How to Get Started
pip install requestsCode language: Bash (bash)
import requests
api_url = "https://api.regolo.ai/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_REGOLO_KEY"
}
data = {
"model": "qwen3.5-122b",
"messages": [
{
"role": "user",
"content": "If a train travels 60 km/h for 2 hours and then 80 km/h for 1.5 hours, what is the total distance covered?"
}
],
"reasoning_effort": "medium"
}
response = requests.post(api_url, headers=headers, json=data)
print(response.json())
Code language: Python (python)
Output
{
"id": "chatcmpl-8458b75f1bedcde1",
"created": 1772819136,
"model": "hosted_vllm/qwen3.5-122b",
"object": "chat.completion",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "To find the total distance covered, we calculate the distance for each part of the trip and then add them together.\n\n**Formula:** $\\text{Distance} = \\text{Speed} \\times \\text{Time}$\n\n1. **First part of the trip:**\n * Speed: $60\\text{ km/h}$\n * Time: $2\\text{ hours}$\n * Distance: $60 \\times 2 = 120\\text{ km}$\n\n2. **Second part of the trip:**\n * Speed: $80\\text{ km/h}$\n * Time: $1.5\\text{ hours}$\n * Distance: $80 \\times 1.5 = 120\\text{ km}$\n\n3. **Total distance:**\n * $120\\text{ km} + 120\\text{ km} = 240\\text{ km}$\n\nThe total distance covered is **240 km**.",
"role": "assistant",
"reasoning_content": "Thinking Process:\n\n1. **Analyze the Request:** The user is asking for the total distance covered by a train given two segments of travel with different speeds and durations.\n\n2. **Identify the Given Information:**\n * Segment 1: Speed ($v_1$) = 60 km/h, Time ($t_1$) = 2 hours.\n * Segment 2: Speed ($v_2$) = 80 km/h, Time ($t_2$) = 1.5 hours.\n\n3. **Identify the Formula:** The fundamental formula for distance is $Distance = Speed \\times Time$.\n\n4. **Calculate Distance for Segment 1:**\n * $d_1 = v_1 \\times t_1$\n * $d_1 = 60 \\text{ km/h} \\times 2 \\text{ h}$\n * $d_1 = 120 \\text{ km}$\n\n5. **Calculate Distance for Segment 2:**\n * $d_2 = v_2 \\times t_2$\n * $d_2 = 80 \\text{ km/h} \\times 1.5 \\text{ h}$\n * Calculation: $80 \\times 1 = 80$, $80 \\times 0.5 = 40$. $80 + 40 = 120$.\n * $d_2 = 120 \\text{ km}$\n\n6. **Calculate Total Distance:**\n * $Total Distance = d_1 + d_2$\n * $Total Distance = 120 \\text{ km} + 120 \\text{ km}$\n * $Total Distance = 240 \\text{ km}$\n\n7. **Final Review:** Does the answer make sense?\n * First part: 60 km/h for 2 hours -> 120 km. Correct.\n * Second part: 80 km/h for 1.5 hours -> 120 km. Correct.\n * Total: 120 + 120 = 240 km. Correct.\n\n8. **Construct the Output:** Present the steps clearly to the user.\n * State the formula.\n * Calculate the first part.\n * Calculate the second part.\n * Sum them up.\n * State the final answer.cw\n"
},
"provider_specific_fields": {
"stop_reason": null,
"token_ids": null
}
}
],
"usage": {
"completion_tokens": 788,
"prompt_tokens": 44,
"total_tokens": 832
}
}Code language: JSON / JSON with Comments (json)
Applications & use cases
- Multimodal assistants that can understand text, images, and video in a single workflow.
- Document understanding and media analysis for reports, visual assets, and mixed-content knowledge bases.
- Customer support automation with multilingual coverage across a broad set of languages.
- Coding copilots and software engineering assistants for development and debugging tasks.
- Agent-style systems that use reasoning and tool-oriented workflows to complete multi-step tasks.
- Enterprise knowledge assistants for long-context search, synthesis, and internal documentation workflows.
- Research and analysis tasks that benefit from a native 262K-token context and extended long-context configurations.
