File size: 11,697 Bytes
2aa8b3a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 | """Tools that hold state for the model instead of asking it to.
**What the literature actually supports.** The oldest and best-established
result here is Nye et al., *Show Your Work: Scratchpads for Intermediate
Computation with Language Models* (ICLR 2022): a model allowed to emit
intermediate steps into a buffer before answering solves multi-step problems
that the same model cannot solve in one shot. The mechanism is not
mysterious. It moves working memory out of a single forward pass and into
text, and the smaller the model the more of its capacity a single pass has to
spend on the question itself.
The efficient-agent literature since then has converged on a second idea from
the same direction: an agent that keeps its own list of what remains to be
done, and reads it back, degrades far more slowly as a task lengthens than one
carrying that list in context. That is the case for a to-do list as a tool
rather than as a prompt convention.
**Where this stops, and why that matters here.** ``TOOL_CATEGORIES_REVIEW.md``
already records the one direct small-model measurement this project found, and
it is a negative result: Llama-1B performed *worse* when planning for itself.
So the claim these tools are built on is narrow and it is worth stating
exactly. Externalising state is well supported. Asking a small model to
*reason about strategy* is not. ``make_plan`` and the two tools here are
therefore all storage: they write down what the model tells them, hand it back
unchanged, and never summarise, rank, infer or advise. A tool that told a 0.6B
model what to do next would be adding an opinion nobody measured.
**Why per-run and not persistent.** Both of these live for the length of one
run and are erased with its sandbox. A scratchpad that survived would be a
second, unerasable copy of the user's prompt data sitting on a volunteer's
machine, which is the one thing this network promises does not happen. The
cost is that a model cannot carry notes between turns; the alternative is a
promise this product could not keep.
Like the rest of the library, nothing here opens a socket, spawns a process,
reads a file or consults a model. Every call answers from memory, which is the
same viability bar the local tool set applies below 7B.
"""
from __future__ import annotations
import threading
from collections.abc import Mapping
from .core import Registry, ToolContext, ToolInputError, ToolSpec
#: Bounds. A small model with a runaway loop must not be able to turn a
#: scratchpad into an unbounded allocation on a volunteer's machine.
MAX_NOTES = 40
MAX_NOTE_CHARACTERS = 2_000
MAX_SCRATCHPAD_CHARACTERS = 20_000
MAX_TASKS = 40
MAX_TASK_CHARACTERS = 400
_ACTIONS_NOTE = ("write", "read", "clear")
_ACTIONS_TASK = ("add", "complete", "list", "clear")
class _RunState:
"""Per-run storage, keyed by the run that owns it.
A dictionary keyed by run id rather than a single buffer, because one
worker runs several requests and two users' notes sharing a buffer would
be both a correctness bug and a disclosure. Guarded by a lock for the same
reason the queue is: a worker may execute concurrently.
"""
def __init__(self) -> None:
self._lock = threading.RLock()
self._notes: dict[str, list[str]] = {}
self._tasks: dict[str, list[dict[str, object]]] = {}
def notes(self, run_id: str) -> list[str]:
with self._lock:
return list(self._notes.get(run_id, ()))
def append_note(self, run_id: str, text: str) -> int:
with self._lock:
notes = self._notes.setdefault(run_id, [])
if len(notes) >= MAX_NOTES:
raise ToolInputError(
f"the scratchpad holds at most {MAX_NOTES} notes; clear it to continue"
)
if sum(len(item) for item in notes) + len(text) > MAX_SCRATCHPAD_CHARACTERS:
raise ToolInputError(
f"the scratchpad holds at most {MAX_SCRATCHPAD_CHARACTERS} "
"characters; clear it to continue"
)
notes.append(text)
return len(notes)
def clear_notes(self, run_id: str) -> int:
with self._lock:
return len(self._notes.pop(run_id, ()))
def tasks(self, run_id: str) -> list[dict[str, object]]:
with self._lock:
return [dict(task) for task in self._tasks.get(run_id, ())]
def add_task(self, run_id: str, text: str) -> int:
with self._lock:
tasks = self._tasks.setdefault(run_id, [])
if len(tasks) >= MAX_TASKS:
raise ToolInputError(
f"the list holds at most {MAX_TASKS} tasks; complete or clear some"
)
tasks.append({"task": text, "done": False})
return len(tasks)
def complete_task(self, run_id: str, number: int) -> dict[str, object]:
with self._lock:
tasks = self._tasks.get(run_id, [])
if not 1 <= number <= len(tasks):
raise ToolInputError(
f"there is no task {number}; the list has {len(tasks)}"
)
tasks[number - 1]["done"] = True
return dict(tasks[number - 1])
def clear_tasks(self, run_id: str) -> int:
with self._lock:
return len(self._tasks.pop(run_id, ()))
def forget(self, run_id: str) -> None:
"""Erase everything this run wrote. Called when its sandbox closes."""
