[Evals] End project (#14324)

Also does some cleanup.

Now that we support updating/ending projects, do this automatically.
Then you can edit the name of the project in the app.

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -2,10 +2,12 @@
 
 from __future__ import annotations
 
+import dataclasses
 import functools
 import inspect
 import logging
 import uuid
+from datetime import datetime
 from enum import Enum
 from typing import (
     TYPE_CHECKING,
@@ -32,11 +34,12 @@ from langchain_core.tracers.evaluation import (
 )
 from langchain_core.tracers.langchain import LangChainTracer
 from langsmith.client import Client
-from langsmith.evaluation import RunEvaluator
+from langsmith.evaluation import EvaluationResult, RunEvaluator
 from langsmith.run_helpers import as_runnable, is_traceable_function
-from langsmith.schemas import Dataset, DataType, Example
+from langsmith.schemas import Dataset, DataType, Example, TracerSession
 from langsmith.utils import LangSmithError
 from requests import HTTPError
+from typing_extensions import TypedDict
 
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.base import Chain
@@ -919,9 +922,12 @@ def _prepare_eval_run(
     project_name: str,
     project_metadata: Optional[Dict[str, Any]] = None,
     tags: Optional[List[str]] = None,
-) -> Tuple[MCF, str, Dataset, List[Example]]:
+) -> Tuple[MCF, TracerSession, Dataset, List[Example]]:
     wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
     dataset = client.read_dataset(dataset_name=dataset_name)
+    examples = list(client.list_examples(dataset_id=dataset.id))
+    if not examples:
+        raise ValueError(f"Dataset {dataset_name} has no example rows.")
 
     try:
         project_extra: dict = {"metadata": project_metadata} if project_metadata else {}
@@ -953,13 +959,105 @@ run_on_dataset(
         f"View all tests for Dataset {dataset_name} at:\n{dataset.url}",
         flush=True,
     )
-    examples = list(client.list_examples(dataset_id=dataset.id))
+    return wrapped_model, project, dataset, examples
-    if not examples:
-        raise ValueError(f"Dataset {dataset_name} has no example rows.")
-    return wrapped_model, project_name, dataset, examples
 
