Python
API Overview
This section serves as a comprehensive table of contents for the aiXplain Python SDK API Reference. It provides an organized overview of all modules, classes, and functions available in the SDK.
Modules
aixplain: aiXplain SDK Library.aixplain.cli_groupsaixplain.decoratorsaixplain.decorators.api_key_checkeraixplain.enumsaixplain.enums.asset_statusaixplain.enums.data_splitaixplain.enums.data_subtypeaixplain.enums.data_typeaixplain.enums.error_handleraixplain.enums.file_typeaixplain.enums.functionaixplain.enums.languageaixplain.enums.licenseaixplain.enums.onboard_statusaixplain.enums.ownership_typeaixplain.enums.privacyaixplain.enums.response_statusaixplain.enums.sort_byaixplain.enums.sort_orderaixplain.enums.storage_typeaixplain.enums.supplieraixplain.factories: aiXplain SDK Library.aixplain.factories.agent_factoryaixplain.factories.agent_factory.utilsaixplain.factories.api_key_factoryaixplain.factories.asset_factoryaixplain.factories.benchmark_factoryaixplain.factories.corpus_factoryaixplain.factories.data_factoryaixplain.factories.dataset_factoryaixplain.factories.file_factoryaixplain.factories.finetune_factoryaixplain.factories.finetune_factory.prompt_validatoraixplain.factories.metric_factoryaixplain.factories.model_factoryaixplain.factories.model_factory.utilsaixplain.factories.pipeline_factoryaixplain.factories.pipeline_factory.utilsaixplain.factories.script_factoryaixplain.factories.team_agent_factoryaixplain.factories.team_agent_factory.utilsaixplain.factories.wallet_factoryaixplain.modules: aiXplain SDK Library.aixplain.modules.agentaixplain.modules.agent.output_formataixplain.modules.agent.toolaixplain.modules.agent.tool.model_toolaixplain.modules.agent.tool.pipeline_toolaixplain.modules.agent.utilsaixplain.modules.api_keyaixplain.modules.assetaixplain.modules.benchmarkaixplain.modules.benchmark_jobaixplain.modules.content_intervalaixplain.modules.corpusaixplain.modules.dataaixplain.modules.datasetaixplain.modules.fileaixplain.modules.finetuneaixplain.modules.finetune.costaixplain.modules.finetune.hyperparametersaixplain.modules.finetune.statusaixplain.modules.metadataaixplain.modules.metricaixplain.modules.model:aixplain.modules.model.llm_modelaixplain.modules.model.responseaixplain.modules.model.utility_model: Copyright 2024 The aiXplain SDK authorsaixplain.modules.model.utilsaixplain.modules.pipelineaixplain.modules.pipeline.assetaixplain.modules.pipeline.defaultaixplain.modules.pipeline.designeraixplain.modules.pipeline.designer.baseaixplain.modules.pipeline.designer.enumsaixplain.modules.pipeline.designer.mixinsaixplain.modules.pipeline.designer.nodesaixplain.modules.pipeline.designer.pipelineaixplain.modules.pipeline.designer.utilsaixplain.modules.pipeline.generateaixplain.modules.pipeline.pipelineaixplain.modules.team_agentaixplain.modules.walletaixplain.processesaixplain.processes.data_onboardingaixplain.processes.data_onboarding.onboard_functionsaixplain.processes.data_onboarding.process_interval_filesaixplain.processes.data_onboarding.process_media_filesaixplain.processes.data_onboarding.process_text_filesaixplain.utils: aiXplain SDK Library.aixplain.utils.cache_utilsaixplain.utils.config: Copyright 2022 The aiXplain SDK authorsaixplain.utils.convert_datatype_utils: Copyright 2022 The aiXplain SDK authorsaixplain.utils.file_utils: Copyright 2022 The aiXplain SDK authorsaixplain.utils.request_utilsaixplain.utils.validation_utils
Classes
asset_status.AssetStatusdata_split.DataSplitdata_subtype.DataSubtypedata_type.DataTypeerror_handler.ErrorHandler: Enumeration class defining different error handler strategies.file_type.FileTypefunction.Functionlanguage.Languagelicense.Licenseonboard_status.OnboardStatusownership_type.OwnershipTypeprivacy.Privacyresponse_status.ResponseStatussort_by.SortBysort_order.SortOrderstorage_type.StorageTypesupplier.Supplieragent_factory.AgentFactoryapi_key_factory.APIKeyFactoryasset_factory.AssetFactorybenchmark_factory.BenchmarkFactory: A static class for creating and managing the Benchmarking experience.corpus_factory.CorpusFactorydata_factory.DataFactory: A static class for creating and exploring Dataset Objects.dataset_factory.DatasetFactory: A static class for creating and exploring Dataset Objects.file_factory.FileFactoryfinetune_factory.FinetuneFactory: A static class for