# 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.chat_app import ChatApp from ...types.chat_app_response import ChatAppResponse from ...types.http_validation_error import HttpValidationError from ...types.input_message import InputMessage from ...types.llm_parameters import LlmParameters from ...types.preset_composite_retrieval_params import PresetCompositeRetrievalParams 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 ChatAppsClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def get_chat_apps_api_v_1_apps_get( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[ChatAppResponse]: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.get_chat_apps_api_v_1_apps_get() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/apps"), 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[ChatAppResponse], _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_chat_app_api_v_1_apps_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: str, retriever_id: str, llm_config: LlmParameters, retrieval_config: PresetCompositeRetrievalParams, ) -> ChatApp: """ Create a new chat app. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: str. Name of the chat app - retriever_id: str. ID of the retriever to use for the chat app - llm_config: LlmParameters. Configuration for the LLM model to use for the chat app - retrieval_config: PresetCompositeRetrievalParams. Configuration for the retrieval model to use for the chat app --- from llama_cloud import ( CompositeRetrievalMode, LlmParameters, PresetCompositeRetrievalParams, ReRankConfig, ReRankerType, SupportedLlmModelNames, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.create_chat_app_api_v_1_apps_post( name="string", retriever_id="string", llm_config=LlmParameters( model_name=SupportedLlmModelNames.GPT_4_O, ), retrieval_config=PresetCompositeRetrievalParams( mode=CompositeRetrievalMode.ROUTING, rerank_config=ReRankConfig( type=ReRankerType.SYSTEM_DEFAULT, ), ), ) """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/apps"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder( { "name": name, "retriever_id": retriever_id, "llm_config": llm_config, "retrieval_config": retrieval_config, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ChatApp, _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_chat_app(self, id: str) -> ChatApp: """ Get a chat app by ID. Parameters: - id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.get_chat_app( id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ChatApp, _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_chat_app( self, id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: typing.Optional[str] = OMIT, llm_config: typing.Optional[LlmParameters] = OMIT, retrieval_config: typing.Optional[PresetCompositeRetrievalParams] = OMIT, ) -> ChatApp: """ Update a chat app. Parameters: - id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: typing.Optional[str]. - llm_config: typing.Optional[LlmParameters]. - retrieval_config: typing.Optional[PresetCompositeRetrievalParams]. --- from llama_cloud import ( CompositeRetrievalMode, LlmParameters, PresetCompositeRetrievalParams, ReRankConfig, ReRankerType, SupportedLlmModelNames, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.update_chat_app( id="string", llm_config=LlmParameters( model_name=SupportedLlmModelNames.GPT_4_O, ), retrieval_config=PresetCompositeRetrievalParams( mode=CompositeRetrievalMode.ROUTING, rerank_config=ReRankConfig( type=ReRankerType.SYSTEM_DEFAULT, ), ), ) """ _request: typing.Dict[str, typing.Any] = {} if name is not OMIT: _request["name"] = name if llm_config is not OMIT: _request["llm_config"] = llm_config if retrieval_config is not OMIT: _request["retrieval_config"] = retrieval_config _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{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(ChatApp, _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_chat_app(self, id: str) -> typing.Any: """ Parameters: - id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.delete_chat_app( id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{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 chat_with_chat_app(self, id: str, *, messages: typing.Optional[typing.List[InputMessage]] = OMIT) -> typing.Any: """ Chat with a chat app. Parameters: - id: str. - messages: typing.Optional[typing.List[InputMessage]]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.chat_apps.chat_with_chat_app( id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if messages is not OMIT: _request["messages"] = messages _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{id}/chat"), json=jsonable_encoder(_request), 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 AsyncChatAppsClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def get_chat_apps_api_v_1_apps_get( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[ChatAppResponse]: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.get_chat_apps_api_v_1_apps_get() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/apps"), 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[ChatAppResponse], _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_chat_app_api_v_1_apps_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: str, retriever_id: str, llm_config: LlmParameters, retrieval_config: PresetCompositeRetrievalParams, ) -> ChatApp: """ Create a new chat app. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: str. Name of the chat app - retriever_id: str. ID of the retriever to use for the chat app - llm_config: LlmParameters. Configuration for the LLM model to use for the chat app - retrieval_config: PresetCompositeRetrievalParams. Configuration for the retrieval model to use for the chat app --- from llama_cloud import ( CompositeRetrievalMode, LlmParameters, PresetCompositeRetrievalParams, ReRankConfig, ReRankerType, SupportedLlmModelNames, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.create_chat_app_api_v_1_apps_post( name="string", retriever_id="string", llm_config=LlmParameters( model_name=SupportedLlmModelNames.GPT_4_O, ), retrieval_config=PresetCompositeRetrievalParams( mode=CompositeRetrievalMode.ROUTING, rerank_config=ReRankConfig( type=ReRankerType.SYSTEM_DEFAULT, ), ), ) """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/apps"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder( { "name": name, "retriever_id": retriever_id, "llm_config": llm_config, "retrieval_config": retrieval_config, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ChatApp, _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_chat_app(self, id: str) -> ChatApp: """ Get a chat app by ID. Parameters: - id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.get_chat_app( id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ChatApp, _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_chat_app( self, id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: typing.Optional[str] = OMIT, llm_config: typing.Optional[LlmParameters] = OMIT, retrieval_config: typing.Optional[PresetCompositeRetrievalParams] = OMIT, ) -> ChatApp: """ Update a chat app. Parameters: - id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: typing.Optional[str]. - llm_config: typing.Optional[LlmParameters]. - retrieval_config: typing.Optional[PresetCompositeRetrievalParams]. --- from llama_cloud import ( CompositeRetrievalMode, LlmParameters, PresetCompositeRetrievalParams, ReRankConfig, ReRankerType, SupportedLlmModelNames, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.update_chat_app( id="string", llm_config=LlmParameters( model_name=SupportedLlmModelNames.GPT_4_O, ), retrieval_config=PresetCompositeRetrievalParams( mode=CompositeRetrievalMode.ROUTING, rerank_config=ReRankConfig( type=ReRankerType.SYSTEM_DEFAULT, ), ), ) """ _request: typing.Dict[str, typing.Any] = {} if name is not OMIT: _request["name"] = name if llm_config is not OMIT: _request["llm_config"] = llm_config if retrieval_config is not OMIT: _request["retrieval_config"] = retrieval_config _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{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(ChatApp, _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_chat_app(self, id: str) -> typing.Any: """ Parameters: - id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.delete_chat_app( id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{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 chat_with_chat_app( self, id: str, *, messages: typing.Optional[typing.List[InputMessage]] = OMIT ) -> typing.Any: """ Chat with a chat app. Parameters: - id: str. - messages: typing.Optional[typing.List[InputMessage]]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.chat_apps.chat_with_chat_app( id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if messages is not OMIT: _request["messages"] = messages _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/apps/{id}/chat"), json=jsonable_encoder(_request), 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)