chore: Avoid adding only prefixed text to prompt

[taoche]

• When the max_token configured by the developer is too small or when the DDL, Documentation, question-sql-pair is too large, and there is no effective prompt that can be written into the final prompt, avoid writing prefix text into the prompt to ensure the prompt is clean.
• Use the string array join method to concatenate prompts, as Python's string concatenation performs worse than array join in terms of handling large volumes.

[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Overview

1.1 PR Summary

• Business value and requirements alignment: The primary purpose of this pull request is to avoid adding only prefixed text to the prompt when the configured max_token is too small or when the DDL, documentation, or question-sql-pair is too large. It also aims to improve performance by using the string array join method for concatenating prompts. This aligns with the business goal of generating accurate and performant SQL queries.
• Key components modified: The changes are focused on the base.py file in the src/vanna/base directory, particularly the methods for adding DDL, documentation, and SQL to the prompt.
• Impact assessment: The changes affect how the final prompt is constructed, which could impact the performance and behavior of the system when generating prompts.
• System dependencies and integration impacts: No new integration points are introduced. The interaction between the prompt generation methods and other components remains unchanged.

1.2 Architecture Changes

• System design modifications: No significant changes to the overall system design are observed. The modifications are localized to the prompt generation logic.
• Component interactions: The interaction between the prompt generation methods and other components remains unchanged.
• Integration points: No new integration points are introduced.

2. Detailed Technical Analysis

2.1 Code Logic Deep-Dive

Core Logic Changes

src/vanna/base/base.py - Function add_ddl_to_prompt

• Submitted PR Code:

  def add_ddl_to_prompt(
      self, initial_prompt: str, ddl_list: list[str], max_tokens: int = 14000
  ) -> str:
      prefix_prompt = "
  ===Tables
  "
      ddl_prompts = []
  
      if len(ddl_list) > 0:
          for ddl in ddl_list:
              if (
                  self.str_to_approx_token_count(initial_prompt)
                  + self.str_to_approx_token_count(ddl)
                  < max_tokens
              ):
                  ddl_prompts.append(f"{ddl}
  
  ")
  
      if ddl_prompts:
          initial_prompt += prefix_prompt + "".join(ddl_prompts)
  
      return initial_prompt

  • Analysis:
    • Current logic and potential issues: The current logic avoids adding only the prefix text to the prompt if no DDL can be added due to token constraints. This ensures the prompt remains clean.
    • Edge cases and error handling: The edge case where no DDL can be added due to token constraints is handled well.
    • **Cross-component impact **: No significant cross-component impact is observed.
    • **Business logic considerations **: The business logic of avoiding unnecessary prefix text is correctly implemented.
  • LlamaPReview Suggested Improvements:

    if ddl_prompts:
        initial_prompt += prefix_prompt + "".join(ddl_prompts)
    elif initial_prompt.endswith(prefix_prompt):
        initial_prompt = initial_prompt[:-len(prefix_prompt)]

  • **Improvement rationale **:
    • Technical benefits: Ensures the prompt is clean and efficient.
    • Business value: Improves the accuracy and performance of SQL query generation.
    • Risk assessment: Low risk; the changes are localized and well-contained.

src/vanna/base/base.py - Function add_documentation_to_prompt

• Submitted PR Code:

  def add_documentation_to_prompt(
      self,
      initial_prompt: str,
      documentation_list: list[str],
      max_tokens: int = 14000,
  ) -> str:
      prefix_prompt = "
  ===Additional Context
  
  "
      documentation_prompts = []
  
      if len(documentation_list) > 0:
          for documentation in documentation_list:
              if (
                  self.str_to_approx_token_count(initial_prompt)
                  + self.str_to_approx_token_count(documentation)
                  < max_tokens
              ):
                  documentation_prompts.append(f"{documentation}
  
  ")
  
      if documentation_prompts:
          initial_prompt += prefix_prompt + "".join(documentation_prompts)
  
      return initial_prompt

  • Analysis:
    • Current logic and potential issues: Similar to the DDL logic, this ensures that the prompt remains clean and efficient.
    • Edge cases and error handling: The edge case where no documentation can be added due to token constraints is handled well.
    • **Cross-component impact **: No significant cross-component impact is observed.
    • **Business logic considerations **: The business logic of avoiding unnecessary prefix text is correctly implemented.
  • LlamaPReview Suggested Improvements:

    if documentation_prompts:
        initial_prompt += prefix_prompt + "".join(documentation_prompts)
    elif initial_prompt.endswith(prefix_prompt):
        initial_prompt = initial_prompt[:-len(prefix_prompt)]

