feat(python-notebook-migration): add LLM client for notebook-to-workflow conversion

[zyratlo]

What changes were proposed in this PR?

Introduces the frontend LLM session class that converts a Jupyter notebook into a Texera workflow JSON plus a bidirectional cell to operator mapping, along with the prompt library it uses. Two files under frontend/src/app/workspace/service/notebook-migration/, totalling ~700 lines (~410 of which is prompt text).

migration-llm.ts — defines NotebookMigrationLLM, an @Injectable class wrapping a Vercel AI SDK chat session against the LiteLLM proxy already exposed on main at /api/chat/completion.

• initialize(modelType, apiKey) — builds an OpenAI-compatible chat client via createOpenAI({ baseURL: AppSettings.getApiEndpoint() }), seeds the message history with Texera documentation as system messages.
• verifyConnection() — does a 10-token ping call to validate that the API key works against the configured model.
• convertNotebookToWorkflow(notebook) — extracts code cells (each tagged with a UUID in metadata.uuid), sends WORKFLOW_PROMPT + the notebook to get a JSON of UDF operators / edges, then sends MAPPING_PROMPT to get the cell↔operator mapping. Assembles a complete Texera workflow JSON (PythonUDFV2 operators with stub input/output ports, links derived from the LLM's edge list, default settings) plus a bidirectional operator_to_cell / cell_to_operator mapping. Returns both as a JSON string.
• close() — clears the message history and the model reference.

migration-prompts.ts — string constants used by migration-llm.ts: TEXERA_OVERVIEW, TUPLE_DOCUMENTATION, TABLE_DOCUMENTATION, OPERATOR_DOCUMENTATION, UDF_INPUT_PORT_DOCUMENTATION, EXAMPLE_OF_GOOD_CONVERSION, VISUALIZER_DOCUMENTATION, EXAMPLE_OF_MULTIPLE_UDF_CONVERSION, WORKFLOW_PROMPT, MAPPING_PROMPT.

Any related issues, documentation, discussions?

Closes #5259
Parent issue #4301

How was this PR tested?

No unit tests were included for these reasons:

• A large portion of the changes are prompt text, which are not testable, only readable. However the prompt text can be changed to improve the performance of the LLM.
• Testing would require mocking a significant amount of logic that will be introduced in later PRs, since the logic in migration-llm.ts is parsing a response.

However I am open to writing tests based on review feedback.

Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Claude Opus 4.7)

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 52.93%. Comparing base (891d2ad) to head (b900b9d).

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #5260      +/-   ##
============================================
- Coverage     52.95%   52.93%   -0.03%     
- Complexity     2627     2630       +3     
============================================
  Files          1090     1090              
  Lines         42210    42210              
  Branches       4534     4534              
============================================
- Hits          22353    22344       -9     
- Misses        18546    18556      +10     
+ Partials       1311     1310       -1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.91% <ø> (ø)		Carriedforward from a090029
agent-service	34.36% <ø> (ø)		Carriedforward from a090029
amber	53.15% <ø> (+0.03%)	⬆️	Carriedforward from a090029
computing-unit-managing-service	1.65% <ø> (ø)		Carriedforward from a090029
config-service	56.71% <ø> (ø)		Carriedforward from a090029
file-service	57.06% <ø> (ø)		Carriedforward from a090029
frontend	47.86% <ø> (-0.07%)	⬇️
pyamber	89.77% <ø> (ø)		Carriedforward from a090029
python	90.73% <ø> (ø)		Carriedforward from a090029
workflow-compiling-service	58.69% <ø> (ø)		Carriedforward from a090029

*This pull request uses carry forward flags. Click here to find out more.

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