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https://github.com/headroomlabs-ai/headroom.git
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f121138c8a
ROOT CAUSE: compress() did not extract the user's question from messages. The pipeline received empty context, so SmartCrusher selected items by statistics only (position, anomaly, boundary) — keeping irrelevant chunks and dropping relevant ones. FIX: _extract_user_query() in compress.py finds the most recent user message and passes it as `context` kwarg through the pipeline. SmartCrusher's RelevanceScorer now receives the actual query and scores items by relevance. Before: 12 RAG chunks → kept hallucination/video (0/6 key terms) After: 12 RAG chunks → kept reward hacking content (3/4 key terms) Also adds: - examples/context_compression_demo.py — real compression demo for OSS PR - examples/test_ccr.py — content preservation verification - OSS_PR_STRATEGY.md — PR target list for LangChain ecosystem Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""Test CCR markers and content preservation in compressed output."""
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from __future__ import annotations
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import json
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import sys
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sys.path.insert(0, ".")
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from examples.context_compression_demo import build_retriever_chunks
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from headroom import compress
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def main():
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chunks = build_retriever_chunks()
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retriever_json = json.dumps(chunks, indent=2)
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messages = [
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{"role": "user", "content": "What are the types of reward hacking discussed in the blogs?"},
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": "call_001",
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"type": "function",
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"function": {
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"name": "retrieve_blog_posts",
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"arguments": json.dumps({"query": "types of reward hacking"}),
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},
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}
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],
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},
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{"role": "tool", "tool_call_id": "call_001", "content": retriever_json},
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]
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result = compress(messages, model="claude-sonnet-4-5-20250929")
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compressed_tool = str(result.messages[2].get("content", ""))
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print("=== Compressed tool output (FULL) ===")
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print(compressed_tool)
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print()
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print(f"Tokens: {result.tokens_before} -> {result.tokens_after} ({result.tokens_saved} saved)")
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print(f"Transforms: {result.transforms_applied}")
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print()
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# Check for CCR markers
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if "hash=" in compressed_tool:
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print("CCR MARKERS FOUND — LLM can retrieve originals")
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else:
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print("No CCR markers")
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print()
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# Check key content
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key_terms = {
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"reward tampering": False,
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"sycophancy": False,
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"specification gaming": False,
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"proxy gaming": False,
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"reward model hacking": False,
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"distribution shift": False,
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}
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for term in key_terms:
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key_terms[term] = term.lower() in compressed_tool.lower()
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status = "FOUND" if key_terms[term] else "MISSING"
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print(f" {term}: {status}")
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found = sum(1 for v in key_terms.values() if v)
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print(f"\n{found}/{len(key_terms)} key concepts preserved in compressed output")
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if __name__ == "__main__":
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main()
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