Close httplib2 connections.
computeTokens(endpoint, body=None, x__xgafv=None)
Return a list of tokens based on the input text.
countTokens(endpoint, body=None, x__xgafv=None)
Perform a token counting.
generateContent(model, body=None, x__xgafv=None)
Generate content with multimodal inputs.
predict(endpoint, body=None, x__xgafv=None)
Perform an online prediction.
rawPredict(endpoint, body=None, x__xgafv=None)
Perform an online prediction with an arbitrary HTTP payload. The response includes the following HTTP headers: * `X-Vertex-AI-Endpoint-Id`: ID of the Endpoint that served this prediction. * `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's DeployedModel that served this prediction.
serverStreamingPredict(endpoint, body=None, x__xgafv=None)
Perform a server-side streaming online prediction request for Vertex LLM streaming.
streamGenerateContent(model, body=None, x__xgafv=None)
Generate content with multimodal inputs with streaming support.
streamRawPredict(endpoint, body=None, x__xgafv=None)
Perform a streaming online prediction with an arbitrary HTTP payload.
close()
Close httplib2 connections.
computeTokens(endpoint, body=None, x__xgafv=None)
Return a list of tokens based on the input text. Args: endpoint: string, Required. The name of the Endpoint requested to get lists of tokens and token ids. (required) body: object, The request body. The object takes the form of: { # Request message for ComputeTokens RPC call. "instances": [ # Required. The instances that are the input to token computing API call. Schema is identical to the prediction schema of the text model, even for the non-text models, like chat models, or Codey models. "", ], } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for ComputeTokens RPC call. "tokensInfo": [ # Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances. { # Tokens info with a list of tokens and the corresponding list of token ids. "tokenIds": [ # A list of token ids from the input. "A String", ], "tokens": [ # A list of tokens from the input. "A String", ], }, ], }
countTokens(endpoint, body=None, x__xgafv=None)
Perform a token counting. Args: endpoint: string, Required. The name of the Endpoint requested to perform token counting. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required) body: object, The request body. The object takes the form of: { # Request message for PredictionService.CountTokens. "contents": [ # Required. Input content. { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, ], "instances": [ # Required. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model. "", ], "model": "A String", # Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for PredictionService.CountTokens. "totalBillableCharacters": 42, # The total number of billable characters counted across all instances from the request. "totalTokens": 42, # The total number of tokens counted across all instances from the request. }
generateContent(model, body=None, x__xgafv=None)
Generate content with multimodal inputs. Args: model: string, Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` (required) body: object, The request body. The object takes the form of: { # Request message for [PredictionService.GenerateContent]. "contents": [ # Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request. { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, ], "generationConfig": { # Generation config. # Optional. Generation config. "candidateCount": 42, # Optional. Number of candidates to generate. "frequencyPenalty": 3.14, # Optional. Frequency penalties. "maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # Optional. Output response mimetype of the generated candidate text. Supported mimetype: - `text/plain`: (default) Text output. - `application/json`: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature. "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], "temperature": 3.14, # Optional. Controls the randomness of predictions. "topK": 3.14, # Optional. If specified, top-k sampling will be used. "topP": 3.14, # Optional. If specified, nucleus sampling will be used. }, "safetySettings": [ # Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates. { # Safety settings. "category": "A String", # Required. Harm category. "method": "A String", # Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score. "threshold": "A String", # Required. The harm block threshold. }, ], "systemInstruction": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, "tools": [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval). "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 64 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "description": "A String", # Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function. "name": "A String", # Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64. "parameters": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema). More fields may be added in the future as needed. # Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1 "default": "", # Optional. Default value of the data. "description": "A String", # Optional. The description of the data. "enum": [ # Optional. Possible values of the element of Type.STRING with enum format. For example we can define an Enum Direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} "A String", ], "example": "", # Optional. Example of the object. Will only populated when the object is the root. "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc "items": # Object with schema name: GoogleCloudAiplatformV1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY. "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY. "maxLength": "A String", # Optional. Maximum length of the Type.STRING "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT. "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY. "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT. "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER "nullable": True or False, # Optional. Indicates if the value may be null. "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression. "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT. "a_key": # Object with schema name: GoogleCloudAiplatformV1Schema }, "required": [ # Optional. Required properties of Type.OBJECT. "A String", ], "title": "A String", # Optional. The title of the Schema. "type": "A String", # Optional. The type of the data. }, }, ], "retrieval": { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation. "disableAttribution": True or False, # Optional. Disable using the result from this tool in detecting grounding attribution. This does not affect how the result is given to the model for generation. "vertexAiSearch": { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation # Set to use data source powered by Vertex AI Search. "datastore": "A String", # Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}` }, }, }, ], } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for [PredictionService.GenerateContent]. "candidates": [ # Output only. Generated candidates. { # A response candidate generated from the model. "citationMetadata": { # A collection of source attributions for a piece of content. # Output only. Source attribution of the generated content. "citations": [ # Output only. List of citations. { # Source attributions for content. "endIndex": 42, # Output only. End index into the content. "license": "A String", # Output only. License of the attribution. "publicationDate": { # Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp # Output only. Publication date of the attribution. "day": 42, # Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant. "month": 42, # Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day. "year": 42, # Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year. }, "startIndex": 42, # Output only. Start index into the content. "title": "A String", # Output only. Title of the attribution. "uri": "A String", # Output only. Url reference of the attribution. }, ], }, "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Output only. Content parts of the candidate. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, "finishMessage": "A String", # Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when `finish_reason` is set. "finishReason": "A String", # Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. "groundingMetadata": { # Metadata returned to client when grounding is enabled. # Output only. Metadata specifies sources used to ground generated content. "searchEntryPoint": { # Google search entry point. # Optional. Google search entry for the following-up web searches. "renderedContent": "A String", # Optional. Web content snippet that can be embedded in a web page or an app webview. "sdkBlob": "A String", # Optional. Base64 encoded JSON representing array of tuple. }, "webSearchQueries": [ # Optional. Web search queries for the following-up web search. "A String", ], }, "index": 42, # Output only. Index of the candidate. "safetyRatings": [ # Output only. List of ratings for the safety of a response candidate. There is at most one rating per category. { # Safety rating corresponding to the generated content. "blocked": True or False, # Output only. Indicates whether the content was filtered out because of this rating. "category": "A String", # Output only. Harm category. "probability": "A String", # Output only. Harm probability levels in the content. "probabilityScore": 3.14, # Output only. Harm probability score. "severity": "A String", # Output only. Harm severity levels in the content. "severityScore": 3.14, # Output only. Harm severity score. }, ], }, ], "promptFeedback": { # Content filter results for a prompt sent in the request. # Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations. "blockReason": "A String", # Output only. Blocked reason. "blockReasonMessage": "A String", # Output only. A readable block reason message. "safetyRatings": [ # Output only. Safety ratings. { # Safety rating corresponding to the generated content. "blocked": True or False, # Output only. Indicates whether the content was filtered out because of this rating. "category": "A String", # Output only. Harm category. "probability": "A String", # Output only. Harm probability levels in the content. "probabilityScore": 3.14, # Output only. Harm probability score. "severity": "A String", # Output only. Harm severity levels in the content. "severityScore": 3.14, # Output only. Harm severity score. }, ], }, "usageMetadata": { # Usage metadata about response(s). # Usage metadata about the response(s). "candidatesTokenCount": 42, # Number of tokens in the response(s). "promptTokenCount": 42, # Number of tokens in the request. "totalTokenCount": 42, }, }
predict(endpoint, body=None, x__xgafv=None)
