"""The LangSmith Client."""
from __future__ import annotations
import collections
import datetime
import functools
import io
import json
import logging
import os
import socket
import uuid
import weakref
from typing import (
TYPE_CHECKING,
Any,
Callable,
DefaultDict,
Dict,
Iterator,
List,
Mapping,
Optional,
Sequence,
Tuple,
Union,
cast,
)
from urllib import parse as urllib_parse
import requests
from requests import adapters as requests_adapters
from urllib3.util import Retry
from langsmith import schemas as ls_schemas
from langsmith import utils as ls_utils
from langsmith.evaluation.evaluator import RunEvaluator
if TYPE_CHECKING:
import pandas as pd
logger = logging.getLogger(__name__)
def _is_localhost(url: str) -> bool:
"""Check if the URL is localhost.
Parameters
----------
url : str
The URL to check.
Returns
-------
bool
True if the URL is localhost, False otherwise.
"""
try:
netloc = urllib_parse.urlsplit(url).netloc.split(":")[0]
ip = socket.gethostbyname(netloc)
return ip == "127.0.0.1" or ip.startswith("0.0.0.0") or ip.startswith("::")
except socket.gaierror:
return False
def _is_langchain_hosted(url: str) -> bool:
"""Check if the URL is langchain hosted.
Parameters
----------
url : str
The URL to check.
Returns
-------
bool
True if the URL is langchain hosted, False otherwise.
"""
try:
netloc = urllib_parse.urlsplit(url).netloc.split(":")[0]
return netloc.endswith("langchain.com")
except Exception:
return False
ID_TYPE = Union[uuid.UUID, str]
def _default_retry_config() -> Retry:
"""Get the default retry configuration.
Returns
-------
Retry
The default retry configuration.
"""
return Retry(
total=3,
allowed_methods=None, # Retry on all methods
status_forcelist=[502, 503, 504, 408, 425, 429],
backoff_factor=0.5,
# Sadly urllib3 1.x doesn't support backoff_jitter
raise_on_redirect=False,
raise_on_status=False,
)
def _serialize_json(obj: Any) -> str:
"""Serialize an object to JSON.
Parameters
----------
obj : Any
The object to serialize.
Returns
-------
str
The serialized JSON string.
Raises
------
TypeError
If the object type is not serializable.
"""
if isinstance(obj, datetime.datetime):
return obj.isoformat()
else:
return str(obj)
def close_session(session: requests.Session) -> None:
"""Close the session.
Parameters
----------
session : Session
The session to close.
"""
logger.debug("Closing Client.session")
session.close()
def _validate_api_key_if_hosted(api_url: str, api_key: Optional[str]) -> None:
"""Verify API key is provided if url not localhost.
Parameters
----------
api_url : str
The API URL.
api_key : str or None
The API key.
Raises
------
LangSmithUserError
If the API key is not provided when using the hosted service.
"""
# If the domain is langchain.com, raise error if no api_key
if not api_key:
if _is_langchain_hosted(api_url):
raise ls_utils.LangSmithUserError(
"API key must be provided when using hosted LangSmith API"
)
def _get_api_key(api_key: Optional[str]) -> Optional[str]:
api_key = api_key if api_key is not None else os.getenv("LANGCHAIN_API_KEY")
if api_key is None or not api_key.strip():
return None
return api_key.strip().strip('"').strip("'")
def _get_api_url(api_url: Optional[str], api_key: Optional[str]) -> str:
_api_url = (
api_url
if api_url is not None
else os.getenv(
"LANGCHAIN_ENDPOINT",
"https://api.smith.langchain.com" if api_key else "http://localhost:1984",
)
)
if not _api_url.strip():
raise ls_utils.LangSmithUserError("LangSmith API URL cannot be empty")
return _api_url.strip().strip('"').strip("'").rstrip("/")
class Client:
"""Client for interacting with the LangSmith API."""
__slots__ = [
"__weakref__",
"api_url",
"api_key",
"retry_config",
"timeout_ms",
"session",
"_get_data_type_cached",
]
def __init__(
self,
api_url: Optional[str] = None,
*,
api_key: Optional[str] = None,
retry_config: Optional[Retry] = None,
timeout_ms: Optional[int] = None,
) -> None:
"""Initialize a Client instance.
Parameters
----------
api_url : str or None, default=None
URL for the LangSmith API. Defaults to the LANGCHAIN_ENDPOINT
environment variable or http://localhost:1984 if not set.
api_key : str or None, default=None
API key for the LangSmith API. Defaults to the LANGCHAIN_API_KEY
environment variable.
retry_config : Retry or None, default=None
Retry configuration for the HTTPAdapter.
timeout_ms : int or None, default=None
Timeout in milliseconds for the HTTPAdapter.
Raises
------
LangSmithUserError
If the API key is not provided when using the hosted service.
"""
self.api_key = _get_api_key(api_key)
self.api_url = _get_api_url(api_url, self.api_key)
_validate_api_key_if_hosted(self.api_url, self.api_key)
self.retry_config = retry_config or _default_retry_config()
self.timeout_ms = timeout_ms or 7000
# Create a session and register a finalizer to close it
self.session = requests.Session()
weakref.finalize(self, close_session, self.session)
# Mount the HTTPAdapter with the retry configuration
adapter = requests_adapters.HTTPAdapter(max_retries=self.retry_config)
self.session.mount("http://", adapter)
self.session.mount("https://", adapter)
self._get_data_type_cached = functools.lru_cache(maxsize=10)(
self._get_data_type
)
def _repr_html_(self) -> str:
"""Return an HTML representation of the instance with a link to the URL.
Returns
-------
str
The HTML representation of the instance.
"""
link = self._host_url
return f'LangSmith Client'
def __repr__(self) -> str:
"""Return a string representation of the instance with a link to the URL.
Returns
-------
str
The string representation of the instance.
"""
return f"Client (API URL: {self.api_url})"
@property
def _host_url(self) -> str:
"""The web host url."""
if _is_localhost(self.api_url):
link = "http://localhost"
elif "dev" in self.api_url.split(".", maxsplit=1)[0]:
link = "https://dev.smith.langchain.com"
else:
link = "https://smith.langchain.com"
return link
@property
def _headers(self) -> Dict[str, str]:
"""Get the headers for the API request.
