e83816a4d1
For each issue anchor present in BASE 63279301bc non-test .py and absent on HEAD, the BASE comment/docstring block was re-attached at the HEAD location of the code it explained (matched by the distinctive code line / enclosing def). Sentences already covered by an existing HEAD comment were deduped; the issue number always survives. Insert-only: no code lines changed.
594 lines
30 KiB
Python
594 lines
30 KiB
Python
"""Plugin LLM facade — host-owned LLM access for trusted plugins (``ctx.llm``).
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``complete`` / ``complete_structured`` (text + image inputs, JSON schema validation)
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and their async siblings. Provider/model/agent_id/profile are explicit keyword
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arguments mirroring the host config shape; the host owns routing, auth, timeouts
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and fallback, so the plugin never sees raw tokens or keys. Every override knob is
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gated by the per-plugin ``plugins.entries.<id>.llm.allow_*_override`` trust flags
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(fail-closed: a missing block means "no overrides"). Backed by
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:func:`agent.auxiliary_client.call_llm`.
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"""
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from __future__ import annotations
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import base64
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import json
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import logging
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import re
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from dataclasses import dataclass, field
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from typing import Any, Awaitable, Callable, Dict, List, Optional, Sequence, Union
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logger = logging.getLogger(__name__)
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@dataclass
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class PluginLlmTextInput:
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"""Text block in a structured input list."""
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text: str
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type: str = "text"
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@dataclass
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class PluginLlmImageInput:
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"""Image block: ``data`` (raw bytes) or ``url`` (http(s)/data: URL).
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``mime_type`` is required for non-PNG bytes to render across providers."""
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data: Optional[bytes] = None
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url: Optional[str] = None
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mime_type: str = "image/png"
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file_name: str = ""
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type: str = "image"
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PluginLlmInput = Union[PluginLlmTextInput, PluginLlmImageInput, Dict[str, Any]]
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"""One structured input block: a dataclass above or a plain dict of the same shape."""
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@dataclass
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class PluginLlmUsage:
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"""Token + cost usage; every field optional. ``cost_usd`` is the host's best estimate."""
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input_tokens: int = 0
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output_tokens: int = 0
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total_tokens: int = 0
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cache_read_tokens: int = 0
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cache_write_tokens: int = 0
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cost_usd: Optional[float] = None
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@dataclass
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class PluginLlmCompleteResult:
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"""Result of :meth:`PluginLlm.complete`."""
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text: str
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provider: str
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model: str
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agent_id: str
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usage: PluginLlmUsage = field(default_factory=PluginLlmUsage)
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audit: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class PluginLlmStructuredResult:
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"""Result of :meth:`PluginLlm.complete_structured`.
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``parsed`` is set only when JSON output was requested AND the response was
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valid JSON; ``content_type`` is then ``"json"``, otherwise ``"text"``."""
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text: str
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provider: str
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model: str
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agent_id: str
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usage: PluginLlmUsage = field(default_factory=PluginLlmUsage)
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parsed: Optional[Any] = None
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content_type: str = "text"
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audit: Dict[str, Any] = field(default_factory=dict)
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@dataclass(frozen=True)
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class _TrustPolicy:
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"""Resolved trust gate for one plugin's LLM access."""
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plugin_id: str
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allow_provider_override: bool = False
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allowed_providers: Optional[frozenset] = None # None = no allowlist
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allow_any_provider: bool = False # True when allowed_providers == ["*"]
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allow_model_override: bool = False
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allowed_models: Optional[frozenset] = None # None = no allowlist
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allow_any_model: bool = False # True when allowed_models == ["*"]
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allow_agent_id_override: bool = False
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allow_profile_override: bool = False
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# Lets ``complete(task=...)`` borrow the host's *built-in* aux task slots.
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# Slots the plugin registered itself are always allowed. Fail-closed.
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allow_task_override: bool = False
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# The ``allow_*_override`` config keys; each is a same-named ``_TrustPolicy`` field.
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_OVERRIDE_FLAGS = ("allow_provider_override", "allow_model_override", "allow_agent_id_override",
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"allow_profile_override", "allow_task_override")
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def _coerce_allowlist(raw: Any) -> tuple[Optional[frozenset], bool]:
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"""YAML list → ``(frozenset_or_None, allow_any)``. A ``"*"`` entry sets
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``allow_any``; missing / non-list → ``(None, False)`` = no allowlist."""
