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732b14f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | """Speculative tool execution for the inspector agent loop (Phase 2)."""
from __future__ import annotations
import asyncio
import json
import logging
from collections.abc import Awaitable, Callable
from dataclasses import dataclass
from typing import Any
from app.config import settings
logger = logging.getLogger(__name__)
DispatchFn = Callable[..., Awaitable[tuple[str, list[Any]]]]
def speculative_execution_active() -> bool:
return bool(settings.enable_async_pipeline and settings.enable_speculative_executor)
@dataclass(frozen=True)
class ToolPattern:
"""Stored tuple: context suffix → predicted tool with empirical probability."""
context_suffix: tuple[str, ...]
predicted_tool: str
param_mapping_fn: Callable[[dict[str, Any]], dict[str, Any]]
empirical_probability: float
@dataclass
class _InflightSpeculation:
tool_name: str
args: dict[str, Any]
task: asyncio.Task[tuple[str, list[Any]]]
def _default_param_mapping(_ctx: dict[str, Any]) -> dict[str, Any]:
return {}
def _mapping_from_last_query(ctx: dict[str, Any]) -> dict[str, Any]:
q = str(ctx.get("last_query") or "").strip()
if not q:
return {"query": "RICS inspection evidence", "k": 14, "rerank_top_n": 7}
return {"query": q, "k": 14, "rerank_top_n": 7}
def _mapping_section_plan(_ctx: dict[str, Any]) -> dict[str, Any]:
return {
"section_code": _ctx.get("section_code"),
"outline": _ctx.get("outline") or "Follow extraction audit.",
}
def _mapping_for_learned_tool(tool_name: str) -> Callable[[dict[str, Any]], dict[str, Any]]:
"""Pick a param mapper for auto-registered patterns."""
if tool_name == "retrieve_survey_rag":
return _mapping_from_last_query
if tool_name == "submit_section_plan":
return _mapping_section_plan
return _default_param_mapping
class PatternRegistry:
"""Exact-sequence pattern store with promotion/cancel speculative dispatch."""
def __init__(
self,
*,
context_window: int | None = None,
probability_threshold: float | None = None,
) -> None:
self._window = int(context_window or settings.speculative_context_window)
self._threshold = float(
probability_threshold or settings.speculative_probability_threshold
)
self._patterns: list[ToolPattern] = []
self._sequence_counts: dict[tuple[str, ...], dict[str, int]] = {}
self._register_builtins()
def _register_builtins(self) -> None:
self.register(
ToolPattern(
context_suffix=("submit_extraction_audit",),
predicted_tool="submit_section_plan",
param_mapping_fn=_mapping_section_plan,
empirical_probability=0.85,
)
)
self.register(
ToolPattern(
context_suffix=("retrieve_survey_rag",),
predicted_tool="retrieve_survey_rag",
param_mapping_fn=_mapping_from_last_query,
empirical_probability=0.78,
)
)
def register(self, pattern: ToolPattern) -> None:
self._patterns.append(pattern)
def match(self, trace_tools: list[str]) -> ToolPattern | None:
if len(trace_tools) < 1:
return None
suffix = tuple(trace_tools[-self._window :])
best: ToolPattern | None = None
for pat in self._patterns:
n = len(pat.context_suffix)
if len(suffix) < n or suffix[-n:] != pat.context_suffix:
continue
if pat.empirical_probability >= self._threshold:
if best is None or pat.empirical_probability > best.empirical_probability:
best = pat
return best
def record_outcome(self, trace_tools: list[str], actual_tool: str, _args: dict[str, Any]) -> None:
"""Promotion mechanism: reinforce sequences that led to ``actual_tool``."""
