Rm deprecated (#15920)

Remove the usage of deprecated methods in the test runner.
2025-05-16 12:32:06 +00:00 · 2024-01-11 18:10:49 -08:00 · 2024-01-11 18:10:49 -08:00 · 129552e3d6
commit 129552e3d6
parent 438beb6c94
2 changed files with 64 additions and 62 deletions
--- a/libs/langchain/langchain/smith/evaluation/runner_utils.py
+++ b/libs/langchain/langchain/smith/evaluation/runner_utils.py
@ -674,15 +674,14 @@ async def _arun_llm(
    """
    if input_mapper is not None:
        prompt_or_messages = input_mapper(inputs)
-        if isinstance(prompt_or_messages, str):
-            return await llm.apredict(
-                prompt_or_messages, callbacks=callbacks, tags=tags
-            )
-        elif isinstance(prompt_or_messages, list) and all(
-            isinstance(msg, BaseMessage) for msg in prompt_or_messages
+        if (
+            isinstance(prompt_or_messages, str)
+            or isinstance(prompt_or_messages, list)
+            and all(isinstance(msg, BaseMessage) for msg in prompt_or_messages)
        ):
-            return await llm.apredict_messages(
-                prompt_or_messages, callbacks=callbacks, tags=tags
+            return await llm.ainvoke(
+                prompt_or_messages,
+                config=RunnableConfig(callbacks=callbacks, tags=tags or []),
            )
        else:
            raise InputFormatError(
@ -694,13 +693,13 @@ async def _arun_llm(
    else:
        try:
            prompt = _get_prompt(inputs)
-            llm_output: Union[str, BaseMessage] = await llm.apredict(
-                prompt, callbacks=callbacks, tags=tags
+            llm_output: Union[str, BaseMessage] = await llm.ainvoke(
+                prompt, config=RunnableConfig(callbacks=callbacks, tags=tags or [])
            )
        except InputFormatError:
            messages = _get_messages(inputs)
-            llm_output = await llm.apredict_messages(
-                messages, callbacks=callbacks, tags=tags
+            llm_output = await llm.ainvoke(
+                messages, config=RunnableConfig(callbacks=callbacks, tags=tags or [])
            )
    return llm_output

@ -722,7 +721,9 @@ async def _arun_chain(
        and chain.input_keys
    ):
        val = next(iter(inputs_.values()))
-        output = await chain.acall(val, callbacks=callbacks, tags=tags)
+        output = await chain.ainvoke(
+            val, config=RunnableConfig(callbacks=callbacks, tags=tags or [])
+        )
    else:
        runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
        output = await chain.ainvoke(inputs_, config=runnable_config)
@ -807,17 +808,17 @@ def _run_llm(
        ValueError: If the LLM type is unsupported.
        InputFormatError: If the input format is invalid.
    """
+    # Most of this is legacy code; we could probably remove a lot of it.
    if input_mapper is not None:
        prompt_or_messages = input_mapper(inputs)
-        if isinstance(prompt_or_messages, str):
-            llm_output: Union[str, BaseMessage] = llm.predict(
-                prompt_or_messages, callbacks=callbacks, tags=tags
-            )
-        elif isinstance(prompt_or_messages, list) and all(
-            isinstance(msg, BaseMessage) for msg in prompt_or_messages
+        if (
+            isinstance(prompt_or_messages, str)
+            or isinstance(prompt_or_messages, list)
+            and all(isinstance(msg, BaseMessage) for msg in prompt_or_messages)
        ):
-            llm_output = llm.predict_messages(
-                prompt_or_messages, callbacks=callbacks, tags=tags
+            llm_output: Union[str, BaseMessage] = llm.invoke(
+                prompt_or_messages,
+                config=RunnableConfig(callbacks=callbacks, tags=tags or []),
            )
        else:
            raise InputFormatError(
@ -828,10 +829,14 @@ def _run_llm(
    else:
        try:
            llm_prompts = _get_prompt(inputs)
-            llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags)
+            llm_output = llm.invoke(
+                llm_prompts, config=RunnableConfig(callbacks=callbacks, tags=tags or [])
+            )
        except InputFormatError:
            llm_messages = _get_messages(inputs)
-            llm_output = llm.predict_messages(llm_messages, callbacks=callbacks)
+            llm_output = llm.invoke(
+                llm_messages, config=RunnableConfig(callbacks=callbacks)
+            )
    return llm_output


