# This file was auto-generated by Fern from our API Definition.

import typing
import urllib.parse
from json.decoder import JSONDecodeError

from ...core.api_error import ApiError
from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper
from ...core.jsonable_encoder import jsonable_encoder
from ...core.remove_none_from_dict import remove_none_from_dict
from ...errors.unprocessable_entity_error import UnprocessableEntityError
from ...types.eval_dataset import EvalDataset
from ...types.http_validation_error import HttpValidationError
from ...types.interval_usage_and_plan import IntervalUsageAndPlan
from ...types.local_eval import LocalEval
from ...types.local_eval_results import LocalEvalResults
from ...types.local_eval_sets import LocalEvalSets
from ...types.project import Project
from ...types.project_create import ProjectCreate
from ...types.prompt_mixin_prompts import PromptMixinPrompts

try:
    import pydantic
    if pydantic.__version__.startswith("1."):
        raise ImportError
    import pydantic.v1 as pydantic  # type: ignore
except ImportError:
    import pydantic  # type: ignore

# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)


class ProjectsClient:
    def __init__(self, *, client_wrapper: SyncClientWrapper):
        self._client_wrapper = client_wrapper

    def list_projects(
        self, *, organization_id: typing.Optional[str] = None, project_name: typing.Optional[str] = None
    ) -> typing.List[Project]:
        """
        List projects or get one by name

        Parameters:
            - organization_id: typing.Optional[str].

            - project_name: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.list_projects()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id, "project_name": project_name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[Project], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 create_project(self, *, organization_id: typing.Optional[str] = None, request: ProjectCreate) -> Project:
        """
        Create a new project.

        Parameters:
            - organization_id: typing.Optional[str].

            - request: ProjectCreate.
        ---
        from llama_cloud import ProjectCreate
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.create_project(
            request=ProjectCreate(
                name="string",
            ),
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_project(self, *, organization_id: typing.Optional[str] = None, request: ProjectCreate) -> Project:
        """
        Upsert a project.
        Updates if a project with the same name already exists. Otherwise, creates a new project.

        Parameters:
            - organization_id: typing.Optional[str].

            - request: ProjectCreate.
        ---
        from llama_cloud import ProjectCreate
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.upsert_project(
            request=ProjectCreate(
                name="string",
            ),
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_project(self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None) -> Project:
        """
        Get a project by ID.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.get_project()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 update_existing_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None, name: str
    ) -> Project:
        """
        Update an existing project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - name: str.
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.update_existing_project(
            name="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"name": name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_project(self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None) -> None:
        """
        Delete a project by ID.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.delete_project()
        """
        _response = self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_project_usage(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> IntervalUsageAndPlan:
        """
        Get usage for a project

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.get_project_usage()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/usage"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(IntervalUsageAndPlan, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_datasets_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[EvalDataset]:
        """
        List eval datasets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.list_datasets_for_project()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/eval/dataset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[EvalDataset], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 create_eval_dataset_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None, name: str
    ) -> EvalDataset:
        """
        Create a new eval dataset for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - name: str. The name of the EvalDataset.
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.create_eval_dataset_for_project(
            name="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/eval/dataset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"name": name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(EvalDataset, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 create_local_eval_set_for_project(
        self,
        project_id: typing.Optional[str],
        *,
        organization_id: typing.Optional[str] = None,
        app_name: str,
        results: typing.Dict[str, typing.List[LocalEval]],
    ) -> typing.List[LocalEvalResults]:
        """
        Create a new local eval set.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - app_name: str. The name of the app.

            - results: typing.Dict[str, typing.List[LocalEval]]. The eval results.
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.create_local_eval_set_for_project(
            app_name="string",
            results={"string": []},
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localevalset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"app_name": app_name, "results": results}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalResults], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_local_evals_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[LocalEvalResults]:
        """
        List local eval results for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.list_local_evals_for_project()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localeval"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalResults], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_local_eval_sets_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[LocalEvalSets]:
        """
        List local eval sets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.list_local_eval_sets_for_project()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localevalsets"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalSets], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_local_eval_set(
        self, project_id: typing.Optional[str], local_eval_set_id: str, *, organization_id: typing.Optional[str] = None
    ) -> typing.Any:
        """
        Delete a local eval set.