Returns
-------
Dict[str, str]
The headers for the API request.
"""
headers = {}
if self.api_key:
headers["x-api-key"] = self.api_key
return headers
def request_with_retries(
self,
request_method: str,
url: str,
request_kwargs: Mapping,
) -> requests.Response:
"""Send a request with retries.
Parameters
----------
request_method : str
The HTTP request method.
url : str
The URL to send the request to.
request_kwargs : Mapping
Additional request parameters.
Returns
-------
Response
The response object.
Raises
------
LangSmithAPIError
If a server error occurs.
LangSmithUserError
If the request fails.
LangSmithConnectionError
If a connection error occurs.
LangSmithError
If the request fails.
"""
try:
response = self.session.request(
request_method, url, stream=False, **request_kwargs
)
ls_utils.raise_for_status_with_text(response)
return response
except requests.HTTPError as e:
if response is not None and response.status_code == 500:
raise ls_utils.LangSmithAPIError(
f"Server error caused failure to {request_method} {url} in"
f" LangSmith API. {e}"
)
else:
raise ls_utils.LangSmithUserError(
f"Failed to {request_method} {url} in LangSmith API. {e}"
)
except requests.ConnectionError as e:
raise ls_utils.LangSmithConnectionError(
f"Connection error caused failure to {request_method} {url}"
" in LangSmith API. Please confirm your LANGCHAIN_ENDPOINT."
) from e
except Exception as e:
raise ls_utils.LangSmithError(
f"Failed to {request_method} {url} in LangSmith API. {e}"
) from e
def _get_with_retries(
self, path: str, params: Optional[Dict[str, Any]] = None
) -> requests.Response:
"""Send a GET request with retries.
Parameters
----------
path : str
The path of the request URL.
params : Dict[str, Any] or None, default=None
The query parameters.
Returns
-------
Response
The response object.
Raises
------
LangSmithAPIError
If a server error occurs.
LangSmithUserError
If the request fails.
LangSmithConnectionError
If a connection error occurs.
LangSmithError
If the request fails.
"""
return self.request_with_retries(
"get",
f"{self.api_url}{path}",
request_kwargs={
"params": params,
"headers": self._headers,
"timeout": self.timeout_ms / 1000,
},
)
def _get_paginated_list(
self, path: str, *, params: Optional[dict] = None
) -> Iterator[dict]:
"""Get a paginated list of items.
Parameters
----------
path : str
The path of the request URL.
params : dict or None, default=None
The query parameters.
Yields
------
dict
The items in the paginated list.
"""
params_ = params.copy() if params else {}
offset = params_.get("offset", 0)
params_["limit"] = params_.get("limit", 100)
while True:
params_["offset"] = offset
response = self._get_with_retries(path, params=params_)
items = response.json()
if not items:
break
yield from items
if len(items) < params_["limit"]:
# offset and limit isn't respected if we're
# querying for specific values
break
offset += len(items)
def upload_dataframe(
self,
df: pd.DataFrame,
name: str,
input_keys: Sequence[str],
output_keys: Sequence[str],
*,
description: Optional[str] = None,
data_type: Optional[ls_schemas.DataType] = ls_schemas.DataType.kv,
) -> ls_schemas.Dataset:
"""Upload a dataframe as individual examples to the LangSmith API.
Parameters
----------
df : pd.DataFrame
The dataframe to upload.
name : str
The name of the dataset.
input_keys : Sequence[str]
The input keys.
output_keys : Sequence[str]
The output keys.
description : str or None, default=None
The description of the dataset.
data_type : DataType or None, default=DataType.kv
The data type of the dataset.
Returns
-------
Dataset
The uploaded dataset.
Raises
------
ValueError
If the csv_file is not a string or tuple.
"""
csv_file = io.BytesIO()
df.to_csv(csv_file, index=False)
csv_file.seek(0)
return self.upload_csv(
("data.csv", csv_file),
input_keys=input_keys,
output_keys=output_keys,
description=description,
name=name,
data_type=data_type,
)
def upload_csv(
self,
csv_file: Union[str, Tuple[str, io.BytesIO]],
input_keys: Sequence[str],
output_keys: Sequence[str],
*,
name: Optional[str] = None,
description: Optional[str] = None,
data_type: Optional[ls_schemas.DataType] = ls_schemas.DataType.kv,
) -> ls_schemas.Dataset:
"""Upload a CSV file to the LangSmith API.
Parameters
----------
csv_file : str or Tuple[str, BytesIO]
The CSV file to upload. If a string, it should be the path
If a tuple, it should be a tuple containing the filename
and a BytesIO object.
input_keys : Sequence[str]
The input keys.
output_keys : Sequence[str]
The output keys.
name : str or None, default=None
The name of the dataset.
description : str or None, default=None
The description of the dataset.
data_type : DataType or None, default=DataType.kv
The data type of the dataset.
Returns
-------
Dataset
The uploaded dataset.
Raises
------
ValueError
If the csv_file is not a string or tuple.
"""
data = {
"input_keys": input_keys,
"output_keys": output_keys,
}
if name:
data["name"] = name
if description:
data["description"] = description
if data_type:
data["data_type"] = ls_utils.get_enum_value(data_type)
if isinstance(csv_file, str):
with open(csv_file, "rb") as f:
file_ = {"file": f}
response = self.session.post(
self.api_url + "/datasets/upload",
headers=self._headers,
data=data,
files=file_,
)
elif isinstance(csv_file, tuple):
response = self.session.post(
self.api_url + "/datasets/upload",
headers=self._headers,
data=data,
files={"file": csv_file},
)
else:
raise ValueError("csv_file must be a string or tuple")
ls_utils.raise_for_status_with_text(response)
result = response.json()
# TODO: Make this more robust server-side
if "detail" in result and "already exists" in result["detail"]:
file_name = csv_file if isinstance(csv_file, str) else csv_file[0]
file_name = file_name.split("/")[-1]
raise ValueError(f"Dataset {file_name} already exists")
return ls_schemas.Dataset(**result, _host_url=self._host_url)
def create_run(
self,
name: str,
inputs: Dict[str, Any],
run_type: str,
*,
execution_order: Optional[int] = None,
**kwargs: Any,
) -> None:
"""Persist a run to the LangSmith API.