creating and managing the FineTune experience.metric_factory.MetricFactory: A static class for creating and exploring Metric Objects.model_factory.ModelFactory: A static class for creating and exploring Model Objects.pipeline_factory.PipelineFactory: A static class for creating and exploring Pipeline Objects.script_factory.ScriptFactoryteam_agent_factory.TeamAgentFactorywallet_factory.WalletFactoryagent.Agent: Advanced AI system capable of performing tasks by leveraging specialized software tools and resources from aiXplain marketplace.output_format.OutputFormattool.Tool: Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.model_tool.ModelTool: Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.pipeline_tool.PipelineTool: Specialized software or resource designed to assist the AI in executing specific tasks or functions based on user commands.api_key.APIKeyapi_key.APIKeyLimitsapi_key.APIKeyUsageLimitasset.Assetbenchmark.Benchmark: Benchmark is a powerful tool for benchmarking machine learning models and evaluating their performance on specific tasks.benchmark_job.BenchmarkJob: Benchmark Job Represents a single run of an already created Benchmark.content_interval.AudioContentInterval: AudioContentInterval(content: str, content_id: int, start_time: float, end_time: float)content_interval.ContentInterval: ContentInterval(content: str, content_id: int)content_interval.ImageContentInterval: ImageContentInterval(content: str, content_id: int, x: Union[float, List[float]], y: Union[float, List[float]], width: Optional[float] = None, height: Optional[float] = None, rotation: Optional[float] = None)content_interval.TextContentInterval: TextContentInterval(content: str, content_id: int, start: Union[int, Tuple[int, int]], end: Union[int, Tuple[int, int]])content_interval.VideoContentInterval: VideoContentInterval(content: str, content_id: int, start_time: float, end_time: float, x: Union[float, List[float], NoneType] = None, y: Union[float, List[float], NoneType] = None, width: Optional[float] = None, height: Optional[float] = None, rotation: Optional[float] = None)corpus.Corpusdata.Datadataset.Dataset: Dataset is a collection of data intended to be used for a specific function.file.Filefinetune.Finetune: FineTune is a powerful tool for fine-tuning machine learning models and using your own datasets for specific tasks.cost.FinetuneCosthyperparameters.Hyperparameters: Hyperparameters(epochs: int = 1, train_batch_size: int = 4, eval_batch_size: int = 4, learning_rate: float = 1e-05, max_seq_length: int = 4096, warmup_ratio: float = 0.0, warmup_steps: int = 0, lr_scheduler_type: aixplain.modules.finetune.hyperparameters.SchedulerType = <SchedulerType.LINEAR: 'linear'>)hyperparameters.SchedulerTypestatus.FinetuneStatus: FinetuneStatus(status: 'AssetStatus', model_status: 'AssetStatus', epoch: Optional[float] = None, training_loss: Optional[float] = None, validation_loss: Optional[float] = None)metadata.MetaDatametric.Metric: Represents a metric to be computed on one or more peices of data. It is usually linked to a machine learning task.model.Model: This is ready-to-use AI model. This model can be run in both synchronous and asynchronous manner.llm_model.LLM: Ready-to-use LLM model. This model can be run in both synchronous and asynchronous manner.response.ModelResponse: ModelResponse class to store the response of the model run.utility_model.UtilityModel: Ready-to-use Utility Model.utility_model.UtilityModelInput: UtilityModelInput(name: str, description: str, type: aixplain.enums.data_type.DataType = <DataType.TEXT: 'text'>)asset.Pipeline: Representing a custom pipeline that was created on the aiXplain Platformdefault.DefaultPipelinebase.InputParambase.Inputsbase.Link: Link class, this class will be used to link the output of the node to thebase.Node: Node class is the base class for all the nodes in the pipeline. This classbase.OutputParambase.Outputsbase.Param: Param class, this class will be used to create the parameters of the node.base.ParamProxybase.Serializableenums.AssetTypeenums.FunctionTypeenums.NodeTypeenums.Operationenums.ParamTypeenums.RouteTypemixins.LinkableMixin: Linkable mixin class, this class will be used to link the output of themixins.OutputableMixin: Outputable mixin class, this class will be used to link the output of