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client.py
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2130 lines (1830 loc) · 72.3 KB
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# This file was auto-generated by Fern from our API Definition.
import typing
from ..core.client_wrapper import SyncClientWrapper
from ..types.project_sort_by import ProjectSortBy
from ..types.sort_order import SortOrder
from ..core.request_options import RequestOptions
from ..core.pagination import SyncPager
from ..types.dataset_response import DatasetResponse
from ..types.paginated_dataset_response import PaginatedDatasetResponse
from ..core.unchecked_base_model import construct_type
from ..errors.unprocessable_entity_error import UnprocessableEntityError
from ..types.http_validation_error import HttpValidationError
from json.decoder import JSONDecodeError
from ..core.api_error import ApiError
from ..requests.create_datapoint_request import CreateDatapointRequestParams
from ..types.update_dateset_action import UpdateDatesetAction
from ..core.serialization import convert_and_respect_annotation_metadata
from ..core.jsonable_encoder import jsonable_encoder
from ..types.datapoint_response import DatapointResponse
from ..types.paginated_datapoint_response import PaginatedDatapointResponse
from ..types.version_status import VersionStatus
from ..types.list_datasets import ListDatasets
from .. import core
from ..types.file_environment_response import FileEnvironmentResponse
from ..core.client_wrapper import AsyncClientWrapper
from ..core.pagination import AsyncPager
# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)
class DatasetsClient:
def __init__(self, *, client_wrapper: SyncClientWrapper):
self._client_wrapper = client_wrapper
def list(
self,
*,
page: typing.Optional[int] = None,
size: typing.Optional[int] = None,
name: typing.Optional[str] = None,
user_filter: typing.Optional[str] = None,
sort_by: typing.Optional[ProjectSortBy] = None,
order: typing.Optional[SortOrder] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> SyncPager[DatasetResponse]:
"""
List all Datasets.
Parameters
----------
page : typing.Optional[int]
Page offset for pagination.
size : typing.Optional[int]
Page size for pagination. Number of Datasets to fetch.
name : typing.Optional[str]
Case-insensitive filter for Dataset name.
user_filter : typing.Optional[str]
Case-insensitive filter for users in the Dataset. This filter matches against both email address and name of users.
sort_by : typing.Optional[ProjectSortBy]
Field to sort Datasets by
order : typing.Optional[SortOrder]
Direction to sort by.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
SyncPager[DatasetResponse]
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
response = client.datasets.list(
size=1,
)
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
"""
page = page if page is not None else 1
_response = self._client_wrapper.httpx_client.request(
"datasets",
method="GET",
params={
"page": page,
"size": size,
"name": name,
"user_filter": user_filter,
"sort_by": sort_by,
"order": order,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_parsed_response = typing.cast(
PaginatedDatasetResponse,
construct_type(
type_=PaginatedDatasetResponse, # type: ignore
object_=_response.json(),
),
)
_has_next = True
_get_next = lambda: self.list(
page=page + 1,
size=size,
name=name,
user_filter=user_filter,
sort_by=sort_by,
order=order,
request_options=request_options,
)
_items = _parsed_response.records
return SyncPager(has_next=_has_next, items=_items, get_next=_get_next)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def upsert(
self,
*,
datapoints: typing.Sequence[CreateDatapointRequestParams],
version_id: typing.Optional[str] = None,
environment: typing.Optional[str] = None,
path: typing.Optional[str] = OMIT,
id: typing.Optional[str] = OMIT,
action: typing.Optional[UpdateDatesetAction] = OMIT,
attributes: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = OMIT,
commit_message: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> DatasetResponse:
"""
Create a Dataset or update it with a new version if it already exists.
Datasets are identified by the `ID` or their `path`. The datapoints determine the versions of the Dataset.
By default, the new Dataset version will be set to the list of Datapoints provided in
the request. You can also create a new version by adding or removing Datapoints from an existing version
by specifying `action` as `add` or `remove` respectively. In this case, you may specify
the `version_id` or `environment` query parameters to identify the existing version to base
the new version on. If neither is provided, the default deployed version will be used.
If you provide a commit message, then the new version will be committed;
otherwise it will be uncommitted. If you try to commit an already committed version,
an exception will be raised.
Humanloop also deduplicates Datapoints. If you try to add a Datapoint that already
exists, it will be ignored. If you intentionally want to add a duplicate Datapoint,
you can add a unique identifier to the Datapoint's inputs such as `{_dedupe_id: <unique ID>}`.