Parameters
----------
name : str
The name of the run.
inputs : Dict[str, Any]
The input values for the run.
run_type : str
The type of the run, such as such as tool, chain, llm, retriever,
embedding, prompt, or parser.
execution_order : int or None, default=None
The execution order of the run.
**kwargs : Any
Additional keyword arguments.
Raises
------
LangSmithUserError
If the API key is not provided when using the hosted service.
"""
project_name = kwargs.pop(
"project_name",
kwargs.pop(
"session_name",
os.environ.get(
# TODO: Deprecate LANGCHAIN_SESSION
"LANGCHAIN_PROJECT",
os.environ.get("LANGCHAIN_SESSION", "default"),
),
),
)
run_create = {
**kwargs,
"session_name": project_name,
"name": name,
"inputs": inputs,
"run_type": run_type,
"execution_order": execution_order if execution_order is not None else 1,
}
run_extra = cast(dict, run_create.setdefault("extra", {}))
runtime = run_extra.setdefault("runtime", {})
runtime_env = ls_utils.get_runtime_environment()
run_extra["runtime"] = {**runtime_env, **runtime}
headers = {**self._headers, "Accept": "application/json"}
self.request_with_retries(
"post",
f"{self.api_url}/runs",
request_kwargs={
"data": json.dumps(run_create, default=_serialize_json),
"headers": headers,
"timeout": self.timeout_ms / 1000,
},
)
def update_run(
self,
run_id: ID_TYPE,
*,
end_time: Optional[datetime.datetime] = None,
error: Optional[str] = None,
inputs: Optional[Dict] = None,
outputs: Optional[Dict] = None,
events: Optional[Sequence[dict]] = None,
**kwargs: Any,
) -> None:
"""Update a run in the LangSmith API.
Parameters
----------
run_id : str or UUID
The ID of the run to update.
end_time : datetime or None
The end time of the run.
error : str or None, default=None
The error message of the run.
inputs : Dict or None, default=None
The input values for the run.
outputs : Dict or None, default=None
The output values for the run.
events : Sequence[dict] or None, default=None
The events for the run.
**kwargs : Any
Kwargs are ignored.
"""
headers = {**self._headers, "Accept": "application/json"}
data: Dict[str, Any] = {}
if end_time is not None:
data["end_time"] = end_time.isoformat()
if error is not None:
data["error"] = error
if inputs is not None:
data["inputs"] = inputs
if outputs is not None:
data["outputs"] = outputs
if events is not None:
data["events"] = events
self.request_with_retries(
"patch",
f"{self.api_url}/runs/{run_id}",
request_kwargs={
"data": json.dumps(data, default=_serialize_json),
"headers": headers,
"timeout": self.timeout_ms / 1000,
},
)
def _load_child_runs(self, run: ls_schemas.Run) -> ls_schemas.Run:
"""Load child runs for a given run.
Parameters
----------
run : Run
The run to load child runs for.
Returns
-------
Run
The run with loaded child runs.
Raises
------
LangSmithError
If a child run has no parent.
"""
child_runs = self.list_runs(id=run.child_run_ids)
treemap: DefaultDict[uuid.UUID, List[ls_schemas.Run]] = collections.defaultdict(
list
)
runs: Dict[uuid.UUID, ls_schemas.Run] = {}
for child_run in sorted(child_runs, key=lambda r: r.execution_order):
if child_run.parent_run_id is None:
raise ls_utils.LangSmithError(f"Child run {child_run.id} has no parent")
treemap[child_run.parent_run_id].append(child_run)
runs[child_run.id] = child_run
run.child_runs = treemap.pop(run.id, [])
for run_id, children in treemap.items():
runs[run_id].child_runs = children
return run
def read_run(
self, run_id: ID_TYPE, load_child_runs: bool = False
) -> ls_schemas.Run:
"""Read a run from the LangSmith API.
Parameters
----------
run_id : str or UUID
The ID of the run to read.
load_child_runs : bool, default=False
Whether to load nested child runs.
Returns
-------
Run
The run.
"""
response = self._get_with_retries(f"/runs/{run_id}")
run = ls_schemas.Run(**response.json(), _host_url=self._host_url)
if load_child_runs and run.child_run_ids:
run = self._load_child_runs(run)
return run
def list_runs(
self,
*,
project_id: Optional[ID_TYPE] = None,
project_name: Optional[str] = None,
run_type: Optional[str] = None,
reference_example_id: Optional[ID_TYPE] = None,
query: Optional[str] = None,
filter: Optional[str] = None,
execution_order: Optional[int] = None,
parent_run_id: Optional[ID_TYPE] = None,
start_time: Optional[datetime.datetime] = None,
error: Optional[bool] = None,
run_ids: Optional[List[ID_TYPE]] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
**kwargs: Any,
) -> Iterator[ls_schemas.Run]:
"""List runs from the LangSmith API.
Parameters
----------
project_id : UUID or None, default=None
The ID of the project to filter by.
project_name : str or None, default=None
The name of the project to filter by.
run_type : str or None, default=None
The type of the runs to filter by.
reference_example_id : UUID or None, default=None
The ID of the reference example to filter by.
query : str or None, default=None
The query string to filter by.
filter : str or None, default=None
The filter string to filter by.
execution_order : int or None, default=None
The execution order to filter by.
parent_run_id : UUID or None, default=None
The ID of the parent run to filter by.
start_time : datetime or None, default=None
The start time to filter by.
error : bool or None, default=None
Whether to filter by error status.
run_ids : List[str or UUID] or None, default=None
The IDs of the runs to filter by.
limit : int or None, default=None
The maximum number of runs to return.
offset : int or None, default=None
The number of runs to skip.
**kwargs : Any
Additional keyword arguments.
Yields
------
Run
The runs.
