Add LLMCompress subsystem - sandbox for symbolic compression of LLM behavior
New subsystem fully self-contained: Components: - LLMCompress/llm_adapter.py: LLMAdapter + LLMResponse (abstract over LLM backends) - LLMCompress/compression_report.py: CompressionReport (symbolic analysis results) - LLMCompress/llm_compressor.py: compress_interaction() and compress_session() - LLMCompress/tests/test_llm_compress.py: 5 comprehensive tests Integration: - Uses GlyphOS Cognitive Kernel for symbolic analysis - Integrates with GlyphOS Event System - Emits cognition.started and cognition.completed events - Supports in-memory GX execution via execute_symbolic() Test Coverage: - LLMCompress tests: 5/5 PASS - All existing tests still pass (52/52) - Total: 57 tests passing Bug fixes in cognitive_kernel.py: - Fixed execute_symbolic() method calls to use correct function signatures - normalize_segments(manifest, segments, payload) - map_lanes(segments) - build_envelope(manifest, lanes, payload, context) - execute_with_lain(envelope) Constraints: - No modifications to gx_compiler/* - No modifications to glyphs/super_registry.py - Self-contained subsystem with proper isolation - Full backward compatibility maintained
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GlyphRunner System
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"""LLM symbolic compressor.
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Feeds LLM interactions through the GlyphOS Cognitive Kernel and produces
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a symbolic CompressionReport.
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"""
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from __future__ import annotations
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import json
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from typing import Any, Dict, List, Optional
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from glyphos.cognitive_kernel import get_kernel
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from glyphos.events import emit
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from .llm_adapter import LLMAdapter, LLMResponse
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from .compression_report import CompressionReport
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def _interaction_to_payload(interaction: LLMResponse) -> bytes:
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"""Serialize a single interaction into a payload for symbolic analysis."""
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data = interaction.to_dict()
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return json.dumps(data, ensure_ascii=False, sort_keys=True).encode("utf-8")
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def compress_interaction(
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adapter: LLMAdapter,
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prompt: str,
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*,
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mode: str = "analyze",
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context: Optional[Dict[str, Any]] = None,
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**llm_kwargs: Any,
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) -> CompressionReport:
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"""Run a single LLM interaction through the symbolic stack."""
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interaction = adapter.run(prompt, **llm_kwargs)
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emit(
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"cognition.started",
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{
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"source": "LLMCompress",
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"mode": mode,
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"prompt_preview": prompt[:120],
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},
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)
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manifest: Dict[str, Any] = {
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"type": "llm_interaction",
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"source": "LLMCompress",
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"model_name": interaction.model_name,
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}
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segments: List[Dict[str, Any]] = []
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payload: bytes = _interaction_to_payload(interaction)
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kernel = get_kernel()
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exec_context: Dict[str, Any] = context.copy() if context else {}
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exec_context.setdefault("source", "LLMCompress")
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exec_context.setdefault("interaction_type", "single")
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result = kernel.execute_symbolic(
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manifest=manifest,
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segments=segments,
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payload=payload,
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mode=mode,
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context=exec_context,
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)
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fused_symbol = result.get("fused_symbol", {})
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diagnostics = result.get("diagnostics", {})
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cognition_trace = result.get("cognition_trace", [])
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glyph_res = diagnostics.get("glyph_resonance") or {}
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glyph_ids: List[str] = []
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if isinstance(glyph_res, dict):
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gid = glyph_res.get("glyph_id")
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if isinstance(gid, str):
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glyph_ids.append(gid)
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report = CompressionReport(
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interactions=[interaction.to_dict()],
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fused_symbol=fused_symbol,
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diagnostics=diagnostics,
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cognition_trace=cognition_trace,
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glyph_ids=glyph_ids,
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glyph_resonance=glyph_res or None,
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tags=["llm_compress", mode],
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metadata={"model_name": interaction.model_name},
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)
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emit(
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"cognition.completed",
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{
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"source": "LLMCompress",
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"mode": mode,
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"model_name": interaction.model_name,
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"glyph_resonance": glyph_res or None,
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"summary": fused_symbol.get("summary"),
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},
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)
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return report
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def compress_session(
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adapter: LLMAdapter,
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prompts: List[str],
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*,
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mode: str = "analyze",
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context: Optional[Dict[str, Any]] = None,
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**llm_kwargs: Any,
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) -> CompressionReport:
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"""Compress a multi-turn LLM session into a single symbolic report."""
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interactions = [adapter.run(p, **llm_kwargs) for p in prompts]
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session_data = [i.to_dict() for i in interactions]
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payload = json.dumps(
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{"session": session_data},
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ensure_ascii=False,
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sort_keys=True,
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).encode("utf-8")
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manifest: Dict[str, Any] = {
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"type": "llm_session",
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"source": "LLMCompress",
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"turns": len(interactions),
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"model_name": interactions[0].model_name if interactions else None,
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}
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segments: List[Dict[str, Any]] = []
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kernel = get_kernel()
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exec_context: Dict[str, Any] = context.copy() if context else {}
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exec_context.setdefault("source", "LLMCompress")
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exec_context.setdefault("interaction_type", "session")
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emit(
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"cognition.started",
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{
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"source": "LLMCompress",
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"mode": mode,
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"turns": len(interactions),
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},
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)
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result = kernel.execute_symbolic(
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manifest=manifest,
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segments=segments,
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payload=payload,
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mode=mode,
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context=exec_context,
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)
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fused_symbol = result.get("fused_symbol", {})
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diagnostics = result.get("diagnostics", {})
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cognition_trace = result.get("cognition_trace", [])
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glyph_res = diagnostics.get("glyph_resonance") or {}
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glyph_ids: List[str] = []
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if isinstance(glyph_res, dict):
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gid = glyph_res.get("glyph_id")
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if isinstance(gid, str):
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glyph_ids.append(gid)
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report = CompressionReport(
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interactions=session_data,
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fused_symbol=fused_symbol,
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diagnostics=diagnostics,
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cognition_trace=cognition_trace,
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glyph_ids=glyph_ids,
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glyph_resonance=glyph_res or None,
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tags=["llm_compress", "session", mode],
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metadata={
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"model_name": manifest.get("model_name"),
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"turns": len(interactions),
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},
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)
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emit(
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"cognition.completed",
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{
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"source": "LLMCompress",
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"mode": mode,
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"turns": len(interactions),
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"glyph_resonance": glyph_res or None,
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"summary": fused_symbol.get("summary"),
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},
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)
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return report
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