themixins.RoutableMixin: Routable mixin class, this class will be used to route the input data tonodes.AssetNode: Asset node class, this node will be used to fetch the asset from thenodes.BareAssetnodes.BareAssetInputsnodes.BareAssetOutputsnodes.BareMetricnodes.BareReconstructor: Reconstructor node class, this node will be used to reconstruct thenodes.BareSegmentor: Segmentor node class, this node will be used to segment the input datanodes.BaseMetricnodes.BaseReconstructor: Reconstructor node class, this node will be used to reconstruct thenodes.BaseSegmentor: Segmentor node class, this node will be used to segment the input datanodes.Decision: Decision node class, this node will be used to make decisions based onnodes.DecisionInputsnodes.DecisionOutputsnodes.Input: Input node class, this node will be used to input the data to thenodes.InputInputsnodes.InputOutputsnodes.MetricInputsnodes.MetricOutputsnodes.Output: Output node class, this node will be used to output the result of thenodes.OutputInputsnodes.OutputOutputsnodes.ReconstructorInputsnodes.ReconstructorOutputsnodes.Route: Route class, this class will be used to route the input data to differentnodes.Router: Router node class, this node will be used to route the input data tonodes.RouterInputsnodes.RouterOutputsnodes.Script: Script node class, this node will be used to run a script on the inputnodes.SegmentorInputsnodes.SegmentorOutputspipeline.DesignerPipelinepipeline.AsrAgeClassification: The ASR Age Classification function is designed to analyze audio recordings ofpipeline.AsrAgeClassificationInputspipeline.AsrAgeClassificationOutputspipeline.AsrGenderClassification: The ASR Gender Classification function analyzes audio recordings to determinepipeline.AsrGenderClassificationInputspipeline.AsrGenderClassificationOutputspipeline.AsrQualityEstimation: ASR Quality Estimation is a process that evaluates the accuracy and reliabilitypipeline.AsrQualityEstimationInputspipeline.AsrQualityEstimationOutputspipeline.AudioEmotionDetection: Audio Emotion Detection is a technology that analyzes vocal characteristics andpipeline.AudioEmotionDetectionInputspipeline.AudioEmotionDetectionOutputspipeline.AudioForcedAlignment: Audio Forced Alignment is a process that synchronizes a given audio recordingpipeline.AudioForcedAlignmentInputspipeline.AudioForcedAlignmentOutputspipeline.AudioGenerationMetric: The Audio Generation Metric is a quantitative measure used to evaluate thepipeline.AudioGenerationMetricInputspipeline.AudioGenerationMetricOutputspipeline.AudioIntentDetection: Audio Intent Detection is a process that involves analyzing audio signals topipeline.AudioIntentDetectionInputspipeline.AudioIntentDetectionOutputspipeline.AudioLanguageIdentification: Audio Language Identification is a process that involves analyzing an audiopipeline.AudioLanguageIdentificationInputspipeline.AudioLanguageIdentificationOutputspipeline.AudioReconstruction: Audio Reconstruction is the process of restoring or recreating audio signalspipeline.AudioReconstructionInputspipeline.AudioReconstructionOutputspipeline.AudioTranscriptAnalysis: Audio Transcript Analysis is a process that involves converting spoken languagepipeline.AudioTranscriptAnalysisInputspipeline.AudioTranscriptAnalysisOutputspipeline.AudioTranscriptImprovement: Audio Transcript Improvement is a function that enhances the accuracy andpipeline.AudioTranscriptImprovementInputspipeline.AudioTranscriptImprovementOutputspipeline.BaseModel: The Base-Model function serves as a foundational framework designed to providepipeline.BaseModelInputspipeline.BaseModelOutputspipeline.BenchmarkScoringAsr: Benchmark Scoring ASR is a function that evaluates and compares the performancepipeline.BenchmarkScoringAsrInputspipeline.BenchmarkScoringAsrOutputspipeline.BenchmarkScoringMt: Benchmark Scoring MT is a function designed to evaluate and score machinepipeline.BenchmarkScoringMtInputspipeline.BenchmarkScoringMtOutputspipeline.ClassificationMetric: A Classification Metric is a quantitative measure used to evaluate the qualitypipeline.ClassificationMetricInputspipeline.ClassificationMetricOutputspipeline.DepthEstimation: Depth estimation is a computational process that determines the distance ofpipeline.DepthEstimationInputspipeline.DepthEstimationOutputspipeline.Diacritization: Diacritization is the process of adding diacritical marks to letters in a textpipeline.DiacritizationInputspipeline.DiacritizationOutputspipeline.DialectDetection: Dialect Detection is a function that identifies and classifies the specificpipeline.DialectDetectionInputspipeline.DialectDetectionOutputspipeline.DocumentImageParsing: Document Image Parsing is the process of analyzing and converting scanned orpipeline.DocumentImageParsingInputspipeline.DocumentImageParsingOutputspipeline.DocumentInformationExtraction: Document Information Extraction is the process of automatically identifying,pipeline.DocumentInformationExtractionInputspipeline.DocumentInformationExtractionOutputspipeline.EmotionDetection: Emotion Detection is a process that involves analyzing text to identify andpipeline.EmotionDetectionInputspipeline.EmotionDetectionOutputspipeline.EntityLinking: Entity Linking is the process of identifying and connecting mentions ofpipeline.EntityLinkingInputspipeline.EntityLinkingOutputspipeline.ExtractAudioFromVideo: The "Extract Audio From Video" function allows users to separate and save thepipeline.ExtractAudioFromVideoInputspipeline.ExtractAudioFromVideoOutputspipeline.FactChecking: Fact Checking is the process of verifying the accuracy and truthfulness ofpipeline.FactCheckingInputspipeline.FactCheckingOutputspipeline.FillTextMask: The "Fill Text Mask" function takes a text input with masked or placeholderpipeline.FillTextMaskInputspipeline.FillTextMaskOutputspipeline.ImageCaptioning: Image Captioning is a process that involves generating a textual description ofpipeline.ImageCaptioningInputspipeline.ImageCaptioningOutputspipeline.ImageColorization: Image colorization is a process that involves adding color to grayscale images,pipeline.ImageColorizationInputspipeline.ImageColorizationOutputspipeline.ImageCompression: Image compression is a process that reduces the file size of an image bypipeline.ImageCompressionInputspipeline.ImageCompressionOutputspipeline.ImageContentModeration: Image Content Moderation is a process that involves analyzing and filteringpipeline.ImageContentModerationInputspipeline.ImageContentModerationOutputspipeline.ImageEmbedding: Image Embedding is a process that transforms an image into a fixed-dimensionalpipeline.ImageEmbeddingInputspipeline.ImageEmbeddingOutputspipeline.ImageImpainting: Image inpainting is a process that involves filling in missing or damaged partspipeline.ImageImpaintingInputspipeline.ImageImpaintingOutputspipeline.ImageLabelDetection: Image Label Detection is a function that automatically identifies and assignspipeline.ImageLabelDetectionInputspipeline.ImageLabelDetectionOutputspipeline.ImageManipulation: Image Manipulation refers to the process of altering or enhancing digitalpipeline.ImageManipulationInputspipeline.ImageManipulationOutputspipeline.ImageToVideoGeneration: The Image To Video Generation function transforms a series of static imagespipeline.ImageToVideoGenerationInputspipeline.ImageToVideoGenerationOutputspipeline.InstanceSegmentation: Instance segmentation is a computer vision task that involves detecting andpipeline.InstanceSegmentationInputspipeline.InstanceSegmentationOutputspipeline.IntentClassification: Intent Classification is a natural language processing task that involvespipeline.IntentClassificationInputspipeline.IntentClassificationOutputspipeline.InverseTextNormalization: Inverse Text Normalization is the process of converting spoken or writtenpipeline.InverseTextNormalizationInputspipeline.InverseTextNormalizationOutputspipeline.KeywordSpotting: Keyword Spotting is a function that enables the detection and identification ofpipeline.KeywordSpottingInputspipeline.KeywordSpottingOutputspipeline.LanguageIdentification: Language Identification is the process of automatically determining thepipeline.LanguageIdentificationAudio: The Language Identification Audio function analyzes audio input to determinepipeline.LanguageIdentificationAudioInputspipeline.LanguageIdentificationAudioOutputspipeline.LanguageIdentificationInputspipeline.LanguageIdentificationOutputspipeline.Loglikelihood: The Log Likelihood function measures the probability of observing the givenpipeline.LoglikelihoodInputspipeline.LoglikelihoodOutputspipeline.MetricAggregation: Metric Aggregation is a