"""
if project_name is not None:
if project_id is not None:
raise ValueError("Only one of project_id or project_name may be given")
project_id = self.read_project(project_name=project_name).id
query_params: Dict[str, Any] = {
"session": project_id,
"run_type": run_type,
**kwargs,
}
if reference_example_id is not None:
query_params["reference_example"] = reference_example_id
if query is not None:
query_params["query"] = query
if filter is not None:
query_params["filter"] = filter
if execution_order is not None:
query_params["execution_order"] = execution_order
if parent_run_id is not None:
query_params["parent_run"] = parent_run_id
if start_time is not None:
query_params["start_time"] = start_time.isoformat()
if error is not None:
query_params["error"] = error
if run_ids is not None:
query_params["id"] = run_ids
if limit is not None:
query_params["limit"] = limit
if offset is not None:
query_params["offset"] = offset
yield from (
ls_schemas.Run(**run, _host_url=self._host_url)
for run in self._get_paginated_list("/runs", params=query_params)
)
def share_run(self, run_id: ID_TYPE, *, share_id: Optional[ID_TYPE] = None) -> str:
"""Get a share link for a run."""
data = {
"run_id": str(run_id),
"share_token": share_id or str(uuid.uuid4()),
}
response = self.session.put(
f"{self.api_url}/runs/{run_id}/share",
headers=self._headers,
json=data,
)
ls_utils.raise_for_status_with_text(response)
share_token = response.json()["share_token"]
return f"{self._host_url}/public/{share_token}/r"
def unshare_run(self, run_id: ID_TYPE) -> None:
"""Delete share link for a run."""
response = self.session.delete(
f"{self.api_url}/runs/{run_id}/share",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
def read_run_shared_link(self, run_id: ID_TYPE) -> Optional[str]:
response = self.session.get(
f"{self.api_url}/runs/{run_id}/share",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
result = response.json()
if result is None or "share_token" not in result:
return None
return f"{self._host_url}/public/{result['share_token']}/r"
def run_is_shared(self, run_id: ID_TYPE) -> bool:
"""Get share state for a run."""
link = self.read_run_shared_link(run_id)
return link is not None
def create_project(
self,
project_name: str,
*,
project_extra: Optional[dict] = None,
upsert: bool = False,
) -> ls_schemas.TracerSession:
"""Create a project on the LangSmith API.
Parameters
----------
project_name : str
The name of the project.
project_extra : dict or None, default=None
Additional project information.
upsert : bool, default=False
Whether to update the project if it already exists.
Returns
-------
TracerSession
The created project.
"""
endpoint = f"{self.api_url}/sessions"
body = {
"name": project_name,
"extra": project_extra,
}
params = {}
if upsert:
params["upsert"] = True
response = self.session.post(
endpoint,
headers=self._headers,
json=body,
)
ls_utils.raise_for_status_with_text(response)
return ls_schemas.TracerSession(**response.json(), _host_url=self._host_url)
@ls_utils.xor_args(("project_id", "project_name"))
def read_project(
self, *, project_id: Optional[str] = None, project_name: Optional[str] = None
) -> ls_schemas.TracerSessionResult:
"""Read a project from the LangSmith API.
Parameters
----------
project_id : str or None, default=None
The ID of the project to read.
project_name : str or None, default=None
The name of the project to read.
Note: Only one of project_id or project_name may be given.
Returns
-------
TracerSessionResult
The project.
"""
path = "/sessions"
params: Dict[str, Any] = {"limit": 1}
if project_id is not None:
path += f"/{project_id}"
elif project_name is not None:
params["name"] = project_name
else:
raise ValueError("Must provide project_name or project_id")
response = self._get_with_retries(path, params=params)
result = response.json()
if isinstance(result, list):
if len(result) == 0:
raise ls_utils.LangSmithError(f"Project {project_name} not found")
return ls_schemas.TracerSessionResult(**result[0], _host_url=self._host_url)
return ls_schemas.TracerSessionResult(
**response.json(), _host_url=self._host_url
)
def list_projects(
self,
project_ids: Optional[List[ID_TYPE]] = None,
name: Optional[str] = None,
name_contains: Optional[str] = None,
reference_dataset_id: Optional[ID_TYPE] = None,
reference_dataset_name: Optional[str] = None,
reference_free: Optional[bool] = None,
) -> Iterator[ls_schemas.TracerSession]:
"""
List projects from the LangSmith API.
Parameters
----------
project_ids : Optional[List[ID_TYPE]], optional
A list of project IDs to filter by, by default None
name : Optional[str], optional
The name of the project to filter by, by default None
name_contains : Optional[str], optional
A string to search for in the project name, by default None
reference_dataset_id : Optional[List[ID_TYPE]], optional
A dataset ID to filter by, by default None
reference_dataset_name : Optional[str], optional
The name of the reference dataset to filter by, by default None
reference_free : Optional[bool], optional
Whether to filter for only projects not associated with a dataset.
Yields
------
TracerSession
The projects.
"""
params: Dict[str, Any] = {}
if project_ids is not None:
params["id"] = project_ids
if name is not None:
params["name"] = name
if name_contains is not None:
params["name_contains"] = name_contains
if reference_dataset_id is not None:
if reference_dataset_name is not None:
raise ValueError(
"Only one of reference_dataset_id or"
" reference_dataset_name may be given"
)
params["reference_dataset"] = reference_dataset_id
elif reference_dataset_name is not None:
reference_dataset_id = self.read_dataset(
dataset_name=reference_dataset_name
).id
params["reference_dataset"] = reference_dataset_id
if reference_free is not None:
params["reference_free"] = reference_free
yield from (
ls_schemas.TracerSession(**project, _host_url=self._host_url)
for project in self._get_paginated_list("/sessions", params=params)
)
@ls_utils.xor_args(("project_name", "project_id"))
def delete_project(
self, *, project_name: Optional[str] = None, project_id: Optional[str] = None
) -> None:
"""Delete a project from the LangSmith API.
Parameters
----------
project_name : str or None, default=None
The name of the project to delete.
project_id : str or None, default=None
The ID of the project to delete.
"""
if project_name is not None:
project_id = str(self.read_project(project_name=project_name).id)
elif project_id is None:
raise ValueError("Must provide project_name or project_id")
response = self.session.delete(
self.api_url + f"/sessions/{project_id}",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
def create_dataset(
self,
dataset_name: str,
*,
description: Optional[str] = None,
data_type: ls_schemas.DataType = ls_schemas.DataType.kv,
) -> ls_schemas.Dataset:
"""Create a dataset in the LangSmith API.
