# AI

# To Markdown

## Convert Files into Markdown

`radar.ai.to_markdown.create(ToMarkdownCreateParams**kwargs)  -> SyncSinglePage[ToMarkdownCreateResponse]`

**post** `/accounts/{account_id}/ai/tomarkdown`

Converts uploaded files into Markdown format using Workers AI.

### Parameters

- `account_id: str`

- `files: Sequence[FileTypes]`

### Returns

- `class ToMarkdownCreateResponse: …`

  - `data: str`

  - `format: str`

  - `mime_type: str`

  - `name: str`

  - `tokens: str`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
page = client.radar.ai.to_markdown.create(
    account_id="023e105f4ecef8ad9ca31a8372d0c353",
    files=[b"Example data"],
)
page = page.result[0]
print(page.data)
```

#### Response

```json
{
  "result": [
    {
      "data": "data",
      "format": "format",
      "mimeType": "mimeType",
      "name": "name",
      "tokens": "tokens"
    }
  ],
  "success": true
}
```

## Domain Types

### To Markdown Create Response

- `class ToMarkdownCreateResponse: …`

  - `data: str`

  - `format: str`

  - `mime_type: str`

  - `name: str`

  - `tokens: str`

# Inference

## Get Workers AI inference distribution by dimension

`radar.ai.inference.summary_v2(Literal["MODEL", "TASK"]dimension, InferenceSummaryV2Params**kwargs)  -> InferenceSummaryV2Response`

**get** `/radar/ai/inference/summary/{dimension}`

Retrieves an aggregated summary of the number of inferences run on Workers AI, grouped by the specified dimension.

### Parameters

- `dimension: Literal["MODEL", "TASK"]`

  Specifies the attribute by which to group the results.

  - `"MODEL"`

  - `"TASK"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class InferenceSummaryV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.summary_v2(
    dimension="MODEL",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "@cf/meta/llama-3-8b-instruct": "8.381743",
      "@cf/meta/m2m100-1.2b": "22.904",
      "@cf/stabilityai/stable-diffusion-xl-base-1.0": "10.274394"
    }
  },
  "success": true
}
```

## Get time series distribution of Workers AI inference by dimension.

`radar.ai.inference.timeseries_groups_v2(Literal["MODEL", "TASK"]dimension, InferenceTimeseriesGroupsV2Params**kwargs)  -> InferenceTimeseriesGroupsV2Response`

**get** `/radar/ai/inference/timeseries_groups/{dimension}`

Retrieves the distribution of the number of inferences run on Workers AI, grouped by the specified dimension over time.

### Parameters

- `dimension: Literal["MODEL", "TASK"]`

  Specifies the attribute by which to group the results.

  - `"MODEL"`

  - `"TASK"`

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `normalization: Optional[Literal["PERCENTAGE", "MIN0_MAX"]]`

  Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

  - `"PERCENTAGE"`

  - `"MIN0_MAX"`

### Returns

- `class InferenceTimeseriesGroupsV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.timeseries_groups_v2(
    dimension="MODEL",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Domain Types

### Inference Summary V2 Response

- `class InferenceSummaryV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Inference Timeseries Groups V2 Response

- `class InferenceTimeseriesGroupsV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

# Summary

## Get Workers AI models summary

`radar.ai.inference.summary.model(SummaryModelParams**kwargs)  -> SummaryModelResponse`

**get** `/radar/ai/inference/summary/model`

Retrieves the distribution of the number of inferences by model.

### Parameters

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class SummaryModelResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.summary.model()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "@cf/meta/llama-3-8b-instruct": "8.381743",
      "@cf/meta/m2m100-1.2b": "22.904",
      "@cf/stabilityai/stable-diffusion-xl-base-1.0": "10.274394"
    }
  },
  "success": true
}
```

## Get Workers AI tasks summary

`radar.ai.inference.summary.task(SummaryTaskParams**kwargs)  -> SummaryTaskResponse`

**get** `/radar/ai/inference/summary/task`

Retrieves the distribution of the number of inferences by task.

### Parameters

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class SummaryTaskResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.summary.task()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "text generation": "10.274394",
      "text-to-image": "22.904"
    }
  },
  "success": true
}
```

## Domain Types

### Summary Model Response

- `class SummaryModelResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Summary Task Response

