test_prompt_with_llm() — langchain Function Reference

Architecture documentation for the test_prompt_with_llm() function in test_runnable.py from the langchain codebase.

Function python LangChainCore Runnables calls 1

Entity Profile

LangChainCore→ Runnables→ test_prompt_with_llm() — langchain Function Reference

Dependency Diagram

graph TD
  f1012a38_ff36_4def_e0f2_df496716b208["test_prompt_with_llm()"]
  26df6ad8_0189_51d0_c3c1_6c3248893ff5["test_runnable.py"]
  f1012a38_ff36_4def_e0f2_df496716b208 -->|defined in| 26df6ad8_0189_51d0_c3c1_6c3248893ff5
  8652094c_ec57_c551_fc44_9566d00cf872["abatch()"]
  f1012a38_ff36_4def_e0f2_df496716b208 -->|calls| 8652094c_ec57_c551_fc44_9566d00cf872
  style f1012a38_ff36_4def_e0f2_df496716b208 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/tests/unit_tests/runnables/test_runnable.py lines 1994–2186

async def test_prompt_with_llm(
    mocker: MockerFixture, snapshot: SnapshotAssertion
) -> None:
    prompt = (
        SystemMessagePromptTemplate.from_template("You are a nice assistant.")
        + "{question}"
    )
    llm = FakeListLLM(responses=["foo", "bar"])

    chain = prompt | llm

    assert isinstance(chain, RunnableSequence)
    assert chain.first == prompt
    assert chain.middle == []
    assert chain.last == llm
    assert dumps(chain, pretty=True) == snapshot

    # Test invoke
    prompt_spy = mocker.spy(prompt.__class__, "ainvoke")
    llm_spy = mocker.spy(llm.__class__, "ainvoke")
    tracer = FakeTracer()
    assert (
        await chain.ainvoke({"question": "What is your name?"}, {"callbacks": [tracer]})
        == "foo"
    )
    assert prompt_spy.call_args.args[1] == {"question": "What is your name?"}
    assert llm_spy.call_args.args[1] == ChatPromptValue(
        messages=[
            SystemMessage(content="You are a nice assistant."),
            HumanMessage(content="What is your name?"),
        ]
    )
    assert tracer.runs == snapshot
    mocker.stop(prompt_spy)
    mocker.stop(llm_spy)

    # Test batch
    prompt_spy = mocker.spy(prompt.__class__, "abatch")
    llm_spy = mocker.spy(llm.__class__, "abatch")
    tracer = FakeTracer()
    assert await chain.abatch(
        [
            {"question": "What is your name?"},
            {"question": "What is your favorite color?"},
        ],
        {"callbacks": [tracer]},
    ) == ["bar", "foo"]
    assert prompt_spy.call_args.args[1] == [
        {"question": "What is your name?"},
        {"question": "What is your favorite color?"},
    ]
    assert llm_spy.call_args.args[1] == [
        ChatPromptValue(
            messages=[
                SystemMessage(content="You are a nice assistant."),
                HumanMessage(content="What is your name?"),
            ]
        ),
        ChatPromptValue(
            messages=[
                SystemMessage(content="You are a nice assistant."),
                HumanMessage(content="What is your favorite color?"),
            ]
        ),
    ]
    assert tracer.runs == snapshot
    mocker.stop(prompt_spy)
    mocker.stop(llm_spy)

    # Test stream
    prompt_spy = mocker.spy(prompt.__class__, "ainvoke")
    llm_spy = mocker.spy(llm.__class__, "astream")
    tracer = FakeTracer()
    assert [
        token
        async for token in chain.astream(
            {"question": "What is your name?"}, {"callbacks": [tracer]}
        )
    ] == ["bar"]
    assert prompt_spy.call_args.args[1] == {"question": "What is your name?"}
    assert llm_spy.call_args.args[1] == ChatPromptValue(