fixed kwargs ask method

[gzileni]

• added docs
• added kwargs arguments to ask method

[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Overview

1.1 Core Changes

• Primary purpose and scope: The primary purpose of this PR is to enhance the flexibility of the ask method by introducing kwargs to allow for more customizable interactions with the underlying SQL generation and LLM (Large Language Model) components.
• Key components modified: The ask method in base.py and the submit_prompt method in ollama.py are the primary components modified. The kwargs are passed down to these methods, enabling more configurable behavior.
• Cross-component impacts: This change impacts the interaction between the ask method and downstream components like generate_sql and the LLM client. It does not fundamentally alter the core architecture but enhances the flexibility of existing components.
• Business value alignment: This PR aligns with the business requirement to provide more control and customization options to users, enabling them to tailor the behavior of the ask method to their specific needs.

1.2 Technical Architecture

• System design modifications: The introduction of kwargs to the ask method adds a new layer of configurability without altering the core system design.
• Component interaction changes: The ask method now passes kwargs to generate_sql and further to submit_prompt, allowing for more flexible control over the LLM interaction.
• Integration points impact: The integration points with the LLM client and SQL generation components are affected, as they now need to handle and utilize the passed kwargs.
• Dependency changes and implications: The ask method relies on downstream components to correctly handle kwargs, which could introduce new dependencies and potential points of failure if not managed properly.

2. Critical Findings

2.1 Must Fix (P0🔴)

None identified in the code changes themselves.

2.2 Should Fix (P1🟡)

Issue: Insufficient documentation for ask and submit_prompt methods.

• Impact: Users may struggle to understand how to use kwargs effectively, leading to reduced usability and potential misuse.
• Suggested Solution: Significantly improve the documentation to explain the purpose, usage, and supported kwargs. Provide examples to demonstrate how to use kwargs to customize the behavior of the ask method.

Issue: Missing testing coverage for kwargs functionality.

• Impact: The new kwargs functionality may not be thoroughly tested, leading to potential bugs and reliability issues.
• Suggested Solution: Add unit tests to verify the correct behavior of ask with different kwargs scenarios. Ensure tests cover various use cases and edge cases.

Issue: Review documentation in local.py, remote.py, and utils.py for accuracy and completeness.

• Impact: Incomplete or inaccurate documentation can lead to confusion and maintenance challenges.
• Suggested Solution: Ensure that the added documentation is well-formatted, accurate, and provides sufficient information about the classes and methods.

2.3 Consider (P2🟢)

Area: Potential explicit arguments for common LLM parameters in submit_prompt.

• Improvement Opportunity: Adding explicit arguments for common LLM parameters (e.g., temperature, top_p) in addition to **kwargs would improve discoverability and type hinting for common use cases. This can make the API easier to use and understand, especially for users who want to fine-tune the LLM behavior.

Area: Input validation or sanitization for kwargs.

• Improvement Opportunity: Considering input validation or sanitization for kwargs if there's a potential security risk or if you want to enforce specific types or values for certain keyword arguments in the future. This can help prevent unexpected behavior or errors.

2.4 Summary of Action Items

• High Priority (P1):
  • Improve documentation for the ask and submit_prompt methods.
  • Add unit tests to cover kwargs functionality.
  • Review documentation in local.py, remote.py, and utils.py.
• Medium Priority (P2):
  • Consider adding explicit arguments for common LLM parameters in submit_prompt.
  • Consider input validation or sanitization for kwargs.

3. Technical Analysis

3.1 Code Logic Analysis

📁 src/vanna/base/base.py - ask method

• Submitted PR Code:

    def ask(
        self,
        question: Union[str, None] = None,
        print_results: bool = True,
        auto_train: bool = True,
        visualize: bool = True,  # if False, will not generate plotly code
        allow_llm_to_see_data: bool = False,
        **kwargs: Any,
    ) -> Union[
        Tuple[
            Union[str, None],
            Union[pd.DataFrame, None],
            Union[plotly.graph_objs.Figure, None],
        ],
        None,
    ]:
        # ...
        try:
            sql = self.generate_sql(question=question, allow_llm_to_see_data=allow_llm_to_see_data, **kwargs)
        except Exception as e:
            print(e)
            return None, None, None
        # ...

