test_batch_size() — langchain Function Reference

Architecture documentation for the test_batch_size() function in test_base.py from the langchain codebase.

Function python LangChainCore LanguageModelBase

Entity Profile

LangChainCore→ LanguageModelBase→ test_batch_size() — langchain Function Reference

Dependency Diagram

graph TD
  337d7973_60b3_88f5_cab6_c04b4a499b37["test_batch_size()"]
  8cb88ac4_61d9_baf3_9df4_9b3f5095927e["test_base.py"]
  337d7973_60b3_88f5_cab6_c04b4a499b37 -->|defined in| 8cb88ac4_61d9_baf3_9df4_9b3f5095927e
  style 337d7973_60b3_88f5_cab6_c04b4a499b37 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/tests/unit_tests/language_models/chat_models/test_base.py lines 97–118

def test_batch_size(messages: list[BaseMessage], messages_2: list[BaseMessage]) -> None:
    # The base endpoint doesn't support native batching,
    # so we expect batch_size to always be 1
    llm = FakeListChatModel(responses=[str(i) for i in range(100)])
    with collect_runs() as cb:
        llm.batch([messages, messages_2], {"callbacks": [cb]})
        assert len(cb.traced_runs) == 2
        assert all((r.extra or {}).get("batch_size") == 1 for r in cb.traced_runs)
    with collect_runs() as cb:
        llm.batch([messages], {"callbacks": [cb]})
        assert all((r.extra or {}).get("batch_size") == 1 for r in cb.traced_runs)
        assert len(cb.traced_runs) == 1

    with collect_runs() as cb:
        llm.invoke(messages)
        assert len(cb.traced_runs) == 1
        assert (cb.traced_runs[0].extra or {}).get("batch_size") == 1

    with collect_runs() as cb:
        list(llm.stream(messages))
        assert len(cb.traced_runs) == 1
        assert (cb.traced_runs[0].extra or {}).get("batch_size") == 1