@ -852,7 +857,9 @@ def _run_chain(
        and chain.input_keys
    ):
        val = next(iter(inputs_.values()))
-        output = chain(val, callbacks=callbacks, tags=tags)
+        output = chain.invoke(
+            val, config=RunnableConfig(callbacks=callbacks, tags=tags or [])
+        )
    else:
        runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
        output = chain.invoke(inputs_, config=runnable_config)
@ -1313,7 +1320,7 @@ Examples
 .. code-block:: python

    from langsmith import Client
-    from langchain_community.chat_models import ChatOpenAI
+    from langchain_openai import ChatOpenAI
    from langchain.chains import LLMChain
    from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset

--- a/libs/langchain/tests/integration_tests/smith/evaluation/test_runner_utils.py
+++ b/libs/langchain/tests/integration_tests/smith/evaluation/test_runner_utils.py
@ -1,4 +1,4 @@
-from typing import Iterator, List
+from typing import Iterator, List, Optional
 from uuid import uuid4

 import pytest
@ -7,7 +7,8 @@ from langchain_community.llms.openai import OpenAI
 from langchain_core.messages import BaseMessage, HumanMessage
 from langchain_core.prompts.chat import ChatPromptTemplate
 from langsmith import Client as Client
-from langsmith.schemas import DataType
+from langsmith.evaluation import run_evaluator
+from langsmith.schemas import DataType, Example, Run

 from langchain.chains.llm import LLMChain
 from langchain.evaluation import EvaluatorType
@ -26,7 +27,15 @@ def _check_all_feedback_passed(_project_name: str, client: Client) -> None:
    feedback = list(client.list_feedback(run_ids=[run.id for run in runs]))
    if not feedback:
        return
-    assert all([f.score == 1 for f in feedback])
+    assert all([bool(f.score) for f in feedback])
+
+
+@run_evaluator
+def not_empty(run: Run, example: Optional[Example] = None) -> dict:
+    return {
+        "score": run.outputs and next(iter(run.outputs.values())),
+        "key": "not_empty",
+    }


@pytest.fixture
@ -81,7 +90,9 @@ def test_chat_model(
    kv_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = ChatOpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(
+        evaluators=[EvaluatorType.QA], custom_evaluators=[not_empty]
+    )
    with pytest.raises(ValueError, match="Must specify reference_key"):
        run_on_dataset(
            dataset_name=kv_dataset_name,
@ -90,7 +101,7 @@ def test_chat_model(
            client=client,
        )
    eval_config = RunEvalConfig(
-        evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
+        evaluators=[EvaluatorType.QA],
        reference_key="some_output",
    )
    with pytest.raises(
@ -109,9 +120,8 @@ def test_chat_model(
    run_on_dataset(
        client=client,
        dataset_name=kv_dataset_name,
-        llm_or_chain_factory=llm,
+        llm_or_chain_factory=input_mapper | llm,
        evaluation=eval_config,
-        input_mapper=input_mapper,
        project_name=eval_project_name,
        tags=["shouldpass"],
    )
@ -120,7 +130,7 @@ def test_chat_model(

