Define the Scaffolding for the getDataTool

langchain chat comparison of the databricks tool vs the sql_database tool:

The two approaches for creating tools in LangChain for Databricks and SQL databases exhibit distinct design philosophies and functionalities. Below is a comparison of the pros and cons of each approach:

Databricks Tool Approach Pros: Dynamic Type Handling: The _uc_type_to_pydantic_type function dynamically maps user-defined types from Databricks to Pydantic types, allowing for flexible and accurate data validation based on the function's input parameters. Structured Tool Creation: The use of StructuredTool allows for a clear definition of input parameters and their types, enhancing the usability and clarity of the tool. Automatic Tool Generation: The include method can automatically include multiple functions from a schema, reducing manual effort and potential errors in tool creation. Integration with Databricks SDK: The toolkit leverages the Databricks SDK for seamless interaction with Databricks functions, making it easier to execute and manage functions within the Databricks environment.

Cons: Complexity: The approach involves multiple layers of abstraction and type handling, which may increase complexity and make it harder to maintain or extend. Error Handling: The reliance on assertions and type checks may lead to runtime errors if the expected types do not match, potentially complicating debugging. Dependency on Databricks SDK: The toolkit requires the Databricks SDK, which adds an additional dependency that must be managed.

SQL Database Tool Approach Pros: Simplicity: The SQL database tools are straightforward, focusing on executing SQL queries and retrieving metadata without complex type handling. Clear Separation of Concerns: Each tool (e.g., querying, getting schema, listing tables) has a clear and distinct purpose, making the code easier to understand and maintain. Direct Interaction with SQL: The tools directly interact with the SQL database, allowing for immediate execution of queries and retrieval of results, which can be more intuitive for users familiar with SQL. Use of Pydantic for Validation: The use of Pydantic models for input validation ensures that the input parameters are well-defined and validated before execution.

Cons: Limited Type Handling: The SQL tools do not dynamically handle different SQL types, which may lead to issues if the database schema changes or if complex types are involved. Less Flexibility: The tools are more rigid in their design, focusing on specific SQL operations without the ability to easily extend or modify for different use cases. Error Handling: While the tools provide some error handling, they may not be as robust as the Databricks approach, which includes detailed type checks and assertions.

Summary The Databricks tool approach is more complex and flexible, allowing for dynamic type handling and automatic tool generation, but it may introduce additional complexity and dependencies. The SQL database tool approach is simpler and more straightforward, focusing on clear functionality and ease of use, but it lacks the dynamic type handling and flexibility of the Databricks approach. Choosing between these approaches depends on the specific requirements of the application, such as the need for dynamic type handling versus simplicity and ease of use

Tab-SE / tableau_langchain

Define the Scaffolding for the getDataTool #36