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"""Scale test for IntelligentContextManager TOIN + CCR integration.
This tests that:
1. Dropped messages are stored in CCR
2. Drops are recorded to TOIN
3. The marker includes CCR reference
4. TOIN patterns accumulate across multiple compressions
"""
import json
import os
# Set API key from environment or use provided key
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
from headroom.cache.compression_store import get_compression_store
from headroom.config import IntelligentContextConfig
from headroom.telemetry import get_toin
from headroom.tokenizer import Tokenizer
from headroom.tokenizers import EstimatingTokenCounter
from headroom.transforms.intelligent_context import IntelligentContextManager
def create_large_conversation(num_turns: int = 50) -> list[dict]:
"""Create a large conversation with varied content."""
messages = [{"role": "system", "content": "You are a helpful coding assistant."}]
for i in range(num_turns):
# Vary content to create different importance levels
if i % 10 == 0:
# Error messages (should be preserved)
messages.append(
{"role": "user", "content": f"I'm getting an error: TypeError at line {i * 10}"}
)
messages.append(
{
"role": "assistant",
"content": f"The TypeError at line {i * 10} is caused by a type mismatch. "
f"Here's the fix:\n```python\n# Fix for error {i}\ndef fix_{i}():\n pass\n```",
}
)
elif i % 7 == 0:
# Tool calls (should stay atomic)
messages.append({"role": "user", "content": f"Search for files matching pattern_{i}"})
messages.append(
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": f"call_{i}",
"type": "function",
"function": {
"name": "search_files",
"arguments": f'{{"pattern": "pattern_{i}"}}',
},
}
],
}
)
messages.append(
{
"role": "tool",
"tool_call_id": f"call_{i}",
"content": json.dumps([f"file_{i}_a.py", f"file_{i}_b.py", f"file_{i}_c.py"]),
}
)
else:
# Regular conversation (lower priority)
messages.append(
{"role": "user", "content": f"Question {i}: Can you explain how feature_{i} works?"}
)
messages.append(
{
"role": "assistant",
"content": f"Feature_{i} is a component that handles processing. "
f"It works by iterating through the data and applying "
f"transformations. Here's a brief overview of the key aspects "
f"and how they interact with other parts of the system. "
f"The main entry point is the process() method which takes "
f"input data and returns the transformed output.",
}
)
return messages
def test_toin_ccr_integration():
"""Test TOIN + CCR integration with IntelligentContextManager."""
print("=" * 70)
print("TOIN + CCR Integration Test for IntelligentContextManager")
print("=" * 70)
# Get TOIN and CCR store
toin = get_toin()
store = get_compression_store()
# Record initial state
initial_patterns = len(toin._patterns) if hasattr(toin, "_patterns") else 0
# CCR store uses a backend, not direct _store
if hasattr(store, "_backend") and hasattr(store._backend, "_store"):
initial_store_size = len(store._backend._store)
else:
initial_store_size = 0
print("\nInitial state:")
print(f" TOIN patterns: {initial_patterns}")
print(f" CCR store entries: {initial_store_size}")
# Create manager with TOIN
config = IntelligentContextConfig(
enabled=True,
keep_system=True,
keep_last_turns=3,
output_buffer_tokens=2000,
use_importance_scoring=True,
)
manager = IntelligentContextManager(config=config, toin=toin)
tokenizer = Tokenizer(EstimatingTokenCounter())
# Run multiple compression cycles to accumulate TOIN patterns
print("\n" + "-" * 70)
print("Running compression cycles...")
print("-" * 70)
all_ccr_refs = []
for cycle in range(5):
# Create fresh conversation each cycle
messages = create_large_conversation(num_turns=30 + cycle * 5)
tokens_before = tokenizer.count_messages(messages)
# Set a tight limit to force dropping
model_limit = tokens_before // 2
result = manager.apply(
messages,
tokenizer,
model_limit=model_limit,
output_buffer=1000,
)
# Extract CCR reference from marker if present
ccr_ref = None
for marker in result.markers_inserted:
if "ccr_retrieve" in marker and "reference '" in marker:
start = marker.find("reference '") + len("reference '")
end = marker.find("'", start)
ccr_ref = marker[start:end]
all_ccr_refs.append(ccr_ref)
print(f"\nCycle {cycle + 1}:")
print(f" Messages: {len(messages)} → {len(result.messages)}")
print(
f" Tokens: {result.tokens_before} → {result.tokens_after} "
f"({100 * (1 - result.tokens_after / result.tokens_before):.1f}% reduction)"
)
print(f" Transforms: {result.transforms_applied}")
print(f" CCR reference: {ccr_ref or 'None'}")
# Check final state
final_patterns = len(toin._patterns) if hasattr(toin, "_patterns") else 0
if hasattr(store, "_backend") and hasattr(store._backend, "_store"):
final_store_size = len(store._backend._store)
else:
final_store_size = 0
print("\n" + "-" * 70)
print("Final state:")
print("-" * 70)
print(
f" TOIN patterns: {initial_patterns} → {final_patterns} (+{final_patterns - initial_patterns})"
)
print(
f" CCR store entries: {initial_store_size} → {final_store_size} (+{final_store_size - initial_store_size})"
)
print(f" CCR references created: {len(all_ccr_refs)}")
# Test retrieval from CCR
if all_ccr_refs:
print("\n" + "-" * 70)
print("Testing CCR retrieval...")