 
-def _prepare_run_on_dataset(
+class _RowResult(TypedDict, total=False):
+    """A dictionary of the results for a single example row."""
+
+    feedback: Optional[List[EvaluationResult]]
+    execution_time: Optional[float]
+    run_id: Optional[str]
+
+
+@dataclasses.dataclass
+class _DatasetRunContainer:
+    """A container to help manage the state of a eval run."""
+
+    client: Client
+    project: TracerSession
+    wrapped_model: MCF
+    examples: List[Example]
+    configs: List[RunnableConfig]
+
+    def _merge_test_outputs(
+        self,
+        batch_results: list,
+        all_eval_results: Dict[str, _RowResult],
+    ) -> dict:
+        results: dict = {}
+        for example, output in zip(self.examples, batch_results):
+            row_result = cast(_RowResult, all_eval_results.get(str(example.id), {}))
+            results[str(example.id)] = {
+                "input": example.inputs,
+                "feedback": row_result.get("feedback", []),
+                "execution_time": row_result.get("execution_time"),
+                "run_id": row_result.get("run_id"),
+            }
+            if isinstance(output, EvalError):
+                results[str(example.id)]["Error"] = output.Error
+            else:
+                results[str(example.id)]["output"] = output
+            if example.outputs:
+                results[str(example.id)]["reference"] = example.outputs
+        return results
+
+    def _collect_metrics(self) -> Dict[str, _RowResult]:
+        all_eval_results: dict = {}
+        for c in self.configs:
+            for callback in cast(list, c["callbacks"]):
+                if isinstance(callback, EvaluatorCallbackHandler):
+                    eval_results = callback.logged_eval_results
+                    for (_, example_id), v in eval_results.items():
+                        all_eval_results.setdefault(str(example_id), {}).update(
+                            {"feedback": v}
+                        )
+                elif isinstance(callback, LangChainTracer):
+                    run = callback.latest_run
+                    execution_time = (
+                        (run.end_time - run.start_time).total_seconds()
+                        if run and run.end_time
+                        else None
+                    )
+                    run_id = str(run.id) if run else None
+                    all_eval_results.setdefault(str(callback.example_id), {}).update(
+                        {
+                            "execution_time": execution_time,
+                            "run_id": run_id,
+                        }
+                    )
+        return cast(Dict[str, _RowResult], all_eval_results)
+
+    def _collect_test_results(
+        self,
+        batch_results: List[Union[dict, str, LLMResult, ChatResult]],
+    ) -> TestResult:
+        wait_for_all_evaluators()
+        all_eval_results = self._collect_metrics()
+        results = self._merge_test_outputs(batch_results, all_eval_results)
+        return TestResult(
+            project_name=self.project.name,
+            results=results,
+        )
+
+    def finish(self, batch_results: list, verbose: bool = False) -> TestResult:
+        results = self._collect_test_results(batch_results)
+        if verbose:
+            try:
+                agg_feedback = results.get_aggregate_feedback()
+                _display_aggregate_results(agg_feedback)
+            except Exception as e:
+                logger.debug(f"Failed to print aggregate feedback: {repr(e)}")
+        try:
+            # Closing the project permits name changing and metric optimizations
+            self.client.update_project(self.project.id, end_time=datetime.utcnow())
+        except Exception as e:
+            logger.debug(f"Failed to close project: {repr(e)}")
+        return results
+
+    @classmethod
+    def prepare(
+        cls,
         client: Client,
         dataset_name: str,
         llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
@@ -969,9 +1067,9 @@ def _prepare_run_on_dataset(
         input_mapper: Optional[Callable[[Dict], Any]] = None,
         concurrency_level: int = 5,
         project_metadata: Optional[Dict[str, Any]] = None,
-) -> Tuple[MCF, str, List[Example], List[RunnableConfig]]:
+    ) -> _DatasetRunContainer:
         project_name = project_name or name_generation.random_name()
-    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
+        wrapped_model, project, dataset, examples = _prepare_eval_run(
             client,
             dataset_name,
             llm_or_chain_factory,
@@ -989,7 +1087,7 @@ def _prepare_run_on_dataset(
             RunnableConfig(
                 callbacks=[
                     LangChainTracer(
-                    project_name=project_name,
+                        project_name=project.name,
                         client=client,
                         use_threading=False,
                         example_id=example.id,
@@ -1007,56 +1105,12 @@ def _prepare_run_on_dataset(
             )
             for example in examples
         ]
-    return wrapped_model, project_name, examples, configs
+        return cls(
-
+            client=client,
-
+            project=project,
-def _collect_test_results(
+            wrapped_model=wrapped_model,
-    examples: List[Example],
+            examples=examples,
-    batch_results: List[Union[dict, str, LLMResult, ChatResult]],
+            configs=configs,
-    configs: List[RunnableConfig],
-    project_name: str,
-) -> TestResult:
-    wait_for_all_evaluators()
-    all_eval_results = {}
-    all_execution_time = {}
-    all_run_ids = {}
-    for c in configs:
-        for callback in cast(list, c["callbacks"]):
-            if isinstance(callback, EvaluatorCallbackHandler):
-                eval_results = callback.logged_eval_results
-                all_eval_results.update(
-                    {example_id: v for (_, example_id), v in eval_results.items()}
-                )
-            elif isinstance(callback, LangChainTracer):
-                run = callback.latest_run
-                example_id = callback.example_id
-                run_id = str(run.id) if run else None
-                execution_time = (
-                    (run.end_time - run.start_time).total_seconds()
-                    if run and run.end_time
-                    else None
-                )
-                all_execution_time[str(example_id)] = execution_time
-                all_run_ids[str(example_id)] = run_id
-
-    results: dict = {}
-    for example, output in zip(examples, batch_results):
-        feedback = all_eval_results.get(str(example.id), [])
-        results[str(example.id)] = {
-            "input": example.inputs,
-            "feedback": feedback,
-            "execution_time": all_execution_time.get(str(example.id)),
-            "run_id": all_run_ids.get(str(example.id)),
-        }
-        if isinstance(output, EvalError):
-            results[str(example.id)]["Error"] = output.Error
-        else:
-            results[str(example.id)]["output"] = output
-        if example.outputs:
-            results[str(example.id)]["reference"] = example.outputs
-    return TestResult(
-        project_name=project_name,
-        results=results,
         )
 
 
@@ -1125,7 +1179,7 @@ async def arun_on_dataset(
             removal="0.0.305",
         )
     client = client or Client()
-    wrapped_model, project_name, examples, configs = _prepare_run_on_dataset(
+    container = _DatasetRunContainer.prepare(
         client,
         dataset_name,
         llm_or_chain_factory,
@@ -1137,26 +1191,18 @@ async def arun_on_dataset(
         project_metadata=project_metadata,
     )
     batch_results = await runnable_utils.gather_with_concurrency(
-        configs[0].get("max_concurrency"),
+        container.configs[0].get("max_concurrency"),
         *map(
             functools.partial(
                 _arun_llm_or_chain,
-                llm_or_chain_factory=wrapped_model,
+                llm_or_chain_factory=container.wrapped_model,
                 input_mapper=input_mapper,
             ),
-            examples,
+            container.examples,
-            configs,
+            container.configs,
         ),
     )
-    results = _collect_test_results(examples, batch_results, configs, project_name)
+    return container.finish(batch_results, verbose=verbose)
-    if verbose:
-        try:
-            agg_feedback = results.get_aggregate_feedback()
-            print("\n Eval quantiles:")
-            print(agg_feedback)
-        except Exception as e:
-            logger.debug(f"Failed to print aggregate feedback: {repr(e)}")
-    return results
 
