LlamaIndex Testing: Pytest Implementation for Language Model Framework
The llama_index repository implements a comprehensive testing strategy centered around pytest for unit testing critical components of the framework. The test suite, comprising 54 test cases, covers essential functionality like chat engine operations, index querying, token counting, and embedding systems. The testing approach emphasizes verifying core features through isolated unit tests, ensuring reliability and maintainability of the codebase. Qodo Tests Hub provides developers with detailed insights into llama_index's testing patterns, making it easier to understand how different components are tested. Through the platform, developers can explore real-world examples of pytest implementations, study test organization strategies, and learn best practices for testing complex functionality like embedding generation, query composition, and chat engine operations. This practical knowledge helps teams implement more effective testing approaches in their own projects.
| Path | Test Type | Language | Description |
|---|---|---|---|
| llama-index-core/tests/chat_engine/test_simple.py |
unit
|
python | This Python unit test verifies SimpleChatEngine’s message handling, history management, and response formatting capabilities. |
| llama-index-core/tests/embeddings/todo_hf_test_utils.py |
unit
|
python | This pytest unit test verifies the proper resolution and initialization of different embedding models in LlamaIndex. |
| llama-index-core/tests/agent/function_calling/test_step.py |
unit
|
python | This pytest unit test verifies function calling agent workflow and tool handling in the llama_index framework. |
| llama-index-core/tests/indices/tree/test_embedding_retriever.py |
unit
|
python | This pytest unit test verifies embedding-based retrieval functionality in TreeIndex component with similarity matching. |
| llama-index-core/tests/agent/custom/test_query_pipeline.py |
unit
|
python | This pytest unit test verifies the functionality of QueryPipelineAgentWorker with various component configurations and execution patterns. |
| llama-index-core/tests/agent/memory/test_simple_composable.py |
unit
|
python | This pytest unit test verifies SimpleComposableMemory functionality with vector-based message storage and retrieval in LlamaIndex. |
| llama-index-core/tests/agent/memory/test_vector_memory.py |
unit
|
python | This Python unit test verifies VectorMemory’s ability to store and retrieve chat messages based on semantic similarity using vector embeddings. |
| llama-index-core/tests/chat_engine/test_condense_question.py |
unit
|
python | This Python unit test verifies the CondenseQuestionChatEngine’s message handling, history management, and state reset functionality. |
| llama-index-core/tests/embeddings/test_base.py |
unit
|
python | This Python unit test verifies text embedding generation, similarity calculations, and embedding aggregation in the LlamaIndex core embedding system. |
| llama-index-core/tests/evaluation/test_metrics.py |
unit
|
python | This pytest unit test verifies the accuracy of information retrieval evaluation metrics including Hit Rate, MRR, Precision, Recall, Average Precision, and NDCG calculations. |