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Add Comprehensive Test Coverage for rag_service.py #3

Description

@brylie

Objective

Implement a robust test suite for the rag_service.py module, which handles the Retrieval-Augmented Generation (RAG) functionality. Aim for 80-90% test coverage to ensure reliability and correctness of the RAG process.

Description

The rag_service.py module contains the RAGService class, which is responsible for retrieving relevant context and preparing messages for the chat model. We need to create a comprehensive set of tests to cover all critical paths and ensure the RAG process works correctly under various scenarios.

Tasks

  1. Create a test file test_rag_service.py in the tests/ directory.
  2. Implement unit tests for the following methods of the RAGService class:
    • get_relevant_context
    • prepare_messages_with_sources
    • prepare_messages
  3. Mock the VectorStore dependency to isolate the RAG service in tests.
  4. Test various scenarios including:
    • Successful context retrieval and message preparation
    • Edge cases (e.g., no relevant context found, empty query)
    • Different lengths of chat history
  5. Verify that the RagCitation objects are correctly created and returned.
  6. Test the integration of context into the system prompt.
  7. Ensure that the service correctly handles different types of input (e.g., various message structures).

Acceptance Criteria

  • All tests pass successfully.
  • Test coverage is between 80-90% as measured by a coverage tool.
  • All public methods of the RAGService class are thoroughly tested.
  • Mocking is used appropriately to isolate the RAG service from its dependencies.
  • Tests verify both the structure and content of returned messages and citations.
  • Edge cases and potential error scenarios are adequately covered.

Additional Notes

  • Use pytest as the testing framework.
  • Use pytest-asyncio for testing asynchronous code.
  • Use pytest-cov to measure test coverage.
  • Ensure tests are independent and can run in any order.
  • Consider using parameterized tests for functions with multiple input scenarios.

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