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if not isinstance(raw, list):
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return None, False
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normalized = [item.strip().lower() for item in raw if isinstance(item, str)]
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return frozenset(item for item in normalized if item and item != "*"), "*" in normalized
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def _resolve_trust_policy(plugin_id: str) -> _TrustPolicy:
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"""Read ``plugins.entries.<plugin_id>.llm`` from config.yaml (missing → fully
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restrictive). Resolved per call so config edits apply without a restart."""
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if not plugin_id:
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return _TrustPolicy(plugin_id="")
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try:
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from hermes_cli.config import load_config_readonly
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llm_cfg: Any = (load_config_readonly() or {}).get("plugins")
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except Exception: # pragma: no cover — config IO failure
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llm_cfg = None
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for key in ("entries", plugin_id, "llm"):
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llm_cfg = llm_cfg.get(key) if isinstance(llm_cfg, dict) else None
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if not isinstance(llm_cfg, dict):
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return _TrustPolicy(plugin_id=plugin_id)
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allowed_models, allow_any_model = _coerce_allowlist(llm_cfg.get("allowed_models"))
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allowed_providers, allow_any_provider = _coerce_allowlist(llm_cfg.get("allowed_providers"))
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return _TrustPolicy(
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plugin_id=plugin_id, allowed_providers=allowed_providers, allow_any_provider=allow_any_provider,
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allowed_models=allowed_models, allow_any_model=allow_any_model,
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**{name: bool(llm_cfg.get(name, False)) for name in _OVERRIDE_FLAGS},
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)
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class PluginLlmTrustError(PermissionError):
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"""Raised when a plugin attempts an LLM override without trust."""
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def _denied(plugin_id: str, what: str, flag: str) -> PluginLlmTrustError:
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return PluginLlmTrustError(
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f"Plugin {plugin_id!r} cannot {what} (set plugins.entries.{plugin_id}.llm.{flag} to true to allow).")
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def _gate_ref_override(policy: _TrustPolicy, kind: str, requested: str) -> str:
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"""Gate a ``provider`` / ``model`` override: trust flag, then optional allowlist."""
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if not getattr(policy, f"allow_{kind}_override"):
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raise _denied(policy.plugin_id, f"override the {kind}", f"allow_{kind}_override")
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allowed = getattr(policy, f"allowed_{kind}s")
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if not getattr(policy, f"allow_any_{kind}") and allowed is not None and requested.strip().lower() not in allowed:
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raise PluginLlmTrustError(f"Plugin {policy.plugin_id!r} {kind} override {requested!r} is not in "
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f"plugins.entries.{policy.plugin_id}.llm.allowed_{kind}s.")
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return requested.strip()
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# Overrides gated by a bare trust flag (no allowlist): ``kind`` -> denial wording.
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_FLAG_ONLY_OVERRIDES = {
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"agent_id": "run completions against a non-default agent id", "profile": "override the auth profile",
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}
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def _check_overrides(
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policy: _TrustPolicy, *, requested_provider: Optional[str], requested_model: Optional[str],
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requested_agent_id: Optional[str], requested_profile: Optional[str],
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) -> tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
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"""Gate each override independently, in the order provider, model, agent_id,
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profile. Returns ``(provider, model, agent_id, profile)`` (agent_id unstripped)."""
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final_provider = _gate_ref_override(policy, "provider", requested_provider) if requested_provider else None
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final_model = _gate_ref_override(policy, "model", requested_model) if requested_model else None
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for kind, requested in (("agent_id", requested_agent_id), ("profile", requested_profile)):
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if requested and not getattr(policy, f"allow_{kind}_override"):
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raise _denied(policy.plugin_id, _FLAG_ONLY_OVERRIDES[kind], f"allow_{kind}_override")
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return final_provider, final_model, requested_agent_id, requested_profile.strip() if requested_profile else None
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def _resolve_task_ownership(plugin_id: str) -> tuple[frozenset, frozenset]:
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"""``(owned_keys, builtin_keys)`` for the task trust gate.
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Imports are lazy (circular import at plugin discovery); an unreadable registry
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yields empty sets, failing the gate closed. Ownership matches on the canonical id
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``ctx.llm`` is bound to (``manifest.key or manifest.name``), which is what
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``register_auxiliary_task`` stores as the entry's ``plugin``."""