print("-" * 70)
ref = all_ccr_refs[-1] # Use the most recent reference
entry = store.retrieve(ref)
if entry:
# Parse the retrieved content from the CompressionEntry
try:
dropped_messages = json.loads(entry.original_content)
print(f" Retrieved {len(dropped_messages)} dropped messages from CCR")
print(f" First message role: {dropped_messages[0].get('role', 'unknown')}")
print(f" Content preview: {str(dropped_messages[0].get('content', ''))[:100]}...")
print(" Entry metadata:")
print(f" - Tool: {entry.tool_name}")
print(f" - Original tokens: {entry.original_tokens}")
print(f" - Compressed tokens: {entry.compressed_tokens}")
except json.JSONDecodeError:
print(f" Retrieved content (not JSON): {entry.original_content[:200]}...")
else:
print(f" WARNING: Could not retrieve CCR reference {ref}")
# Debug: check what's in the store
print(f" Store backend type: {type(store._backend)}")
if hasattr(store._backend, "_store"):
print(f" Backend store keys: {list(store._backend._store.keys())[:5]}...")
# Print TOIN statistics
print("\n" + "-" * 70)
print("TOIN Statistics:")
print("-" * 70)
stats = toin.get_stats()
print(f" Total patterns: {stats.get('total_patterns', 0)}")
print(f" Total compressions: {stats.get('total_compressions', 0)}")
print(f" Total retrievals: {stats.get('total_retrievals', 0)}")
print(f" Retrieval rate: {stats.get('retrieval_rate', 0):.1%}")
# Check for intelligent_context_drop patterns
drop_patterns = (
[
p
for p in toin._patterns.values()
if hasattr(p, "tool_name") and "intelligent_context" in str(getattr(p, "tool_name", ""))
]
if hasattr(toin, "_patterns")
else []
)
print(f" IntelligentContext drop patterns: {len(drop_patterns)}")
print("\n" + "=" * 70)
print("TEST COMPLETE")
print("=" * 70)
# Assertions
assert final_patterns >= initial_patterns, "TOIN should have recorded new patterns"
assert len(all_ccr_refs) > 0, "Should have created CCR references"
# CCR store entries should exist (though count may vary due to TTL)
if final_store_size == 0 and initial_store_size == 0:
print(" Note: CCR store size shows 0 (entries may have different backend)")
else:
assert final_store_size > initial_store_size, "CCR store should have new entries"
print("\n✓ All assertions passed!")
return True
def test_with_real_llm():
"""Test with a real LLM call to verify end-to-end flow."""
print("\n" + "=" * 70)
print("Real LLM Integration Test")
print("=" * 70)
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
print("Skipping real LLM test - OPENAI_API_KEY not set")
return
try:
from openai import OpenAI
client = OpenAI()
except ImportError:
print("Skipping real LLM test - openai package not installed")
return
# Create a conversation that will be compressed
messages = create_large_conversation(num_turns=20)
# Apply IntelligentContext compression
toin = get_toin()
config = IntelligentContextConfig(
enabled=True,
keep_system=True,
keep_last_turns=2,
)
manager = IntelligentContextManager(config=config, toin=toin)
tokenizer = Tokenizer(EstimatingTokenCounter())
tokens_before = tokenizer.count_messages(messages)
result = manager.apply(
messages,
tokenizer,
model_limit=tokens_before // 3, # Force significant compression
output_buffer=500,
)
print("\nCompression result:")
print(f" Messages: {len(messages)} → {len(result.messages)}")
print(f" Tokens: {result.tokens_before} → {result.tokens_after}")
# Convert to OpenAI format (filter out tool messages with None content)
openai_messages = []
for msg in result.messages:
if msg.get("role") == "tool":
continue # Skip tool messages for this test
if msg.get("content") is None:
continue # Skip messages with None content
openai_messages.append({"role": msg["role"], "content": msg["content"]})
# Add a question about the compressed context
openai_messages.append(
{
"role": "user",
"content": "Based on our conversation, what errors did we discuss? "
"If you see a message about compressed context, note the CCR reference.",
}
)
print(f"\nSending {len(openai_messages)} messages to OpenAI...")
try:
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=openai_messages,
max_tokens=500,
)
print("\nLLM Response:")
print("-" * 40)
print(response.choices[0].message.content)
print("-" * 40)
print(f"\nTokens used: {response.usage.total_tokens}")
except Exception as e:
print(f"LLM call failed: {e}")
if __name__ == "__main__":
# Run the TOIN + CCR integration test
test_toin_ccr_integration()
# Run real LLM test if API key available
test_with_real_llm()
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