 def test_llm(kv_dataset_name: str, eval_project_name: str, client: Client) -> None:
    llm = OpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA])
    with pytest.raises(ValueError, match="Must specify reference_key"):
        run_on_dataset(
            dataset_name=kv_dataset_name,
@ -132,9 +142,7 @@ def test_llm(kv_dataset_name: str, eval_project_name: str, client: Client) -> No
        evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
        reference_key="some_output",
    )
-    with pytest.raises(
-        InputFormatError, match="Example inputs do not match language model"
-    ):
+    with pytest.raises(InputFormatError, match="Example inputs"):
        run_on_dataset(
            dataset_name=kv_dataset_name,
            llm_or_chain_factory=llm,
@ -148,9 +156,8 @@ def test_llm(kv_dataset_name: str, eval_project_name: str, client: Client) -> No
    run_on_dataset(
        client=client,
        dataset_name=kv_dataset_name,
-        llm_or_chain_factory=llm,
+        llm_or_chain_factory=input_mapper | llm,
        evaluation=eval_config,
-        input_mapper=input_mapper,
        project_name=eval_project_name,
        tags=["shouldpass"],
    )
@ -172,9 +179,7 @@ def test_chain(kv_dataset_name: str, eval_project_name: str, client: Client) ->
        evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
        reference_key="some_output",
    )
-    with pytest.raises(
-        InputFormatError, match="Example inputs do not match chain input keys"
-    ):
+    with pytest.raises(InputFormatError, match="Example inputs"):
        run_on_dataset(
            dataset_name=kv_dataset_name,
            llm_or_chain_factory=lambda: chain,
@ -182,19 +187,9 @@ def test_chain(kv_dataset_name: str, eval_project_name: str, client: Client) ->
            client=client,
        )

-    def input_mapper(d: dict) -> dict:
-        return {"input": d["some_input"]}
-
-    with pytest.raises(
-        InputFormatError,
-        match=" match the chain's expected input keys.",
-    ):
-        run_on_dataset(
-            dataset_name=kv_dataset_name,
-            llm_or_chain_factory=lambda: input_mapper | chain,
-            client=client,
-            evaluation=eval_config,
-        )
+    eval_config = RunEvalConfig(
+        custom_evaluators=[not_empty],
+    )

    def right_input_mapper(d: dict) -> dict:
        return {"question": d["some_input"]}
@ -259,7 +254,7 @@ def test_chat_model_on_chat_dataset(
    chat_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = ChatOpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        dataset_name=chat_dataset_name,
        llm_or_chain_factory=llm,
@ -274,7 +269,7 @@ def test_llm_on_chat_dataset(
    chat_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = OpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        dataset_name=chat_dataset_name,
        llm_or_chain_factory=llm,
@ -337,7 +332,7 @@ def test_chat_model_on_llm_dataset(
    llm_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = ChatOpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        client=client,
        dataset_name=llm_dataset_name,
@ -353,7 +348,7 @@ def test_llm_on_llm_dataset(
    llm_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = OpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        client=client,
        dataset_name=llm_dataset_name,
@ -431,7 +426,7 @@ def test_llm_on_kv_singleio_dataset(
    kv_singleio_dataset_name: str, eval_project_name: str, client: Client
 ) -> None:
    llm = OpenAI(temperature=0)
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        dataset_name=kv_singleio_dataset_name,
        llm_or_chain_factory=llm,
@ -448,7 +443,7 @@ def test_chain_on_kv_singleio_dataset(
 ) -> None:
    llm = ChatOpenAI(temperature=0)
    chain = LLMChain.from_string(llm, "The answer to the {question} is: ")
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    run_on_dataset(
        dataset_name=kv_singleio_dataset_name,
        llm_or_chain_factory=lambda: chain,
@ -467,7 +462,7 @@ async def test_runnable_on_kv_singleio_dataset(
        ChatPromptTemplate.from_messages([("human", "{the wackiest input}")])
        | ChatOpenAI()
    )
-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    await arun_on_dataset(
        dataset_name=kv_singleio_dataset_name,
        llm_or_chain_factory=runnable,
@ -496,7 +491,7 @@ async def test_arb_func_on_kv_singleio_dataset(
                f"Expected message with content type string, got {content}"
            )

-    eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
+    eval_config = RunEvalConfig(custom_evaluators=[not_empty])
    await arun_on_dataset(
        dataset_name=kv_singleio_dataset_name,
        llm_or_chain_factory=my_func,