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owned: set = set()
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builtin: set = set()
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try:
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from hermes_cli.plugins import get_plugin_auxiliary_tasks
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owned = {e.get("key") for e in get_plugin_auxiliary_tasks()
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if e.get("plugin") == plugin_id and isinstance(e.get("key"), str) and e.get("key")}
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except Exception: # pragma: no cover — registry unavailable
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pass
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try:
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from hermes_cli.main import _AUX_TASKS
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builtin = {k for k, _name, _desc in _AUX_TASKS}
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except Exception: # pragma: no cover — main import failure
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pass
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return frozenset(owned), frozenset(builtin)
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def _check_task(policy: _TrustPolicy, *, plugin_id: str, requested_task: Optional[str]) -> Optional[str]:
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"""Validate a plugin's requested auxiliary ``task`` key.
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unset / ``""`` / ``"auto"`` → ``None`` (main-model path); a key the plugin
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registered itself → allowed; a built-in key → only with ``allow_task_override``;
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anything else raises + logs. Never silently downgraded to ``auto``: that would
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mask the misconfiguration and could route to a main model the user steered
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elsewhere on purpose.
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A foreign/unknown key raises :class:`PluginLlmTrustError` and logs a warning naming the offending plugin
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and key. See #64174, #64182.
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"""
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task = (requested_task or "").strip()
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if not task or task.lower() == "auto":
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return None
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owned, builtin = _resolve_task_ownership(plugin_id)
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if task in owned or (task in builtin and policy.allow_task_override):
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return task
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if task in builtin:
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logger.warning("plugin_llm task routing denied: plugin %r requested built-in "
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"auxiliary task %r without plugins.entries.%s.llm.allow_task_override", plugin_id, task, plugin_id)
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raise _denied(plugin_id, f"route through the built-in auxiliary task {task!r}", "allow_task_override")
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logger.warning("plugin_llm task routing denied: plugin %r requested auxiliary task %r it did not register",
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plugin_id, task)
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raise PluginLlmTrustError(
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f"Plugin {plugin_id!r} cannot route through auxiliary task {task!r} — a "
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f"plugin may only pass a task key it registered itself via "
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f"ctx.register_auxiliary_task() (or a built-in key when plugins.entries."
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f"{plugin_id}.llm.allow_task_override is true)."
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)
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def _normalize_input_block(block: PluginLlmInput) -> Dict[str, Any]:
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"""Coerce a structured input block to a plain dict. Unknown shapes raise ``ValueError``."""
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if isinstance(block, PluginLlmTextInput):
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return {"type": "text", "text": block.text}
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if isinstance(block, PluginLlmImageInput):
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d: Dict[str, Any] = {"type": "image", "mime_type": block.mime_type, "file_name": block.file_name}
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if block.data is not None:
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d["data"] = block.data
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if block.url:
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d["url"] = block.url
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return d
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if not isinstance(block, dict):
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raise ValueError(f"Unsupported input block: {type(block).__name__}")
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kind = block.get("type")
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if kind == "text":
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if not isinstance(block.get("text"), str):
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raise ValueError("text input block requires 'text' string")
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return {"type": "text", "text": block["text"]}
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if kind == "image":
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if "data" not in block and not block.get("url"):
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raise ValueError("image input block requires 'data' bytes or 'url'")
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return {"type": "image", "data": block.get("data"), "url": block.get("url"),
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"mime_type": block.get("mime_type") or "image/png", "file_name": block.get("file_name") or ""}
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raise ValueError(f"Unknown input block type: {kind!r}")
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def _image_part(norm: Dict[str, Any]) -> Dict[str, Any]:
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"""Normalized image block → OpenAI ``image_url`` part (data: URL for bytes)."""
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url = norm.get("url")
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if not url:
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data = norm.get("data") or b""
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if not isinstance(data, (bytes, bytearray)):
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raise ValueError("image input 'data' must be bytes")
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url = f"data:{norm.get('mime_type') or 'image/png'};base64,{base64.b64encode(data).decode('ascii')}"
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return {"type": "image_url", "image_url": {"url": url}}
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def _build_structured_messages(
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*, instructions: str, inputs: Sequence[PluginLlmInput], json_mode: bool,
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json_schema: Optional[Any], schema_name: Optional[str], system_prompt: Optional[str],
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) -> List[Dict[str, Any]]:
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"""OpenAI-style messages for a structured call: optional system message (prompt +
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JSON-only directive), then a user message whose first text part is the
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instructions (+ schema name / JSON schema) followed by the input blocks."""
