Session to project (#6249)

Sessions are being renamed to projects in the tracer

langchain/callbacks/manager.py

@@ -106,7 +106,7 @@ def wandb_tracing_enabled(
 
 @contextmanager
 def tracing_v2_enabled(
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     *,
     example_id: Optional[Union[str, UUID]] = None,
 ) -> Generator[None, None, None]:
@@ -120,7 +120,7 @@ def tracing_v2_enabled(
         example_id = UUID(example_id)
     cb = LangChainTracer(
         example_id=example_id,
-        session_name=session_name,
+        project_name=project_name,
     )
     tracing_v2_callback_var.set(cb)
     yield
@@ -131,12 +131,12 @@ def tracing_v2_enabled(
 def trace_as_chain_group(
     group_name: str,
     *,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     example_id: Optional[Union[str, UUID]] = None,
 ) -> Generator[CallbackManager, None, None]:
     """Get a callback manager for a chain group in a context manager."""
     cb = LangChainTracer(
-        session_name=session_name,
+        project_name=project_name,
         example_id=example_id,
     )
     cm = CallbackManager.configure(
@@ -152,12 +152,12 @@ def trace_as_chain_group(
 async def atrace_as_chain_group(
     group_name: str,
     *,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     example_id: Optional[Union[str, UUID]] = None,
 ) -> AsyncGenerator[AsyncCallbackManager, None]:
     """Get a callback manager for a chain group in a context manager."""
     cb = LangChainTracer(
-        session_name=session_name,
+        project_name=project_name,
         example_id=example_id,
     )
     cm = AsyncCallbackManager.configure(
@@ -1039,10 +1039,10 @@ def _configure(
     tracing_v2_enabled_ = (
         env_var_is_set("LANGCHAIN_TRACING_V2") or tracer_v2 is not None
     )
-    tracer_session = os.environ.get("LANGCHAIN_SESSION")
+    tracer_project = os.environ.get(
+        "LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default")
+    )
     debug = _get_debug()
-    if tracer_session is None:
-        tracer_session = "default"
     if (
         verbose
         or debug
@@ -1072,7 +1072,7 @@ def _configure(
                 callback_manager.add_handler(tracer, True)
             else:
                 handler = LangChainTracerV1()
-                handler.load_session(tracer_session)
+                handler.load_session(tracer_project)
                 callback_manager.add_handler(handler, True)
         if wandb_tracing_enabled_ and not any(
             isinstance(handler, WandbTracer) for handler in callback_manager.handlers
@@ -1090,7 +1090,7 @@ def _configure(
                 callback_manager.add_handler(tracer_v2, True)
             else:
                 try:
-                    handler = LangChainTracer(session_name=tracer_session)
+                    handler = LangChainTracer(project_name=tracer_project)
                     callback_manager.add_handler(handler, True)
                 except Exception as e:
                     logger.warning(

langchain/callbacks/tracers/langchain.py

@@ -45,7 +45,7 @@ class LangChainTracer(BaseTracer):
     def __init__(
         self,
         example_id: Optional[Union[UUID, str]] = None,
-        session_name: Optional[str] = None,
+        project_name: Optional[str] = None,
         client: Optional[LangChainPlusClient] = None,
         **kwargs: Any,
     ) -> None:
@@ -55,7 +55,9 @@ class LangChainTracer(BaseTracer):
         self.example_id = (
             UUID(example_id) if isinstance(example_id, str) else example_id
         )
-        self.session_name = session_name or os.getenv("LANGCHAIN_SESSION", "default")
+        self.project_name = project_name or os.getenv(
+            "LANGCHAIN_PROJECT", os.getenv("LANGCHAIN_SESSION", "default")
+        )
         # set max_workers to 1 to process tasks in order
         self.executor = ThreadPoolExecutor(max_workers=1)
         self.client = client or LangChainPlusClient()
@@ -103,7 +105,7 @@ class LangChainTracer(BaseTracer):
         extra["runtime"] = get_runtime_environment()
         run_dict["extra"] = extra
         try:
-            self.client.create_run(**run_dict, session_name=self.session_name)
+            self.client.create_run(**run_dict, project_name=self.project_name)
         except Exception as e:
             # Errors are swallowed by the thread executor so we need to log them here
             log_error_once("post", e)

langchain/client/runner_utils.py

@@ -237,18 +237,18 @@ async def _gather_with_concurrency(
     return results
 
 
-async def _tracer_initializer(session_name: Optional[str]) -> Optional[LangChainTracer]:
+async def _tracer_initializer(project_name: Optional[str]) -> Optional[LangChainTracer]:
     """
     Initialize a tracer to share across tasks.
 