function that computes and summarizes numerical data bypipeline.MetricAggregationInputspipeline.MetricAggregationOutputspipeline.MultiClassImageClassification: Multi Class Image Classification is a machine learning task where an algorithmpipeline.MultiClassImageClassificationInputspipeline.MultiClassImageClassificationOutputspipeline.MultiClassTextClassification: Multi Class Text Classification is a natural language processing task thatpipeline.MultiClassTextClassificationInputspipeline.MultiClassTextClassificationOutputspipeline.MultiLabelTextClassification: Multi Label Text Classification is a natural language processing task where apipeline.MultiLabelTextClassificationInputspipeline.MultiLabelTextClassificationOutputspipeline.MultilingualSpeechRecognition: Multilingual Speech Recognition is a technology that enables the automaticpipeline.MultilingualSpeechRecognitionInputspipeline.MultilingualSpeechRecognitionOutputspipeline.NamedEntityRecognition: Named Entity Recognition (NER) is a natural language processing task thatpipeline.NamedEntityRecognitionInputspipeline.NamedEntityRecognitionOutputspipeline.NoiseRemoval: Noise Removal is a process that involves identifying and eliminating unwantedpipeline.NoiseRemovalInputspipeline.NoiseRemovalOutputspipeline.ObjectDetection: Object Detection is a computer vision technology that identifies and locatespipeline.ObjectDetectionInputspipeline.ObjectDetectionOutputspipeline.Ocr: OCR, or Optical Character Recognition, is a technology that converts differentpipeline.OcrInputspipeline.OcrOutputspipeline.OffensiveLanguageIdentification: Offensive Language Identification is a function that analyzes text to detectpipeline.OffensiveLanguageIdentificationInputspipeline.OffensiveLanguageIdentificationOutputspipeline.OtherMultipurpose: The "Other (Multipurpose)" function serves as a versatile category designed topipeline.OtherMultipurposeInputspipeline.OtherMultipurposeOutputspipeline.PartOfSpeechTagging: Part of Speech Tagging is a natural language processing task that involvespipeline.PartOfSpeechTaggingInputspipeline.PartOfSpeechTaggingOutputspipeline.Pipelinepipeline.ReferencelessAudioGenerationMetric: The Referenceless Audio Generation Metric is a tool designed to evaluate thepipeline.ReferencelessAudioGenerationMetricInputspipeline.ReferencelessAudioGenerationMetricOutputspipeline.ReferencelessTextGenerationMetric: The Referenceless Text Generation Metric is a method for evaluating the qualitypipeline.ReferencelessTextGenerationMetricDefault: The Referenceless Text Generation Metric Default is a function designed topipeline.ReferencelessTextGenerationMetricDefaultInputspipeline.ReferencelessTextGenerationMetricDefaultOutputspipeline.ReferencelessTextGenerationMetricInputspipeline.ReferencelessTextGenerationMetricOutputspipeline.ScriptExecution: Script Execution refers to the process of running a set of programmedpipeline.ScriptExecutionInputspipeline.ScriptExecutionOutputspipeline.Search: The "Search" function allows users to input keywords or phrases to quicklypipeline.SearchInputspipeline.SearchOutputspipeline.SemanticSegmentation: Semantic segmentation is a computer vision process that involves classifyingpipeline.SemanticSegmentationInputspipeline.SemanticSegmentationOutputspipeline.SentimentAnalysis: Sentiment Analysis is a natural language processing technique used to determinepipeline.SentimentAnalysisInputspipeline.SentimentAnalysisOutputspipeline.SpeakerDiarizationAudio: Speaker Diarization Audio is a process that involves segmenting an audiopipeline.SpeakerDiarizationAudioInputspipeline.SpeakerDiarizationAudioOutputspipeline.SpeakerDiarizationVideo: The Speaker Diarization Video function identifies and segments differentpipeline.SpeakerDiarizationVideoInputspipeline.SpeakerDiarizationVideoOutputspipeline.SpeechClassification: Speech Classification is a process that involves analyzing and categorizingpipeline.SpeechClassificationInputspipeline.SpeechClassificationOutputspipeline.SpeechEmbedding: Speech Embedding is a process that transforms spoken language into a fixed-pipeline.SpeechEmbeddingInputspipeline.SpeechEmbeddingOutputspipeline.SpeechNonSpeechClassification: The function "Speech or Non-Speech Classification" is designed to analyze