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messages: List[Dict[str, Any]] = []
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sys_parts: List[str] = [system_prompt.strip()] if system_prompt else []
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if json_mode or json_schema is not None:
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sys_parts.append("Respond with a single JSON object that matches the requested shape. "
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"Do not include prose or markdown fences.")
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if sys_parts:
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messages.append({"role": "system", "content": "\n\n".join(sys_parts)})
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header = instructions.strip()
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if schema_name:
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header = f"{header}\n\nSchema name: {schema_name}"
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if json_schema is not None:
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try:
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schema_text = json.dumps(json_schema, ensure_ascii=False, sort_keys=True)
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except (TypeError, ValueError):
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schema_text = str(json_schema)
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header = f"{header}\n\nJSON schema:\n{schema_text}"
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user_parts: List[Dict[str, Any]] = [{"type": "text", "text": header}]
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for block in inputs:
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norm = _normalize_input_block(block) # always "text" or "image"
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user_parts.append({"type": "text", "text": norm["text"]} if norm["type"] == "text" else _image_part(norm))
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messages.append({"role": "user", "content": user_parts})
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return messages
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_FENCE_RE = re.compile(r"```(?:json)?\s*(.+?)```", re.DOTALL | re.IGNORECASE)
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def _strip_code_fences(text: str) -> str:
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"""The first fenced code block's body, or the stripped text when unfenced."""
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match = _FENCE_RE.search(text)
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return match.group(1).strip() if match else text.strip()
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def _parse_structured_text(*, text: str, json_mode: bool, json_schema: Optional[Any]) -> tuple[Optional[Any], str]:
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"""``(parsed, content_type)``: ``"json"`` when parsing (and schema validation, if
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given) succeeded, ``"text"`` otherwise. Schema violations raise ``ValueError``;
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a missing ``jsonschema`` package skips validation with a debug log."""
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if not (json_mode or json_schema is not None) or not text:
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return None, "text"
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try:
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parsed = json.loads(_strip_code_fences(text))
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except (json.JSONDecodeError, ValueError):
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return None, "text"
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if json_schema is not None:
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try:
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import jsonschema # type: ignore[import-untyped]
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jsonschema.validate(parsed, json_schema)
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except ImportError:
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logger.debug("jsonschema unavailable; skipping schema validation")
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except jsonschema.ValidationError as exc: # type: ignore[attr-defined]
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raise ValueError(f"Plugin LLM structured output did not match schema: {exc.message}") from exc
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return parsed, "json"
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def _extract_usage(response: Any) -> PluginLlmUsage:
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"""Token usage from an OpenAI-shaped response, tolerating provider naming
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(``prompt_tokens``/``completion_tokens`` vs ``input_tokens``/``output_tokens``,
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``cache_read_input_tokens`` vs ``cache_read_tokens``)."""
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raw = getattr(response, "usage", None)
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if raw is None:
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return PluginLlmUsage()
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def _g(*names: str) -> int:
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for name in names:
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v = getattr(raw, name, None)
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if v is None and isinstance(raw, dict):
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v = raw.get(name)
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try:
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if v is not None and int(v):
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return int(v)
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except (TypeError, ValueError):
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pass
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return 0
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inp, out = _g("prompt_tokens", "input_tokens"), _g("completion_tokens", "output_tokens")
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return PluginLlmUsage(
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input_tokens=inp, output_tokens=out, total_tokens=_g("total_tokens") or (inp + out),
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cache_read_tokens=_g("cache_read_input_tokens", "cache_read_tokens"),
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cache_write_tokens=_g("cache_creation_input_tokens", "cache_write_tokens"),
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)
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def _extract_text(response: Any) -> str:
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"""Assistant text of an OpenAI-shaped response (string or text-part list content)."""
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try:
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content = getattr(response.choices[0].message, "content", None)
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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texts = ((part.get("text") if part.get("type") == "text" else None)
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if isinstance(part, dict) else getattr(part, "text", None) for part in content)
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return "".join(t for t in texts if isinstance(t, str))
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except (AttributeError, IndexError, TypeError):
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pass
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return ""
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def _main_config_value(reader: str, default: str) -> str:
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"""Read the current main provider/model via ``agent.auxiliary_client``."""
