Chapter 3: Quick Start Guide¶
Your First 5 Minutes¶
This guide will get you up and running with Virginia Clemm Poe in just a few minutes. By the end, you'll have:
- ✅ Installed and configured the package
- ✅ Updated your local model dataset
- ✅ Found and analyzed AI models
- ✅ Used both Python API and CLI
Step 1: Installation and Setup¶
# Install the package
pip install virginia-clemm-poe
# Set up browser for web scraping
virginia-clemm-poe setup
# Set your Poe API key
export POE_API_KEY="your_poe_api_key_here"
Get Your API Key
Visit Poe.com → Settings → API to generate your free API key.
Step 2: Initial Data Update¶
This command will: - Fetch all models from the Poe API - Scrape pricing information from the website - Save the enriched dataset locally
First Run
The first update may take 5-10 minutes as it scrapes data for hundreds of models. Subsequent updates are much faster as they only update changed models.
Step 3: Basic CLI Usage¶
Search for Models¶
# Find Claude models
virginia-clemm-poe search "claude"
# Find GPT models
virginia-clemm-poe search "gpt"
# Find models by capability
virginia-clemm-poe search "image"
List All Models¶
# Show all available models
virginia-clemm-poe list
# Show only models with pricing data
virginia-clemm-poe list --with-pricing
# Show models in JSON format
virginia-clemm-poe list --format json
Get Model Details¶
Step 4: Basic Python API Usage¶
Create a Python script to explore the model data:
# quick_start.py
from virginia_clemm_poe import api
def main():
# Search for models
print("🔍 Searching for Claude models...")
claude_models = api.search_models(query="claude")
print(f"Found {len(claude_models)} Claude models")
# Get a specific model
print("\n📊 Getting Claude 3 Opus details...")
opus = api.get_model_by_id("claude-3-opus")
if opus:
print(f"Model: {opus.model_name}")
print(f"Description: {opus.description}")
if opus.pricing:
input_cost = opus.pricing.details.get("Input (text)", "N/A")
print(f"Input cost: {input_cost}")
# List all models with pricing
print("\n💰 Models with pricing data...")
models_with_pricing = api.list_models(with_pricing=True)
print(f"Found {len(models_with_pricing)} models with pricing")
# Find cheapest text model
print("\n🎯 Finding cheapest text models...")
text_models = [m for m in models_with_pricing
if m.pricing and "Input (text)" in m.pricing.details]
if text_models:
# Sort by input cost (assuming cost is in format like "$0.015 / 1k tokens")
def extract_cost(model):
cost_str = model.pricing.details.get("Input (text)", "$999")
# Simple extraction - in real use, you'd want more robust parsing
try:
return float(cost_str.replace("$", "").split()[0])
except:
return 999.0
cheapest = min(text_models, key=extract_cost)
print(f"Cheapest: {cheapest.model_name}")
print(f"Cost: {cheapest.pricing.details['Input (text)']}")
if __name__ == "__main__":
main()
Run the script:
Common Use Cases¶
Use Case 1: Find Models by Price Range¶
from virginia_clemm_poe import api
def find_affordable_models(max_cost=0.01):
"""Find models under a certain cost threshold."""
models = api.list_models(with_pricing=True)
affordable = []
for model in models:
if model.pricing and "Input (text)" in model.pricing.details:
cost_str = model.pricing.details["Input (text)"]
# Extract numeric cost (simplified)
try:
cost = float(cost_str.replace("$", "").split()[0])
if cost <= max_cost:
affordable.append((model.model_name, cost))
except:
continue
return sorted(affordable, key=lambda x: x[1])
# Find models under $0.01 per 1k tokens
cheap_models = find_affordable_models(0.01)
for name, cost in cheap_models[:5]:
print(f"{name}: ${cost}")
Use Case 2: Compare Model Capabilities¶
from virginia_clemm_poe import api
def compare_models(model_ids):
"""Compare multiple models side by side."""