• Analysis:
  • Current logic and potential issues: The ask method now accepts arbitrary keyword arguments (kwargs) and passes them to generate_sql. This allows for more flexible control over the SQL generation process. The type hint Any for kwargs is very broad, reducing type safety and discoverability.
  • Edge cases and error handling: The try-except block catches exceptions during generate_sql, which remains unchanged.
  • Cross-component impact: The change impacts the interaction between the ask method and downstream components like generate_sql and the LLM client.
  • Business logic considerations: The introduction of kwargs enhances the flexibility of the ask method, allowing users to customize the behavior of the LLM and SQL generation process.
• LlamaPReview Suggested Improvements:

        **kwargs: Any
            Optional keyword arguments that can be passed to customize the behavior of the `ask` method.
            These arguments are forwarded to the `generate_sql` method and potentially to the underlying
            Large Language Model (LLM) to influence the SQL generation process.

            Currently supported kwargs (subject to change, refer to documentation for updates):
            - `temperature` (float):  Controls the randomness of the LLM's output. Higher values (e.g., 1.0) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic.  This is passed to the underlying LLM client (e.g., Ollama, OpenAI).
            - ... (Add other supported kwargs as they become defined)

            Example:
```python
            vn.ask("What are the top 5 customers by revenue?", temperature=0.7)

        In this example, `temperature=0.7` is passed as a kwarg to influence the LLM's SQL generation.

  - Improvement rationale:
    * Technical benefits: Clear documentation improves usability and reduces the learning curve for users who want to leverage the `kwargs` functionality. It also enhances maintainability by making the intended usage of `kwargs` explicit.
    * Business value: A more user-friendly and customizable `ask` method can attract more users and enable more advanced use cases.
    * Risk assessment: The risk of misuse or unexpected behavior is mitigated by clear documentation and testing.

📁 **src/vanna/ollama/ollama.py** - **submit_prompt method**
  - Submitted PR Code:
```python
    def submit_prompt(self, prompt, **kwargs) -> str:
        """
        Submits a prompt to the Ollama client and returns the response content.
        Args:
          prompt (dict): The prompt message to be sent to the Ollama client.
          **kwargs: Additional keyword arguments.
        Returns:
          str: The content of the response message from the Ollama client.
        Logs:
          - Ollama parameters including model, options, and keep_alive status.
          - The content of the prompt being submitted.
          - The response received from the Ollama client.
        """
        self.log(
          f"Ollama parameters:
"
          f"model={self.model},
"
          f"options={self.ollama_options},
"
          f"keep_alive={self.keep_alive}")
        self.log(f"Prompt Content:
{json.dumps(prompt, ensure_ascii=False)}")
        response_dict = self.ollama_client.chat(model=self.model,
                                                 messages=prompt,
                                                 stream=False,
                                                 options=self.ollama_options,
                                                 **kwargs) # kwargs are passed here now
        return response_dict["message"]["content"]

• Analysis:
  • Current logic and potential issues: The submit_prompt method now accepts kwargs and passes them to the ollama_client.chat method, enabling customization of the LLM interaction. The docstring is added, which is good, but it's still generic.
  • Edge cases and error handling: The method does not introduce new error handling mechanisms.
  • Cross-component impact: The change impacts the interaction between the submit_prompt method and the Ollama client.
  • Business logic considerations: The introduction of kwargs enhances the flexibility of the LLM interaction, allowing users to customize the behavior of the LLM.
• LlamaPReview Suggested Improvements:

    def submit_prompt(self, prompt, temperature: float = None, top_p: float = None, **kwargs) -> str:
        """
        Submits a prompt to the Ollama client and returns the response content.

