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
- Create a test file
test_rag_service.py in the tests/ directory.
- Implement unit tests for the following methods of the
RAGService class:
get_relevant_context
prepare_messages_with_sources
prepare_messages
- Mock the
VectorStore dependency to isolate the RAG service in tests.
- 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
- Verify that the
RagCitation objects are correctly created and returned.
- Test the integration of context into the system prompt.
- 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.
Objective
Implement a robust test suite for the
rag_service.pymodule, 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.pymodule contains theRAGServiceclass, 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
test_rag_service.pyin thetests/directory.RAGServiceclass:get_relevant_contextprepare_messages_with_sourcesprepare_messagesVectorStoredependency to isolate the RAG service in tests.RagCitationobjects are correctly created and returned.Acceptance Criteria
RAGServiceclass are thoroughly tested.Additional Notes
pytestas the testing framework.pytest-asynciofor testing asynchronous code.pytest-covto measure test coverage.