Parameters
----------
dataset_name : str
The name of the dataset.
description : str or None, default=None
The description of the dataset.
data_type : DataType or None, default=DataType.kv
The data type of the dataset.
Returns
-------
Dataset
The created dataset.
"""
dataset = ls_schemas.DatasetCreate(
name=dataset_name,
description=description,
data_type=data_type,
)
response = self.session.post(
self.api_url + "/datasets",
headers=self._headers,
data=dataset.json(),
)
ls_utils.raise_for_status_with_text(response)
return ls_schemas.Dataset(**response.json(), _host_url=self._host_url)
@ls_utils.xor_args(("dataset_name", "dataset_id"))
def read_dataset(
self,
*,
dataset_name: Optional[str] = None,
dataset_id: Optional[ID_TYPE] = None,
) -> ls_schemas.Dataset:
"""Read a dataset from the LangSmith API.
Parameters
----------
dataset_name : str or None, default=None
The name of the dataset to read.
dataset_id : UUID or None, default=None
The ID of the dataset to read.
Returns
-------
Dataset
The dataset.
"""
path = "/datasets"
params: Dict[str, Any] = {"limit": 1}
if dataset_id is not None:
path += f"/{dataset_id}"
elif dataset_name is not None:
params["name"] = dataset_name
else:
raise ValueError("Must provide dataset_name or dataset_id")
response = self._get_with_retries(
path,
params=params,
)
result = response.json()
if isinstance(result, list):
if len(result) == 0:
raise ls_utils.LangSmithError(f"Dataset {dataset_name} not found")
return ls_schemas.Dataset(**result[0], _host_url=self._host_url)
return ls_schemas.Dataset(**result, _host_url=self._host_url)
def list_datasets(
self,
*,
dataset_ids: Optional[List[ID_TYPE]] = None,
data_type: Optional[str] = None,
dataset_name: Optional[str] = None,
dataset_name_contains: Optional[str] = None,
) -> Iterator[ls_schemas.Dataset]:
"""List the datasets on the LangSmith API.
Yields
------
Dataset
The datasets.
"""
params: Dict[str, Any] = {}
if dataset_ids is not None:
params["id"] = dataset_ids
if data_type is not None:
params["data_type"] = data_type
if dataset_name is not None:
params["name"] = dataset_name
if dataset_name_contains is not None:
params["name_contains"] = dataset_name_contains
yield from (
ls_schemas.Dataset(**dataset, _host_url=self._host_url)
for dataset in self._get_paginated_list("/datasets", params=params)
)
@ls_utils.xor_args(("dataset_id", "dataset_name"))
def delete_dataset(
self,
*,
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
) -> None:
"""Delete a dataset from the LangSmith API.
Parameters
----------
dataset_id : UUID or None, default=None
The ID of the dataset to delete.
dataset_name : str or None, default=None
The name of the dataset to delete.
"""
if dataset_name is not None:
dataset_id = self.read_dataset(dataset_name=dataset_name).id
if dataset_id is None:
raise ValueError("Must provide either dataset name or ID")
response = self.session.delete(
f"{self.api_url}/datasets/{dataset_id}",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
def _get_data_type(self, dataset_id: ID_TYPE) -> ls_schemas.DataType:
dataset = self.read_dataset(dataset_id=dataset_id)
return dataset.data_type
@ls_utils.xor_args(("dataset_id", "dataset_name"))
def create_llm_example(
self,
prompt: str,
generation: Optional[str] = None,
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
created_at: Optional[datetime.datetime] = None,
) -> ls_schemas.Example:
"""Add an example (row) to an LLM-type dataset."""
return self.create_example(
inputs={"input": prompt},
outputs={"output": generation},
dataset_id=dataset_id,
dataset_name=dataset_name,
created_at=created_at,
)
@ls_utils.xor_args(("dataset_id", "dataset_name"))
def create_chat_example(
self,
messages: List[Mapping[str, Any]],
generations: Optional[Mapping[str, Any]] = None,
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
created_at: Optional[datetime.datetime] = None,
) -> ls_schemas.Example:
"""Add an example (row) to a Chat-type dataset."""
return self.create_example(
inputs={"input": messages},
outputs={"output": generations},
dataset_id=dataset_id,
dataset_name=dataset_name,
created_at=created_at,
)
def create_example_from_run(
self,
run: ls_schemas.Run,
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
created_at: Optional[datetime.datetime] = None,
) -> ls_schemas.Example:
"""Add an example (row) to an LLM-type dataset."""
if dataset_id is None:
dataset_id = self.read_dataset(dataset_name=dataset_name).id
dataset_name = None # Nested call expects only 1 defined
dataset_type = self._get_data_type_cached(dataset_id)
if dataset_type == ls_schemas.DataType.llm:
if run.run_type != "llm":
raise ValueError(
f"Run type {run.run_type} is not supported"
" for dataset of type 'LLM'"
)
try:
prompt = ls_utils.get_prompt_from_inputs(run.inputs)
except ValueError:
raise ValueError(
"Error converting LLM run inputs to prompt for run"
f" {run.id} with inputs {run.inputs}"
)
inputs: Dict[str, Any] = {"input": prompt}
if not run.outputs:
outputs: Optional[Dict[str, Any]] = None
else:
try:
generation = ls_utils.get_llm_generation_from_outputs(run.outputs)
except ValueError:
raise ValueError(
"Error converting LLM run outputs to generation for run"
f" {run.id} with outputs {run.outputs}"
)
outputs = {"output": generation}
elif dataset_type == ls_schemas.DataType.chat:
if run.run_type != "llm":
raise ValueError(
f"Run type {run.run_type} is not supported"
" for dataset of type 'chat'"
)
try:
inputs = {"input": ls_utils.get_messages_from_inputs(run.inputs)}
except ValueError:
raise ValueError(
"Error converting LLM run inputs to chat messages for run"
f" {run.id} with inputs {run.inputs}"
)
if not run.outputs:
outputs = None
else:
try:
outputs = {
"output": ls_utils.get_message_generation_from_outputs(
run.outputs
)
}
except ValueError:
raise ValueError(
"Error converting LLM run outputs to chat generations"
f" for run {run.id} with outputs {run.outputs}"
)
elif dataset_type == ls_schemas.DataType.kv:
# Anything goes
inputs = run.inputs
outputs = run.outputs
else:
raise ValueError(f"Dataset type {dataset_type} not recognized.")
return self.create_example(
inputs=inputs,
outputs=outputs,
dataset_id=dataset_id,
dataset_name=dataset_name,
created_at=created_at,
)
@ls_utils.xor_args(("dataset_id", "dataset_name"))
def create_example(
self,
inputs: Mapping[str, Any],
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
created_at: Optional[datetime.datetime] = None,
outputs: Optional[Mapping[str, Any]] = None,
) -> ls_schemas.Example:
"""Create a dataset example in the LangSmith API.