Perform an online prediction. Args: endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required) body: object, The request body. The object takes the form of: { # Request message for PredictionService.Predict. "instances": [ # Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri. "", ], "parameters": "", # The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri. } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for PredictionService.Predict. "deployedModelId": "A String", # ID of the Endpoint's DeployedModel that served this prediction. "metadata": "", # Output only. Request-level metadata returned by the model. The metadata type will be dependent upon the model implementation. "model": "A String", # Output only. The resource name of the Model which is deployed as the DeployedModel that this prediction hits. "modelDisplayName": "A String", # Output only. The display name of the Model which is deployed as the DeployedModel that this prediction hits. "modelVersionId": "A String", # Output only. The version ID of the Model which is deployed as the DeployedModel that this prediction hits. "predictions": [ # The predictions that are the output of the predictions call. The schema of any single prediction may be specified via Endpoint's DeployedModels' Model's PredictSchemata's prediction_schema_uri. "", ], }
rawPredict(endpoint, body=None, x__xgafv=None)
Perform an online prediction with an arbitrary HTTP payload. The response includes the following HTTP headers: * `X-Vertex-AI-Endpoint-Id`: ID of the Endpoint that served this prediction. * `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's DeployedModel that served this prediction. Args: endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required) body: object, The request body. The object takes the form of: { # Request message for PredictionService.RawPredict. "httpBody": { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. # The prediction input. Supports HTTP headers and arbitrary data payload. A DeployedModel may have an upper limit on the number of instances it supports per request. When this limit it is exceeded for an AutoML model, the RawPredict method returns an error. When this limit is exceeded for a custom-trained model, the behavior varies depending on the model. You can specify the schema for each instance in the predict_schemata.instance_schema_uri field when you create a Model. This schema applies when you deploy the `Model` as a `DeployedModel` to an Endpoint and use the `RawPredict` method. "contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body. "data": "A String", # The HTTP request/response body as raw binary. "extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], }, } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. "contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body. "data": "A String", # The HTTP request/response body as raw binary. "extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], }
serverStreamingPredict(endpoint, body=None, x__xgafv=None)
Perform a server-side streaming online prediction request for Vertex LLM streaming. Args: endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required) body: object, The request body. The object takes the form of: { # Request message for PredictionService.StreamingPredict. The first message must contain endpoint field and optionally input. The subsequent messages must contain input. "inputs": [ # The prediction input. { # A tensor value type. "boolVal": [ # Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL True or False, ], "bytesVal": [ # STRING "A String", ], "doubleVal": [ # DOUBLE 3.14, ], "dtype": "A String", # The data type of tensor. "floatVal": [ # FLOAT 3.14, ], "int64Val": [ # INT64 "A String", ], "intVal": [ # INT_8 INT_16 INT_32 42, ], "listVal": [ # A list of tensor values. # Object with schema name: GoogleCloudAiplatformV1Tensor ], "shape": [ # Shape of the tensor. "A String", ], "stringVal": [ # STRING "A String", ], "structVal": { # A map of string to tensor. "a_key": # Object with schema name: GoogleCloudAiplatformV1Tensor }, "tensorVal": "A String", # Serialized raw tensor content. "uint64Val": [ # UINT64 "A String", ], "uintVal": [ # UINT8 UINT16 UINT32 42, ], }, ], "parameters": { # A tensor value type. # The parameters that govern the prediction. "boolVal": [ # Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL True or False, ], "bytesVal": [ # STRING "A String", ], "doubleVal": [ # DOUBLE 3.14, ], "dtype": "A String", # The data type of tensor. "floatVal": [ # FLOAT 3.14, ], "int64Val": [ # INT64 "A String", ], "intVal": [ # INT_8 INT_16 INT_32 42, ], "listVal": [ # A list of tensor values. # Object with schema name: GoogleCloudAiplatformV1Tensor ], "shape": [ # Shape of the tensor. "A String", ], "stringVal": [ # STRING "A String", ], "structVal": { # A map of string to tensor. "a_key": # Object with schema name: GoogleCloudAiplatformV1Tensor }, "tensorVal": "A String", # Serialized raw tensor content. "uint64Val": [ # UINT64 "A String", ], "uintVal": [ # UINT8 UINT16 UINT32 42, ], }, } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for PredictionService.StreamingPredict. "outputs": [ # The prediction output. { # A tensor value type. "boolVal": [ # Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL True or False, ], "bytesVal": [ # STRING "A String", ], "doubleVal": [ # DOUBLE 3.14, ], "dtype": "A String", # The data type of tensor. "floatVal": [ # FLOAT 3.14, ], "int64Val": [ # INT64 "A String", ], "intVal": [ # INT_8 INT_16 INT_32 42, ], "listVal": [ # A list of tensor values. # Object