        Parameters:
            - project_id: typing.Optional[str].

            - local_eval_set_id: str.

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.delete_local_eval_set(
            local_eval_set_id="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/projects/{project_id}/localevalset/{local_eval_set_id}",
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_promptmixin_prompts(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[PromptMixinPrompts]:
        """
        List PromptMixin prompt sets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.list_promptmixin_prompts()
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PromptMixinPrompts], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 create_prompt_mixin_prompts(
        self,
        project_id: typing.Optional[str],
        *,
        organization_id: typing.Optional[str] = None,
        request: PromptMixinPrompts,
    ) -> PromptMixinPrompts:
        """
        Create a new PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - request: PromptMixinPrompts.
        ---
        from llama_cloud import PromptMixinPrompts
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.create_prompt_mixin_prompts(
            request=PromptMixinPrompts(
                project_id="string",
                name="string",
                prompts=[],
            ),
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PromptMixinPrompts, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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 update_promptmixin_prompts(
        self,
        project_id: typing.Optional[str],
        prompt_set_id: str,
        *,
        organization_id: typing.Optional[str] = None,
        request: PromptMixinPrompts,
    ) -> PromptMixinPrompts:
        """
        Update a PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - prompt_set_id: str.

            - organization_id: typing.Optional[str].

            - request: PromptMixinPrompts.
        ---
        from llama_cloud import PromptMixinPrompts
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.update_promptmixin_prompts(
            prompt_set_id="string",
            request=PromptMixinPrompts(
                project_id="string",
                name="string",
                prompts=[],
            ),
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts/{prompt_set_id}"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PromptMixinPrompts, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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_prompt_mixin_prompts(
        self, project_id: typing.Optional[str], prompt_set_id: str, *, organization_id: typing.Optional[str] = None
    ) -> typing.Any:
        """
        Delete a PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - prompt_set_id: str.

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.projects.delete_prompt_mixin_prompts(
            prompt_set_id="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts/{prompt_set_id}"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)


class AsyncProjectsClient:
    def __init__(self, *, client_wrapper: AsyncClientWrapper):
        self._client_wrapper = client_wrapper

    async def list_projects(
        self, *, organization_id: typing.Optional[str] = None, project_name: typing.Optional[str] = None
    ) -> typing.List[Project]:
        """
        List projects or get one by name

        Parameters:
            - organization_id: typing.Optional[str].

            - project_name: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.list_projects()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id, "project_name": project_name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[Project], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def create_project(self, *, organization_id: typing.Optional[str] = None, request: ProjectCreate) -> Project:
        """
        Create a new project.

        Parameters:
            - organization_id: typing.Optional[str].

            - request: ProjectCreate.
        ---
        from llama_cloud import ProjectCreate
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.create_project(
            request=ProjectCreate(
                name="string",
            ),
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def upsert_project(self, *, organization_id: typing.Optional[str] = None, request: ProjectCreate) -> Project:
        """
        Upsert a project.
        Updates if a project with the same name already exists. Otherwise, creates a new project.

        Parameters:
            - organization_id: typing.Optional[str].

            - request: ProjectCreate.
        ---
        from llama_cloud import ProjectCreate
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.upsert_project(
            request=ProjectCreate(
                name="string",
            ),
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/projects"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def get_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> Project:
        """
        Get a project by ID.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.get_project()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def update_existing_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None, name: str
    ) -> Project:
        """
        Update an existing project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - name: str.
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.update_existing_project(
            name="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"name": name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Project, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def delete_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> None:
        """
        Delete a project by ID.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.delete_project()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def get_project_usage(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> IntervalUsageAndPlan:
        """
        Get usage for a project

