mirror of
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c58d35765d
and: - remove dataset name from autogenerated project name - print out project name to view
350 lines
10 KiB
Python
350 lines
10 KiB
Python
"""Test the LangSmith evaluation helpers."""
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import uuid
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from datetime import datetime
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from typing import Any, Dict, Iterator, List, Optional, Union
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from unittest import mock
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import pytest
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from langsmith.client import Client
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from langsmith.schemas import Dataset, Example
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from langchain.chains.base import Chain
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from langchain.chains.transform import TransformChain
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.smith.evaluation.runner_utils import (
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InputFormatError,
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_get_messages,
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_get_prompt,
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_run_llm,
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_run_llm_or_chain,
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_validate_example_inputs_for_chain,
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_validate_example_inputs_for_language_model,
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arun_on_dataset,
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)
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from tests.unit_tests.llms.fake_chat_model import FakeChatModel
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from tests.unit_tests.llms.fake_llm import FakeLLM
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_CREATED_AT = datetime(2015, 1, 1, 0, 0, 0)
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_TENANT_ID = "7a3d2b56-cd5b-44e5-846f-7eb6e8144ce4"
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_EXAMPLE_MESSAGE = {
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"data": {"content": "Foo", "example": False, "additional_kwargs": {}},
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"type": "human",
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}
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_VALID_MESSAGES = [
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{"messages": [_EXAMPLE_MESSAGE], "other_key": "value"},
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{"messages": [], "other_key": "value"},
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{
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"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE]],
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"other_key": "value",
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},
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{"any_key": [_EXAMPLE_MESSAGE]},
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{"any_key": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE]]},
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]
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_VALID_PROMPTS = [
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{"prompts": ["foo"], "other_key": "value"},
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{"prompt": "foo", "other_key": ["bar", "baz"]},
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{"some_key": "foo"},
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{"some_key": ["foo"]},
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]
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_INVALID_PROMPTS = (
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[
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{"prompts": "foo"},
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{"prompt": ["foo"]},
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{"some_key": 3},
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{"some_key": "foo", "other_key": "bar"},
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],
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)
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@pytest.mark.parametrize(
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"inputs",
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_VALID_MESSAGES,
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)
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def test__get_messages_valid(inputs: Dict[str, Any]) -> None:
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{"messages": []}
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_get_messages(inputs)
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@pytest.mark.parametrize(
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"inputs",
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_VALID_PROMPTS,
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)
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def test__get_prompts_valid(inputs: Dict[str, Any]) -> None:
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_get_prompt(inputs)
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@pytest.mark.parametrize(
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"inputs",
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_VALID_PROMPTS,
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)
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def test__validate_example_inputs_for_language_model(inputs: Dict[str, Any]) -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = inputs
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_validate_example_inputs_for_language_model(mock_, None)
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@pytest.mark.parametrize(
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"inputs",
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_INVALID_PROMPTS,
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)
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def test__validate_example_inputs_for_language_model_invalid(
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inputs: Dict[str, Any]
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) -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = inputs
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with pytest.raises(InputFormatError):
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_validate_example_inputs_for_language_model(mock_, None)
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def test__validate_example_inputs_for_chain_single_input() -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = {"foo": "bar"}
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chain = mock.MagicMock()
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chain.input_keys = ["def not foo"]
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_validate_example_inputs_for_chain(mock_, chain, None)
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def test__validate_example_inputs_for_chain_input_mapper() -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = {"foo": "bar", "baz": "qux"}
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chain = mock.MagicMock()
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chain.input_keys = ["not foo", "not baz", "not qux"]
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def wrong_output_format(inputs: dict) -> str:
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assert "foo" in inputs
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assert "baz" in inputs
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return "hehe"
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with pytest.raises(InputFormatError, match="must be a dictionary"):
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_validate_example_inputs_for_chain(mock_, chain, wrong_output_format)
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def wrong_output_keys(inputs: dict) -> dict:
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assert "foo" in inputs
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assert "baz" in inputs
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return {"not foo": "foo", "not baz": "baz"}
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with pytest.raises(InputFormatError, match="keys that match"):
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_validate_example_inputs_for_chain(mock_, chain, wrong_output_keys)
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def input_mapper(inputs: dict) -> dict:
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assert "foo" in inputs
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assert "baz" in inputs
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return {"not foo": inputs["foo"], "not baz": inputs["baz"], "not qux": "qux"}
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_validate_example_inputs_for_chain(mock_, chain, input_mapper)
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def test__validate_example_inputs_for_chain_multi_io() -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = {"foo": "bar", "baz": "qux"}
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chain = mock.MagicMock()
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chain.input_keys = ["foo", "baz"]
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_validate_example_inputs_for_chain(mock_, chain, None)
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def test__validate_example_inputs_for_chain_single_input_multi_expect() -> None:
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mock_ = mock.MagicMock()
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mock_.inputs = {"foo": "bar"}
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chain = mock.MagicMock()
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chain.input_keys = ["def not foo", "oh here is another"]
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with pytest.raises(
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InputFormatError, match="Example inputs do not match chain input keys."