Parameters
----------
datapoints : typing.Sequence[CreateDatapointRequestParams]
The Datapoints to create this Dataset version with. Modify the `action` field to determine how these Datapoints are used.
version_id : typing.Optional[str]
ID of the specific Dataset version to base the created Version on. Only used when `action` is `"add"` or `"remove"`.
environment : typing.Optional[str]
Name of the Environment identifying a deployed Version to base the created Version on. Only used when `action` is `"add"` or `"remove"`.
path : typing.Optional[str]
Path of the Dataset, including the name. This locates the Dataset in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
id : typing.Optional[str]
ID for an existing Dataset.
action : typing.Optional[UpdateDatesetAction]
The action to take with the provided Datapoints.
- If `"set"`, the created version will only contain the Datapoints provided in this request.
- If `"add"`, the created version will contain the Datapoints provided in this request in addition to the Datapoints in the target version.
- If `"remove"`, the created version will contain the Datapoints in the target version except for the Datapoints provided in this request.
If `"add"` or `"remove"`, one of the `version_id` or `environment` query parameters may be provided.
attributes : typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]
Additional fields to describe the Dataset. Helpful to separate Dataset versions from each other with details on how they were created or used.
commit_message : typing.Optional[str]
Message describing the changes made. If provided, a committed version of the Dataset is created. Otherwise, an uncommitted version is created.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.upsert(
path="test-questions",
datapoints=[
{
"inputs": {"question": "What is the capital of France?"},
"target": {"answer": "Paris"},
},
{
"inputs": {"question": "Who wrote Hamlet?"},
"target": {"answer": "William Shakespeare"},
},
],
action="set",
commit_message="Add two new questions and answers",
)
"""
_response = self._client_wrapper.httpx_client.request(
"datasets",
method="POST",
params={
"version_id": version_id,
"environment": environment,
},
json={
"path": path,
"id": id,
"datapoints": convert_and_respect_annotation_metadata(
object_=datapoints, annotation=typing.Sequence[CreateDatapointRequestParams], direction="write"
),
"action": action,
"attributes": attributes,
"commit_message": commit_message,
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def get(
self,
id: str,
*,
version_id: typing.Optional[str] = None,
environment: typing.Optional[str] = None,
include_datapoints: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> DatasetResponse:
"""
Retrieve the Dataset with the given ID.
Unless `include_datapoints` is set to `true`, the response will not include
the Datapoints.
Use the List Datapoints endpoint (`GET /{id}/datapoints`) to efficiently
retrieve Datapoints for a large Dataset.
By default, the deployed version of the Dataset is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Dataset.
Parameters
----------
id : str
Unique identifier for Dataset.
version_id : typing.Optional[str]
A specific Version ID of the Dataset to retrieve.
environment : typing.Optional[str]
Name of the Environment to retrieve a deployed Version from.
include_datapoints : typing.Optional[bool]
If set to `true`, include all Datapoints in the response. Defaults to `false`. Consider using the paginated List Datapoints endpoint instead.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.get(
id="ds_b0baF1ca7652",
version_id="dsv_6L78pqrdFi2xa",
include_datapoints=True,
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}",
method="GET",
params={
"version_id": version_id,
"environment": environment,
"include_datapoints": include_datapoints,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def delete(self, id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None:
"""
Delete the Dataset with the given ID.
Parameters
----------
id : str
Unique identifier for Dataset.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
None
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.delete(
id="id",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}",
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def move(
self,
id: str,
*,
path: typing.Optional[str] = OMIT,
name: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> DatasetResponse:
"""
Move the Dataset to a different path or change the name.
Parameters
----------
id : str
Unique identifier for Dataset.
path : typing.Optional[str]
Path of the Dataset including the Dataset name, which is used as a unique identifier.
name : typing.Optional[str]
Name of the Dataset, which is used as a unique identifier.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.move(
id="id",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}",
method="PATCH",
json={
"path": path,
"name": name,
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def list_datapoints(
self,
id: str,
*,
version_id: typing.Optional[str] = None,
environment: typing.Optional[str] = None,
page: typing.Optional[int] = None,
size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> SyncPager[DatapointResponse]:
"""
List all Datapoints for the Dataset with the given ID.
Parameters
----------
id : str
Unique identifier for Dataset.
version_id : typing.Optional[str]
A specific Version ID of the Dataset to retrieve.
environment : typing.Optional[str]
Name of the Environment to retrieve a deployed Version from.
page : typing.Optional[int]
Page number for pagination.
size : typing.Optional[int]
Page size for pagination. Number of Datapoints to fetch.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
SyncPager[DatapointResponse]
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
response = client.datasets.list_datapoints(
id="ds_b0baF1ca7652",
size=1,
)
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
"""
page = page if page is not None else 1
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/datapoints",
method="GET",
params={
"version_id": version_id,
"environment": environment,
"page": page,
"size": size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_parsed_response = typing.cast(
PaginatedDatapointResponse,
construct_type(
type_=PaginatedDatapointResponse, # type: ignore
object_=_response.json(),
),
)
_has_next = True
_get_next = lambda: self.list_datapoints(
id,
version_id=version_id,
environment=environment,
page=page + 1,
size=size,
request_options=request_options,
)
_items = _parsed_response.records
return SyncPager(has_next=_has_next, items=_items, get_next=_get_next)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def list_versions(
self,
id: str,
*,
status: typing.Optional[VersionStatus] = None,
include_datapoints: typing.Optional[typing.Literal["latest_committed"]] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> ListDatasets:
"""
Get a list of the versions for a Dataset.