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.get_project_usage()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/usage"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(IntervalUsageAndPlan, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def list_datasets_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[EvalDataset]:
        """
        List eval datasets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.list_datasets_for_project()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/eval/dataset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[EvalDataset], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def create_eval_dataset_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None, name: str
    ) -> EvalDataset:
        """
        Create a new eval dataset for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - name: str. The name of the EvalDataset.
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.create_eval_dataset_for_project(
            name="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/eval/dataset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"name": name}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(EvalDataset, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def create_local_eval_set_for_project(
        self,
        project_id: typing.Optional[str],
        *,
        organization_id: typing.Optional[str] = None,
        app_name: str,
        results: typing.Dict[str, typing.List[LocalEval]],
    ) -> typing.List[LocalEvalResults]:
        """
        Create a new local eval set.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - app_name: str. The name of the app.

            - results: typing.Dict[str, typing.List[LocalEval]]. The eval results.
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.create_local_eval_set_for_project(
            app_name="string",
            results={"string": []},
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localevalset"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder({"app_name": app_name, "results": results}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalResults], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def list_local_evals_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[LocalEvalResults]:
        """
        List local eval results for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.list_local_evals_for_project()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localeval"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalResults], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def list_local_eval_sets_for_project(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[LocalEvalSets]:
        """
        List local eval sets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.list_local_eval_sets_for_project()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/localevalsets"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[LocalEvalSets], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def delete_local_eval_set(
        self, project_id: typing.Optional[str], local_eval_set_id: str, *, organization_id: typing.Optional[str] = None
    ) -> typing.Any:
        """
        Delete a local eval set.

        Parameters:
            - project_id: typing.Optional[str].

            - local_eval_set_id: str.

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.delete_local_eval_set(
            local_eval_set_id="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/projects/{project_id}/localevalset/{local_eval_set_id}",
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def list_promptmixin_prompts(
        self, project_id: typing.Optional[str], *, organization_id: typing.Optional[str] = None
    ) -> typing.List[PromptMixinPrompts]:
        """
        List PromptMixin prompt sets for a project.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.list_promptmixin_prompts()
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PromptMixinPrompts], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def create_prompt_mixin_prompts(
        self,
        project_id: typing.Optional[str],
        *,
        organization_id: typing.Optional[str] = None,
        request: PromptMixinPrompts,
    ) -> PromptMixinPrompts:
        """
        Create a new PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - organization_id: typing.Optional[str].

            - request: PromptMixinPrompts.
        ---
        from llama_cloud import PromptMixinPrompts
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.create_prompt_mixin_prompts(
            request=PromptMixinPrompts(
                project_id="string",
                name="string",
                prompts=[],
            ),
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts"),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PromptMixinPrompts, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def update_promptmixin_prompts(
        self,
        project_id: typing.Optional[str],
        prompt_set_id: str,
        *,
        organization_id: typing.Optional[str] = None,
        request: PromptMixinPrompts,
    ) -> PromptMixinPrompts:
        """
        Update a PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - prompt_set_id: str.

            - organization_id: typing.Optional[str].

            - request: PromptMixinPrompts.
        ---
        from llama_cloud import PromptMixinPrompts
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.update_promptmixin_prompts(
            prompt_set_id="string",
            request=PromptMixinPrompts(
                project_id="string",
                name="string",
                prompts=[],
            ),
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts/{prompt_set_id}"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PromptMixinPrompts, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)

    async def delete_prompt_mixin_prompts(
        self, project_id: typing.Optional[str], prompt_set_id: str, *, organization_id: typing.Optional[str] = None
    ) -> typing.Any:
        """
        Delete a PromptMixin prompt set.

        Parameters:
            - project_id: typing.Optional[str].

            - prompt_set_id: str.

            - organization_id: typing.Optional[str].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.projects.delete_prompt_mixin_prompts(
            prompt_set_id="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "DELETE",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/projects/{project_id}/prompts/{prompt_set_id}"
            ),
            params=remove_none_from_dict({"organization_id": organization_id}),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.Any, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _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)