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):
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_validate_example_inputs_for_chain(mock_, chain, None)
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@pytest.mark.parametrize("inputs", _INVALID_PROMPTS)
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def test__get_prompts_invalid(inputs: Dict[str, Any]) -> None:
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with pytest.raises(InputFormatError):
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_get_prompt(inputs)
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def test_run_llm_or_chain_with_input_mapper() -> None:
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example = Example(
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id=uuid.uuid4(),
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created_at=_CREATED_AT,
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inputs={"the wrong input": "1", "another key": "2"},
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outputs={"output": "2"},
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dataset_id=str(uuid.uuid4()),
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)
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def run_val(inputs: dict) -> dict:
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assert "the right input" in inputs
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return {"output": "2"}
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mock_chain = TransformChain(
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input_variables=["the right input"],
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output_variables=["output"],
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transform=run_val,
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)
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def input_mapper(inputs: dict) -> dict:
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assert "the wrong input" in inputs
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return {"the right input": inputs["the wrong input"]}
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result = _run_llm_or_chain(
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example, lambda: mock_chain, n_repetitions=1, input_mapper=input_mapper
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)
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assert len(result) == 1
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assert result[0] == {"output": "2", "the right input": "1"}
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bad_result = _run_llm_or_chain(
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example,
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lambda: mock_chain,
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n_repetitions=1,
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)
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assert len(bad_result) == 1
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assert "Error" in bad_result[0]
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# Try with LLM
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def llm_input_mapper(inputs: dict) -> str:
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assert "the wrong input" in inputs
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return "the right input"
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mock_llm = FakeLLM(queries={"the right input": "somenumber"})
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result = _run_llm_or_chain(
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example, mock_llm, n_repetitions=1, input_mapper=llm_input_mapper
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)
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assert len(result) == 1
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llm_result = result[0]
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assert isinstance(llm_result, str)
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assert llm_result == "somenumber"
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@pytest.mark.parametrize(
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"inputs",
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[
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{"one_key": [_EXAMPLE_MESSAGE], "other_key": "value"},
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{
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"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], _EXAMPLE_MESSAGE],
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"other_key": "value",
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},
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{"prompts": "foo"},
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{},
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],
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)
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def test__get_messages_invalid(inputs: Dict[str, Any]) -> None:
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with pytest.raises(InputFormatError):
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_get_messages(inputs)
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@pytest.mark.parametrize("inputs", _VALID_PROMPTS + _VALID_MESSAGES)
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def test_run_llm_all_formats(inputs: Dict[str, Any]) -> None:
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llm = FakeLLM()
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_run_llm(llm, inputs, mock.MagicMock())
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@pytest.mark.parametrize("inputs", _VALID_MESSAGES + _VALID_PROMPTS)
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def test_run_chat_model_all_formats(inputs: Dict[str, Any]) -> None:
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llm = FakeChatModel()
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_run_llm(llm, inputs, mock.MagicMock())
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@pytest.mark.asyncio
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async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
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dataset = Dataset(
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id=uuid.uuid4(),
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name="test",
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description="Test dataset",
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owner_id="owner",
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created_at=_CREATED_AT,
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tenant_id=_TENANT_ID,
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)
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uuids = [
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"0c193153-2309-4704-9a47-17aee4fb25c8",
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"0d11b5fd-8e66-4485-b696-4b55155c0c05",
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"90d696f0-f10d-4fd0-b88b-bfee6df08b84",
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"4ce2c6d8-5124-4c0c-8292-db7bdebcf167",
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"7b5a524c-80fa-4960-888e-7d380f9a11ee",
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]
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examples = [
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Example(
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id=uuids[0],
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created_at=_CREATED_AT,
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inputs={"input": "1"},
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outputs={"output": "2"},
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dataset_id=str(uuid.uuid4()),
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),
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Example(
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id=uuids[1],
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created_at=_CREATED_AT,
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inputs={"input": "3"},
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outputs={"output": "4"},
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dataset_id=str(uuid.uuid4()),
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),
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Example(
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id=uuids[2],
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created_at=_CREATED_AT,
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inputs={"input": "5"},
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outputs={"output": "6"},
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dataset_id=str(uuid.uuid4()),
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),
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Example(
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id=uuids[3],
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created_at=_CREATED_AT,
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inputs={"input": "7"},
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outputs={"output": "8"},
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dataset_id=str(uuid.uuid4()),
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),
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Example(
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id=uuids[4],
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created_at=_CREATED_AT,
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inputs={"input": "9"},
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outputs={"output": "10"},
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dataset_id=str(uuid.uuid4()),
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),
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]
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def mock_read_dataset(*args: Any, **kwargs: Any) -> Dataset:
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return dataset
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def mock_list_examples(*args: Any, **kwargs: Any) -> Iterator[Example]:
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return iter(examples)
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async def mock_arun_chain(
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example: Example,
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llm_or_chain: Union[BaseLanguageModel, Chain],
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n_repetitions: int,
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tags: Optional[List[str]] = None,
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callbacks: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Dict[str, Any]]:
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return [
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{"result": f"Result for example {example.id}"} for _ in range(n_repetitions)
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]
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def mock_create_project(*args: Any, **kwargs: Any) -> Any:
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proj = mock.MagicMock()
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proj.id = "123"
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return proj
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with mock.patch.object(
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Client, "read_dataset", new=mock_read_dataset
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), mock.patch.object(Client, "list_examples", new=mock_list_examples), mock.patch(
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"langchain.smith.evaluation.runner_utils._arun_llm_or_chain",
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new=mock_arun_chain,
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), mock.patch.object(
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Client, "create_project", new=mock_create_project
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):
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client = Client(api_url="http://localhost:1984", api_key="123")
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chain = mock.MagicMock()
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chain.input_keys = ["foothing"]
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num_repetitions = 3
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results = await arun_on_dataset(
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dataset_name="test",
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llm_or_chain_factory=lambda: chain,
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concurrency_level=2,
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project_name="test_project",
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num_repetitions=num_repetitions,
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client=client,
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)
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expected = {
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uuid_: [
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{"result": f"Result for example {uuid.UUID(uuid_)}"}
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for _ in range(num_repetitions)
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]
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for uuid_ in uuids
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}
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assert results["results"] == expected
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