Parameters
----------
inputs : Mapping[str, Any]
The input values for the example.
dataset_id : UUID or None, default=None
The ID of the dataset to create the example in.
dataset_name : str or None, default=None
The name of the dataset to create the example in.
created_at : datetime or None, default=None
The creation timestamp of the example.
outputs : Mapping[str, Any] or None, default=None
The output values for the example.
Returns
-------
Example
The created example.
"""
if dataset_id is None:
dataset_id = self.read_dataset(dataset_name=dataset_name).id
data = {
"inputs": inputs,
"outputs": outputs,
"dataset_id": dataset_id,
}
if created_at:
data["created_at"] = created_at.isoformat()
example = ls_schemas.ExampleCreate(**data)
response = self.session.post(
f"{self.api_url}/examples", headers=self._headers, data=example.json()
)
ls_utils.raise_for_status_with_text(response)
result = response.json()
return ls_schemas.Example(**result)
def read_example(self, example_id: ID_TYPE) -> ls_schemas.Example:
"""Read an example from the LangSmith API.
Parameters
----------
example_id : str or UUID
The ID of the example to read.
Returns
-------
Example
The example.
"""
response = self._get_with_retries(f"/examples/{example_id}")
return ls_schemas.Example(**response.json())
def list_examples(
self,
dataset_id: Optional[ID_TYPE] = None,
dataset_name: Optional[str] = None,
example_ids: Optional[List[ID_TYPE]] = None,
) -> Iterator[ls_schemas.Example]:
"""List the examples on the LangSmith API.
Parameters
----------
dataset_id : UUID or None, default=None
The ID of the dataset to filter by.
dataset_name : str or None, default=None
The name of the dataset to filter by.
example_ids : List[UUID] or None, default=None
The IDs of the examples to filter by.
Yields
------
Example
The examples.
"""
params: Dict[str, Any] = {}
if dataset_id is not None:
params["dataset"] = dataset_id
elif dataset_name is not None:
dataset_id = self.read_dataset(dataset_name=dataset_name).id
params["dataset"] = dataset_id
else:
pass
if example_ids is not None:
params["id"] = example_ids
yield from (
ls_schemas.Example(**dataset)
for dataset in self._get_paginated_list("/examples", params=params)
)
def update_example(
self,
example_id: str,
*,
inputs: Optional[Dict[str, Any]] = None,
outputs: Optional[Mapping[str, Any]] = None,
dataset_id: Optional[ID_TYPE] = None,
) -> Dict[str, Any]:
"""Update a specific example.
Parameters
----------
example_id : str or UUID
The ID of the example to update.
inputs : Dict[str, Any] or None, default=None
The input values to update.
outputs : Mapping[str, Any] or None, default=None
The output values to update.
dataset_id : UUID or None, default=None
The ID of the dataset to update.
Returns
-------
Dict[str, Any]
The updated example.
"""
example = ls_schemas.ExampleUpdate(
inputs=inputs,
outputs=outputs,
dataset_id=dataset_id,
)
response = self.session.patch(
f"{self.api_url}/examples/{example_id}",
headers=self._headers,
data=example.json(exclude_none=True),
)
ls_utils.raise_for_status_with_text(response)
return response.json()
def delete_example(self, example_id: ID_TYPE) -> None:
"""Delete an example by ID.
Parameters
----------
example_id : str or UUID
The ID of the example to delete.
"""
response = self.session.delete(
f"{self.api_url}/examples/{example_id}",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
def _resolve_run_id(
self,
run: Union[ls_schemas.Run, ls_schemas.RunBase, str, uuid.UUID],
load_child_runs: bool,
) -> ls_schemas.Run:
"""Resolve the run ID.
Parameters
----------
run : Run or RunBase or str or UUID
The run to resolve.
load_child_runs : bool
Whether to load child runs.
Returns
-------
Run
The resolved run.
Raises
------
TypeError
If the run type is invalid.
"""
if isinstance(run, (str, uuid.UUID)):
run_ = self.read_run(run, load_child_runs=load_child_runs)
elif isinstance(run, ls_schemas.Run):
run_ = run
elif isinstance(run, ls_schemas.RunBase):
run_ = ls_schemas.Run(**run.dict())
else:
raise TypeError(f"Invalid run type: {type(run)}")
return run_
def _resolve_example_id(
self,
example: Union[ls_schemas.Example, str, uuid.UUID, dict, None],
run: ls_schemas.Run,
) -> Optional[ls_schemas.Example]:
"""Resolve the example ID.
Parameters
----------
example : Example or str or UUID or dict or None
The example to resolve.
run : Run
The run associated with the example.
Returns
-------
Example or None
The resolved example.
"""
if isinstance(example, (str, uuid.UUID)):
reference_example_ = self.read_example(example)
elif isinstance(example, ls_schemas.Example):
reference_example_ = example
elif isinstance(example, dict):
reference_example_ = ls_schemas.Example(**example)
elif run.reference_example_id is not None:
reference_example_ = self.read_example(run.reference_example_id)
else:
reference_example_ = None
return reference_example_
def evaluate_run(
self,
run: Union[ls_schemas.Run, ls_schemas.RunBase, str, uuid.UUID],
evaluator: RunEvaluator,
*,
source_info: Optional[Dict[str, Any]] = None,
reference_example: Optional[
Union[ls_schemas.Example, str, dict, uuid.UUID]
] = None,
load_child_runs: bool = False,
) -> ls_schemas.Feedback:
"""Evaluate a run.