models = [api.get_model_by_id(mid) for mid in model_ids]
print(f"{'Model':<20} {'Input Cost':<15} {'Output Cost':<15}")
print("-" * 50)
for model in models:
if model and model.pricing:
input_cost = model.pricing.details.get("Input (text)", "N/A")
output_cost = model.pricing.details.get("Bot message", "N/A")
print(f"{model.model_name:<20} {input_cost:<15} {output_cost:<15}")
# Compare popular models
compare_models([
"claude-3-opus",
"gpt-4",
"claude-3-sonnet"
])
Use Case 3: Monitor Model Availability¶
#!/bin/bash
# monitor_models.sh - Check if specific models are available
models=("claude-3-opus" "gpt-4" "gemini-pro")
for model in "${models[@]}"; do
echo "Checking $model..."
virginia-clemm-poe info "$model" > /dev/null 2>&1
if [ $? -eq 0 ]; then
echo "✅ $model is available"
else
echo "❌ $model is not available"
fi
done
CLI Workflow Examples¶
Daily Update Routine¶
#!/bin/bash
# daily_update.sh - Daily model data maintenance
echo "🔄 Starting daily update..."
# Update models that might have changed
virginia-clemm-poe update --pricing --changed-only
# Check for new models
virginia-clemm-poe update --new-only
# Generate a summary report
virginia-clemm-poe stats
echo "✅ Daily update complete"
Research Workflow¶
# 1. Update dataset
virginia-clemm-poe update --all
# 2. Search for specific capabilities
virginia-clemm-poe search "vision" > vision_models.txt
virginia-clemm-poe search "code" > coding_models.txt
# 3. Get detailed pricing for interesting models
virginia-clemm-poe info "claude-3-opus" --format json > opus_details.json
virginia-clemm-poe info "gpt-4-vision" --format json > gpt4v_details.json
# 4. Generate comparison report
virginia-clemm-poe compare "claude-3-opus" "gpt-4" --output report.html
Integration Examples¶
Jupyter Notebook Integration¶
# In Jupyter notebook
import pandas as pd
from virginia_clemm_poe import api
# Load all models into a DataFrame
models = api.list_models(with_pricing=True)
df = pd.DataFrame([
{
'name': m.model_name,
'provider': m.bot_info.creator if m.bot_info else 'Unknown',
'input_cost': m.pricing.details.get('Input (text)', 'N/A') if m.pricing else 'N/A',
'description': m.description[:100] + '...' if len(m.description) > 100 else m.description
}
for m in models
])
# Analyze the data
print(f"Total models: {len(df)}")
print(f"Unique providers: {df['provider'].nunique()}")
df.head()
FastAPI Integration¶
from fastapi import FastAPI
from virginia_clemm_poe import api
app = FastAPI()
@app.get("/models/search/{query}")
def search_models(query: str):
"""Search for models matching the query."""
models = api.search_models(query=query)
return {"query": query, "count": len(models), "models": models}
@app.get("/models/{model_id}")
def get_model(model_id: str):
"""Get detailed information about a specific model."""
model = api.get_model_by_id(model_id)
if not model:
return {"error": "Model not found"}
return model
@app.get("/stats")
def get_stats():
"""Get statistics about the model dataset."""
all_models = api.list_models()
with_pricing = api.list_models(with_pricing=True)
return {
"total_models": len(all_models),
"models_with_pricing": len(with_pricing),
"coverage": len(with_pricing) / len(all_models) * 100
}
Next Steps¶
Now that you've got the basics down, explore:
- Python API Reference - Complete API documentation
- CLI Commands - All available command-line options
- Data Models - Understanding the data structures
- Configuration - Advanced configuration options
Quick Reference¶
Essential Commands¶
# Setup
virginia-clemm-poe setup
virginia-clemm-poe update --pricing
# Search and explore
virginia-clemm-poe search "query"
virginia-clemm-poe list --with-pricing
virginia-clemm-poe info "model-id"
# Maintenance
virginia-clemm-poe update --changed-only
virginia-clemm-poe stats
virginia-clemm-poe diagnose
Essential Python Imports¶
Performance Tips
- Use
--changed-onlyfor faster updates - Cache search results for repeated queries
- Use
--format jsonfor programmatic processing - Monitor logs with
--verbosefor debugging