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try:
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import agent.auxiliary_client as ac
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return (getattr(ac, reader)() or "").strip() or default
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except Exception: # pragma: no cover — defensive
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return default
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def _resolve_attribution(*, provider_override: Optional[str], model_override: Optional[str], response: Any,
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route_info: Optional[Dict[str, str]] = None) -> tuple[str, str]:
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"""``(provider, model)`` to record on the result.
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Provider: route selected by ``auxiliary_client`` > explicit override > current
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main provider > ``"auto"``. Model: ``response.model`` (the canonical id that
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actually ran) > route > override > current main model > ``"default"``."""
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route_info = route_info or {}
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provider = route_info.get("provider") or provider_override or _main_config_value("_read_main_provider", "auto")
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response_model = getattr(response, "model", None)
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if isinstance(response_model, str) and response_model.strip():
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return provider, response_model.strip()
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return provider, route_info.get("model") or model_override or _main_config_value("_read_main_model", "default")
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def _json_response_format(*, json_mode: bool, json_schema: Optional[Any]) -> Optional[Dict[str, Any]]:
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"""``extra_body.response_format``; falls back to ``json_object`` without a
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schema so schema-blind providers still get a hint."""
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if json_schema is not None:
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schema = {"name": "plugin_structured_output", "schema": json_schema, "strict": False}
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return {"response_format": {"type": "json_schema", "json_schema": schema}}
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if json_mode:
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return {"response_format": {"type": "json_object"}}
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return None
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def _structured_spec(
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name: str, instructions: str, input: Sequence[PluginLlmInput], system_prompt: Optional[str],
|
|
json_mode: bool, json_schema: Optional[Any], schema_name: Optional[str],
|
|
) -> Dict[str, Any]:
|
|
"""Argument check for the structured methods (runs before the trust gate)."""
|
|
if not instructions or not instructions.strip():
|
|
raise ValueError(f"{name} requires non-empty instructions")
|
|
if not input:
|
|
raise ValueError(f"{name} requires at least one input block")
|
|
return dict(instructions=instructions, inputs=list(input), system_prompt=system_prompt,
|
|
json_mode=json_mode, json_schema=json_schema, schema_name=schema_name)
|
|
|
|
|
|
class PluginLlm:
|
|
"""Host-owned LLM access for one trusted plugin.
|
|
|
|
Constructed by :class:`hermes_cli.plugins.PluginContext` and exposed as
|
|
``ctx.llm``; the constructor binds plugin identity for trust enforcement, so
|
|
plugins should not instantiate it directly. Every public method is ``_gate``
|
|
(trust checks → call kwargs) → ``_invoke_*`` (host ``call_llm`` or injected
|
|
caller) → ``_finish`` (result + audit log)."""
|
|
|
|
def __init__(
|
|
self, *, plugin_id: str, policy_loader: Optional[Callable[[str], _TrustPolicy]] = None,
|
|
sync_caller: Optional[Callable[..., Any]] = None,
|
|
async_caller: Optional[Callable[..., Awaitable[Any]]] = None,
|
|
) -> None:
|
|
self._plugin_id = plugin_id
|
|
self._policy_loader = policy_loader or _resolve_trust_policy
|
|
self._sync_caller = sync_caller
|
|
self._async_caller = async_caller
|
|
|
|
def complete(
|
|
self, messages: List[Dict[str, Any]], *, provider: Optional[str] = None, model: Optional[str] = None,
|
|
temperature: Optional[float] = None, max_tokens: Optional[int] = None,
|
|
timeout: Optional[float] = None, agent_id: Optional[str] = None, profile: Optional[str] = None,
|
|
purpose: Optional[str] = None, task: Optional[str] = None,
|
|
) -> PluginLlmCompleteResult:
|
|
"""Run a host-owned chat completion against the user's active model.
|
|
|
|
``provider``/``model``/``agent_id``/``profile`` are each gated by
|
|
``plugins.entries.<id>.llm.allow_*_override``. ``task`` routes through a
|
|
plugin-registered auxiliary slot (see :func:`_check_task`)."""
