Discovery Engine API . projects . locations

close()

Close httplib2 connections.

obtainCrawlRate(location, body=None, x__xgafv=None)

Obtains the time series data of organic or dedicated crawl rate for monitoring. When dedicated crawl rate is not set, it will return vertex AI's organic crawl rate time series. Organic crawl means Google automatically crawl the internet at its own convenience. When dedicated crawl rate is set, it will return vertex AI's dedicated crawl rate time series.

Args:
  location: string, Required. The location resource where crawl rate management will be performed. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for CrawlRateManagementService.ObtainCrawlRate method.
  "crawlRateScope": "A String", # Required. The scope of the crawl rate that the user wants to monitor. Currently, only domain and host name are supported. A domain name example: `example.com`. A host name example: `www.example.com`. Please do not include `/` in the domain or host name.
}

  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 CrawlRateManagementService.ObtainCrawlRate method. The response contains organcic or dedicated crawl rate time series data for monitoring, depending on whether dedicated crawl rate is set.
  "dedicatedCrawlRateTimeSeries": { # The historical dedicated crawl rate timeseries data, used for monitoring. Dedicated crawl is used by Vertex AI to crawl the user's website when dedicate crawl is set. # The historical dedicated crawl rate timeseries data, used for monitoring.
    "autoRefreshCrawlErrorRate": { # The historical crawl rate timeseries data, used for monitoring. # Vertex AI's error rate time series of auto-refresh dedicated crawl.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
    "autoRefreshCrawlRate": { # The historical crawl rate timeseries data, used for monitoring. # Vertex AI's dedicated crawl rate time series of auto-refresh, which is the crawl rate of Google-CloudVertexBot when dedicate crawl is set, and the crawl rate is for best effort use cases like refreshing urls periodically.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
    "userTriggeredCrawlErrorRate": { # The historical crawl rate timeseries data, used for monitoring. # Vertex AI's error rate time series of user triggered dedicated crawl.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
    "userTriggeredCrawlRate": { # The historical crawl rate timeseries data, used for monitoring. # Vertex AI's dedicated crawl rate time series of user triggered crawl, which is the crawl rate of Google-CloudVertexBot when dedicate crawl is set, and user triggered crawl rate is for deterministic use cases like crawling urls or sitemaps specified by users.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
  },
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Errors from service when handling the request.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  "organicCrawlRateTimeSeries": { # The historical organic crawl rate timeseries data, used for monitoring. Organic crawl is auto-determined by Google to crawl the user's website when dedicate crawl is not set. Crawl rate is the QPS of crawl request Google sends to the user's website. # The historical organic crawl rate timeseries data, used for monitoring.
    "googleOrganicCrawlRate": { # The historical crawl rate timeseries data, used for monitoring. # Google's organic crawl rate time series, which is the sum of all googlebots' crawl rate. Please refer to https://developers.google.com/search/docs/crawling-indexing/overview-google-crawlers for more details about googlebots.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
    "vertexAiOrganicCrawlRate": { # The historical crawl rate timeseries data, used for monitoring. # Vertex AI's organic crawl rate time series, which is the crawl rate of Google-CloudVertexBot when dedicate crawl is not set. Please refer to https://developers.google.com/search/docs/crawling-indexing/google-common-crawlers#google-cloudvertexbot for more details about Google-CloudVertexBot.
      "qpsTimeSeries": { # A collection of data points that describes the time-varying values of a metric. A time series is identified by a combination of a fully-specified monitored resource and a fully-specified metric. This type is used for both listing and creating time series. # The QPS of the crawl rate.
        "description": "A String", # Input only. A detailed description of the time series that will be associated with the google.api.MetricDescriptor for the metric. Once set, this field cannot be changed through CreateTimeSeries.
        "metadata": { # Auxiliary metadata for a MonitoredResource object. MonitoredResource objects contain the minimum set of information to uniquely identify a monitored resource instance. There is some other useful auxiliary metadata. Monitoring and Logging use an ingestion pipeline to extract metadata for cloud resources of all types, and store the metadata in this message. # Output only. The associated monitored resource metadata. When reading a time series, this field will include metadata labels that are explicitly named in the reduction. When creating a time series, this field is ignored.
          "systemLabels": { # Output only. Values for predefined system metadata labels. System labels are a kind of metadata extracted by Google, including "machine_image", "vpc", "subnet_id", "security_group", "name", etc. System label values can be only strings, Boolean values, or a list of strings. For example: { "name": "my-test-instance", "security_group": ["a", "b", "c"], "spot_instance": false }
            "a_key": "", # Properties of the object.
          },
          "userLabels": { # Output only. A map of user-defined metadata labels.
            "a_key": "A String",
          },
        },
        "metric": { # A specific metric, identified by specifying values for all of the labels of a `MetricDescriptor`. # The associated metric. A fully-specified metric used to identify the time series.
          "labels": { # The set of label values that uniquely identify this metric. All labels listed in the `MetricDescriptor` must be assigned values.
            "a_key": "A String",
          },
          "type": "A String", # An existing metric type, see google.api.MetricDescriptor. For example, `custom.googleapis.com/invoice/paid/amount`.
        },
        "metricKind": "A String", # The metric kind of the time series. When listing time series, this metric kind might be different from the metric kind of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the metric kind of the associated metric. If the associated metric's descriptor must be auto-created, then this field specifies the metric kind of the new descriptor and must be either `GAUGE` (the default) or `CUMULATIVE`.
        "points": [ # The data points of this time series. When listing time series, points are returned in reverse time order. When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be `BOOL`, `INT64`, `DOUBLE`, or `DISTRIBUTION`.
          { # A single data point in a time series.
