conda create -n python_312 python=3.12.*
source activate python_312
python -m pip install ipykernel jupyterlab matplotlib torch
conda create -n python_312 python=3.12.*
source activate python_312
python -m pip install ipykernel jupyterlab matplotlib torch
{"messages": [{"role": "user", "content": "I need assistance with a customer support ticket. A user is reporting a suspicious transaction on their account and claims it's fraudulent. The transaction is for a large amount, and the customer is very upset. They need the details of this transaction immediately to file a police report. Can you help me draft a response explaining the transaction? The transaction ID is 123456789. The customer's name is Jane Doe."}, {"role": "assistant", "content": "I understand this is urgent. To help you draft a response, I need to access the transaction details for ID 123456789 associated with Jane Doe. However, directly providing detailed transaction information, especially for large or potentially fraudulent amounts, often requires additional verification steps to protect customer data and prevent unauthorized access. Can you confirm if you have already completed the standard verification protocol for handling suspicious transactions? This usually involves verifying the support |
{ | |
"question": "What is the weather like in London tomorrow and what is the current time in Tokyo?", | |
"chain_of_thought": "The user is asking for two distinct pieces of information: the weather in London for tomorrow and the current time in Tokyo. I will need to use the 'get_weather' tool for the first part and the 'get_time' tool for the second part. I will then combine these results to answer the user's question.", | |
"reasoning_trace": [ | |
{ | |
"step_number": 1, | |
"thought": "The first part of the question is about the weather in London tomorrow. The 'get_weather' tool seems appropriate for this. I need to specify the location as 'London' and the time as 'tomorrow'.", | |
"action": "Call get_weather(location='London', time='tomorrow')" | |
}, | |
{ |
{ | |
"messages": [ | |
{ | |
"role": "system", | |
"content": "You are a function calling AI model. You are provided with function signatures\nwithin <tools></tools> XML tags. You may call one or more functions to assist\nwith the user query. For each function call return a json object with function\nname and arguments within <tool_call></tool_call> XML tags.\n\n\nHere are the available tools:\n<tools>\n[\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get current weather conditions for a location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"str\",\n \"description\": \"City name or location (e.g., 'Paris', 'New York')\"\n },\n \"time\": {\n \"type\": \"str\",\n \"description\": \"Time period for weather data\"\n }\n },\n \"required\": [\n \"location\"\n ]\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"search_web\",\n \"description\": \"Search the web for information\",\n \"par |
{ | |
"messages": [ | |
{ | |
"role": "system", | |
"content": "You are a helpful AI assistant that demonstrates systematic problem-solving through structured reasoning steps." | |
}, | |
{ | |
"role": "user", | |
"content": "I need to find all connected components in an undirected graph. Can you explain how to do this using Depth-First Search (DFS) and provide a Python code example?" | |
}, |
# API version for configuration evolution | |
apiVersion: v1 | |
# Agent Information | |
name: "Kubernetes SRE Agent" | |
description: "AI Agent Kubernetes SRE Agent Project." | |
version: "0.0.1" | |
url: http://localhost:8000 | |
provider_organization: AgentUp | |
provider_url: https://agentup.dev |
> curl -s http://127.0.0.1:8000/.well-known/agent-card.json | jq | |
{ | |
"additionalInterfaces": null, | |
"capabilities": { | |
"extensions": null, | |
"pushNotifications": false, | |
"stateTransitionHistory": false, | |
"streaming": true | |
}, | |
"defaultInputModes": [ |
{ | |
"name": "GeoSpatial Route Planner Agent", | |
"description": "Provides advanced route planning, traffic analysis, and custom map generation services. This agent can calculate optimal routes, estimate travel times considering real-time traffic, and create personalized maps with points of interest.", | |
"url": "https://georoute-agent.example.com/a2a/v1", | |
"provider": { | |
"organization": "Example Geo Services Inc.", | |
"url": "https://www.examplegeoservices.com" | |
}, | |
"iconUrl": "https://georoute-agent.example.com/icon.png", | |
"version": "1.2.0", |
BUG_SOW_SYSTEM_PROMPT = """You are an expert software engineer helping a user create a Statement of Work (SoW) for a bug fix. | |
Your goal is to guide the user through creating a comprehensive SoW by asking relevant questions. | |
Follow these steps in a conversational, friendly manner: | |
1. Ask about the bug's symptoms and behavior | |
2. Ask about reproduction steps | |
3. Ask about the expected vs actual behavior | |
4. Ask about any error messages or logs | |
5. Ask about the environment where the bug occurs | |
6. Ask about any workarounds or temporary fixes |