     Args:
-        session_name: The session name for the tracer.
+        project_name: The project name for the tracer.
 
     Returns:
-        A LangChainTracer instance with an active session.
+        A LangChainTracer instance with an active project.
     """
-    if session_name:
+    if project_name:
-        tracer = LangChainTracer(session_name=session_name)
+        tracer = LangChainTracer(project_name=project_name)
         return tracer
     else:
         return None
@@ -260,12 +260,12 @@ async def arun_on_examples(
     *,
     concurrency_level: int = 5,
     num_repetitions: int = 1,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     verbose: bool = False,
     tags: Optional[List[str]] = None,
 ) -> Dict[str, Any]:
     """
-    Run the chain on examples and store traces to the specified session name.
+    Run the chain on examples and store traces to the specified project name.
 
     Args:
         examples: Examples to run the model or chain over
@@ -276,7 +276,7 @@ async def arun_on_examples(
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
-        session_name: Session name to use when tracing runs.
+        project_name: Project name to use when tracing runs.
         verbose: Whether to print progress.
         tags: Tags to add to the traces.
 
@@ -307,7 +307,7 @@ async def arun_on_examples(
 
     await _gather_with_concurrency(
         concurrency_level,
-        functools.partial(_tracer_initializer, session_name),
+        functools.partial(_tracer_initializer, project_name),
         *(functools.partial(process_example, e) for e in examples),
     )
     return results
@@ -386,11 +386,11 @@ def run_on_examples(
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     *,
     num_repetitions: int = 1,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     verbose: bool = False,
     tags: Optional[List[str]] = None,
 ) -> Dict[str, Any]:
-    """Run the chain on examples and store traces to the specified session name.
+    """Run the chain on examples and store traces to the specified project name.
 
     Args:
         examples: Examples to run model or chain over.
@@ -401,14 +401,14 @@ def run_on_examples(
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
-        session_name: Session name to use when tracing runs.
+        project_name: Project name to use when tracing runs.
         verbose: Whether to print progress.
         tags: Tags to add to the run traces.
     Returns:
         A dictionary mapping example ids to the model outputs.
     """
     results: Dict[str, Any] = {}
-    tracer = LangChainTracer(session_name=session_name) if session_name else None
+    tracer = LangChainTracer(project_name=project_name) if project_name else None
     for i, example in enumerate(examples):
         result = run_llm_or_chain(
             example,
@@ -425,13 +425,13 @@ def run_on_examples(
     return results
 
 
-def _get_session_name(
+def _get_project_name(
-    session_name: Optional[str],
+    project_name: Optional[str],
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     dataset_name: str,
 ) -> str:
-    if session_name is not None:
+    if project_name is not None:
-        return session_name
+        return project_name
     current_time = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
     if isinstance(llm_or_chain_factory, BaseLanguageModel):
         model_name = llm_or_chain_factory.__class__.__name__
@@ -446,13 +446,13 @@ async def arun_on_dataset(
     *,
     concurrency_level: int = 5,
     num_repetitions: int = 1,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     verbose: bool = False,
     client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
 ) -> Dict[str, Any]:
     """
-    Run the chain on a dataset and store traces to the specified session name.
+    Run the chain on a dataset and store traces to the specified project name.
 
     Args:
         client: Client to use to read the dataset.
@@ -464,7 +464,7 @@ async def arun_on_dataset(
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
-        session_name: Name of the session to store the traces in.
+        project_name: Name of the project to store the traces in.
             Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
         client: Client to use to read the dataset. If not provided, a new
@@ -472,11 +472,10 @@ async def arun_on_dataset(
         tags: Tags to add to each run in the sesssion.
 