            "interval": { # A time interval extending just after a start time through an end time. If the start time is the same as the end time, then the interval represents a single point in time. # The time interval to which the data point applies. For `GAUGE` metrics, the start time is optional, but if it is supplied, it must equal the end time. For `DELTA` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying contiguous and non-overlapping intervals. For `CUMULATIVE` metrics, the start and end time should specify a non-zero interval, with subsequent points specifying the same start time and increasing end times, until an event resets the cumulative value to zero and sets a new start time for the following points.
              "endTime": "A String", # Required. The end of the time interval.
              "startTime": "A String", # Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.
            },
            "value": { # A single strongly-typed value. # The value of the data point.
              "boolValue": True or False, # A Boolean value: `true` or `false`.
              "distributionValue": { # `Distribution` contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths. Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                "bucketCounts": [ # The number of values in each bucket of the histogram, as described in `bucket_options`. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in `bucket_counts` must equal the value in the `count` field of the distribution. If present, `bucket_counts` should contain N values, where N is the number of buckets specified in `bucket_options`. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in `bucket_counts` follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in `bucket_counts` is the count for the overflow bucket (number N-1).
                  "A String",
                ],
                "bucketOptions": { # `BucketOptions` describes the bucket boundaries used to create a histogram for the distribution. The buckets can be in a linear sequence, an exponential sequence, or each bucket can be specified explicitly. `BucketOptions` does not include the number of values in each bucket. A bucket has an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket must be strictly greater than the lower bound. The sequence of N buckets for a distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. # Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
                  "explicitBuckets": { # Specifies a set of buckets with arbitrary widths. There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] The `bounds` field must contain at least one element. If `bounds` has only one element, then there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                    "bounds": [ # The values must be monotonically increasing.
                      3.14,
                    ],
                  },
                  "exponentialBuckets": { # Specifies an exponential sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                    "growthFactor": 3.14, # Must be greater than 1.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "scale": 3.14, # Must be greater than 0.
                  },
                  "linearBuckets": { # Specifies a linear sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the following boundaries: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                    "numFiniteBuckets": 42, # Must be greater than 0.
                    "offset": 3.14, # Lower bound of the first bucket.
                    "width": 3.14, # Must be greater than 0.
                  },
                },
                "count": "A String", # The number of values in the population. Must be non-negative. This value must equal the sum of the values in `bucket_counts` if a histogram is provided.
                "exemplars": [ # Must be in increasing order of `value` field.
                  { # Exemplars are example points that may be used to annotate aggregated distribution values. They are metadata that gives information about a particular value added to a Distribution bucket, such as a trace ID that was active when a value was added. They may contain further information, such as a example values and timestamps, origin, etc.
                    "attachments": [ # Contextual information about the example value. Examples are: Trace: type.googleapis.com/google.monitoring.v3.SpanContext Literal string: type.googleapis.com/google.protobuf.StringValue Labels dropped during aggregation: type.googleapis.com/google.monitoring.v3.DroppedLabels There may be only a single attachment of any given message type in a single exemplar, and this is enforced by the system.
                      {
                        "a_key": "", # Properties of the object. Contains field @type with type URL.
                      },
                    ],
                    "timestamp": "A String", # The observation (sampling) time of the above value.
                    "value": 3.14, # Value of the exemplar point. This value determines to which bucket the exemplar belongs.
                  },
                ],
                "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                  "max": 3.14, # The maximum of the population values.
                  "min": 3.14, # The minimum of the population values.
                },
                "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
              },
              "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
              "stringValue": "A String", # A variable-length string value.
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"project_id"`, `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "project_id": "my-project", "instance_id": "12345678901234", "zone": "us-central1-a" }} # The associated monitored resource. Custom metrics can use only certain monitored resource types in their time series data. For more information, see [Monitored resources for custom metrics](https://cloud.google.com/monitoring/custom-metrics/creating-metrics#custom-metric-resources).
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Compute Engine VM instances use the labels `"project_id"`, `"instance_id"`, and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Compute Engine VM instance is `gce_instance`. Some descriptors include the service name in the type; for example, the type of a Datastream stream is `datastream.googleapis.com/Stream`.
        },
        "unit": "A String", # The units in which the metric value is reported. It is only applicable if the `value_type` is `INT64`, `DOUBLE`, or `DISTRIBUTION`. The `unit` defines the representation of the stored metric values. This field can only be changed through CreateTimeSeries when it is empty.
        "valueType": "A String", # The value type of the time series. When listing time series, this value type might be different from the value type of the associated metric if this time series is an alignment or reduction of other time series. When creating a time series, this field is optional. If present, it must be the same as the type of the data in the `points` field.
      },
    },
  },
  "state": "A String", # Output only. The state of the response.
}