- `class SummaryTaskResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

# Timeseries Groups

# Summary

## Get Workers AI models time series

`radar.ai.inference.timeseries_groups.summary.model(SummaryModelParams**kwargs)  -> SummaryModelResponse`

**get** `/radar/ai/inference/timeseries_groups/model`

Retrieves the distribution of the number of inferences by model over time.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class SummaryModelResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.timeseries_groups.summary.model()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Get Workers AI tasks time series

`radar.ai.inference.timeseries_groups.summary.task(SummaryTaskParams**kwargs)  -> SummaryTaskResponse`

**get** `/radar/ai/inference/timeseries_groups/task`

Retrieves the distribution of the number of inferences by task over time.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class SummaryTaskResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.inference.timeseries_groups.summary.task()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Domain Types

### Summary Model Response

- `class SummaryModelResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Summary Task Response

- `class SummaryTaskResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

# Bots

## Get AI bots HTTP requests distribution by dimension

`radar.ai.bots.summary_v2(Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]dimension, BotSummaryV2Params**kwargs)  -> BotSummaryV2Response`

**get** `/radar/ai/bots/summary/{dimension}`

Retrieves an aggregated summary of AI bots HTTP requests grouped by the specified dimension.

### Parameters

- `dimension: Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]`

  Specifies the attribute by which to group the results.

  - `"USER_AGENT"`

  - `"CRAWL_PURPOSE"`

  - `"INDUSTRY"`

  - `"VERTICAL"`

  - `"CONTENT_TYPE"`

  - `"RESPONSE_STATUS"`

  - `"RESPONSE_STATUS_CATEGORY"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class BotSummaryV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.bots.summary_v2(
    dimension="USER_AGENT",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "Amazonbot": "10.274394",
      "Bytespider": "8.381743",
      "facebookexternalhit": "63.40249"
    }
  },
  "success": true
}
```

## Get AI bots HTTP requests time series

`radar.ai.bots.timeseries(BotTimeseriesParams**kwargs)  -> BotTimeseriesResponse`

**get** `/radar/ai/bots/timeseries`

Retrieves AI bots HTTP request volume over time.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class BotTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.bots.timeseries()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    }
  },
  "success": true
}
```

## Get time series distribution of AI bots HTTP requests by dimension.

`radar.ai.bots.timeseries_groups(Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]dimension, BotTimeseriesGroupsParams**kwargs)  -> BotTimeseriesGroupsResponse`

**get** `/radar/ai/bots/timeseries_groups/{dimension}`

Retrieves the distribution of HTTP requests from AI bots, grouped by the specified dimension over time.

### Parameters

- `dimension: Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]`

  Specifies the attribute by which to group the results.

  - `"USER_AGENT"`

  - `"CRAWL_PURPOSE"`

  - `"INDUSTRY"`

  - `"VERTICAL"`

  - `"CONTENT_TYPE"`

  - `"RESPONSE_STATUS"`

  - `"RESPONSE_STATUS_CATEGORY"`

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `normalization: Optional[Literal["PERCENTAGE", "MIN0_MAX", "PERCENTAGE_CHANGE"]]`

  Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/). `PERCENTAGE_CHANGE` requires exactly one comparison series (e.g. a `control` date range).

  - `"PERCENTAGE"`

  - `"MIN0_MAX"`

  - `"PERCENTAGE_CHANGE"`

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class BotTimeseriesGroupsResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.bots.timeseries_groups(
    dimension="USER_AGENT",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Domain Types

### Bot Summary V2 Response

- `class BotSummaryV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Bot Timeseries Response

- `class BotTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

### Bot Timeseries Groups Response

- `class BotTimeseriesGroupsResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

# Summary

## Get AI user agents summary

`radar.ai.bots.summary.user_agent(SummaryUserAgentParams**kwargs)  -> SummaryUserAgentResponse`

**get** `/radar/ai/bots/summary/user_agent`

Retrieves the distribution of traffic by AI user agent.

