | ||
| async-all-the-way-down | ||
| data-designer-got-skills | ||
| design-principles | ||
| owning-the-model-stack | ||
| search-agent | ||
| structured-outputs-from-nemotron | ||
| text-to-sql | ||