if len(trace_tools) < 1:
return
prefix = tuple(trace_tools[:-1]) if len(trace_tools) > 1 else tuple()
key = prefix[-self._window :] if prefix else tuple()
bucket = self._sequence_counts.setdefault(key, {})
bucket[actual_tool] = bucket.get(actual_tool, 0) + 1
self._maybe_register_learned_pattern(key, actual_tool)
def _maybe_register_learned_pattern(
self,
context_key: tuple[str, ...],
predicted_tool: str,
) -> None:
if not context_key:
return
min_obs = int(getattr(settings, "speculative_learn_min_observations", 5))
bucket = self._sequence_counts.get(context_key, {})
count = int(bucket.get(predicted_tool, 0))
if count < min_obs:
return
total = sum(bucket.values())
if total < min_obs:
return
probability = count / total
if probability < self._threshold:
return
suffix = context_key[-self._window :]
for pat in self._patterns:
if pat.context_suffix == suffix and pat.predicted_tool == predicted_tool:
return
self.register(
ToolPattern(
context_suffix=suffix,
predicted_tool=predicted_tool,
param_mapping_fn=_mapping_for_learned_tool(predicted_tool),
empirical_probability=probability,
)
)
logger.info(
"speculative learned pattern suffix=%s -> %s p=%.2f",
suffix,
predicted_tool,
probability,
)
@staticmethod
def _args_compatible(expected: dict[str, Any], actual: dict[str, Any]) -> bool:
for k, v in expected.items():
if k not in actual:
continue
if json.dumps(actual[k], sort_keys=True, default=str) != json.dumps(
v, sort_keys=True, default=str
):
return False
return True
class SpeculativeToolDispatcher:
"""Transparent wrapper around ``_dispatch_tool`` with speculate / promote / cancel."""
def __init__(
self,
*,
registry: PatternRegistry,
dispatch_fn: DispatchFn,
context: dict[str, Any],
) -> None:
self._registry = registry
self._dispatch_fn = dispatch_fn
self._context = context
self._trace: list[str] = []
self._inflight: dict[str, _InflightSpeculation] = {}
@property
def trace_tools(self) -> list[str]:
return list(self._trace)
def _task_key(self, tool_name: str, args: dict[str, Any]) -> str:
blob = json.dumps({"tool": tool_name, "args": args}, sort_keys=True, default=str)
return blob
def _cancel_inflight(self, *, except_key: str | None = None) -> None:
for key, spec in list(self._inflight.items()):
if except_key is not None and key == except_key:
continue
if not spec.task.done():
spec.task.cancel()
del self._inflight[key]
async def maybe_start_speculation(self) -> None:
if not speculative_execution_active():
return
pat = self._registry.match(self._trace)
if pat is None:
return
args = pat.param_mapping_fn(dict(self._context))
key = self._task_key(pat.predicted_tool, args)
if key in self._inflight:
return
async def _run() -> tuple[str, list[Any]]:
return await self._dispatch_fn(name=pat.predicted_tool, args=args)
task = asyncio.create_task(_run())
self._inflight[key] = _InflightSpeculation(
tool_name=pat.predicted_tool,
args=args,
task=task,
)
logger.debug(
"speculative dispatch started tool=%s suffix=%s",
pat.predicted_tool,
pat.context_suffix,
)
async def dispatch(self, *, name: str, args: dict[str, Any]) -> tuple[str, list[Any]]:
key = self._task_key(name, args)
spec = self._inflight.pop(key, None)
if spec is not None and spec.tool_name == name:
if PatternRegistry._args_compatible(spec.args, args):
if spec.task.done() and not spec.task.cancelled():
try:
result = spec.task.result()
self._trace.append(name)
self._registry.record_outcome(self._trace, name, args)
self._cancel_inflight()
await self.maybe_start_speculation()
logger.debug("speculative promote tool=%s", name)
return result
except Exception: # noqa: BLE001
logger.debug("speculative promote failed tool=%s", name, exc_info=True)
elif not spec.task.done():
self._cancel_inflight(except_key=key)
result = await spec.task
self._trace.append(name)
self._registry.record_outcome(self._trace, name, args)
await self.maybe_start_speculation()
return result
# Cancel path: LLM chose a different tool or params than speculated.
self._cancel_inflight()
result = await self._dispatch_fn(name=name, args=args)
self._trace.append(name)
self._registry.record_outcome(self._trace, name, args)
if isinstance(args.get("query"), str):
self._context["last_query"] = args["query"]
await self.maybe_start_speculation()
return result
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