### Parameters

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class SummaryUserAgentResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.bots.summary.user_agent()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "Amazonbot": "10.274394",
      "Bytespider": "8.381743",
      "facebookexternalhit": "63.40249"
    }
  },
  "success": true
}
```

## Domain Types

### Summary User Agent Response

- `class SummaryUserAgentResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

# Timeseries Groups

## Get AI user agents time series

`radar.ai.timeseries_groups.user_agent(TimeseriesGroupUserAgentParams**kwargs)  -> TimeseriesGroupUserAgentResponse`

**get** `/radar/ai/bots/timeseries_groups/user_agent`

Retrieves the distribution of traffic by AI user agent over time.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class TimeseriesGroupUserAgentResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.timeseries_groups.user_agent()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Get AI bots HTTP requests distribution by dimension

`radar.ai.timeseries_groups.summary(Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]dimension, TimeseriesGroupSummaryParams**kwargs)  -> TimeseriesGroupSummaryResponse`

**get** `/radar/ai/bots/summary/{dimension}`

Retrieves an aggregated summary of AI bots HTTP requests grouped by the specified dimension.

### Parameters

- `dimension: Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]`

  Specifies the attribute by which to group the results.

  - `"USER_AGENT"`

  - `"CRAWL_PURPOSE"`

  - `"INDUSTRY"`

  - `"VERTICAL"`

  - `"CONTENT_TYPE"`

  - `"RESPONSE_STATUS"`

  - `"RESPONSE_STATUS_CATEGORY"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class TimeseriesGroupSummaryResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.timeseries_groups.summary(
    dimension="USER_AGENT",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "Amazonbot": "10.274394",
      "Bytespider": "8.381743",
      "facebookexternalhit": "63.40249"
    }
  },
  "success": true
}
```

## Get AI bots HTTP requests time series

`radar.ai.timeseries_groups.timeseries(TimeseriesGroupTimeseriesParams**kwargs)  -> TimeseriesGroupTimeseriesResponse`

**get** `/radar/ai/bots/timeseries`

Retrieves AI bots HTTP request volume over time.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class TimeseriesGroupTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.timeseries_groups.timeseries()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    }
  },
  "success": true
}
```

## Get time series distribution of AI bots HTTP requests by dimension.

`radar.ai.timeseries_groups.timeseries_groups(Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]dimension, TimeseriesGroupTimeseriesGroupsParams**kwargs)  -> TimeseriesGroupTimeseriesGroupsResponse`

**get** `/radar/ai/bots/timeseries_groups/{dimension}`

Retrieves the distribution of HTTP requests from AI bots, grouped by the specified dimension over time.

### Parameters

- `dimension: Literal["USER_AGENT", "CRAWL_PURPOSE", "INDUSTRY", 4 more]`

  Specifies the attribute by which to group the results.

  - `"USER_AGENT"`

  - `"CRAWL_PURPOSE"`

  - `"INDUSTRY"`

  - `"VERTICAL"`

  - `"CONTENT_TYPE"`

  - `"RESPONSE_STATUS"`

  - `"RESPONSE_STATUS_CATEGORY"`

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[Sequence[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `content_type: Optional[List[Literal["HTML", "IMAGES", "JSON", 13 more]]]`

  Filters results by content type category. When set, results can only be further filtered by location, continent, or Autonomous System.

  - `"HTML"`

  - `"IMAGES"`

  - `"JSON"`

  - `"JAVASCRIPT"`

  - `"CSS"`

  - `"PLAIN_TEXT"`

  - `"FONTS"`

  - `"XML"`

  - `"YAML"`

  - `"VIDEO"`

  - `"AUDIO"`

  - `"MARKDOWN"`

  - `"DOCUMENTS"`

  - `"BINARY"`

  - `"SERIALIZATION"`

  - `"OTHER"`

- `continent: Optional[Sequence[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `crawl_purpose: Optional[Sequence[str]]`

  Filters results by bot crawl purpose.

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `industry: Optional[Sequence[str]]`

  Filters results by industry.

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category. Only supported on high-cardinality dimensions; otherwise the request is rejected. Minimum value is 2.

- `location: Optional[Sequence[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

- `normalization: Optional[Literal["PERCENTAGE", "MIN0_MAX", "PERCENTAGE_CHANGE"]]`

  Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/). `PERCENTAGE_CHANGE` requires exactly one comparison series (e.g. a `control` date range).

  - `"PERCENTAGE"`

  - `"MIN0_MAX"`

  - `"PERCENTAGE_CHANGE"`

- `response_status: Optional[Sequence[str]]`

  Filters results by HTTP response status code (e.g. 200, 403, 404). Only [IANA-registered codes](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml) are accepted.

- `response_status_category: Optional[List[Literal["INFORMATIONAL", "SUCCESS", "REDIRECTION", 2 more]]]`

  Filters results by HTTP response status code category.

  - `"INFORMATIONAL"`

  - `"SUCCESS"`

  - `"REDIRECTION"`

  - `"CLIENT_ERROR"`

  - `"SERVER_ERROR"`

- `user_agent: Optional[Sequence[str]]`

  Filters results by user agent.

- `vertical: Optional[Sequence[str]]`

  Filters results by vertical.

### Returns

- `class TimeseriesGroupTimeseriesGroupsResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.timeseries_groups.timeseries_groups(
    dimension="USER_AGENT",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```

## Domain Types

### Timeseries Group User Agent Response

- `class TimeseriesGroupUserAgentResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Timeseries Group Summary Response

- `class TimeseriesGroupSummaryResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Dict[str, str]`

### Timeseries Group Timeseries Response

- `class TimeseriesGroupTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

### Timeseries Group Timeseries Groups Response

- `class TimeseriesGroupTimeseriesGroupsResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

# Markdown For Agents

## Get AI markdown for agents reduction ratio summary

`radar.ai.markdown_for_agents.summary(MarkdownForAgentSummaryParams**kwargs)  -> MarkdownForAgentSummaryResponse`

**get** `/radar/ai/markdown_for_agents/summary`

Retrieves the overall median HTML-to-markdown reduction ratio for AI agent requests over the given date range.

### Parameters

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class MarkdownForAgentSummaryResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Summary0`

    - `value: str`

      A numeric string that can include decimals and infinity values.

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.markdown_for_agents.summary()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "summary_0": {
      "value": "10.6"
    }
  },
  "success": true
}
```