     Returns:
-        A dictionary containing the run's session name and the resulting model outputs.
+        A dictionary containing the run's project name and the resulting model outputs.
     """
     client_ = client or LangChainPlusClient()
-    session_name = _get_session_name(session_name, llm_or_chain_factory, dataset_name)
+    project_name = _get_project_name(project_name, llm_or_chain_factory, dataset_name)
-    client_.create_session(session_name, mode="eval")
     dataset = client_.read_dataset(dataset_name=dataset_name)
     examples = client_.list_examples(dataset_id=str(dataset.id))
 
@@ -485,12 +484,12 @@ async def arun_on_dataset(
         llm_or_chain_factory,
         concurrency_level=concurrency_level,
         num_repetitions=num_repetitions,
-        session_name=session_name,
+        project_name=project_name,
         verbose=verbose,
         tags=tags,
     )
     return {
-        "session_name": session_name,
+        "project_name": project_name,
         "results": results,
     }
 
@@ -500,12 +499,12 @@ def run_on_dataset(
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     *,
     num_repetitions: int = 1,
-    session_name: Optional[str] = None,
+    project_name: Optional[str] = None,
     verbose: bool = False,
     client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
 ) -> Dict[str, Any]:
-    """Run the chain on a dataset and store traces to the specified session name.
+    """Run the chain on a dataset and store traces to the specified project name.
 
     Args:
         dataset_name: Name of the dataset to run the chain on.
@@ -516,7 +515,7 @@ def run_on_dataset(
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
-        session_name: Name of the session to store the traces in.
+        project_name: Name of the project to store the traces in.
             Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
         client: Client to use to access the dataset. If None, a new client
@@ -524,22 +523,21 @@ def run_on_dataset(
         tags: Tags to add to each run in the sesssion.
 
     Returns:
-        A dictionary containing the run's session name and the resulting model outputs.
+        A dictionary containing the run's project name and the resulting model outputs.
     """
     client_ = client or LangChainPlusClient()
-    session_name = _get_session_name(session_name, llm_or_chain_factory, dataset_name)
+    project_name = _get_project_name(project_name, llm_or_chain_factory, dataset_name)
-    client_.create_session(session_name, mode="eval")
     dataset = client_.read_dataset(dataset_name=dataset_name)
     examples = client_.list_examples(dataset_id=str(dataset.id))
     results = run_on_examples(
         examples,
         llm_or_chain_factory,
         num_repetitions=num_repetitions,
-        session_name=session_name,
+        project_name=project_name,
         verbose=verbose,
         tags=tags,
     )
     return {
-        "session_name": session_name,
+        "project_name": project_name,
         "results": results,
     }

