chore(wren-ai-service): minor updates

[cyyeh]

Summary by CodeRabbit

• Documentation
  • Refined configuration examples by localizing timeout settings to individual models and standardizing model parameters and output formats across various AI service integrations.
• Tests
  • Expanded support by introducing a new "stripe" use case option for additional dataset integration.

[coderabbitai]

Walkthrough

This pull request updates several configuration files for the Wren AI service by refining timeout settings and model parameters. Global timeout values have been localized to specific model configurations (e.g., azure/gpt-4, groq/llama-3.3-70b-specdec), and new timeout and kwargs settings have been added in multiple config files. In addition, the deepseek configuration has been updated to reorder model names, adjust response format, and remove certain API base entries. A new "stripe" use case has also been added in the test suite to enable support for the bigquery dataset.

Changes

Files	Change Summary
wren-ai-service/.../config.azure.yaml, wren-ai-service/.../config.groq.yaml	Moved global timeout (120) to a model-specific configuration for llm (azure/gpt-4 and groq/llama-3.3-70b-specdec respectively)
wren-ai-service/.../config.deepseek.yaml	Reordered model names; added timeout: 120 for each model; updated response_format (for deepseek-coder) and removed api_base entries
wren-ai-service/.../config.google_ai_studio.yaml	Added timeout: 120 to LLM and embedder models; updated kwargs for gemini/gemini-2.0-flash-exp to include response_format: json_object, n, and temperature
wren-ai-service/.../config.ollama.yaml	Increased timeout values from 120 to 600 seconds for both llm and embedder model configurations
wren-ai-service/.../test_usecases.py	Introduced a new "stripe" use case by adding it to the usecase_to_dataset_type mapping and argument parser choices

Sequence Diagram(s)

sequenceDiagram
    participant U as User
    participant A as Argument Parser
    participant P as Usecase Processor
    participant D as Dataset Loader

    U->>A: Input usecases including "stripe"
    A->>P: Parse and validate usecase options
    P->>D: Map "stripe" to "bigquery" dataset
    D-->>P: Return dataset configuration
    P-->>U: Process usecase with loaded dataset

Loading

Possibly related PRs

• Create config.azure.yaml #1248: Adjusts Azure configuration with a timeout parameter for the litellm_llm provider, aligning with the timeout changes for azure/gpt-4.
• chore(wren-ai-service): fix config examples #1267: Modifies timeout settings for embedder models in deepseek, similar in scope to the adjustments made in this PR.
• chore(wren-ai-service): minor-updates #1265: Introduces an engine_timeout parameter in configuration files, directly related to the re-scoped timeout changes featured here.

Suggested reviewers

• paopa
• wwwy3y3

Poem

Hoppin’ through the code, I spy a change so neat,
Timeouts now live with models, making configs complete.
Deepseek and Google shine with kwargs and fresh flair,
Ollama’s leap to 600 shows we truly care.
Stripe joins the crew in our test parade—
A bunny’s cheer for a well-tuned update!
(_/)

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📥 Commits

Reviewing files that changed from the base of the PR and between cd2b361 and 165a176.

📒 Files selected for processing (6)

• wren-ai-service/docs/config_examples/config.azure.yaml (1 hunks)
• wren-ai-service/docs/config_examples/config.deepseek.yaml (1 hunks)
• wren-ai-service/docs/config_examples/config.google_ai_studio.yaml (1 hunks)
• wren-ai-service/docs/config_examples/config.groq.yaml (1 hunks)
• wren-ai-service/docs/config_examples/config.ollama.yaml (2 hunks)
• wren-ai-service/tests/pytest/test_usecases.py (2 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (5)

• GitHub Check: pytest
• GitHub Check: pytest
• GitHub Check: Analyze (python)
• GitHub Check: Analyze (javascript-typescript)
• GitHub Check: Analyze (go)

🔇 Additional comments (12)

wren-ai-service/tests/pytest/test_usecases.py (2)

161-161: LGTM! Added stripe use case with bigquery dataset type.

The mapping is consistent with other bigquery-based use cases.

---

172-177: LGTM! Updated CLI arguments to include stripe use case.

The changes to parser choices and default use cases list are consistent.

wren-ai-service/docs/config_examples/config.ollama.yaml (2)

10-10: LLM Model Timeout Increased
The timeout for the LLM model has been raised from 120 to 600 seconds. This adjustment allows a longer response window when interacting with the Ollama host. Please verify that your downstream services can tolerate this higher timeout.

---

22-22: Embedder Timeout Increased
Similarly, the embedder’s timeout is now set to 600 seconds. This update ensures both model types have consistent timeout settings, which may be beneficial in scenarios with slower external API responses.

wren-ai-service/docs/config_examples/config.azure.yaml (1)

8-8: Timeout Localization for Azure LLM
The timeout parameter (120 seconds) has been relocated from a global setting to directly reside within the model configuration for azure/gpt-4. This improves clarity by ensuring that the timeout value explicitly applies to the intended model.

wren-ai-service/docs/config_examples/config.groq.yaml (1)

10-10: Explicit Timeout for GROQ LLM Model
A timeout of 120 seconds has now been added under the specific model configuration for groq/llama-3.3-70b-specdec. This change makes the timeout configuration explicit and model-specific.

wren-ai-service/docs/config_examples/config.google_ai_studio.yaml (2)

10-13: Gemini LLM Base Configuration Update
For the gemini/gemini-2.0-flash-exp model, a timeout of 120 seconds has been set and the kwargs (n and temperature) are explicitly defined. This provides better control over model invocation parameters.

---

14-21: Alias Configuration with Enhanced Response Format
The alias entry (gemini-llm-for-chart) also now has a timeout of 120 seconds along with its dedicated kwargs. Additionally, a response_format with type json_object has been introduced, which should help standardize and parse outputs effectively for chart generation.

wren-ai-service/docs/config_examples/config.deepseek.yaml (4)

9-15: Deepseek Model – First Entry Configuration
The first model block now explicitly declares a timeout of 120 seconds and includes the standard kwargs. Note that the AI summary mentions a change in model signature (expecting a permutation from deepseek/deepseek-reasoner to deepseek/deepseek-chat); please verify that the current identifier “deepseek/deepseek-reasoner” is as intended.

---

17-23: Deepseek Model – Second Entry Configuration
The second model’s configuration now features an explicit timeout of 120 seconds and the kwargs have been maintained. Confirm that the model identifier (deepseek/deepseek-chat) aligns with the intended update, especially considering the permutation noted in the summary.

---

25-32: Deepseek Model – Third Entry Configuration
The third model entry sets a timeout of 120 seconds and updates the response_format to json_object. This change is particularly relevant for scenarios expecting structured JSON responses. Ensure that the new response format is supported by downstream components.

---

39-41: Embedder Configuration Consistency
The embedder section now includes a timeout of 120 seconds for the text-embedding-3-large model, ensuring that timeout settings across different model types remain consistent.

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