docs: update compression example with larger input context

- Add substantial AI history document as context (~3000+ chars)
- Demonstrate meaningful compression on large input
- Show percentage of tokens saved
- Explain that compression works on input tokens

Author: CLEMENTINATOR

example/compression.py

@@ -1,9 +1,12 @@
 """Example: Token compression with Edgee Gateway SDK
 
 This example demonstrates how to:
-1. Enable compression for a request
+1. Enable compression for a request with a large input context
 2. Set a custom compression rate
 3. Access compression metrics from the response
+
+Note: Compression works on INPUT tokens, so this example includes a large
+context document to demonstrate meaningful compression savings.
 """
 
 import os
@@ -17,18 +20,73 @@
 # Initialize the client
 edgee = Edgee(os.environ.get("EDGEE_API_KEY"))
 
+# Large context document to demonstrate input compression
+LARGE_CONTEXT = """
+The History and Impact of Artificial Intelligence
+
+Artificial intelligence (AI) has evolved from a theoretical concept to a 
+transformative technology that influences nearly every aspect of modern life. 
+The field began in earnest in the 1950s when pioneers like Alan Turing and 
+John McCarthy laid the groundwork for machine intelligence.
+
+Early developments focused on symbolic reasoning and expert systems. These 
+rule-based approaches dominated the field through the 1970s and 1980s, with 
+systems like MYCIN demonstrating practical applications in medical diagnosis. 
+However, these early systems were limited by their inability to learn from data 
+and adapt to new situations.
+
+The resurgence of neural networks in the 1980s and 1990s, particularly with 
+backpropagation algorithms, opened new possibilities. Yet it wasn't until the 
+2010s, with the advent of deep learning and the availability of massive datasets 
+and computational power, that AI truly began to revolutionize industries.
+
+Modern AI applications span numerous domains:
+- Natural language processing enables machines to understand and generate human language
+- Computer vision allows machines to interpret visual information from the world
+- Robotics combines AI with mechanical systems for autonomous operation
+- Healthcare uses AI for diagnosis, drug discovery, and personalized treatment
+- Finance leverages AI for fraud detection, algorithmic trading, and risk assessment
+- Transportation is being transformed by autonomous vehicles and traffic optimization
+
+The development of large language models like GPT, BERT, and others has 
+particularly accelerated progress in natural language understanding and generation. 
+These models, trained on vast amounts of text data, can perform a wide range of 
+language tasks with remarkable proficiency.
+
+Despite remarkable progress, significant challenges remain. Issues of bias, 
+interpretability, safety, and ethical considerations continue to be areas of 
+active research and debate. The AI community is working to ensure that these 
+powerful technologies are developed and deployed responsibly, with consideration 
+for their societal impact.
+
+Looking forward, AI is expected to continue advancing rapidly, with potential 
+breakthroughs in areas like artificial general intelligence, quantum machine 
+learning, and brain-computer interfaces. The integration of AI into daily life 
+will likely deepen, raising important questions about human-AI collaboration, 
+workforce transformation, and the future of human cognition itself.
+"""
+
 print("=" * 70)
 print("Edgee Token Compression Example")
 print("=" * 70)
 print()
 
-# Example 1: Request with compression enabled
-print("Example 1: Request with compression enabled")
+# Example: Request with compression enabled and large input
+print("Example: Large context with compression enabled")
 print("-" * 70)
+print(f"Input context length: {len(LARGE_CONTEXT)} characters")
+print()
+
 response = edgee.send(
     model="gpt-4o",
     input={
-        "messages": [{"role": "user", "content": "Explain quantum computing in simple terms."}],
+        "messages": [
+            {"role": "system", "content": LARGE_CONTEXT},
+            {
+                "role": "user",
+                "content": "Based on the context above, summarize the key milestones in AI development in 3 bullet points.",
+            },
+        ],
         "enable_compression": True,
         "compression_rate": 0.5,
     },
@@ -51,7 +109,18 @@
     print(f"  Input tokens:  {response.compression.input_tokens}")
     print(f"  Saved tokens:  {response.compression.saved_tokens}")
     print(f"  Compression rate: {response.compression.rate:.2%}")
-    print(f"  Token savings: {response.compression.saved_tokens} tokens saved!")
+    savings_pct = (
+        (response.compression.saved_tokens / response.compression.input_tokens * 100)
+        if response.compression.input_tokens > 0
+        else 0
+    )
+    print(f"  Savings: {savings_pct:.1f}% of input tokens saved!")
+    print()
+    print(f"  💡 Without compression, this request would have used")
+    print(f"     {response.compression.input_tokens} input tokens.")
+    print(
+        f"     With compression, only {response.compression.input_tokens - response.compression.saved_tokens} tokens were processed!"
+    )
 else:
     print("No compression data available in response.")
     print("Note: Compression data is only returned when compression is enabled")