Parameters
----------
id : str
Unique identifier for Dataset.
status : typing.Optional[VersionStatus]
Filter versions by status: 'uncommitted', 'committed'. If no status is provided, all versions are returned.
include_datapoints : typing.Optional[typing.Literal["latest_committed"]]
If set to 'latest_committed', include the Datapoints for the latest committed version. Defaults to `None`.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
ListDatasets
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.list_versions(
id="ds_b0baF1ca7652",
status="committed",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/versions",
method="GET",
params={
"status": status,
"include_datapoints": include_datapoints,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
ListDatasets,
construct_type(
type_=ListDatasets, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def commit(
self, id: str, version_id: str, *, commit_message: str, request_options: typing.Optional[RequestOptions] = None
) -> DatasetResponse:
"""
Commit a version of the Dataset with a commit message.
If the version is already committed, an exception will be raised.
Parameters
----------
id : str
Unique identifier for Dataset.
version_id : str
Unique identifier for the specific version of the Dataset.
commit_message : str
Message describing the changes made.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.commit(
id="ds_b0baF1ca7652",
version_id="dsv_6L78pqrdFi2xa",
commit_message="initial commit",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/versions/{jsonable_encoder(version_id)}/commit",
method="POST",
json={
"commit_message": commit_message,
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def upload_csv(
self,
id: str,
*,
file: core.File,
commit_message: str,
version_id: typing.Optional[str] = None,
environment: typing.Optional[str] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> DatasetResponse:
"""
Add Datapoints from a CSV file to a Dataset.
This will create a new committed version of the Dataset with the Datapoints from the CSV file.
If either `version_id` or `environment` is provided, the new version will be based on the specified version,
with the Datapoints from the CSV file added to the existing Datapoints in the version.
If neither `version_id` nor `environment` is provided, the new version will be based on the version
of the Dataset that is deployed to the default Environment.
Parameters
----------
id : str
Unique identifier for the Dataset
file : core.File
See core.File for more documentation
commit_message : str
Commit message for the new Dataset version.
version_id : typing.Optional[str]
ID of the specific Dataset version to base the created Version on.
environment : typing.Optional[str]
Name of the Environment identifying a deployed Version to base the created Version on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.upload_csv(
id="id",
commit_message="commit_message",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/datapoints/csv",
method="POST",
params={
"version_id": version_id,
"environment": environment,
},
data={
"commit_message": commit_message,
},
files={
"file": file,
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def set_deployment(
self, id: str, environment_id: str, *, version_id: str, request_options: typing.Optional[RequestOptions] = None
) -> DatasetResponse:
"""
Deploy Dataset to Environment.
Set the deployed version for the specified Environment.
Parameters
----------
id : str
Unique identifier for Dataset.
environment_id : str
Unique identifier for the Environment to deploy the Version to.
version_id : str
Unique identifier for the specific version of the Dataset.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DatasetResponse
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.set_deployment(
id="ds_b0baF1ca7652",
environment_id="staging",
version_id="dsv_6L78pqrdFi2xa",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/environments/{jsonable_encoder(environment_id)}",
method="POST",
params={
"version_id": version_id,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return typing.cast(
DatasetResponse,
construct_type(
type_=DatasetResponse, # type: ignore
object_=_response.json(),
),
)
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def remove_deployment(
self, id: str, environment_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> None:
"""
Remove deployed Dataset from Environment.
Remove the deployed version for the specified Environment.
Parameters
----------
id : str
Unique identifier for Dataset.
environment_id : str
Unique identifier for the Environment to remove the deployment from.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
None
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.datasets.remove_deployment(
id="ds_b0baF1ca7652",
environment_id="staging",
)
"""
_response = self._client_wrapper.httpx_client.request(
f"datasets/{jsonable_encoder(id)}/environments/{jsonable_encoder(environment_id)}",
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return
if _response.status_code == 422:
raise UnprocessableEntityError(
typing.cast(
HttpValidationError,
construct_type(
type_=HttpValidationError, # type: ignore
object_=_response.json(),
),
)
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, body=_response.text)
raise ApiError(status_code=_response.status_code, body=_response_json)
def list_environments(
self, id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.List[FileEnvironmentResponse]:
"""
List all Environments and their deployed versions for the Dataset.
Parameters
----------
id : str
Unique identifier for Dataset.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[FileEnvironmentResponse]
Successful Response
Examples
--------
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)