# This file was auto-generated by Fern from our API Definition. import typing from json.decoder import JSONDecodeError from ....core.api_error import ApiError from ....core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ....core.http_response import AsyncHttpResponse, HttpResponse from ....core.request_options import RequestOptions from ....core.serialization import convert_and_respect_annotation_metadata from ....core.unchecked_base_model import construct_type from ....errors.bad_request_error import BadRequestError from ....requests.read_v1request import ReadV1RequestParams from ....types.read_v1response import ReadV1Response from .types.text_analyze_request_callback_method import TextAnalyzeRequestCallbackMethod from .types.text_analyze_request_custom_intent_mode import TextAnalyzeRequestCustomIntentMode from .types.text_analyze_request_custom_topic_mode import TextAnalyzeRequestCustomTopicMode from .types.text_analyze_request_summarize import TextAnalyzeRequestSummarize # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class RawTextClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def analyze( self, *, request: ReadV1RequestParams, callback: typing.Optional[str] = None, callback_method: typing.Optional[TextAnalyzeRequestCallbackMethod] = None, sentiment: typing.Optional[bool] = None, summarize: typing.Optional[TextAnalyzeRequestSummarize] = None, tag: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, topics: typing.Optional[bool] = None, custom_topic: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, custom_topic_mode: typing.Optional[TextAnalyzeRequestCustomTopicMode] = None, intents: typing.Optional[bool] = None, custom_intent: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, custom_intent_mode: typing.Optional[TextAnalyzeRequestCustomIntentMode] = None, language: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> HttpResponse[ReadV1Response]: """ Analyze text content using Deepgrams text analysis API Parameters ---------- request : ReadV1RequestParams callback : typing.Optional[str] URL to which we'll make the callback request callback_method : typing.Optional[TextAnalyzeRequestCallbackMethod] HTTP method by which the callback request will be made sentiment : typing.Optional[bool] Recognizes the sentiment throughout a transcript or text summarize : typing.Optional[TextAnalyzeRequestSummarize] Summarize content. For Listen API, supports string version option. For Read API, accepts boolean only. tag : typing.Optional[typing.Union[str, typing.Sequence[str]]] Label your requests for the purpose of identification during usage reporting topics : typing.Optional[bool] Detect topics throughout a transcript or text custom_topic : typing.Optional[typing.Union[str, typing.Sequence[str]]] Custom topics you want the model to detect within your input audio or text if present Submit up to `100`. custom_topic_mode : typing.Optional[TextAnalyzeRequestCustomTopicMode] Sets how the model will interpret strings submitted to the `custom_topic` param. When `strict`, the model will only return topics submitted using the `custom_topic` param. When `extended`, the model will return its own detected topics in addition to those submitted using the `custom_topic` param intents : typing.Optional[bool] Recognizes speaker intent throughout a transcript or text custom_intent : typing.Optional[typing.Union[str, typing.Sequence[str]]] Custom intents you want the model to detect within your input audio if present custom_intent_mode : typing.Optional[TextAnalyzeRequestCustomIntentMode] Sets how the model will interpret intents submitted to the `custom_intent` param. When `strict`, the model will only return intents submitted using the `custom_intent` param. When `extended`, the model will return its own detected intents in the `custom_intent` param. language : typing.Optional[str] The [BCP-47 language tag](https://tools.ietf.org/html/bcp47) that hints at the primary spoken language. Depending on the Model and API endpoint you choose only certain languages are available request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- HttpResponse[ReadV1Response] Successful text analysis """ _response = self._client_wrapper.httpx_client.request( "v1/read", base_url=self._client_wrapper.get_environment().base, method="POST", params={ "callback": callback, "callback_method": callback_method, "sentiment": sentiment, "summarize": summarize, "tag": tag, "topics": topics, "custom_topic": custom_topic, "custom_topic_mode": custom_topic_mode, "intents": intents, "custom_intent": custom_intent, "custom_intent_mode": custom_intent_mode, "language": language, }, json=convert_and_respect_annotation_metadata( object_=request, annotation=ReadV1RequestParams, direction="write" ), headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: _data = typing.cast( ReadV1Response, construct_type( type_=ReadV1Response, # type: ignore object_=_response.json(), ), ) return HttpResponse(response=_response, data=_data) if _response.status_code == 400: raise BadRequestError( headers=dict(_response.headers), body=typing.cast( typing.Any, construct_type( type_=typing.Any, # type: ignore object_=_response.json(), ), ), ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text) raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) class AsyncRawTextClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def analyze( self, *, request: ReadV1RequestParams, callback: typing.Optional[str] = None, callback_method: typing.Optional[TextAnalyzeRequestCallbackMethod] = None, sentiment: typing.Optional[bool] = None, summarize: typing.Optional[TextAnalyzeRequestSummarize] = None, tag: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, topics: typing.Optional[bool] = None, custom_topic: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, custom_topic_mode: typing.Optional[TextAnalyzeRequestCustomTopicMode] = None, intents: typing.Optional[bool] = None, custom_intent: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, custom_intent_mode: typing.Optional[TextAnalyzeRequestCustomIntentMode] = None, language: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> AsyncHttpResponse[ReadV1Response]: """ Analyze text content using Deepgrams text analysis API Parameters ---------- request : ReadV1RequestParams callback : typing.Optional[str] URL to which we'll make the callback request callback_method : typing.Optional[TextAnalyzeRequestCallbackMethod] HTTP method by which the callback request will be made sentiment : typing.Optional[bool] Recognizes the sentiment throughout a transcript or text summarize : typing.Optional[TextAnalyzeRequestSummarize] Summarize content. For Listen API, supports string version option. For Read API, accepts boolean only. tag : typing.Optional[typing.Union[str, typing.Sequence[str]]] Label your requests for the purpose of identification during usage reporting topics : typing.Optional[bool] Detect topics throughout a transcript or text custom_topic : typing.Optional[typing.Union[str, typing.Sequence[str]]] Custom topics you want the model to detect within your input audio or text if present Submit up to `100`. custom_topic_mode : typing.Optional[TextAnalyzeRequestCustomTopicMode] Sets how the model will interpret strings submitted to the `custom_topic` param. When `strict`, the model will only return topics submitted using the `custom_topic` param. When `extended`, the model will return its own detected topics in addition to those submitted using the `custom_topic` param intents : typing.Optional[bool] Recognizes speaker intent throughout a transcript or text custom_intent : typing.Optional[typing.Union[str, typing.Sequence[str]]] Custom intents you want the model to detect within your input audio if present custom_intent_mode : typing.Optional[TextAnalyzeRequestCustomIntentMode] Sets how the model will interpret intents submitted to the `custom_intent` param. When `strict`, the model will only return intents submitted using the `custom_intent` param. When `extended`, the model will return its own detected intents in the `custom_intent` param. language : typing.Optional[str] The [BCP-47 language tag](https://tools.ietf.org/html/bcp47) that hints at the primary spoken language. Depending on the Model and API endpoint you choose only certain languages are available request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AsyncHttpResponse[ReadV1Response] Successful text analysis """ _response = await self._client_wrapper.httpx_client.request( "v1/read", base_url=self._client_wrapper.get_environment().base, method="POST", params={ "callback": callback, "callback_method": callback_method, "sentiment": sentiment, "summarize": summarize, "tag": tag, "topics": topics, "custom_topic": custom_topic, "custom_topic_mode": custom_topic_mode, "intents": intents, "custom_intent": custom_intent, "custom_intent_mode": custom_intent_mode, "language": language, }, json=convert_and_respect_annotation_metadata( object_=request, annotation=ReadV1RequestParams, direction="write" ), headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: _data = typing.cast( ReadV1Response, construct_type( type_=ReadV1Response, # type: ignore object_=_response.json(), ), ) return AsyncHttpResponse(response=_response, data=_data) if _response.status_code == 400: raise BadRequestError( headers=dict(_response.headers), body=typing.cast( typing.Any, construct_type( type_=typing.Any, # type: ignore object_=_response.json(), ), ), ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text) raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)