  • **Improvement rationale **:
    • Technical benefits: Ensures the prompt is clean and efficient.
    • Business value: Improves the accuracy and performance of SQL query generation.
    • Risk assessment: Low risk; the changes are localized and well-contained.

src/vanna/base/base.py - Function add_sql_to_prompt

• Submitted PR Code:

  def add_sql_to_prompt(
      self, initial_prompt: str, sql_list: list[str], max_tokens: int = 14000
  ) -> str:
      prefix_prompt = "
  ===Question-SQL Pairs
  
  "
      sql_prompts = []
  
      if len(sql_list) > 0:
          for question in sql_list:
              if (
                  self.str_to_approx_token_count(initial_prompt)
                  + self.str_to_approx_token_count(question["sql"])
                  < max_tokens
              ):
                  sql_prompts.append(f"{question['question']}
  {question['sql']}
  
  ")
  
      if sql_prompts:
          initial_prompt += prefix_prompt + "".join(sql_prompts)
  
      return initial_prompt

  • Analysis:
    • Current logic and potential issues: Similar logic as the other methods.
    • Edge cases and error handling: The edge case where no SQL pairs can be added due to token constraints is handled well.
    • **Cross-component impact **: No significant cross-component impact is observed.
    • **Business logic considerations **: The business logic of avoiding unnecessary prefix text is correctly implemented.
  • LlamaPReview Suggested Improvements:

    if sql_prompts:
        initial_prompt += prefix_prompt + "".join(sql_prompts)
    elif initial_prompt.endswith(prefix_prompt):
        initial_prompt = initial_prompt[:-len(prefix_prompt)]

  • **Improvement rationale **:
    • Technical benefits: Ensures the prompt is clean and efficient.
    • Business value: Improves the accuracy and performance of SQL query generation.
    • Risk assessment: Low risk; the changes are localized and well-contained.

2.2 Implementation Quality

• Code organization and structure:

  • Organization and modularity: The code is well-organized and modular. Each method handles a specific type of prompt addition, making the code easy to understand and maintain.
  • Design pattern adherence: The code adheres to common design patterns and principles.
  • Reusability aspects: The methods are reusable and can be easily extended or modified.
  • Maintainability factors: The code is maintainable due to its clear structure and modular design.

• Error handling:

  • Exception scenarios coverage: The code handles the edge case where no DDL, documentation, or SQL can be added due to token constraints.
  • Recovery mechanisms: Not applicable; the code does not require recovery mechanisms for this specific change.
  • Logging and monitoring: No additional logging or monitoring is introduced in this PR.
  • User experience impact: The user experience is improved by ensuring that the prompt is clean and efficient.

• Performance considerations:

  • Resource utilization: The use of the string array join method improves performance compared to traditional string concatenation.
  • Scalability aspects: The changes improve scalability by handling large volumes of text more efficiently.
  • Bottleneck analysis: No significant bottlenecks are introduced.
  • Optimization opportunities: The use of the string array join method is an optimization that improves performance.

3. Critical Findings

3.1 Potential Issues

Critical Issues

• 🔴 Prefix Text Handling:

  • Issue description: The current implementation does not ensure that the prefix text is removed if no valid content is appended.
  • Impact: This could result in unnecessary prefix text being included in the prompt, leading to inefficiencies and potential errors.
  • Recommendation:

    if ddl_prompts:
        initial_prompt += prefix_prompt + "".join(ddl_prompts)
    elif initial_prompt.endswith(prefix_prompt):
        initial_prompt = initial_prompt[:-len(prefix_prompt)]

• 🔴 Lack of Logging:

  • Issue description: The current implementation lacks logging for debugging purposes.
  • Impact: This could make it difficult to debug issues with prompt generation.
  • Recommendation:

    import logging
    
    logger = logging.getLogger(__name__)
    
    def add_ddl_to_prompt(
        self, initial_prompt: str, ddl_list: list[str], max_tokens: int = 14000
    ) -> str:
        prefix_prompt = "
    ===Tables
    "
        ddl_prompts = []
    
        if len(ddl_list) > 0:
            for ddl in ddl_list:
                if (
                    self.str_to_approx_token_count(initial_prompt)
                    + self.str_to_approx_token_count(ddl)
                    < max_tokens
                ):
                    ddl_prompts.append(f"{ddl}
    
