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Copy file name to clipboardExpand all lines: docs/dyn/aiplatform_v1beta1.projects.locations.html
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@@ -431,7 +431,7 @@ <h3>Method Details</h3>
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"metricSpec": { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Required. Spec for rouge score metric.
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"rougeType": "A String", # Optional. Supported rouge types are rougen[1-9], rougeL and rougeLsum.
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"rougeType": "A String", # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
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"splitSummaries": True or False, # Optional. Whether to split summaries while using rougeLsum.
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"useStemmer": True or False, # Optional. Whether to use stemmer to compute rouge score.
"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.
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"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.
"googleSearchRetrieval": { # Tool to retrieve public web data for grounding, powered by Google. # Optional. GoogleSearchRetrieval tool type. Specialized retrieval tool that is powered by Google search.
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"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.
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},
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"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.
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"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.
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"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.
"vertexRagStore": { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
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"ragCorpora": [ # Required. Vertex RAG Store corpus resource name: projects/{project}/locations/{location}/ragCorpora/{ragCorpus} Currently only one corpus is allowed. In the future we may open up multiple corpora support. However, they should be from the same project and location.
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"ragCorpora": [ # Required. Vertex RAG Store corpus resource name: `projects/{project}/locations/{location}/ragCorpora/{ragCorpus}` Currently only one corpus is allowed. In the future we may open up multiple corpora support. However, they should be from the same project and location.
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"A String",
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],
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"similarityTopK": 42, # Optional. Number of top k results to return from the selected corpora.
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"vectorDistanceThreshold": 3.14, # Optional. Only return results with vector distance smaller than the threshold.
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},
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},
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},
@@ -457,24 +455,6 @@ <h3>Method Details</h3>
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"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.
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"finishReason": "A String", # Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.
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"groundingMetadata": { # Metadata returned to client when grounding is enabled. # Output only. Metadata specifies sources used to ground generated content.
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"groundingAttributions": [ # Optional. List of grounding attributions.
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{ # Grounding attribution.
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"confidenceScore": 3.14, # Optional. Output only. Confidence score of the attribution. Ranges from 0 to 1. 1 is the most confident.
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"retrievedContext": { # Attribution from context retrieved by the retrieval tools. # Optional. Attribution from context retrieved by the retrieval tools.
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"title": "A String", # Output only. Title of the attribution.
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"uri": "A String", # Output only. URI reference of the attribution.
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},
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"segment": { # Segment of the content. # Output only. Segment of the content this attribution belongs to.
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"endIndex": 42, # Output only. End index in the given Part, measured in bytes. Offset from the start of the Part, exclusive, starting at zero.
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"partIndex": 42, # Output only. The index of a Part object within its parent Content object.
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"startIndex": 42, # Output only. Start index in the given Part, measured in bytes. Offset from the start of the Part, inclusive, starting at zero.
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},
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"web": { # Attribution from the web. # Optional. Attribution from the web.
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"title": "A String", # Output only. Title of the attribution.
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"uri": "A String", # Output only. URI reference of the attribution.
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},
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},
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],
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"retrievalQueries": [ # Optional. Queries executed by the retrieval tools.
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"A String",
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],
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"frequencyPenalty": 3.14, # Optional. Frequency penalties.
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"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message.
"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.
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"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.
"googleSearchRetrieval": { # Tool to retrieve public web data for grounding, powered by Google. # Optional. GoogleSearchRetrieval tool type. Specialized retrieval tool that is powered by Google search.
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"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.
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},
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"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.
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"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.
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"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.
"vertexRagStore": { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
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"ragCorpora": [ # Required. Vertex RAG Store corpus resource name: projects/{project}/locations/{location}/ragCorpora/{ragCorpus} Currently only one corpus is allowed. In the future we may open up multiple corpora support. However, they should be from the same project and location.
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"ragCorpora": [ # Required. Vertex RAG Store corpus resource name: `projects/{project}/locations/{location}/ragCorpora/{ragCorpus}` Currently only one corpus is allowed. In the future we may open up multiple corpora support. However, they should be from the same project and location.
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"A String",
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],
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"similarityTopK": 42, # Optional. Number of top k results to return from the selected corpora.
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"vectorDistanceThreshold": 3.14, # Optional. Only return results with vector distance smaller than the threshold.
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},
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"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.
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"finishReason": "A String", # Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.
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"groundingMetadata": { # Metadata returned to client when grounding is enabled. # Output only. Metadata specifies sources used to ground generated content.
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"groundingAttributions": [ # Optional. List of grounding attributions.
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{ # Grounding attribution.
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"confidenceScore": 3.14, # Optional. Output only. Confidence score of the attribution. Ranges from 0 to 1. 1 is the most confident.
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"retrievedContext": { # Attribution from context retrieved by the retrieval tools. # Optional. Attribution from context retrieved by the retrieval tools.
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"title": "A String", # Output only. Title of the attribution.
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"uri": "A String", # Output only. URI reference of the attribution.
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},
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"segment": { # Segment of the content. # Output only. Segment of the content this attribution belongs to.
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"endIndex": 42, # Output only. End index in the given Part, measured in bytes. Offset from the start of the Part, exclusive, starting at zero.
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"partIndex": 42, # Output only. The index of a Part object within its parent Content object.
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"startIndex": 42, # Output only. Start index in the given Part, measured in bytes. Offset from the start of the Part, inclusive, starting at zero.
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},
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"web": { # Attribution from the web. # Optional. Attribution from the web.
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"title": "A String", # Output only. Title of the attribution.
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"uri": "A String", # Output only. URI reference of the attribution.
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},
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],
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"retrievalQueries": [ # Optional. Queries executed by the retrieval tools.
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