docs: add text-to-sql dev note (#349)

* 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>

Author: dhruvnathawani

docs/assets/recipes/code_generation/enterprise_text_to_sql.py

@@ -543,7 +543,7 @@ def build_config(model_alias: str, dialect: str = "sqlite") -> dd.DataDesignerCo
             config_builder.add_column(
                 dd.ExpressionColumnConfig(
                     name=f"{prefix}_{rubric}_score",
-                    expr=f"{{{{ {judge_name}.{rubric}.score if {judge_name}.{rubric}.score else ' ' }}}}",
+                    expr=f"{{{{ {judge_name}.{rubric}.score if {judge_name}.{rubric}.score is not none else '' }}}}",
                 )
             )
 

docs/devnotes/.authors.yml

@@ -23,6 +23,14 @@ authors:
     name: Dhruv Nathawani
     description: Researcher at NVIDIA
     avatar: https://avatars.githubusercontent.com/u/128275431?v=4
+  ymeyer:
+    name: Yev Meyer
+    description: Principal Research Scientist at NVIDIA
+    avatar: https://avatars.githubusercontent.com/u/11296522?v=4
+  mvansegbroeck:
+    name: Maarten Van Segbroeck
+    description: Director of Research at NVIDIA
+    avatar: https://avatars.githubusercontent.com/u/67658125?v=4
   jgreco:
     name: Johnny Greco
     description: Researcher at NVIDIA