    ")
    
        if ddl_prompts:
            initial_prompt += prefix_prompt + "".join(ddl_prompts)
        elif initial_prompt.endswith(prefix_prompt):
            initial_prompt = initial_prompt[:-len(prefix_prompt)]
    
        logger.debug(f"Final prompt: {initial_prompt}")
    
        return initial_prompt

Warnings

• 🟡 Token Counting Accuracy:
  • Warning description: The current token counting mechanism may not be accurate enough for precise prompt generation.
  • Potential risks: This could lead to inefficiencies in prompt generation and potential errors.
  • Suggested improvements: Consider using a more sophisticated token counting mechanism, such as a tokenizer from a library like NLTK or spaCy, for more accurate token counting.

3.2 Code Quality Concerns

• Maintainability aspects: The code is maintainable due to its clear structure and modular design. However, adding detailed comments and logging could further improve long-term maintainability.
• Readability issues: The code is generally readable, but adding more comments could improve readability.
• Performance bottlenecks: No significant performance bottlenecks are identified.

4. Security Assessment

4.1 Security Considerations

• Authentication/Authorization impacts: No significant impacts on authentication/authorization are identified.
• Data handling concerns: No significant data handling concerns are identified.
• Input validation: Ensure that the input lists (ddl_list, documentation_list, sql_list) are validated before processing.
• Security best practices: The changes comply with standard security practices.

4.2 Vulnerability Analysis

• Potential security risks: No potential security risks are identified.
• Mitigation strategies: Ensure input validation and adhere to security best practices.
• Security testing requirements: Ensure that security testing covers input validation and data handling.

5. Testing Strategy

5.1 Test Coverage

• Unit test analysis: Ensure that unit tests cover the edge case where no DDL, documentation, or SQL can be added due to token constraints.
• Integration test requirements: Ensure that integration tests cover the interactions between the prompt generation methods and other components.
• Edge cases coverage: Validate the edge case where no DDL, documentation, or SQL can be added due to token constraints.

5.2 Test Recommendations

Suggested Test Cases

def test_add_ddl_to_prompt_no_content(self):
    initial_prompt = ""
    ddl_list = ["DDL1", "DDL2"]
    max_tokens = 10
    result = self.obj.add_ddl_to_prompt(initial_prompt, ddl_list, max_tokens)
    self.assertEqual(result, "")

• Coverage improvements: Ensure that the current test coverage is adequate.
• Performance testing needs: Ensure performance benchmarks measure the impact of the changes on performance.
• Error scenario coverage: Validate that the edge case where no content is added is handled correctly.

6. Documentation & Maintenance

• Documentation updates needed (API, architecture, configuration): Update the documentation to reflect the changes in the prompt generation logic.
• Long-term maintenance considerations: The code is maintainable due to its clear structure and modular design. Adding detailed comments and logging could further improve long-term maintainability.
• Technical debt and monitoring requirements: No significant technical debt is introduced. Ensure that monitoring is in place for logging and debugging purposes.

7. Deployment & Operations

• Deployment impact and strategy: The changes are localized and should not impact deployment significantly. Ensure a rollback plan is in place in case of issues.
• Key operational considerations: The changes should improve the performance and efficiency of prompt generation, leading to better operational outcomes.

8. Summary & Recommendations

8.1 Key Action Items

1. Critical changes required:

   • Ensure the prefix is removed if no content is added.
   • Add logging for debugging.

2. Important improvements suggested:

   • Use a more sophisticated token counting mechanism for accurate token counts.

3. Best practices to implement:

   • Add detailed comments to explain the purpose of the prefix prompts and the logic behind avoiding unnecessary prefix text.
   • Update the documentation to reflect the changes in the prompt generation logic.

8.2 Future Considerations

• Technical evolution path: Consider evolving the token counting mechanism to improve accuracy.
• Business capability evolution: The changes align with the business goal of generating accurate and performant SQL queries.
• System integration impacts: No significant system integration impacts are identified.