with schema name: GoogleCloudAiplatformV1Tensor ], "shape": [ # Shape of the tensor. "A String", ], "stringVal": [ # STRING "A String", ], "structVal": { # A map of string to tensor. "a_key": # Object with schema name: GoogleCloudAiplatformV1Tensor }, "tensorVal": "A String", # Serialized raw tensor content. "uint64Val": [ # UINT64 "A String", ], "uintVal": [ # UINT8 UINT16 UINT32 42, ], }, ], "parameters": { # A tensor value type. # The parameters that govern the prediction. "boolVal": [ # Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL True or False, ], "bytesVal": [ # STRING "A String", ], "doubleVal": [ # DOUBLE 3.14, ], "dtype": "A String", # The data type of tensor. "floatVal": [ # FLOAT 3.14, ], "int64Val": [ # INT64 "A String", ], "intVal": [ # INT_8 INT_16 INT_32 42, ], "listVal": [ # A list of tensor values. # Object with schema name: GoogleCloudAiplatformV1Tensor ], "shape": [ # Shape of the tensor. "A String", ], "stringVal": [ # STRING "A String", ], "structVal": { # A map of string to tensor. "a_key": # Object with schema name: GoogleCloudAiplatformV1Tensor }, "tensorVal": "A String", # Serialized raw tensor content. "uint64Val": [ # UINT64 "A String", ], "uintVal": [ # UINT8 UINT16 UINT32 42, ], }, }
streamGenerateContent(model, body=None, x__xgafv=None)
Generate content with multimodal inputs with streaming support. Args: model: string, Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` (required) body: object, The request body. The object takes the form of: { # Request message for [PredictionService.GenerateContent]. "contents": [ # Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request. { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, ], "generationConfig": { # Generation config. # Optional. Generation config. "candidateCount": 42, # Optional. Number of candidates to generate. "frequencyPenalty": 3.14, # Optional. Frequency penalties. "maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # Optional. Output response mimetype of the generated candidate text. Supported mimetype: - `text/plain`: (default) Text output. - `application/json`: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature. "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], "temperature": 3.14, # Optional. Controls the randomness of predictions. "topK": 3.14, # Optional. If specified, top-k sampling will be used. "topP": 3.14, # Optional. If specified, nucleus sampling will be used. }, "safetySettings": [ # Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates. { # Safety settings. "category": "A String", # Required. Harm category. "method": "A String", # Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score. "threshold": "A String", # Required. The harm block threshold. }, ], "systemInstruction": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, "tools": [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval). "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 64 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "description": "A String", # Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function. "name": "A String", # Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64. "parameters": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema). More fields may be added in the future as needed. # Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1 "default": "", # Optional. Default value of the data. "description": "A String", # Optional. The description of the data. "enum": [ # Optional. Possible values of the element of Type.STRING with enum format. For example we can define an Enum Direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} "A String", ], "example": "", # Optional. Example of the object. Will only populated when the object is the root. "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc "items": # Object with schema name: GoogleCloudAiplatformV1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY. "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY. "maxLength": "A String", # Optional. Maximum length of the Type.STRING "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT. "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY. "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT. "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER "nullable": True or False, # Optional. Indicates if the value may be null. "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression. "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT. "a_key": # Object with schema name: GoogleCloudAiplatformV1Schema }, "required": [ # Optional. Required properties of Type.OBJECT. "A String", ], "title": "A String", # Optional. The title of the Schema. "type": "A String", # Optional. The type of the data. }, }, ], "retrieval": { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation. "disableAttribution": True or False, # Optional. Disable using the result from this tool in detecting grounding attribution. This does not affect how the result is given to the model for generation. "vertexAiSearch": { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation # Set to use data source powered by Vertex AI Search. "datastore": "A String", # Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}` }, }, }, ], } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for [PredictionService.GenerateContent]. "candidates": [ # Output only. Generated candidates. { # A response candidate generated from the model. "citationMetadata": { # A