[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 {}
@@ -953,111 +959,159 @@ 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):
-    client: Client,
+    """A dictionary of the results for a single example row."""
-    dataset_name: str,
+
-    llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
+    feedback: Optional[List[EvaluationResult]]
-    project_name: Optional[str],
+    execution_time: Optional[float]
-    evaluation: Optional[smith_eval.RunEvalConfig] = None,
+    run_id: Optional[str]
-    tags: Optional[List[str]] = None,
+
-    input_mapper: Optional[Callable[[Dict], Any]] = None,
+
-    concurrency_level: int = 5,
+@dataclasses.dataclass
-    project_metadata: Optional[Dict[str, Any]] = None,
+class _DatasetRunContainer:
-) -> Tuple[MCF, str, List[Example], List[RunnableConfig]]:
+    """A container to help manage the state of a eval run."""
-    project_name = project_name or name_generation.random_name()
+
-    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
+    client: Client
-        client,
+    project: TracerSession
-        dataset_name,
+    wrapped_model: MCF
-        llm_or_chain_factory,
+    examples: List[Example]
-        project_name,
+    configs: List[RunnableConfig]
-        project_metadata=project_metadata,
+
-        tags=tags,
+    def _merge_test_outputs(
-    )
+        self,
-    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
+        batch_results: list,
-    run_evaluators = _setup_evaluation(
+        all_eval_results: Dict[str, _RowResult],
-        wrapped_model, examples, evaluation, dataset.data_type or DataType.kv
+    ) -> dict:
-    )
+        results: dict = {}
-    _validate_example_inputs(examples[0], wrapped_model, input_mapper)
+        for example, output in zip(self.examples, batch_results):
-    progress_bar = progress.ProgressBarCallback(len(examples))
+            row_result = cast(_RowResult, all_eval_results.get(str(example.id), {}))
-    configs = [
+            results[str(example.id)] = {
-        RunnableConfig(
+                "input": example.inputs,
-            callbacks=[
+                "feedback": row_result.get("feedback", []),
-                LangChainTracer(
+                "execution_time": row_result.get("execution_time"),
-                    project_name=project_name,
+                "run_id": row_result.get("run_id"),
-                    client=client,
+            }
-                    use_threading=False,
+            if isinstance(output, EvalError):
-                    example_id=example.id,
+                results[str(example.id)]["Error"] = output.Error
-                ),
+            else:
-                EvaluatorCallbackHandler(
+                results[str(example.id)]["output"] = output
-                    evaluators=run_evaluators or [],
+            if example.outputs:
-                    client=client,
+                results[str(example.id)]["reference"] = example.outputs
-                    example_id=example.id,
+        return results
-                    max_concurrency=0,
+
-                ),
+    def _collect_metrics(self) -> Dict[str, _RowResult]:
-                progress_bar,
+        all_eval_results: dict = {}
-            ],
+        for c in self.configs:
-            tags=tags or [],
+            for callback in cast(list, c["callbacks"]):
-            max_concurrency=concurrency_level,
+                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,
         )
-        for example in examples
-    ]
-    return wrapped_model, project_name, examples, configs
 
+    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
 
-def _collect_test_results(
+    @classmethod
-    examples: List[Example],
+    def prepare(
-    batch_results: List[Union[dict, str, LLMResult, ChatResult]],
+        cls,
-    configs: List[RunnableConfig],
+        client: Client,
-    project_name: str,
+        dataset_name: str,
-) -> TestResult:
+        llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
-    wait_for_all_evaluators()
+        project_name: Optional[str],
-    all_eval_results = {}
+        evaluation: Optional[smith_eval.RunEvalConfig] = None,
-    all_execution_time = {}
+        tags: Optional[List[str]] = None,
-    all_run_ids = {}
+        input_mapper: Optional[Callable[[Dict], Any]] = None,
-    for c in configs:
+        concurrency_level: int = 5,
-        for callback in cast(list, c["callbacks"]):
+        project_metadata: Optional[Dict[str, Any]] = None,
-            if isinstance(callback, EvaluatorCallbackHandler):
+    ) -> _DatasetRunContainer:
-                eval_results = callback.logged_eval_results
+        project_name = project_name or name_generation.random_name()
-                all_eval_results.update(
+        wrapped_model, project, dataset, examples = _prepare_eval_run(
-                    {example_id: v for (_, example_id), v in eval_results.items()}
+            client,
-                )
+            dataset_name,
-            elif isinstance(callback, LangChainTracer):
+            llm_or_chain_factory,
-                run = callback.latest_run
+            project_name,
-                example_id = callback.example_id
+            project_metadata=project_metadata,
-                run_id = str(run.id) if run else None
+            tags=tags,
-                execution_time = (
+        )
-                    (run.end_time - run.start_time).total_seconds()
+        wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
-                    if run and run.end_time
+        run_evaluators = _setup_evaluation(
-                    else None
+            wrapped_model, examples, evaluation, dataset.data_type or DataType.kv
-                )
+        )
-                all_execution_time[str(example_id)] = execution_time
+        _validate_example_inputs(examples[0], wrapped_model, input_mapper)
-                all_run_ids[str(example_id)] = run_id
+        progress_bar = progress.ProgressBarCallback(len(examples))
-
+        configs = [
-    results: dict = {}
+            RunnableConfig(
-    for example, output in zip(examples, batch_results):
+                callbacks=[
-        feedback = all_eval_results.get(str(example.id), [])
+                    LangChainTracer(
-        results[str(example.id)] = {
+                        project_name=project.name,
-            "input": example.inputs,
+                        client=client,
-            "feedback": feedback,
+                        use_threading=False,
-            "execution_time": all_execution_time.get(str(example.id)),
+                        example_id=example.id,
-            "run_id": all_run_ids.get(str(example.id)),
+                    ),
-        }
+                    EvaluatorCallbackHandler(
-        if isinstance(output, EvalError):
+                        evaluators=run_evaluators or [],
-            results[str(example.id)]["Error"] = output.Error
+                        client=client,
-        else:
+                        example_id=example.id,
-            results[str(example.id)]["output"] = output
+                        max_concurrency=0,
-        if example.outputs:
+                    ),
-            results[str(example.id)]["reference"] = example.outputs
+                    progress_bar,
-    return TestResult(
+                ],
-        project_name=project_name,
+                tags=tags or [],
-        results=results,
+                max_concurrency=concurrency_level,
-    )
+            )
+            for example in examples
+        ]
+        return cls(
+            client=client,
+            project=project,
+            wrapped_model=wrapped_model,
+            examples=examples,
+            configs=configs,
+        )
 
