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chore: import upstream snapshot with attribution
2026-07-13 13:10:45 +08:00

48 lines
1.6 KiB
Python

"""Session-scoped grounding context aggregating the LLM grounding caches.
A single :class:`GroundingContext` owns one instance of each cached grounding
reference (docs, AGENTS.md). It is created per ``Session`` and threaded
through prompt assembly, so the grounding caches have a clear, process-scoped
lifetime with no module-level mutable globals.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from core.agent_harness.grounding.agents_md_reference import (
AgentsMdReference,
)
from core.agent_harness.grounding.diagnostics import (
GroundingSource,
log_grounding_cache_diagnostics,
)
from core.agent_harness.grounding.docs_reference import DocsReference
@dataclass
class GroundingContext:
"""Owns the per-session repo-level grounding caches and exposes diagnostics."""
docs: DocsReference = field(default_factory=DocsReference)
agents_md: AgentsMdReference = field(default_factory=AgentsMdReference)
def iter_sources(self) -> list[GroundingSource]:
"""Return each cache as a :class:`GroundingSource` for diagnostics display."""
return [
self.docs.as_grounding_source(),
self.agents_md.as_grounding_source(),
]
def log_cache_diagnostics(self, reason: str) -> None:
"""Log all grounding cache stats when ``TRACER_VERBOSE=1``."""
log_grounding_cache_diagnostics(self.iter_sources(), reason)
def invalidate(self) -> None:
"""Drop every grounding cache (tests, forced refresh)."""
self.docs.invalidate()
self.agents_md.invalidate()
__all__ = ["GroundingContext"]