test_schema_complex_seq() — langchain Function Reference

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

Function python LangChainCore Runnables

Entity Profile

LangChainCore→ Runnables→ test_schema_complex_seq() — langchain Function Reference

Dependency Diagram

graph TD
  cd29b59a_2e4d_7f89_c2ec_514b1a240603["test_schema_complex_seq()"]
  26df6ad8_0189_51d0_c3c1_6c3248893ff5["test_runnable.py"]
  cd29b59a_2e4d_7f89_c2ec_514b1a240603 -->|defined in| 26df6ad8_0189_51d0_c3c1_6c3248893ff5
  style cd29b59a_2e4d_7f89_c2ec_514b1a240603 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/tests/unit_tests/runnables/test_runnable.py lines 685–739

def test_schema_complex_seq() -> None:
    prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?")
    prompt2 = ChatPromptTemplate.from_template(
        "what country is the city {city} in? respond in {language}"
    )

    model = FakeListChatModel(responses=[""])

    chain1: Runnable = RunnableSequence(
        prompt1, model, StrOutputParser(), name="city_chain"
    )

    assert chain1.name == "city_chain"

    chain2: Runnable = (
        {"city": chain1, "language": itemgetter("language")}
        | prompt2
        | model
        | StrOutputParser()
    )

    assert chain2.get_input_jsonschema() == {
        "title": "RunnableParallel<city,language>Input",
        "type": "object",
        "properties": {
            "person": {"title": "Person", "type": "string"},
            "language": {"title": "Language"},
        },
        "required": ["person", "language"],
    }

    assert chain2.get_output_jsonschema() == {
        "title": "StrOutputParserOutput",
        "type": "string",
    }

    assert chain2.with_types(input_type=str).get_input_jsonschema() == {
        "title": "RunnableSequenceInput",
        "type": "string",
    }

    assert chain2.with_types(input_type=int).get_output_jsonschema() == {
        "title": "StrOutputParserOutput",
        "type": "string",
    }

    class InputType(BaseModel):
        person: str

    assert chain2.with_types(input_type=InputType).get_input_jsonschema() == {
        "title": "InputType",
        "type": "object",
        "properties": {"person": {"title": "Person", "type": "string"}},
        "required": ["person"],
    }