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Update docs to Plano (#639)
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139 changed files with 4407 additions and 24735 deletions
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version: v0.1
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listener:
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address: 127.0.0.1
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port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
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message_format: huggingface
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# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
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llm_providers:
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- name: OpenAI
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provider: openai
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access_key: $OPENAI_API_KEY
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model: gpt-3.5-turbo
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default: true
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# default system prompt used by all prompt targets
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system_prompt: |
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You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.
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prompt_targets:
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- name: network_qa
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endpoint:
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name: app_server
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path: /agent/network_summary
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description: Handle general Q/A related to networking.
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default: true
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- name: reboot_devices
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description: Reboot specific devices or device groups
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endpoint:
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name: app_server
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path: /agent/device_reboot
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parameters:
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- name: device_ids
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type: list
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description: A list of device identifiers (IDs) to reboot.
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required: true
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- name: device_summary
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description: Retrieve statistics for specific devices within a time range
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endpoint:
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name: app_server
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path: /agent/device_summary
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parameters:
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- name: device_ids
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type: list
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description: A list of device identifiers (IDs) to retrieve statistics for.
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required: true # device_ids are required to get device statistics
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- name: time_range
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type: int
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description: Time range in days for which to gather device statistics. Defaults to 7.
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default: 7
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# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
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endpoints:
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app_server:
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# value could be ip address or a hostname with port
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# this could also be a list of endpoints for load balancing
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# for example endpoint: [ ip1:port, ip2:port ]
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endpoint: host.docker.internal:18083
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# max time to wait for a connection to be established
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connect_timeout: 0.005s
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After Width: | Height: | Size: 297 KiB |
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from flask import Flask, request, jsonify
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app = Flask(__name__)
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@app.route("/agent/device_summary", methods=["POST"])
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def get_device_summary():
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"""
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Endpoint to retrieve device statistics based on device IDs and an optional time range.
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"""
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data = request.get_json()
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# Validate 'device_ids' parameter
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device_ids = data.get("device_ids")
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if not device_ids or not isinstance(device_ids, list):
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return (
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jsonify({"error": "'device_ids' parameter is required and must be a list"}),
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400,
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)
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# Validate 'time_range' parameter (optional, defaults to 7)
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time_range = data.get("time_range", 7)
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if not isinstance(time_range, int):
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return jsonify({"error": "'time_range' must be an integer"}), 400
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# Simulate retrieving statistics for the given device IDs and time range
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# In a real application, you would query your database or external service here
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statistics = []
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for device_id in device_ids:
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# Placeholder for actual data retrieval
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stats = {
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"device_id": device_id,
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"time_range": f"Last {time_range} days",
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"data": f"Statistics data for device {device_id} over the last {time_range} days.",
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}
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statistics.append(stats)
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response = {"statistics": statistics}
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return jsonify(response), 200
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if __name__ == "__main__":
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app.run(debug=True)
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import os
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import gradio as gr
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from typing import Optional
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from openai import OpenAI
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from common import create_gradio_app
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app = FastAPI()
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# Define the request model
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class EnergySourceRequest(BaseModel):
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energy_source: str
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consideration: Optional[str] = None
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class EnergySourceResponse(BaseModel):
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energy_source: str
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consideration: Optional[str] = None
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# Post method for device summary
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@app.post("/agent/energy_source_info")
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def get_workforce(request: EnergySourceRequest):
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"""
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Endpoint to get details about energy source
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"""
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considertion = "You don't have any specific consideration. Feel free to talk in a more open ended fashion"
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if request.consideration is not None:
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considertion = f"Add specific focus on the following consideration when you summarize the content for the energy source: {request.consideration}"
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response = {
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"energy_source": request.energy_source,
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"consideration": considertion,
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}
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return response
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After Width: | Height: | Size: 852 KiB |
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@ -0,0 +1,35 @@
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version: v0.1
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listener:
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address: 127.0.0.1
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port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
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message_format: huggingface
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# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
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llm_providers:
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- name: OpenAI
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provider: openai
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access_key: $OPENAI_API_KEY
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model: gpt-3.5-turbo
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default: true
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# default system prompt used by all prompt targets
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system_prompt: |
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You are a helpful assistant and can offer information about energy sources. You will get a JSON object with energy_source and consideration fields. Focus on answering using those fields
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prompt_targets:
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- name: get_info_for_energy_source
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description: get information about an energy source
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parameters:
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- name: energy_source
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type: str
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description: a source of energy
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required: true
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enum: [renewable, fossil]
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- name: consideration
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type: str
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description: a specific type of consideration for an energy source
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enum: [cost, economic, technology]
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endpoint:
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name: rag_energy_source_agent
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path: /agent/energy_source_info
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http_method: POST
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from flask import Flask, request, jsonify
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app = Flask(__name__)
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@app.route("/agent/device_summary", methods=["POST"])
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def get_device_summary():
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"""
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Endpoint to retrieve device statistics based on device IDs and an optional time range.
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"""
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data = request.get_json()
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# Validate 'device_ids' parameter
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device_ids = data.get("device_ids")
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if not device_ids or not isinstance(device_ids, list):
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return (
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jsonify({"error": "'device_ids' parameter is required and must be a list"}),
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400,
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)
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# Validate 'time_range' parameter (optional, defaults to 7)
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time_range = data.get("time_range", 7)
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if not isinstance(time_range, int):
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return jsonify({"error": "'time_range' must be an integer"}), 400
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# Simulate retrieving statistics for the given device IDs and time range
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# In a real application, you would query your database or external service here
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statistics = []
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for device_id in device_ids:
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# Placeholder for actual data retrieval
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stats = {
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"device_id": device_id,
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"time_range": f"Last {time_range} days",
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"data": f"Statistics data for device {device_id} over the last {time_range} days.",
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}
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statistics.append(stats)
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response = {"statistics": statistics}
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return jsonify(response), 200
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if __name__ == "__main__":
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app.run(debug=True)
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prompt_targets:
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- name: get_device_statistics
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description: Retrieve and present the relevant data based on the specified devices and time range
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path: /agent/device_summary
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parameters:
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- name: device_ids
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type: list
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description: A list of device identifiers (IDs) to reboot.
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required: true
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- name: time_range
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type: int
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description: The number of days in the past over which to retrieve device statistics
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required: false
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default: 7
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