collection of source attributions for a piece of content. # Output only. Source attribution of the generated content. "citations": [ # Output only. List of citations. { # Source attributions for content. "endIndex": 42, # Output only. End index into the content. "license": "A String", # Output only. License of the attribution. "publicationDate": { # Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp # Output only. Publication date of the attribution. "day": 42, # Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant. "month": 42, # Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day. "year": 42, # Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year. }, "startIndex": 42, # Output only. Start index into the content. "title": "A String", # Output only. Title of the attribution. "uri": "A String", # Output only. Url reference of the attribution. }, ], }, "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Output only. Content parts of the candidate. "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes. "fileData": { # URI based data. # Optional. URI based data. "fileUri": "A String", # Required. URI. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. "a_key": "", # Properties of the object. }, "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. }, "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. "response": { # Required. The function response in JSON object format. "a_key": "", # Properties of the object. }, }, "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. "data": "A String", # Required. Raw bytes. "mimeType": "A String", # Required. The IANA standard MIME type of the source data. }, "text": "A String", # Optional. Text part (can be code). "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. "endOffset": "A String", # Optional. The end offset of the video. "startOffset": "A String", # Optional. The start offset of the video. }, }, ], "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. }, "finishMessage": "A String", # Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when `finish_reason` is set. "finishReason": "A String", # Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. "groundingMetadata": { # Metadata returned to client when grounding is enabled. # Output only. Metadata specifies sources used to ground generated content. "searchEntryPoint": { # Google search entry point. # Optional. Google search entry for the following-up web searches. "renderedContent": "A String", # Optional. Web content snippet that can be embedded in a web page or an app webview. "sdkBlob": "A String", # Optional. Base64 encoded JSON representing array of tuple. }, "webSearchQueries": [ # Optional. Web search queries for the following-up web search. "A String", ], }, "index": 42, # Output only. Index of the candidate. "safetyRatings": [ # Output only. List of ratings for the safety of a response candidate. There is at most one rating per category. { # Safety rating corresponding to the generated content. "blocked": True or False, # Output only. Indicates whether the content was filtered out because of this rating. "category": "A String", # Output only. Harm category. "probability": "A String", # Output only. Harm probability levels in the content. "probabilityScore": 3.14, # Output only. Harm probability score. "severity": "A String", # Output only. Harm severity levels in the content. "severityScore": 3.14, # Output only. Harm severity score. }, ], }, ], "promptFeedback": { # Content filter results for a prompt sent in the request. # Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations. "blockReason": "A String", # Output only. Blocked reason. "blockReasonMessage": "A String", # Output only. A readable block reason message. "safetyRatings": [ # Output only. Safety ratings. { # Safety rating corresponding to the generated content. "blocked": True or False, # Output only. Indicates whether the content was filtered out because of this rating. "category": "A String", # Output only. Harm category. "probability": "A String", # Output only. Harm probability levels in the content. "probabilityScore": 3.14, # Output only. Harm probability score. "severity": "A String", # Output only. Harm severity levels in the content. "severityScore": 3.14, # Output only. Harm severity score. }, ], }, "usageMetadata": { # Usage metadata about response(s). # Usage metadata about the response(s). "candidatesTokenCount": 42, # Number of tokens in the response(s). "promptTokenCount": 42, # Number of tokens in the request. "totalTokenCount": 42, }, }
streamRawPredict(endpoint, body=None, x__xgafv=None)
Perform a streaming online prediction with an arbitrary HTTP payload. Args: endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required) body: object, The request body. The object takes the form of: { # Request message for PredictionService.StreamRawPredict. "httpBody": { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. # The prediction input. Supports HTTP headers and arbitrary data payload. "contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body. "data": "A String", # The HTTP request/response body as raw binary. "extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], }, } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. "contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body. "data": "A String", # The HTTP request/response body as raw binary. "extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], }