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feat(docs): remove reference tool command from model configs API documentation

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The `python scripts/show_model_context_limits.py` command was removed from the documentation as it is no longer necessary or relevant for users configuring model context limits. The remaining content still provides clear guidance on calculating context limits in K tokens.
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YueGuobin 2026-03-06 01:54:04 +08:00
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5
docs/gns3-copilot/llm-model-configs-api.md
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@ -816,11 +816,6 @@ The `context_limit` value is specified in **K tokens** (thousands of tokens) to
GPT-4o: 128,000 tokens / 1000 = 128
```
3. **Use the reference tool**:
```bash
python scripts/show_model_context_limits.py
```
### Example: Common Models
| Model | Official Value | Configuration |

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scripts/show_model_context_limits.py
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@ -1,157 +0,0 @@
#!/usr/bin/env python3
"""
Reference tool for LLM model context limits.
This script provides reference context limit values for common LLM models.
It helps users find the correct context_limit value when creating LLM model configurations.
IMPORTANT:
- context_limit unit is K tokens (1 K = 1000 tokens)
- This tool only displays reference values. You MUST manually configure
context_limit when creating or updating LLM model configurations via API.
Usage:
python scripts/show_model_context_limits.py
For official documentation, always check:
- OpenAI: https://platform.openai.com/docs/models
- Anthropic: https://docs.anthropic.com/claude/docs/models-overview
- Google: https://ai.google.dev/gemini-api/docs/models
- DeepSeek: https://platform.deepseek.com/api-docs/
"""
import sys
from pathlib import Path
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent))
# Reference context limits (as of 2025)
# Displayed in K tokens for easier configuration
# Users should verify from official provider documentation
MODEL_CONTEXT_LIMITS_K = {
# OpenAI Models
"gpt-4o": 128,
"gpt-4o-mini": 128,
"gpt-4-turbo": 128,
"gpt-4": 8,
"gpt-4-32k": 33,
"gpt-3.5-turbo": 17,
"gpt-3.5-turbo-16k": 17,
# Anthropic Models
"claude-3-5-sonnet-20241022": 200,
"claude-3-5-sonnet-20240620": 200,
"claude-3-opus-20240229": 200,
"claude-3-sonnet-20240229": 200,
"claude-3-haiku-20240307": 200,
# Google Models
"gemini-2.0-flash-exp": 1000,
"gemini-1.5-pro": 2800,
"gemini-1.5-flash": 2800,
"gemini-pro": 92,
# DeepSeek Models
"deepseek-chat": 128,
"deepseek-coder": 128,
# xAI Models
"grok-beta": 128,
}
def find_context_limit_for_model(model_name: str) -> int | None:
"""Find the context limit for a given model name (in K tokens)."""
model_lower = model_name.lower().strip()
# Try exact match
if model_lower in MODEL_CONTEXT_LIMITS_K:
return MODEL_CONTEXT_LIMITS_K[model_lower]
# Try prefix match
for key, limit in MODEL_CONTEXT_LIMITS_K.items():
if model_lower.startswith(key.lower()):
return limit
return None
def main():
print("=" * 70)
print("LLM Model Context Limits Reference Tool")
print("=" * 70)
print()
print("This tool displays reference context limit values for common LLM models.")
print("Please verify from official provider documentation before configuring.")
print()
print("IMPORTANT: context_limit unit is K tokens (1 K = 1,000 tokens)")
print()
print("Official Documentation:")
print(" - OpenAI: https://platform.openai.com/docs/models")
print(" - Anthropic: https://docs.anthropic.com/claude/docs/models-overview")
print(" - Google: https://ai.google.dev/gemini-api/docs/models")
print(" - DeepSeek: https://platform.deepseek.com/api-docs/")
print()
# Display all reference values
print("=" * 70)
print("Reference Context Limits (in K tokens)")
print("=" * 70)
print()
# Group by provider
providers = {
"OpenAI": ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4", "gpt-4-32k", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
"Anthropic": ["claude-3-5-sonnet-20241022", "claude-3-5-sonnet-20240620", "claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"],
"Google": ["gemini-2.0-flash-exp", "gemini-1.5-pro", "gemini-1.5-flash", "gemini-pro"],
"DeepSeek": ["deepseek-chat", "deepseek-coder"],
"xAI": ["grok-beta"],
}
for provider, models in providers.items():
print(f"\n{provider}:")
for model in models:
if model in MODEL_CONTEXT_LIMITS_K:
limit_k = MODEL_CONTEXT_LIMITS_K[model]
limit_actual = limit_k * 1000
print(f" {model:40s} → {limit_k:4d}K (= {limit_actual:,} tokens)")
print()
print("=" * 70)
print()
print("Conversion Examples:")
print()
print(" Official Documentation: 128,000 tokens")
print(" ↓")
print(" API Configuration: \"context_limit\": 128")
print()
print(" Official Documentation: 200,000 tokens")
print(" ↓")
print(" API Configuration: \"context_limit\": 200")
print()
print(" Official Documentation: 2,800,000 tokens")
print(" ↓")
print(" API Configuration: \"context_limit\": 2800")
print()
print("=" * 70)
print()
print("Usage Example:")
print()
print("When creating a model configuration, specify context_limit in K:")
print()
print(' POST /v3/users/{user_id}/llm-model-configs')
print(' {')
print(' "name": "GPT-4o Configuration",')
print(' "provider": "openai",')
print(' "model": "gpt-4o",')
print(' "context_limit": 128, // ← Required: 128K = 128,000 tokens')
print(' "context_strategy": "balanced"')
print(' }')
print()
print("=" * 70)
if __name__ == "__main__":
main()