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92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
"""Deep Solve capability — problem solving driven by the chat agent loop.
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There is no bespoke pipeline anymore. The chat agent loop IS the solver: this
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capability marks the turn as solve mode and resolves a session id, then runs
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the standard agentic chat pipeline. The solve loop capability
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(:class:`deeptutor.capabilities.solve.loop.SolveLoopCapability`) mounts the
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solve tools (``solve_plan`` / ``solve_finish_step`` / ``solve_replan``) plus a
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curated built-in toolset
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(``rag`` / ``code_execution`` / ``geogebra_analysis`` / …) and injects the
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solver playbook; the in-memory :class:`SolveSession` holds the plan, the
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per-step gate, and the replan budget.
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Design axiom (shared with chat / mastery): the intelligence lives at the
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loop's exit — the model plans and solves — while the deterministic spine
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(commit to a plan, don't skip steps, bounded replan) is engine state read and
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written through tools.
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"""
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from __future__ import annotations
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import logging
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import re
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from deeptutor.agents.chat.agentic_pipeline import AgenticChatPipeline
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from deeptutor.capabilities.solve.session import DEFAULT_MAX_REPLANS
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from deeptutor.capabilities.solve.tools import SOLVE_TOOL_NAMES
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from deeptutor.core.capability_protocol import BaseCapability, CapabilityManifest
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from deeptutor.core.context import UnifiedContext
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from deeptutor.core.stream_bus import StreamBus
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from deeptutor.runtime.request_contracts import get_capability_request_schema
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from deeptutor.services.config.capabilities_settings import get_solve_params
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logger = logging.getLogger(__name__)
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_UNSAFE_ID_CHARS = re.compile(r"[^A-Za-z0-9_-]")
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def _sanitize(raw: str) -> str:
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cleaned = _UNSAFE_ID_CHARS.sub("_", raw).strip("_")
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return cleaned or "default"
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def resolve_solve_session_id(context: UnifiedContext) -> str:
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"""Resolve the in-memory session key for this solve turn.
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A solve turn is one-shot, so the turn id (falling back to the session /
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message id) is enough to scope the plan + replan budget; concurrent turns
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get distinct keys and never race.
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"""
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raw = str(
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context.metadata.get("turn_id")
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or context.session_id
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or context.metadata.get("message_id")
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or "default"
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)
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return _sanitize(raw)
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class DeepSolveCapability(BaseCapability):
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manifest = CapabilityManifest(
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name="deep_solve",
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description="Multi-step problem solving driven by the chat agent loop.",
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stages=["responding"],
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tools_used=[*SOLVE_TOOL_NAMES, "rag", "code_execution", "geogebra_analysis", "reason"],
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cli_aliases=["solve"],
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request_schema=get_capability_request_schema("deep_solve"),
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)
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async def run(self, context: UnifiedContext, stream: StreamBus) -> None:
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context.metadata["solve_mode"] = True
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context.metadata["solve_session_id"] = resolve_solve_session_id(context)
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# Read the solve settings and forward them so the page actually drives
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# the loop: max_rounds → the loop's round budget, max_replans → the
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# SolveSession gate (via metadata, read in SolveLoopCapability),
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# temperature / max_tokens → the LLM calls.
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try:
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params = get_solve_params()
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except Exception as exc: # pragma: no cover - defensive config read
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logger.warning("Failed to load solve params, using defaults: %s", exc)
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params = {}
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context.metadata["solve_max_replans"] = int(params.get("max_replans", DEFAULT_MAX_REPLANS))
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pipeline = AgenticChatPipeline(
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language=context.language,
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max_rounds=params.get("max_rounds"),
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temperature=params.get("temperature"),
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max_tokens=params.get("max_tokens"),
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)
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await pipeline.run(context, stream)
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__all__ = ["DeepSolveCapability", "resolve_solve_session_id"]
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