removeDedicatedCrawlRate(location, body=None, x__xgafv=None)

Removes the dedicated crawl rate for a craw_rate_scope. If the dedicated crawl rate was set, this will disable vertex AI's crawl bot from using the dedicated crawl rate for crawling. If the dedicated crawl rate was not set, this is a no-op.

Args:
  location: string, Required. The location resource where crawl rate management will be performed. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for CrawlRateManagementService.RemoveDedicatedCrawlRate method. The user can remove the dedicated crawl rate for a crawl_rate_scope they own, and Google will fall back to organic crawl, and the crawl rate will be determined by Google.
  "crawlRateScope": "A String", # Required. The scope of the crawl rate change. Currently, only domain and host name are supported. A domain name example: `example.com`. A host name example: `www.example.com`. Please do not include `/` in the domain or host name.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

setDedicatedCrawlRate(location, body=None, x__xgafv=None)

Sets the dedicated crawl rate for a crawl_rate_scope. If the dedicated crawl rate was not set, this will enable vertex AI's crawl bot to use the new dedicated crawl rate for crawling. If the dedicated crawl rate was set, vertex AI's crawl bot will try to update the rate to the new value. If the new value is too high, the crawl bot may crawl at a lower rate to avoid overloading the user's website.

Args:
  location: string, Required. The location resource where crawl rate management will be performed. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for CrawlRateManagementService.SetDedicatedCrawlRate method. The user can set the crawl rate for a crawl_rate_scope they own. They can set up an overall crawl rate, or set up a user-triggered crawl rate and a auto-refresh crawl rate separately. If an overall crawl rate is set, Vertex AI will automatically splits crawl_rate into user-triggered and auto-refresh.
  "crawlRate": 42, # Optional. The crawl QPS set by the user. It is not guaranteed that Vertex crawl bot will crawl at this QPS. If the crawl rate is too high, the real QPS may be lower than the value set by the user to avoid overloading the user's website.
  "crawlRateScope": "A String", # Required. The scope of the crawl rate that the user wants to config. Currently, only domain and host name are supported. A domain name example: `example.com`. A host name example: `www.example.com`. Please do not include `/` in the domain or host name.
  "crawlType": "A String", # Optional. Whether it's the crawl rate of user-triggered or auto-refresh.
  "mode": "A String", # Optional. Whether the rate is explicitly set by users, or set by vertex AI.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}