🧭 The Chart Room
Four panels. Four perspectives. One truth.
A multi-model perspective library and web app — route a single query to four different model perspectives, analyze consensus and divergence.
| Panel | Navigator | What They See |
|---|---|---|
| 🎣 Top-left | Fisherman | Ecological detail, bottom structure, local patterns. What's actually there. |
| ⛵ Top-right | Sailor | Systemic flows, weather patterns, navigational structure. How things move. |
| 📸 Bottom-left | Tourist | Surface features, landmarks, narrative salience. What's obvious and memorable. |
| 🏝️ Bottom-right | Native | Negative space, absence patterns, what others don't see. What's missing. |
Each chart is a different attention configuration — a different way of allocating the same fixed cognitive budget (γ + H = C). The overlap is consensus. The divergence is discovery. The negative space is where the future is.
Library Usage
from chartroom import ChartRoom
room = ChartRoom()
result = room.navigate("What is consciousness?")
print(result["fisherman"]["text"]) # Ecological detail
print(result["sailor"]["text"]) # Systemic flows
print(result["tourist"]["text"]) # Surface features
print(result["native"]["text"]) # Negative space
print(result["consensus"]["level"]) # consensus | complementary | contradiction
print(result["divergence"]["points"]) # Unique observations per chart
Without an API key, ChartRoom runs in mock mode — useful for testing and demos.
Single Chart
room = ChartRoom()
result = room.chart("native", "What's missing from this design?")
Async (parallel API calls)
import asyncio
from chartroom import ChartRoom
async def main():
room = ChartRoom(api_key="sk-...")
result = await room.navigate_async("complex query here")
asyncio.run(main())
Web App
pip install chartroom[server]
python app.py
# Open http://localhost:5000
Installation
pip install chartroom # library only
pip install chartroom[server] # library + Flask web app
pip install chartroom[dev] # library + test tools
Configuration
Set via environment variables:
| Variable | Default | Description |
|---|---|---|
OPENAI_API_KEY |
— | API key for model provider |
OPENAI_API_BASE |
https://api.deepinfra.com/v1/openai |
Base URL |
FISHERMAN_MODEL |
deepseek-ai/DeepSeek-V4-Flash |
Fisherman chart model |
SAILOR_MODEL |
ByteDance/Seed-2.0-pro |
Sailor chart model |
TOURIST_MODEL |
deepreinforce-ai/Ornith-1.0-35B |
Tourist chart model |
NATIVE_MODEL |
moonshotai/Kimi-K2.7-Code |
Native chart model |
How It Works
Each chart gets a different system prompt that configures its attention — what it looks for, what it values, how it allocates its token budget. This is chart-system theory applied: same territory, different charts, different distortions.
The consensus analysis finds where charts agree (shared concepts above a frequency threshold). The divergence analysis finds where charts diverge (unique observations only one chart noticed). Together, they map the cognitive terrain.
License
MIT
Related
- chart-system — polyformal navigation framework
- skénna — negative-space navigation
Metadata
Release files for si-chartroom 0.1.0
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| File | Size | Uploaded | Provenance | Actions |
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
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| si_chartroom-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for si_chartroom-0.1.0-py3-none-any.whl |
Total release size: 16.7 kB
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