Jourdain/delhIA-runs
delhIA — Conversation Runs This dataset contains 150 conversation runs on the delhIA Météo-France domain: 50 evaluation scenarios × 3 LLM assistants, produced by a fully automated multi-turn simulation framework. Conversations are stored in OpenAI Chat Completions format (roles: assistant, user, tool). Dataset at a Glance Runs 150 (50 tasks × 3 models) Models gpt-4o, qwen3-32b, qwen3-8b Domain Météo-France weather assistant Language French… See the full description on the dataset page: https://huggingface.co/datasets/Jourdain/delhIA-runs.
delhIA — Conversation Runs
This dataset contains 150 conversation runs on the delhIA Météo-France domain: 50 evaluation scenarios × 3 LLM assistants, produced by a fully automated multi-turn simulation framework.
Conversations are stored in OpenAI Chat Completions format (roles: assistant, user, tool).
Dataset at a Glance
Models
All models used the same assistant system prompt and the same 50 scenarios.
Data Structure
Each record in data/runs.json:
{
"task_id": "0_baseline",
"model": "gpt-4o",
"status": "FINISHED: ...",
"telemetry": {
"token_usage": {"prompt_tokens": 6348, "completion_tokens": 233, "total_tokens": 6581},
"total_calls": 6,
"total_tool_calls": 2,
"average_latency": 2.99
},
"messages": [
{"role": "assistant", "content": "Bonjour ! Comment puis-je vous aider ?"},
{"role": "user", "content": "Bonjour, je voudrais vérifier les alertes..."},
{
"role": "assistant",
"content": null,
"tool_calls": [{
"id": "call_xxx", "type": "function",
"function": {"name": "meteo_alerts", "arguments": "{\"domain\": \"75\"}"}
}]
},
{"role": "tool", "tool_call_id": "call_xxx", "content": "Alertes de vigilance..."},
{"role": "assistant", "content": "Pour le département 75, toutes les alertes sont vertes."},
{
"role": "user",
"tool_calls": [{
"id": "call_yyy", "type": "function",
"function": {"name": "end_simulation", "arguments": "{\"reason\": \"...\"}"}
}]
}
]
}Notes:
end_simulationis a simulation control tool called by the user simulator to signal task completion — not a Météo-France tool.toolrole messages contain raw Météo-France API responses.
Usage
import json
with open("data/runs.json") as f:
runs = json.load(f)
# Filter by model
gpt4o_runs = [r for r in runs if r["model"] == "gpt-4o"]
for run in gpt4o_runs[:1]:
for msg in run["messages"]:
role = msg["role"]
content = msg.get("content") or "[tool call]"
print(f"{role}: {content[:80]}")Companion Dataset
The 50 evaluation scenarios (task definitions, expected tool calls, user instructions) are at [Jourdain/delhIA-scenarios](https://huggingface.co/datasets/Jourdain/delhIA-scenarios).