Parameters
----------
run : Run or RunBase or str or UUID
The run to evaluate.
evaluator : RunEvaluator
The evaluator to use.
source_info : Dict[str, Any] or None, default=None
Additional information about the source of the evaluation to log
as feedback metadata.
reference_example : Example or str or dict or UUID or None, default=None
The example to use as a reference for the evaluation.
If not provided, the run's reference example will be used.
load_child_runs : bool, default=False
Whether to load child runs when resolving the run ID.
Returns
-------
Feedback
The feedback object created by the evaluation.
"""
run_ = self._resolve_run_id(run, load_child_runs=load_child_runs)
reference_example_ = self._resolve_example_id(reference_example, run_)
feedback_result = evaluator.evaluate_run(
run_,
example=reference_example_,
)
source_info = source_info or {}
if feedback_result.evaluator_info:
source_info = {**feedback_result.evaluator_info, **source_info}
return self.create_feedback(
run_.id,
feedback_result.key,
score=feedback_result.score,
value=feedback_result.value,
comment=feedback_result.comment,
correction=feedback_result.correction,
source_info=source_info,
feedback_source_type=ls_schemas.FeedbackSourceType.MODEL,
)
async def aevaluate_run(
self,
run: Union[ls_schemas.Run, str, uuid.UUID],
evaluator: RunEvaluator,
*,
source_info: Optional[Dict[str, Any]] = None,
reference_example: Optional[
Union[ls_schemas.Example, str, dict, uuid.UUID]
] = None,
load_child_runs: bool = False,
) -> ls_schemas.Feedback:
"""Evaluate a run asynchronously.
Parameters
----------
run : Run or str or UUID
The run to evaluate.
evaluator : RunEvaluator
The evaluator to use.
source_info : Dict[str, Any] or None, default=None
Additional information about the source of the evaluation to log
as feedback metadata.
reference_example : Optional Example or UUID, default=None
The example to use as a reference for the evaluation.
If not provided, the run's reference example will be used.
load_child_runs : bool, default=False
Whether to load child runs when resolving the run ID.
Returns
-------
Feedback
The feedback created by the evaluation.
"""
run_ = self._resolve_run_id(run, load_child_runs=load_child_runs)
reference_example_ = self._resolve_example_id(reference_example, run_)
feedback_result = await evaluator.aevaluate_run(
run_,
example=reference_example_,
)
source_info = source_info or {}
if feedback_result.evaluator_info:
source_info = {**feedback_result.evaluator_info, **source_info}
return self.create_feedback(
run_.id,
feedback_result.key,
score=feedback_result.score,
value=feedback_result.value,
comment=feedback_result.comment,
correction=feedback_result.correction,
source_info=source_info,
feedback_source_type=ls_schemas.FeedbackSourceType.MODEL,
)
def create_feedback(
self,
run_id: ID_TYPE,
key: str,
*,
score: Union[float, int, bool, None] = None,
value: Union[float, int, bool, str, dict, None] = None,
correction: Union[dict, None] = None,
comment: Union[str, None] = None,
source_info: Optional[Dict[str, Any]] = None,
feedback_source_type: Union[
ls_schemas.FeedbackSourceType, str
] = ls_schemas.FeedbackSourceType.API,
source_run_id: Optional[ID_TYPE] = None,
) -> ls_schemas.Feedback:
"""Create a feedback in the LangSmith API.
Parameters
----------
run_id : str or UUID
The ID of the run to provide feedback on.
key : str
The name of the metric, tag, or 'aspect' this feedback is about.
score : float or int or bool or None, default=None
The score to rate this run on the metric or aspect.
value : float or int or bool or str or dict or None, default=None
The display value or non-numeric value for this feedback.
correction : dict or None, default=None
The proper ground truth for this run.
comment : str or None, default=None
A comment about this feedback.
source_info : Dict[str, Any] or None, default=None
Information about the source of this feedback.
feedback_source_type : FeedbackSourceType or str, default=FeedbackSourceType.API
The type of feedback source.
source_run_id : str or UUID or None, default=None,
The ID of the run that generated this feedback, if a "model" type.
Returns
-------
Feedback
The created feedback.
"""
if not isinstance(feedback_source_type, ls_schemas.FeedbackSourceType):
feedback_source_type = ls_schemas.FeedbackSourceType(feedback_source_type)
if feedback_source_type == ls_schemas.FeedbackSourceType.API:
feedback_source: ls_schemas.FeedbackSourceBase = (
ls_schemas.APIFeedbackSource(metadata=source_info)
)
elif feedback_source_type == ls_schemas.FeedbackSourceType.MODEL:
feedback_source = ls_schemas.ModelFeedbackSource(metadata=source_info)
else:
raise ValueError(f"Unknown feedback source type {feedback_source_type}")
feedback_source.metadata = (
feedback_source.metadata if feedback_source.metadata is not None else {}
)
if source_run_id is not None and "__run" not in feedback_source.metadata:
feedback_source.metadata["__run"] = {"run_id": str(source_run_id)}
feedback = ls_schemas.FeedbackCreate(
id=uuid.uuid4(),
run_id=run_id,
key=key,
score=score,
value=value,
correction=correction,
comment=comment,
feedback_source=feedback_source,
)
response = self.session.post(
self.api_url + "/feedback",
headers={**self._headers, "Content-Type": "application/json"},
data=feedback.json(exclude_none=True),
)
ls_utils.raise_for_status_with_text(response)
return ls_schemas.Feedback(**response.json())
def update_feedback(
self,
feedback_id: ID_TYPE,
*,
score: Union[float, int, bool, None] = None,
value: Union[float, int, bool, str, dict, None] = None,
correction: Union[dict, None] = None,
comment: Union[str, None] = None,
) -> ls_schemas.Feedback:
"""Update a feedback in the LangSmith API.
Parameters
----------
feedback_id : str or UUID
The ID of the feedback to update.
score : float or int or bool or None, default=None
The score to update the feedback with.
value : float or int or bool or str or dict or None, default=None
The value to update the feedback with.
correction : dict or None, default=None
The correction to update the feedback with.
comment : str or None, default=None
The comment to update the feedback with.
