workflow_example.mdx

Complete Workflow Example

Here's how to use the compressed context in your AI workflow:

```python # Step 1: Compress your context with ScaleDown compressed_response = requests.post( "[https://api.scaledown.xyz/compress/raw/](https://api.scaledown.xyz/compress/raw/)", headers=headers, json={ "context": "Long context about your topic...", "prompt": "What specific question you want answered", "model": "gpt-4o", "scaledown": {"rate": "auto"} } )

compressed_context = compressed_response.json()["compressed_prompt"]

# Step 2: Use compressed context with your AI provider
your_actual_question = "What specific question you want answered"

final_prompt = f"""
System: You are a helpful assistant that answers questions using the provided context
Context: {compressed_context}
User: {your_actual_question}
"""

# Step 3: Send to your AI provider (OpenAI, etc.)
import openai
ai_response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": final_prompt}]
)
```

---

Advanced Configuration Options

Once you're comfortable with basic compression, you can fine-tune your requests:

Real Example

```python # Example: Compress context about Messi payload = { "context": "Lionel Messi is an Argentine professional footballer who plays as a forward...", "prompt": "How many Ballon d'Or awards does Messi have?", "model": "gpt-4o", "scaledown": { "rate": "auto" } }

# The response will contain compressed context that you can use
# to build your final prompt for the AI model
```