 
 def run_on_dataset(
@@ -1185,7 +1231,7 @@ def run_on_dataset(
             removal="0.0.305",
         )
     client = client or Client()
-    wrapped_model, project_name, examples, configs = _prepare_run_on_dataset(
+    container = _DatasetRunContainer.prepare(
         client,
         dataset_name,
         llm_or_chain_factory,
@@ -1201,33 +1247,26 @@ def run_on_dataset(
             _run_llm_or_chain(
                 example,
                 config,
-                llm_or_chain_factory=wrapped_model,
+                llm_or_chain_factory=container.wrapped_model,
                 input_mapper=input_mapper,
             )
-            for example, config in zip(examples, configs)
+            for example, config in zip(container.examples, container.configs)
         ]
     else:
-        with runnable_config.get_executor_for_config(configs[0]) as executor:
+        with runnable_config.get_executor_for_config(container.configs[0]) as executor:
             batch_results = list(
                 executor.map(
                     functools.partial(
                         _run_llm_or_chain,
-                        llm_or_chain_factory=wrapped_model,
+                        llm_or_chain_factory=container.wrapped_model,
                         input_mapper=input_mapper,
                     ),
-                    examples,
+                    container.examples,
-                    configs,
+                    container.configs,
                 )
             )
 
-    results = _collect_test_results(examples, batch_results, configs, project_name)
+    return container.finish(batch_results, verbose=verbose)
-    if verbose:
-        try:
-            agg_feedback = results.get_aggregate_feedback()
-            _display_aggregate_results(agg_feedback)
-        except Exception as e:
-            logger.debug(f"Failed to print aggregate feedback: {repr(e)}")
-    return results
 
 
 _RUN_ON_DATASET_DOCSTRING = """

libs/langchain/poetry.lock

@@ -3133,7 +3133,6 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
@@ -3447,7 +3446,7 @@ files = [
 
 [[package]]
 name = "langchain-community"
-version = "0.0.2"
+version = "0.0.3"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -3475,7 +3474,7 @@ url = "../community"
 
 [[package]]
 name = "langchain-core"
-version = "0.1.0"
+version = "0.1.1"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -3485,7 +3484,7 @@ develop = true
 [package.dependencies]
 anyio = ">=3,<5"
 jsonpatch = "^1.33"
-langsmith = "~0.0.63"
+langsmith = "~0.0.70"
 packaging = "^23.2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -3501,13 +3500,13 @@ url = "../core"
 
 [[package]]
 name = "langsmith"
-version = "0.0.63"
+version = "0.0.70"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.63-py3-none-any.whl", hash = "sha256:43a521dd10d8405ac21a0b959e3de33e2270e4abe6c73cc4036232a6990a0793"},
+    {file = "langsmith-0.0.70-py3-none-any.whl", hash = "sha256:a0d4cac3af94fe44c2ef3814c32b6740f92aebe267e395d62e62040bc5bad343"},
-    {file = "langsmith-0.0.63.tar.gz", hash = "sha256:ddb2dfadfad3e05151ed8ba1643d1c516024b80fbd0c6263024400ced06a3768"},
+    {file = "langsmith-0.0.70.tar.gz", hash = "sha256:3a546c45e67f6600d6669ef63f1f58b772e505703126338ad4f22fe0e2bbf677"},
 ]
 
 [package.dependencies]
@@ -3727,16 +3726,6 @@ files = [
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-    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
-    {file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
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@@ -5262,8 +5251,6 @@ files = [
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-    {file = "psycopg2-2.9.9-cp312-cp312-win32.whl", hash = "sha256:d735786acc7dd25815e89cc4ad529a43af779db2e25aa7c626de864127e5a024"},
-    {file = "psycopg2-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693"},
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@@ -5306,7 +5293,6 @@ files = [
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@@ -5315,8 +5301,6 @@ files = [
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-    {file = "psycopg2_binary-2.9.9-cp312-cp312-win32.whl", hash = "sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93"},
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@@ -9103,4 +9077,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "0b232a037505cefcdf2203edc9d750e70e2e52a297475490022402994c3036a3"
+content-hash = "e93141191088db7b4aec1a976ebd8cb20075e26d4a987bf97c0495ad865b7460"

libs/langchain/pyproject.toml

@@ -80,7 +80,7 @@ cassio = {version = "^0.1.0", optional = true}
 sympy = {version = "^1.12", optional = true}
 rapidfuzz = {version = "^3.1.1", optional = true}
 jsonschema = {version = ">1", optional = true}
-langsmith = "~0.0.63"
+langsmith = "~0.0.70"
 rank-bm25 = {version = "^0.2.2", optional = true}
 geopandas = {version = "^0.13.1", optional = true}
 gitpython = {version = "^3.1.32", optional = true}