some cleanups

tests/test_nous_api_pattern.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Test to understand the pattern of failures - it's not about content length!
+"""
+
+import asyncio
+import os
+from openai import AsyncOpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
+
+nous_client = AsyncOpenAI(
+    api_key=os.getenv("NOUS_API_KEY"),
+    base_url="https://inference-api.nousresearch.com/v1"
+)
+
+MODEL = "gemini-2.5-flash"
+
+async def quick_test(description: str, content: str, **kwargs):
+    """Quick API test."""
+    print(f"\n{description} ({len(content):,} chars)...", end=" ")
+    
+    try:
+        response = await nous_client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {"role": "system", "content": "Summarize this."},
+                {"role": "user", "content": content}
+            ],
+            **kwargs
+        )
+        print(f"✅ SUCCESS")
+        return True
+    except Exception as e:
+        print(f"❌ FAILED: {str(e)[:80]}")
+        return False
+
+async def main():
+    print("Testing different content types and parameters...")
+    
+    # Theory 1: Repeated characters trigger validation
+    print("\n" + "="*60)
+    print("THEORY 1: Repeated characters")
+    print("="*60)
+    await quick_test("Repeated 'A's (5k)", "A" * 5000, temperature=0.1, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("Repeated 'A's (79k)", "A" * 79625, temperature=0.1, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("Varied text (5k)", "Test content. " * 400, temperature=0.1, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("Varied text (79k)", "Test content with variety. " * 3000, temperature=0.1, max_tokens=4000)
+    
+    # Theory 2: max_tokens parameter
+    print("\n" + "="*60)
+    print("THEORY 2: max_tokens parameter")
+    print("="*60)
+    content = "Test " * 4000  # 20k chars
+    await quick_test("max_tokens=4000", content, temperature=0.1, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("max_tokens=8000", content, temperature=0.1, max_tokens=8000)
+    await asyncio.sleep(0.5)
+    await quick_test("max_tokens=2000", content, temperature=0.1, max_tokens=2000)
+    await asyncio.sleep(0.5)
+    await quick_test("No max_tokens", content, temperature=0.1)
+    
+    # Theory 3: Temperature parameter
+    print("\n" + "="*60)
+    print("THEORY 3: Temperature parameter")
+    print("="*60)
+    content = "Test " * 4000
+    await quick_test("temperature=0.1", content, temperature=0.1, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("temperature=0.0", content, temperature=0.0, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("temperature=0.5", content, temperature=0.5, max_tokens=4000)
+    await asyncio.sleep(0.5)
+    await quick_test("No temperature", content, max_tokens=4000)
+    
+    # Theory 4: System prompt impact
+    print("\n" + "="*60)
+    print("THEORY 4: System prompt length")
+    print("="*60)
+    
+    short_system = "Summarize this."
+    long_system = """You are an expert content analyst. Your job is to process web content and create a comprehensive yet concise summary that preserves all important information while dramatically reducing bulk.
+
+Create a well-structured markdown summary that includes:
+1. Key excerpts (quotes, code snippets, important facts) in their original format
+2. Comprehensive summary of all other important information
+3. Proper markdown formatting with headers, bullets, and emphasis
+
+Your goal is to preserve ALL important information while reducing length."""
+    
+    content = "A" * 5000
+    
+    print(f"\nShort system prompt...", end=" ")
+    try:
+        response = await nous_client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {"role": "system", "content": short_system},
+                {"role": "user", "content": content}
+            ],
+            temperature=0.1,
+            max_tokens=4000
+        )
+        print(f"✅ SUCCESS")
+    except Exception as e:
+        print(f"❌ FAILED")
+    
+    await asyncio.sleep(0.5)
+    
+    print(f"Long system prompt...", end=" ")
+    try:
+        response = await nous_client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {"role": "system", "content": long_system},
+                {"role": "user", "content": content}
+            ],
+            temperature=0.1,
+            max_tokens=4000
+        )
+        print(f"✅ SUCCESS")
+    except Exception as e:
+        print(f"❌ FAILED")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+