[Question]: Seeking Guidance on Validating and Testing Results of Text to SQL Code Utilizing Rag-based Llama-index

[Chakradhar448]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

Hello Llama community,

I've been working on developing a text to SQL conversion code leveraging Rag-based Llama-index. While I've made significant progress, I'm now at the stage where I need to validate and test the results thoroughly to ensure accuracy and reliability.

Could you please recommend any algorithms or tools that are commonly used for validating and testing such systems? I'm particularly interested in methods that have been successful in the context of Rag-based Llama-index.

Additionally, if there are any best practices or specific considerations I should keep in mind while conducting these tests, I would greatly appreciate your insights.

Thank you in advance for your assistance!

[dosubot]

To effectively validate and test your text to SQL conversion code using Rag-based Llama-index, consider the following strategies and tools:

1. Unit Testing with pytest: Structure your tests using pytest to ensure each component functions correctly. This approach is beneficial for organizing test cases and verifying the integrity of individual parts of your conversion process.

2. Implement Custom Query Engines: Use custom query engines, like MockQueryEngine, to simulate various behaviors and responses. This method allows you to test how your system handles different inputs and queries, ensuring robustness and flexibility.

3. Diverse Input Formats and Function Schemas: Test your system with a variety of input formats. Utilizing function schemas (fn_schema_cls and fn_schema_obj) helps in processing different types of inputs accurately, which is crucial for a text to SQL tool.

4. Error Handling and Input Resolution: Include tests for error handling and input resolution. Specifically, test how your system behaves when resolve_input_errors is set to False, ensuring it can gracefully handle unexpected inputs.

5. Output Verification: It's essential to verify the output against expected results to confirm the accuracy of your text to SQL conversion. This step ensures that your system not only runs without errors but also produces the correct SQL queries.

Best Practices:

• Aim for comprehensive test coverage, including edge cases and potential error conditions.
• Integrate testing into a CI/CD pipeline for automatic regression detection.
• Consider performance testing for applications requiring high throughput or low latency.
• Include security considerations in your tests, especially to prevent SQL injection or other vulnerabilities.

These strategies, exemplified by the tests in llama-index-legacy/tests/tools/test_query_engine_tool.py, provide a solid foundation for validating and testing your text to SQL conversion tool. For more detailed examples, you can refer to the tests in the llama-index-core and llama-index-legacy repositories.

Details

Sources

• [Bug]: Text-to-SQL have serious SQL injection issue which can execute arbitary SQL statement
• [Tracking]: Evaluation Integrations
• llama-index-core/tests/indices/struct_store/test_sql_query.py
• llama-index-legacy/tests/tools/test_query_engine_tool.py
• llama-index-core/llama_index/core/indices/struct_store/sql_query.py
• llama-datasets/docugami_kg_rag/sec_10_q/llamaindex_baseline.py
• llama-index-core/llama_index/core/utilities/sql_wrapper.py
• docs/docs/optimizing/building_rag_from_scratch.md
• llama-index-core/llama_index/core/query_engine/jsonalyze_query_engine.py

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