langchain/experimental/client/tracing_datasets.ipynb

@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "1a4596ea-a631-416d-a2a4-3577c140493d",
       "metadata": {
@@ -34,6 +35,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "2d77d064-41b4-41fb-82e6-2d16461269ec",
       "metadata": {
@@ -48,19 +50,21 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "7935e832-9ae1-4557-8d08-890c425f18e2",
       "metadata": {},
       "source": [
-    "**NOTE:** You can also use the `tracing_v2_enabled` context manager to capture sessions within a given context:\n",
+        "**NOTE:** You can also use the `tracing_v2_enabled` context manager to capture projects within a given context:\n",
         "```\n",
         "from langchain.callbacks.manager import tracing_v2_enabled\n",
-    "with tracing_v2_enabled(\"My Session Name\"):\n",
+        "with tracing_v2_enabled(\"My Project Name\"):\n",
         "    ...\n",
         "```"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "221b638a-2ae4-46ef-bf6a-d59bf85d587f",
       "metadata": {},
@@ -102,7 +106,7 @@
         "from langchainplus_sdk import LangChainPlusClient\n",
         "\n",
         "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "os.environ[\"LANGCHAIN_SESSION\"] = \"Tracing Walkthrough\"\n",
+        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Tracing Walkthrough\"\n",
         "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.langchain.plus\"  # Uncomment this line if you want to use the hosted version\n",
         "# os.environ[\"LANGCHAIN_API_KEY\"] = \"<YOUR-LANGCHAINPLUS-API-KEY>\"  # Uncomment this line if you want to use the hosted version.\n",
         "\n",
@@ -171,13 +175,14 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "6c43c311-4e09-4d57-9ef3-13afb96ff430",
       "metadata": {},
       "source": [
         "## Creating the Dataset\n",
         "\n",
-    "Now that you've captured a session entitled 'Tracing Walkthrough', it's time to create a dataset. We will do so using the `create_dataset` method below."
+        "Now that you've captured a project entitled 'Tracing Walkthrough', it's time to create a dataset. We will do so using the `create_dataset` method below."
       ]
     },
     {
@@ -207,7 +212,7 @@
         "    dataset_name, description=\"A calculator example dataset\"\n",
         ")\n",
         "runs = client.list_runs(\n",
-    "    session_name=os.environ[\"LANGCHAIN_SESSION\"],\n",
+        "    project_name=os.environ[\"LANGCHAIN_PROJECT\"],\n",
         "    execution_order=1,  # Only return the top-level runs\n",
         "    error=False,  # Only runs that succeed\n",
         ")\n",
@@ -223,6 +228,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "db79dea2-fbaa-4c12-9083-f6154b51e2d3",
       "metadata": {
@@ -235,7 +241,7 @@
         "Alternatively, you could create or edit the dataset in the UI using the following steps:\n",
         "\n",
         "   1. Navigate to the UI by clicking on the link below.\n",
-    "   2. Select the 'search_and_math_chain' session from the list.\n",
+        "   2. Select the 'search_and_math_chain' project from the list.\n",
         "   3. Next to the fist example, click \"+ to Dataset\".\n",
         "   4. Click \"Create Dataset\" and create a title **\"calculator-example-dataset\"**.\n",
         "   5. Add the other examples to the dataset as well\n",
@@ -303,6 +309,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "07885b10",
       "metadata": {
@@ -339,6 +346,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "84094a4a-1d76-461c-bc37-8c537939b466",
       "metadata": {},
@@ -350,7 +358,7 @@
     },
     {
       "cell_type": "code",
-   "execution_count": 10,
+      "execution_count": null,
       "id": "112d7bdf-7e50-4c1a-9285-5bac8473f2ee",
       "metadata": {
         "tags": []
@@ -366,13 +374,13 @@
               "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m    \u001b[0mconcurrency_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m    \u001b[0mnum_repetitions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0msession_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+              "\u001b[0;34m\u001b[0m    \u001b[0mproject_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m    \u001b[0mclient\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[LangChainPlusClient]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m    \u001b[0mtags\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
               "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'Dict[str, Any]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
               "\u001b[0;31mDocstring:\u001b[0m\n",
-       "Run the chain on a dataset and store traces to the specified session name.\n",
+              "Run the chain on a dataset and store traces to the specified project name.\n",
               "\n",
               "Args:\n",
               "    client: Client to use to read the dataset.\n",
@@ -384,7 +392,7 @@
               "    num_repetitions: Number of times to run the model on each example.\n",
               "        This is useful when testing success rates or generating confidence\n",
               "        intervals.\n",
-       "    session_name: Name of the session to store the traces in.\n",
+              "    project_name: Name of the project to store the traces in.\n",
               "        Defaults to {dataset_name}-{chain class name}-{datetime}.\n",
               "    verbose: Whether to print progress.\n",
               "    client: Client to use to read the dataset. If not provided, a new\n",
@@ -392,7 +400,7 @@
               "    tags: Tags to add to each run in the sesssion.\n",
               "\n",
               "Returns:\n",
-       "    A dictionary containing the run's session name and the resulting model outputs.\n",
+              "    A dictionary containing the run's project name and the resulting model outputs.\n",
               "\u001b[0;31mFile:\u001b[0m      ~/code/lc/lckg/langchain/client/runner_utils.py\n",
               "\u001b[0;31mType:\u001b[0m      function"
             ]
@@ -479,6 +487,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "cdacd159-eb4d-49e9-bb2a-c55322c40ed4",
       "metadata": {
@@ -487,7 +496,7 @@
       "source": [
         "### Reviewing the Chain Results\n",
         "\n",
-    "You can review the results of the run in the tracing UI below and navigating to the session \n",
+        "You can review the results of the run in the tracing UI below and navigating to the project \n",
         "with the title **\"Search + Calculator Agent Evaluation\"**"
       ]
     },
@@ -519,6 +528,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "id": "63ed6561-6574-43b3-a653-fe410aa8a617",
       "metadata": {},
@@ -598,9 +608,7 @@
         "from tqdm.notebook import tqdm\n",
         "\n",
         "feedbacks = []\n",
-    "runs = client.list_runs(\n",
+        "runs = client.list_runs(project_name=chain_results[\"project_name\"], execution_order=1, error=False)\n",
-    "    session_name=chain_results[\"session_name\"], execution_order=1, error=False\n",
-    ")\n",
         "for run in tqdm(runs):\n",
         "    if run.outputs is None:\n",
         "        continue\n",

