yanchaomei opened a new pull request, #192:
URL: https://github.com/apache/incubator-hugegraph-ai/pull/192
fix #167
# Comprehensive Test Suite Implementation for HugeGraph-LLM
This PR implements a complete test suite for the HugeGraph-LLM project,
covering all major components and ensuring code quality and reliability.
## Summary of Test Implementation
### 1. Test Infrastructure
- Created `run_tests.py` script for easy test execution
- Implemented `conftest.py` with test configuration and fixtures
- Added test utilities in `test_utils.py` for common testing functions
- Set up test data directories with sample documents, schemas, and prompts
### 2. Document Processing Tests
- `test_document.py`: Tests for document module imports and basic
functionality
- `test_document_splitter.py`: Tests for document chunking in different
languages
- `test_text_loader.py`: Tests for loading text files with various encodings
### 3. Integration Tests
- `test_graph_rag_pipeline.py`: End-to-end tests for graph-based RAG pipeline
- `test_kg_construction.py`: Tests for knowledge graph construction from
documents
- `test_rag_pipeline.py`: Tests for standard RAG pipeline functionality
### 4. Middleware Tests
- `test_middleware.py`: Tests for FastAPI middleware components
### 5. Model Tests
- LLM Tests:
- `test_openai_client.py`: Tests for OpenAI API integration
- `test_qianfan_client.py`: Tests for Baidu Qianfan API integration
- `test_ollama_client.py`: Tests for Ollama local model integration
- Embedding Tests:
- `test_openai_embedding.py`: Tests for OpenAI embedding functionality
- `test_ollama_embedding.py`: Tests for Ollama embedding functionality
- Reranker Tests:
- `test_cohere_reranker.py`: Tests for Cohere reranking API
- `test_siliconflow_reranker.py`: Tests for SiliconFlow reranking API
- `test_init_reranker.py`: Tests for reranker initialization
### 6. Operator Tests
- Common Operations:
- `test_check_schema.py`: Tests for schema validation
- `test_merge_dedup_rerank.py`: Tests for result merging and reranking
- `test_nltk_helper.py`: Tests for NLP utilities
- `test_print_result.py`: Tests for result output formatting
- Document Operations:
- `test_chunk_split.py`: Tests for document chunking strategies
- `test_word_extract.py`: Tests for keyword extraction
- HugeGraph Operations:
- `test_commit_to_hugegraph.py`: Tests for graph data writing
- `test_fetch_graph_data.py`: Tests for graph data retrieval
- `test_graph_rag_query.py`: Tests for graph-based RAG queries
- `test_schema_manager.py`: Tests for graph schema management
- Index Operations:
- `test_build_gremlin_example_index.py`: Tests for Gremlin example indexing
- `test_build_semantic_index.py`: Tests for semantic indexing
- `test_build_vector_index.py`: Tests for vector index construction
- `test_gremlin_example_index_query.py`: Tests for querying Gremlin
examples
- `test_semantic_id_query.py`: Tests for semantic ID queries
- `test_vector_index_query.py`: Tests for vector index queries
- LLM Operations:
- `test_gremlin_generate.py`: Tests for Gremlin query generation
- `test_keyword_extract.py`: Tests for LLM-based keyword extraction
- `test_property_graph_extract.py`: Tests for property graph extraction
## Testing Approach
The test suite employs several testing strategies:
1. **Unit Tests**: Testing individual components in isolation
2. **Integration Tests**: Testing interactions between components
3. **Mock Testing**: Using mocks to simulate external dependencies
4. **Parametrized Tests**: Testing with various input combinations
5. **Exception Testing**: Verifying proper error handling
## Key Features
- **Comprehensive Coverage**: Tests for all major modules and components
- **External Service Handling**: Tests can skip external service
dependencies when needed
- **Mock Implementations**: Provides mock implementations for external
services
- **Test Data**: Includes sample data for consistent test execution
- **Isolation**: Tests are designed to run independently without side effects
## Results
All tests pass successfully, ensuring the reliability and correctness of the
HugeGraph-LLM codebase. The test suite provides a solid foundation for future
development and helps maintain code quality as the project evolves.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