## Get AI markdown for agents reduction ratio time series

`radar.ai.markdown_for_agents.timeseries(MarkdownForAgentTimeseriesParams**kwargs)  -> MarkdownForAgentTimeseriesResponse`

**get** `/radar/ai/markdown_for_agents/timeseries`

Retrieves the median HTML-to-markdown reduction ratio over time for AI agent requests.

### Parameters

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/). When omitted, the interval is auto-selected from the requested date range; finer intervals are only available for shorter ranges. If the requested interval is too granular for the date range, the request is rejected.

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `date_end: Optional[Sequence[Union[str, datetime]]]`

  End of the date range (inclusive). Alternative to `dateRange`; provide together with `dateStart`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `date_range: Optional[Sequence[str]]`

  Filters results by relative date range ending at the current time, with each value producing a separate series. Use `<n>d` for days (up to `364d`) or `<n>w` for weeks (up to `52w`). Append `control` to request the equivalent previous period for comparison: the comparison window is shifted back by the current window's length rounded up to a whole number of weeks, so it keeps the same weekday alignment and does not overlap the current window (e.g. `7dcontrol` covers days -14 to -7, `10dcontrol` covers days -24 to -14). For example, pass `7d` and `7dcontrol` to compare this week with the previous week. All series must resolve to the same duration as the main series; relative ranges (including `control`) satisfy this automatically. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[Sequence[Union[str, datetime]]]`

  Start of the date range. Alternative to `dateRange`; provide together with `dateEnd`. When requesting comparison series, every series must resolve to the same duration as the main series. Each `dateStart`/`dateEnd` is floored to the nearest 15 minutes before evaluation, so windows whose durations match only before alignment may be rejected.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `name: Optional[Sequence[str]]`

  Array of names used to label the series in the response.

### Returns

- `class MarkdownForAgentTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.ai.markdown_for_agents.timeseries()
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z",
            "tags": [
              "BOT_CLASS"
            ]
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    }
  },
  "success": true
}
```

## Domain Types

### Markdown For Agent Summary Response

- `class MarkdownForAgentSummaryResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `summary_0: Summary0`

    - `value: str`

      A numeric string that can include decimals and infinity values.

### Markdown For Agent Timeseries Response

- `class MarkdownForAgentTimeseriesResponse: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://edgetunnel-b2h.pages.dev/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

        - `tags: Optional[List[str]]`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://edgetunnel-b2h.pages.dev/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`