audiopipeline.SpeechNonSpeechClassificationInputspipeline.SpeechNonSpeechClassificationOutputspipeline.SpeechRecognition: Speech recognition is a technology that enables a computer or device topipeline.SpeechRecognitionInputspipeline.SpeechRecognitionOutputspipeline.SpeechSynthesis: Speech synthesis is the artificial production of human speech, typicallypipeline.SpeechSynthesisInputspipeline.SpeechSynthesisOutputspipeline.SpeechTranslation: Speech Translation is a technology that converts spoken language in real-timepipeline.SpeechTranslationInputspipeline.SpeechTranslationOutputspipeline.SplitOnLinebreak: The "Split On Linebreak" function divides a given string into a list ofpipeline.SplitOnLinebreakInputspipeline.SplitOnLinebreakOutputspipeline.SplitOnSilence: The "Split On Silence" function divides an audio recording into separatepipeline.SplitOnSilenceInputspipeline.SplitOnSilenceOutputspipeline.StyleTransfer: Style Transfer is a technique in artificial intelligence that applies thepipeline.StyleTransferInputspipeline.StyleTransferOutputspipeline.Subtitling: Subtitling is the process of displaying written text on a screen to representpipeline.SubtitlingInputspipeline.SubtitlingOutputspipeline.SubtitlingTranslation: Subtitling Translation is the process of converting spoken dialogue from onepipeline.SubtitlingTranslationInputspipeline.SubtitlingTranslationOutputspipeline.TextClassification: Text Classification is a natural language processing task that involvespipeline.TextClassificationInputspipeline.TextClassificationOutputspipeline.TextContentModeration: Text Content Moderation is the process of reviewing, filtering, and managingpipeline.TextContentModerationInputspipeline.TextContentModerationOutputspipeline.TextDenormalization: Text Denormalization is the process of converting abbreviated, contracted, orpipeline.TextDenormalizationInputspipeline.TextDenormalizationOutputspipeline.TextEmbedding: Text embedding is a process that converts text into numerical vectors,pipeline.TextEmbeddingInputspipeline.TextEmbeddingOutputspipeline.TextGeneration: Text Generation is a process in which artificial intelligence models, such aspipeline.TextGenerationInputspipeline.TextGenerationMetric: A Text Generation Metric is a quantitative measure used to evaluate the qualitypipeline.TextGenerationMetricDefault: The "Text Generation Metric Default" function provides a standard set ofpipeline.TextGenerationMetricDefaultInputspipeline.TextGenerationMetricDefaultOutputspipeline.TextGenerationMetricInputspipeline.TextGenerationMetricOutputspipeline.TextGenerationOutputspipeline.TextNormalization: Text normalization is the process of transforming text into a standard,pipeline.TextNormalizationInputspipeline.TextNormalizationOutputspipeline.TextReconstruction: Text Reconstruction is a process that involves piecing together fragmented orpipeline.TextReconstructionInputspipeline.TextReconstructionOutputspipeline.TextSegmenation: Text Segmentation is the process of dividing a continuous text into meaningfulpipeline.TextSegmenationInputspipeline.TextSegmenationOutputspipeline.TextSpamDetection: Text Spam Detection is a process that involves analyzing and identifyingpipeline.TextSpamDetectionInputspipeline.TextSpamDetectionOutputspipeline.TextSummarization: Text summarization is the process of condensing a large body of text into apipeline.TextSummarizationInputspipeline.TextSummarizationOutputspipeline.TextToAudio: The Text to Audio function converts written text into spoken words, allowingpipeline.TextToAudioInputspipeline.TextToAudioOutputspipeline.TextToImageGeneration: Text To Image Generation is a process where a system creates visual imagespipeline.TextToImageGenerationInputspipeline.TextToImageGenerationOutputspipeline.TextToVideoGeneration: Text To Video Generation is a process that converts written descriptions orpipeline.TextToVideoGenerationInputspipeline.TextToVideoGenerationOutputspipeline.TopicClassification: Topic Classification is a natural language processing function that categorizespipeline.TopicClassificationInputspipeline.TopicClassificationOutputspipeline.Translation: Translation is the process of converting text from one language into anpipeline.TranslationInputspipeline.TranslationOutputspipeline.VideoContentModeration: Video Content Moderation is the process of reviewing, analyzing, and