        Args:
          prompt (dict): The prompt message to be sent to the Ollama client.
          temperature (float, optional): Controls the randomness of the output. Higher values (e.g., 1.0) make the output more random. Defaults to None (Ollama default).
          top_p (float, optional):  Nucleus sampling parameter.  Defaults to None (Ollama default).
          **kwargs: Additional keyword arguments to pass directly to the `ollama_client.chat` method.
                   Refer to the Ollama API documentation for available options: [Ollama API Docs Link].

        Returns:
          str: The content of the response message from the Ollama client.

        Logs:
          - Ollama parameters including model, options, and keep_alive status.
          - The content of the prompt being submitted.
          - The response received from the Ollama client.

        Example:
```python
        response = self.submit_prompt(prompt_messages, temperature=0.8, top_p=0.95)

    """
    # ... (rest of the method code, passing temperature, top_p, and **kwargs to ollama_client.chat)

  - Improvement rationale:
    * Technical benefits: More specific documentation and potentially adding explicit arguments for common parameters makes the API easier to use and understand, especially for users who want to fine-tune the LLM behavior. Type hinting for common parameters improves code readability and helps prevent errors.
    * Business value: A more user-friendly and customizable API can attract more users and enable more advanced use cases.
    * Risk assessment: The risk of misuse or unexpected behavior is mitigated by clear documentation and testing.

### 3.2 Key Quality Aspects 
- **System scalability considerations**: This PR is unlikely to have a direct impact on system scalability. Scalability is more dependent on the performance of the underlying LLM and database systems, which are not directly modified by this PR.
- **Performance bottlenecks and optimizations**: The introduction of `kwargs` itself is unlikely to have a significant impact on performance. However, the *use* of these `kwargs` to control LLM behavior could potentially affect performance. For example, if `kwargs` are used to increase the complexity of LLM generation, it could lead to increased processing time and resource consumption on the LLM side.
- **Testing strategy and coverage**: The PR description mentions "added docs" but does not explicitly mention adding or modifying tests. It's crucial to have tests that cover the new `kwargs` functionality in the `ask` method. Tests should verify that `kwargs` are correctly passed to `generate_sql` and downstream components, and that `kwargs` can successfully modify the behavior of the `ask` method.
- **Documentation needs**: The current documentation in the code diffs is descriptive but lacks specific details about the `kwargs`. For example, in `base.py`, the `ask` method documentation mentions `**kwargs: Any`, but doesn't explain *what* `kwargs` are expected or how they are used. Enhance the documentation for the `ask` method and `submit_prompt` (and potentially `generate_sql` if it also utilizes `kwargs`) to clearly specify the purpose of `kwargs`, supported `kwargs`, and examples.

## 4. Overall Evaluation
- **Technical assessment**: The PR is a positive step towards enhancing the flexibility of the `vanna` library by introducing `kwargs` to the `ask` method. The code changes are relatively simple and focused, and they appear to achieve the intended functionality. The addition of documentation is also a positive aspect.
- **Business impact**: The PR significantly increases the configuration flexibility of the `ask` method, allowing users to customize the behavior of the `ask` method by passing keyword arguments. This can attract more users and enable more advanced use cases.
- **Risk evaluation**: The introduction of `kwargs` could potentially introduce security vulnerabilities if not handled carefully. Passing arbitrary `kwargs` down the call chain could expose some configuration options that could be misused. In the context of LLMs, passing arbitrary `kwargs` might expose some configuration options that could be misused. However, in this specific PR, the risk seems low as the `kwargs` are primarily intended for LLM configuration, and the scope is limited to the `ask` method and related functions.
- **Notable positive aspects and good practices**: The PR introduces a new layer of configurability to the `ask` method, which is a positive step towards making the API more versatile. The addition of documentation is also a good practice, although it needs to be significantly improved to explain the purpose and usage of `kwargs`.
- **Implementation quality**: The implementation quality is good, as the code changes are relatively simple and focused. However, the documentation needs to be improved to explain the purpose and usage of `kwargs`.
- **Final recommendation**: **Merge the PR after addressing the P1 issues, especially the documentation and testing.** The P2 issues can be considered for future enhancements. Prioritize improving the documentation to make the `kwargs` functionality usable and understandable for users. Add unit tests to ensure the reliability of this new feature.

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