 
 def _is_jupyter_environment() -> bool:
@@ -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 = [
     {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
-    {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"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
@@ -5262,8 +5251,6 @@ files = [
     {file = "psycopg2-2.9.9-cp310-cp310-win_amd64.whl", hash = "sha256:426f9f29bde126913a20a96ff8ce7d73fd8a216cfb323b1f04da402d452853c3"},
     {file = "psycopg2-2.9.9-cp311-cp311-win32.whl", hash = "sha256:ade01303ccf7ae12c356a5e10911c9e1c51136003a9a1d92f7aa9d010fb98372"},
     {file = "psycopg2-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:121081ea2e76729acfb0673ff33755e8703d45e926e416cb59bae3a86c6a4981"},
-    {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"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win32.whl", hash = "sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win_amd64.whl", hash = "sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a"},
     {file = "psycopg2-2.9.9-cp38-cp38-win32.whl", hash = "sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"},
@@ -5306,7 +5293,6 @@ files = [
     {file = "psycopg2_binary-2.9.9-cp311-cp311-win32.whl", hash = "sha256:dc4926288b2a3e9fd7b50dc6a1909a13bbdadfc67d93f3374d984e56f885579d"},
     {file = "psycopg2_binary-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:b76bedd166805480ab069612119ea636f5ab8f8771e640ae103e05a4aae3e417"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8532fd6e6e2dc57bcb3bc90b079c60de896d2128c5d9d6f24a63875a95a088cf"},
-    {file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b0605eaed3eb239e87df0d5e3c6489daae3f7388d455d0c0b4df899519c6a38d"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f8544b092a29a6ddd72f3556a9fcf249ec412e10ad28be6a0c0d948924f2212"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2d423c8d8a3c82d08fe8af900ad5b613ce3632a1249fd6a223941d0735fce493"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e5afae772c00980525f6d6ecf7cbca55676296b580c0e6abb407f15f3706996"},
@@ -5315,8 +5301,6 @@ files = [
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:cb16c65dcb648d0a43a2521f2f0a2300f40639f6f8c1ecbc662141e4e3e1ee07"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:911dda9c487075abd54e644ccdf5e5c16773470a6a5d3826fda76699410066fb"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:57fede879f08d23c85140a360c6a77709113efd1c993923c59fde17aa27599fe"},
-    {file = "psycopg2_binary-2.9.9-cp312-cp312-win32.whl", hash = "sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93"},
-    {file = "psycopg2_binary-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:81ff62668af011f9a48787564ab7eded4e9fb17a4a6a74af5ffa6a457400d2ab"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:2293b001e319ab0d869d660a704942c9e2cce19745262a8aba2115ef41a0a42a"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03ef7df18daf2c4c07e2695e8cfd5ee7f748a1d54d802330985a78d2a5a6dca9"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a602ea5aff39bb9fac6308e9c9d82b9a35c2bf288e184a816002c9fae930b77"},
@@ -6305,7 +6289,6 @@ files = [
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
-    {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
     {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
     {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
     {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@@ -6313,15 +6296,8 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
-    {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
     {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
-    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
-    {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
-    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
-    {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
-    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
-    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
     {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -6338,7 +6314,6 @@ files = [
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
-    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
     {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
     {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -6346,7 +6321,6 @@ files = [
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
-    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
     {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
     {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -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}