test_prompt_with_llm_parser() — langchain Function Reference

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

Function python LangChainCore Runnables calls 1

Entity Profile

LangChainCore→ Runnables→ test_prompt_with_llm_parser() — langchain Function Reference

Dependency Diagram

graph TD
  d3e93cb3_c29c_f455_2d43_c53eed4a194a["test_prompt_with_llm_parser()"]
  26df6ad8_0189_51d0_c3c1_6c3248893ff5["test_runnable.py"]
  d3e93cb3_c29c_f455_2d43_c53eed4a194a -->|defined in| 26df6ad8_0189_51d0_c3c1_6c3248893ff5
  8652094c_ec57_c551_fc44_9566d00cf872["abatch()"]
  d3e93cb3_c29c_f455_2d43_c53eed4a194a -->|calls| 8652094c_ec57_c551_fc44_9566d00cf872
  style d3e93cb3_c29c_f455_2d43_c53eed4a194a fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/tests/unit_tests/runnables/test_runnable.py lines 2190–2464

async def test_prompt_with_llm_parser(
    mocker: MockerFixture, snapshot: SnapshotAssertion
) -> None:
    prompt = (
        SystemMessagePromptTemplate.from_template("You are a nice assistant.")
        + "{question}"
    )
    llm = FakeStreamingListLLM(responses=["bear, dog, cat", "tomato, lettuce, onion"])
    parser = CommaSeparatedListOutputParser()

    chain = prompt | llm | parser

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

    # Test invoke
    prompt_spy = mocker.spy(prompt.__class__, "ainvoke")
    llm_spy = mocker.spy(llm.__class__, "ainvoke")
    parser_spy = mocker.spy(parser.__class__, "ainvoke")
    tracer = FakeTracer()
    assert await chain.ainvoke(
        {"question": "What is your name?"}, {"callbacks": [tracer]}
    ) == ["bear", "dog", "cat"]
    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 parser_spy.call_args.args[1] == "bear, dog, cat"
    assert tracer.runs == snapshot
    mocker.stop(prompt_spy)
    mocker.stop(llm_spy)
    mocker.stop(parser_spy)

    # Test batch
    prompt_spy = mocker.spy(prompt.__class__, "abatch")
    llm_spy = mocker.spy(llm.__class__, "abatch")
    parser_spy = mocker.spy(parser.__class__, "abatch")
    tracer = FakeTracer()
    assert await chain.abatch(
        [
            {"question": "What is your name?"},
            {"question": "What is your favorite color?"},
        ],
        {"callbacks": [tracer]},
    ) == [["tomato", "lettuce", "onion"], ["bear", "dog", "cat"]]
    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 parser_spy.call_args.args[1] == [
        "tomato, lettuce, onion",
        "bear, dog, cat",
    ]
    assert len(tracer.runs) == 2
    assert all(
        run.name == "RunnableSequence"
        and run.run_type == "chain"
        and len(run.child_runs) == 3
        for run in tracer.runs
    )
    mocker.stop(prompt_spy)