# 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.embedding_model_config import EmbeddingModelConfig from ...types.embedding_model_config_update import EmbeddingModelConfigUpdate from ...types.http_validation_error import HttpValidationError from .types.embedding_model_config_create_embedding_config import EmbeddingModelConfigCreateEmbeddingConfig 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 EmbeddingModelConfigsClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def list_embedding_model_configs( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[EmbeddingModelConfig]: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.embedding_model_configs.list_embedding_model_configs() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "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[EmbeddingModelConfig], _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_embedding_model_config( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: str, embedding_config: EmbeddingModelConfigCreateEmbeddingConfig, ) -> EmbeddingModelConfig: """ Create a new embedding model configuration within a specified project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: str. The name of the embedding model config. - embedding_config: EmbeddingModelConfigCreateEmbeddingConfig. The embedding configuration for the embedding model config. """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"name": name, "embedding_config": embedding_config}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(EmbeddingModelConfig, _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_embedding_model_config( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: EmbeddingModelConfigUpdate, ) -> EmbeddingModelConfig: """ Upserts an embedding model config. Updates if an embedding model config with the same name and project_id already exists. Otherwise, creates a new embedding model config. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: EmbeddingModelConfigUpdate. """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "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(EmbeddingModelConfig, _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_embedding_model_config( self, embedding_model_config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: EmbeddingModelConfigUpdate, ) -> EmbeddingModelConfig: """ Update an embedding model config by ID. Parameters: - embedding_model_config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: EmbeddingModelConfigUpdate. """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/embedding-model-configs/{embedding_model_config_id}" ), params=remove_none_from_dict({"project_id": project_id, "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(EmbeddingModelConfig, _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_embedding_model_config( self, embedding_model_config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Delete an embedding model config by ID. Parameters: - embedding_model_config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.embedding_model_configs.delete_embedding_model_config( embedding_model_config_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/embedding-model-configs/{embedding_model_config_id}" ), params=remove_none_from_dict({"project_id": project_id, "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) class AsyncEmbeddingModelConfigsClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def list_embedding_model_configs( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[EmbeddingModelConfig]: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.embedding_model_configs.list_embedding_model_configs() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "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[EmbeddingModelConfig], _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_embedding_model_config( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: str, embedding_config: EmbeddingModelConfigCreateEmbeddingConfig, ) -> EmbeddingModelConfig: """ Create a new embedding model configuration within a specified project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: str. The name of the embedding model config. - embedding_config: EmbeddingModelConfigCreateEmbeddingConfig. The embedding configuration for the embedding model config. """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"name": name, "embedding_config": embedding_config}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(EmbeddingModelConfig, _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_embedding_model_config( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: EmbeddingModelConfigUpdate, ) -> EmbeddingModelConfig: """ Upserts an embedding model config. Updates if an embedding model config with the same name and project_id already exists. Otherwise, creates a new embedding model config. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: EmbeddingModelConfigUpdate. """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/embedding-model-configs"), params=remove_none_from_dict({"project_id": project_id, "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(EmbeddingModelConfig, _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_embedding_model_config( self, embedding_model_config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: EmbeddingModelConfigUpdate, ) -> EmbeddingModelConfig: """ Update an embedding model config by ID. Parameters: - embedding_model_config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: EmbeddingModelConfigUpdate. """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/embedding-model-configs/{embedding_model_config_id}" ), params=remove_none_from_dict({"project_id": project_id, "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(EmbeddingModelConfig, _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_embedding_model_config( self, embedding_model_config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Delete an embedding model config by ID. Parameters: - embedding_model_config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.embedding_model_configs.delete_embedding_model_config( embedding_model_config_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/embedding-model-configs/{embedding_model_config_id}" ), params=remove_none_from_dict({"project_id": project_id, "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)