Returns
-------
Feedback
The updated feedback.
"""
feedback_update: Dict[str, Any] = {}
if score is not None:
feedback_update["score"] = score
if value is not None:
feedback_update["value"] = value
if correction is not None:
feedback_update["correction"] = correction
if comment is not None:
feedback_update["comment"] = comment
response = self.session.patch(
self.api_url + f"/feedback/{feedback_id}",
headers={**self._headers, "Content-Type": "application/json"},
data=json.dumps(feedback_update, default=_serialize_json),
)
ls_utils.raise_for_status_with_text(response)
return ls_schemas.Feedback(**response.json())
def read_feedback(self, feedback_id: ID_TYPE) -> ls_schemas.Feedback:
"""Read a feedback from the LangSmith API.
Parameters
----------
feedback_id : str or UUID
The ID of the feedback to read.
Returns
-------
Feedback
The feedback.
"""
response = self._get_with_retries(f"/feedback/{feedback_id}")
return ls_schemas.Feedback(**response.json())
def list_feedback(
self,
*,
run_ids: Optional[Sequence[ID_TYPE]] = None,
**kwargs: Any,
) -> Iterator[ls_schemas.Feedback]:
"""List the feedback objects on the LangSmith API.
Parameters
----------
run_ids : List[str or UUID] or None, default=None
The IDs of the runs to filter by.
**kwargs : Any
Additional keyword arguments.
Yields
------
Feedback
The feedback objects.
"""
params = {
"run": run_ids,
**kwargs,
}
yield from (
ls_schemas.Feedback(**feedback)
for feedback in self._get_paginated_list("/feedback", params=params)
)
def delete_feedback(self, feedback_id: ID_TYPE) -> None:
"""Delete a feedback by ID.
Parameters
----------
feedback_id : str or UUID
The ID of the feedback to delete.
"""
response = self.session.delete(
f"{self.api_url}/feedback/{feedback_id}",
headers=self._headers,
)
ls_utils.raise_for_status_with_text(response)
async def arun_on_dataset(
self,
dataset_name: str,
llm_or_chain_factory: Any,
*,
evaluation: Optional[Any] = None,
concurrency_level: int = 5,
project_name: Optional[str] = None,
verbose: bool = False,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Dict[str, Any]:
"""
Asynchronously run the Chain or language model on a dataset
and store traces to the specified project name.
Args:
dataset_name: Name of the dataset to run the chain on.
llm_or_chain_factory: Language model or Chain constructor to run
over the dataset. The Chain constructor is used to permit
independent calls on each example without carrying over state.
evaluation: Optional evaluation configuration to use when evaluating
concurrency_level: The number of async tasks to run concurrently.
project_name: Name of the project to store the traces in.
Defaults to {dataset_name}-{chain class name}-{datetime}.
verbose: Whether to print progress.
tags: Tags to add to each run in the project.
input_mapper: A function to map to the inputs dictionary from an Example
to the format expected by the model to be evaluated. This is useful if
your model needs to deserialize more complex schema or if your dataset
has inputs with keys that differ from what is expected by your chain
or agent.
Returns:
A dictionary containing the run's project name and the
resulting model outputs.
For the synchronous version, see client.run_on_dataset.
Examples
--------
.. code-block:: python
from langsmith import Client
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import RunEvalConfig
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
RunEvalConfig.Criteria("helpfulness"),
RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
client = Client()
await client.arun_on_dataset(
"",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
:class:`StringEvaluator `
or LangSmith's `RunEvaluator` classes.
.. code-block:: python
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
return {"score": prediction == reference}
evaluation_config = RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
await client.arun_on_dataset(
"",
construct_chain,
evaluation=evaluation_config,
)
""" # noqa: E501
try:
from langchain.smith import arun_on_dataset as _arun_on_dataset
except ImportError:
raise ImportError(
"The client.arun_on_dataset function requires the langchain"
"package to run.\nInstall with pip install langchain"
)
return await _arun_on_dataset(
self,
dataset_name,
llm_or_chain_factory,
evaluation=evaluation,
concurrency_level=concurrency_level,
project_name=project_name,
verbose=verbose,
tags=tags,
input_mapper=input_mapper,
)
def run_on_dataset(
self,
dataset_name: str,
llm_or_chain_factory: Any,
*,
evaluation: Optional[Any] = None,
project_name: Optional[str] = None,
verbose: bool = False,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Dict[str, Any]:
"""
Run the Chain or language model on a dataset and store traces
to the specified project name.
Args:
dataset_name: Name of the dataset to run the chain on.
llm_or_chain_factory: Language model or Chain constructor to run
over the dataset. The Chain constructor is used to permit
independent calls on each example without carrying over state.
evaluation: Configuration for evaluators to run on the
results of the chain
project_name: Name of the project to store the traces in.
Defaults to {dataset_name}-{chain class name}-{datetime}.
verbose: Whether to print progress.
tags: Tags to add to each run in the project.
input_mapper: A function to map to the inputs dictionary from an Example
to the format expected by the model to be evaluated. This is useful if
your model needs to deserialize more complex schema or if your dataset
has inputs with keys that differ from what is expected by your chain
or agent.
Returns:
A dictionary containing the run's project name and the resulting model outputs.
For the (usually faster) async version of this function, see `client.arun_on_dataset`.
Examples
--------
.. code-block:: python
from langsmith import Client
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import RunEvalConfig
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
RunEvalConfig.Criteria("helpfulness"),
RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
client = Client()
client.run_on_dataset(
"",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
:class:`StringEvaluator `
or LangSmith's `RunEvaluator` classes.
.. code-block:: python
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
return {"score": prediction == reference}
evaluation_config = RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
client.run_on_dataset(
"",
construct_chain,
evaluation=evaluation_config,
)
""" # noqa: E501
try:
from langchain.smith import run_on_dataset as _run_on_dataset
except ImportError:
raise ImportError(
"The client.run_on_dataset function requires the langchain"
"package to run.\nInstall with pip install langchain"
)
return _run_on_dataset(
self,
dataset_name,
llm_or_chain_factory,
evaluation=evaluation,
project_name=project_name,
verbose=verbose,
tags=tags,
input_mapper=input_mapper,
)