poetry.lock

@@ -4362,13 +4362,13 @@ tests = ["doctest", "pytest", "pytest-mock"]
 
 [[package]]
 name = "langchainplus-sdk"
-version = "0.0.15"
+version = "0.0.17"
-description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform."
+description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchainplus_sdk-0.0.15-py3-none-any.whl", hash = "sha256:e69bdbc8af6007ef2f774248d2483bbaf2d75712b1acc9ea50eda3b9f6dc567d"},
+    {file = "langchainplus_sdk-0.0.17-py3-none-any.whl", hash = "sha256:899675fe850bb0829691ce7643d5c3b4425de1535b6f2d6ce1e5f5457ffb05bf"},
-    {file = "langchainplus_sdk-0.0.15.tar.gz", hash = "sha256:ce40e9e3b6d42741f0a2aa89f83a12f2648f38690a9dd57e5fe3a56f2f232908"},
+    {file = "langchainplus_sdk-0.0.17.tar.gz", hash = "sha256:6520c864a23dcadbe6fb7233a117347f6acc32725a97758e59354704c50de303"},
 ]
 
 [package.dependencies]
@@ -11771,4 +11771,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "09d46ad12369c6a16513558618553623cd520c2855bff3b8fe8248e1b18cbb94"
+content-hash = "6e495e4f58127a5d2001385404b973896e275f5ca71a6ebe856cb114977189d1"

pyproject.toml

@@ -106,7 +106,7 @@ pyspark = {version = "^3.4.0", optional = true}
 clarifai = {version = "9.1.0", optional = true}
 tigrisdb = {version = "^1.0.0b6", optional = true}
 nebula3-python = {version = "^3.4.0", optional = true}
-langchainplus-sdk = ">=0.0.13"
+langchainplus-sdk = ">=0.0.17"
 awadb = {version = "^0.3.3", optional = true}
 azure-search-documents = {version = "11.4.0a20230509004", source = "azure-sdk-dev", optional = true}
 openllm = {version = ">=0.1.6", optional = true}

tests/unit_tests/client/test_runner_utils.py

@@ -176,7 +176,7 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
             {"result": f"Result for example {example.id}"} for _ in range(n_repetitions)
         ]
 
-    def mock_create_session(*args: Any, **kwargs: Any) -> None:
+    def mock_create_project(*args: Any, **kwargs: Any) -> None:
         pass
 
     with mock.patch.object(
@@ -186,7 +186,7 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
     ), mock.patch(
         "langchain.client.runner_utils._arun_llm_or_chain", new=mock_arun_chain
     ), mock.patch.object(
-        LangChainPlusClient, "create_session", new=mock_create_session
+        LangChainPlusClient, "create_project", new=mock_create_project
     ):
         client = LangChainPlusClient(api_url="http://localhost:1984", api_key="123")
         chain = mock.MagicMock()
@@ -195,7 +195,7 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
             dataset_name="test",
             llm_or_chain_factory=lambda: chain,
             concurrency_level=2,
-            session_name="test_session",
+            project_name="test_project",
             num_repetitions=num_repetitions,
             client=client,
         )