GlyphRunner System
1a0b45df9c
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
93 lines
2.6 KiB
Python
93 lines
2.6 KiB
Python
"""LLM Adapter
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Thin abstraction over a concrete LLM backend (local or remote).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, Optional
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@dataclass
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class LLMResponse:
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"""Container for a single LLM interaction."""
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prompt: str
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response: str
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tokens_prompt: Optional[int] = None
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tokens_response: Optional[int] = None
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model_name: Optional[str] = None
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metadata: Dict[str, Any] = None
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def to_dict(self) -> Dict[str, Any]:
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return {
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"prompt": self.prompt,
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"response": self.response,
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"tokens_prompt": self.tokens_prompt,
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"tokens_response": self.tokens_response,
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"model_name": self.model_name,
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"metadata": self.metadata or {},
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}
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class LLMAdapter:
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"""Adapter around a concrete LLM backend.
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backend: Callable that takes (prompt, **kwargs) and returns:
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- str
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- or dict with keys like:
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- response
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- tokens_prompt
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- tokens_response
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- model_name
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"""
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def __init__(
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self,
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backend: Callable[..., Any],
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model_name: Optional[str] = None,
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) -> None:
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self._backend = backend
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self._model_name = model_name or "unknown"
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def run(self, prompt: str, **kwargs: Any) -> LLMResponse:
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"""Run the underlying LLM on a prompt and normalize the result."""
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raw = self._backend(prompt, **kwargs)
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if isinstance(raw, str):
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return LLMResponse(
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prompt=prompt,
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response=raw,
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model_name=self._model_name,
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metadata={},
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)
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if isinstance(raw, dict):
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return LLMResponse(
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prompt=prompt,
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response=str(raw.get("response", "")),
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tokens_prompt=raw.get("tokens_prompt"),
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tokens_response=raw.get("tokens_response"),
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model_name=raw.get("model_name", self._model_name),
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metadata={
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k: v
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for k, v in raw.items()
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if k
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not in {
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"response",
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"tokens_prompt",
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"tokens_response",
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"model_name",
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}
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},
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)
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# Fallback: best-effort stringification
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return LLMResponse(
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prompt=prompt,
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response=str(raw),
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model_name=self._model_name,
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metadata={"raw_type": type(raw).__name__},
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)
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