filteringpipeline.VideoContentModerationInputspipeline.VideoContentModerationOutputspipeline.VideoEmbedding: Video Embedding is a process that transforms video content into a fixed-pipeline.VideoEmbeddingInputspipeline.VideoEmbeddingOutputspipeline.VideoForcedAlignment: Video Forced Alignment is a process that synchronizes video footage withpipeline.VideoForcedAlignmentInputspipeline.VideoForcedAlignmentOutputspipeline.VideoGeneration: Video Generation is the process of creating video content through automated orpipeline.VideoGenerationInputspipeline.VideoGenerationOutputspipeline.VideoLabelDetection: Video Label Detection is a function that automatically identifies and tagspipeline.VideoLabelDetectionInputspipeline.VideoLabelDetectionOutputspipeline.VideoUnderstanding: Video Understanding is the process of analyzing and interpreting video contentpipeline.VideoUnderstandingInputspipeline.VideoUnderstandingOutputspipeline.VisemeGeneration: Viseme Generation is the process of creating visual representations ofpipeline.VisemeGenerationInputspipeline.VisemeGenerationOutputspipeline.VisualQuestionAnswering: Visual Question Answering (VQA) is a task in artificial intelligence thatpipeline.VisualQuestionAnsweringInputspipeline.VisualQuestionAnsweringOutputspipeline.VoiceActivityDetection: Voice Activity Detection (VAD) is a technology that identifies the presence orpipeline.VoiceActivityDetectionInputspipeline.VoiceActivityDetectionOutputspipeline.VoiceCloning: Voice cloning is a technology that uses artificial intelligence to create apipeline.VoiceCloningInputspipeline.VoiceCloningOutputsteam_agent.TeamAgent: Advanced AI system capable of using multiple agents to perform a variety of tasks.wallet.Wallet
Functions
cli_groups.run_cliapi_key_checker.check_api_keyfunction.load_functionslanguage.load_languageslicense.load_licensessupplier.clean_namesupplier.load_suppliersutils.build_agent: Instantiate a new agent in the platform.prompt_validator.validate_promptutils.create_model_from_response: Converts response Json to 'Model' objectutils.get_assets_from_pageutils.build_from_response: Converts response Json to 'Pipeline' objectutils.build_team_agent: Instantiate a new team agent in the platform.utils.process_variablesutils.build_payloadutils.call_run_endpointutils.parse_codeutils.find_prompt_params: This method will find the prompt parameters in the prompt string.generate.fetch_functions: Fetch functions from the backendgenerate.populate_data_types: Populate the data typesgenerate.populate_specs: Populate the function class specsonboard_functions.build_payload_corpus: Create corpus payload to call coreengine on the onboard processonboard_functions.build_payload_data: Create data payload to call coreengine on Corpus/Dataset onboardonboard_functions.build_payload_dataset: Generate onboard payload to coreengineonboard_functions.create_data_asset: Service to call onboard process in coreengineonboard_functions.get_paths: Recursively access all local paths. Check if file extensions are supported.onboard_functions.is_data: Check whether reference data existsonboard_functions.process_data_files: Process a list of local files, compress and upload them to pre-signed URLs in S3onboard_functions.split_data: Split the data according to some split labels and rateprocess_interval_files.compress_folderprocess_interval_files.process_interval: Process text filesprocess_interval_files.run: Process a list of local interval files, compress and upload them to pre-signed URLs in S3process_interval_files.validate_format: Validate the interval formatprocess_media_files.compress_folderprocess_media_files.run: Process a list of local media files, compress and upload them to pre-signed URLs in S3process_text_files.process_text: Process text filesprocess_text_files.run: Process a list of local textual files, compress and upload them to pre-signed URLs in S3cache_utils.load_from_cachecache_utils.save_to_cacheconvert_datatype_utils.dict_to_metadata: Convert all the Dicts to MetaDatafile_utils.download_datafile_utils.s3_to_csv: Convert s3 url to a csv file and download the file indownload_pathfile_utils.save_file: Download and save file from given URLfile_utils.upload_data: Upload files to S3 with pre-signed URLsvalidation_utils.dataset_onboarding_validation: Dataset Onboard Validation