with self._lock:
self._notes.pop(run_id, None)
self._tasks.pop(run_id, None)
STATE = _RunState()
def _run_id(context: ToolContext) -> str:
value = getattr(context, "job_id", "") or getattr(context, "run_id", "")
if not isinstance(value, str) or not value:
raise ToolInputError("this tool needs a run to belong to")
return value
def _text_argument(arguments: Mapping[str, object], key: str, limit: int) -> str:
value = arguments.get(key)
if not isinstance(value, str) or not value.strip():
raise ToolInputError(f"{key} must be a non-empty string")
text = " ".join(value.split())
if len(text) > limit:
raise ToolInputError(f"{key} must be at most {limit} characters")
return text
def _scratchpad(arguments: Mapping[str, object], context: ToolContext) -> Mapping[str, object]:
"""Write a note, read the notes back, or throw them away.
Read returns the notes verbatim and in order. It does not summarise them,
because a summary is a judgement and the model asking for its own notes
back is the one making the judgement.
"""
run_id = _run_id(context)
action = str(arguments.get("action") or "write")
if action not in _ACTIONS_NOTE:
raise ToolInputError(f"action must be one of {', '.join(_ACTIONS_NOTE)}")
if action == "write":
text = _text_argument(arguments, "note", MAX_NOTE_CHARACTERS)
count = STATE.append_note(run_id, text)
return {"ok": True, "result": {"written": text, "note_count": count}}
if action == "read":
notes = STATE.notes(run_id)
return {
"ok": True,
"result": {
"notes": notes,
"note_count": len(notes),
"empty": not notes,
},
}
removed = STATE.clear_notes(run_id)
return {"ok": True, "result": {"cleared": removed}}
def _todo(arguments: Mapping[str, object], context: ToolContext) -> Mapping[str, object]:
"""Keep the list of what is left to do outside the model's context."""
run_id = _run_id(context)
action = str(arguments.get("action") or "list")
if action not in _ACTIONS_TASK:
raise ToolInputError(f"action must be one of {', '.join(_ACTIONS_TASK)}")
if action == "add":
text = _text_argument(arguments, "task", MAX_TASK_CHARACTERS)
number = STATE.add_task(run_id, text)
return {"ok": True, "result": {"added": text, "number": number}}
if action == "complete":
number = arguments.get("number")
if isinstance(number, bool) or not isinstance(number, int):
raise ToolInputError("number must be an integer")
task = STATE.complete_task(run_id, number)
remaining = [item for item in STATE.tasks(run_id) if not item["done"]]
return {
"ok": True,
"result": {"completed": task["task"], "remaining": len(remaining)},
}
if action == "list":
tasks = STATE.tasks(run_id)
return {
"ok": True,
"result": {
"tasks": [
{"number": index, "task": item["task"], "done": bool(item["done"])}
for index, item in enumerate(tasks, start=1)
],
"remaining": sum(1 for item in tasks if not item["done"]),
"empty": not tasks,
},
}
removed = STATE.clear_tasks(run_id)
return {"ok": True, "result": {"cleared": removed}}
WORKSPACE_SPECS: tuple[ToolSpec, ...] = (
ToolSpec(
tool_id="scratchpad",
version="1",
kind="tool",
description=(
"Write a short note to yourself and read your notes back later in this "
"same request. Use it to hold a partial result, a number you worked out, "
"or something you found, so you do not have to carry it in your head "
"while you do the next step. Arguments: action ('write', 'read' or "
"'clear') and, for 'write', note. Notes are erased when this request "
"ends and are never shared with another request."
),
input_schema={
"type": "object",
"additionalProperties": False,
"properties": {
"action": {
"type": "string",
"enum": list(_ACTIONS_NOTE),
"description": "write a note, read them all back, or clear them",
},
"note": {"type": "string", "description": "the note, when writing"},
},
"required": ["action"],
},
),
ToolSpec(
tool_id="todo",
version="1",
kind="tool",
description=(
"Keep a numbered list of what is left to do in this request, and tick "
"items off as you finish them. Use it when the request has several parts "
"so you do not lose one. Arguments: action ('add', 'complete', 'list' or "
"'clear'), task when adding, and number when completing. The list is "
"erased when this request ends."
),
input_schema={
"type": "object",
"additionalProperties": False,
"properties": {
"action": {
"type": "string",
"enum": list(_ACTIONS_TASK),
"description": "add a task, complete one by number, list, or clear",
},
"task": {"type": "string", "description": "the task, when adding"},
"number": {
"type": "integer",
"description": "which task to complete, counting from 1",
},
},
"required": ["action"],
},
),
)
def register_workspace_tools(registry: Registry, *, only: frozenset[str] | None = None) -> None:
handlers = {"scratchpad": _scratchpad, "todo": _todo}
for spec in WORKSPACE_SPECS:
if only is not None and spec.ref not in only:
continue
registry.register(spec, handlers[spec.tool_id])
__all__ = ["WORKSPACE_SPECS", "STATE", "register_workspace_tools"]
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