|
|
agent, kw = self._gate(provider, model, agent_id, profile, task, messages, temperature, max_tokens, timeout)
|
|
return self._finish("complete", agent, kw, self._invoke_sync(kw), purpose)
|
|
|
|
def complete_structured(
|
|
self, *, instructions: str, input: Sequence[PluginLlmInput], json_schema: Optional[Any] = None,
|
|
json_mode: bool = False, schema_name: Optional[str] = None, system_prompt: Optional[str] = None,
|
|
provider: Optional[str] = None, model: Optional[str] = None, temperature: Optional[float] = None,
|
|
max_tokens: Optional[int] = None, timeout: Optional[float] = None, agent_id: Optional[str] = None,
|
|
profile: Optional[str] = None, purpose: Optional[str] = None, task: Optional[str] = None,
|
|
) -> PluginLlmStructuredResult:
|
|
"""Run a bounded host-owned structured completion.
|
|
|
|
``input`` accepts text and image blocks. With ``json_mode=True`` or a
|
|
``json_schema`` the response is parsed (and validated when the optional
|
|
``jsonschema`` package is installed) into ``result.parsed``."""
|
|
spec = _structured_spec("complete_structured", instructions, input, system_prompt, json_mode, json_schema, schema_name)
|
|
agent, kw = self._gate(provider, model, agent_id, profile, task, None, temperature, max_tokens, timeout, spec)
|
|
return self._finish("complete_structured", agent, kw, self._invoke_sync(kw), purpose, spec)
|
|
|
|
async def acomplete(
|
|
self, messages: List[Dict[str, Any]], *, provider: Optional[str] = None, model: Optional[str] = None,
|
|
temperature: Optional[float] = None, max_tokens: Optional[int] = None,
|
|
timeout: Optional[float] = None, agent_id: Optional[str] = None, profile: Optional[str] = None,
|
|
purpose: Optional[str] = None, task: Optional[str] = None,
|
|
) -> PluginLlmCompleteResult:
|
|
"""Async sibling of :meth:`complete`."""
|
|
agent, kw = self._gate(provider, model, agent_id, profile, task, messages, temperature, max_tokens, timeout)
|
|
return self._finish("acomplete", agent, kw, await self._invoke_async(kw), purpose)
|
|
|
|
async def acomplete_structured(
|
|
self, *, instructions: str, input: Sequence[PluginLlmInput], json_schema: Optional[Any] = None,
|
|
json_mode: bool = False, schema_name: Optional[str] = None, system_prompt: Optional[str] = None,
|
|
provider: Optional[str] = None, model: Optional[str] = None, temperature: Optional[float] = None,
|
|
max_tokens: Optional[int] = None, timeout: Optional[float] = None, agent_id: Optional[str] = None,
|
|
profile: Optional[str] = None, purpose: Optional[str] = None, task: Optional[str] = None,
|
|
) -> PluginLlmStructuredResult:
|
|
"""Async sibling of :meth:`complete_structured`."""
|
|
spec = _structured_spec("acomplete_structured", instructions, input, system_prompt, json_mode, json_schema, schema_name)
|
|
agent, kw = self._gate(provider, model, agent_id, profile, task, None, temperature, max_tokens, timeout, spec)
|
|
return self._finish("acomplete_structured", agent, kw, await self._invoke_async(kw), purpose, spec)
|
|
|
|
def _gate(
|
|
self, provider: Optional[str], model: Optional[str], agent_id: Optional[str], profile: Optional[str],
|
|
task: Optional[str], messages: Optional[List[Dict[str, Any]]], temperature: Optional[float],
|
|
max_tokens: Optional[int], timeout: Optional[float], spec: Optional[Dict[str, Any]] = None,
|
|
) -> tuple[Optional[str], Dict[str, Any]]:
|
|
"""Trust gate (task first, then overrides), then — for a structured ``spec`` —
|
|
build messages/response_format (input-shape errors surface only after trust
|
|
passes). Returns the effective agent id and the call kwargs, in the documented
|
|
order: messages, provider_override, model_override, profile_override,
|
|
temperature, max_tokens, timeout, extra_body, task."""
|
|
policy = self._policy_loader(self._plugin_id)
|
|
eff_task = _check_task(policy, plugin_id=self._plugin_id, requested_task=task)
|
|
eff_provider, eff_model, eff_agent, eff_profile = _check_overrides(
|
|
policy, requested_provider=provider, requested_model=model, requested_agent_id=agent_id,
|
|
requested_profile=profile)
|
|
extra_body = None
|
|
if spec is not None:
|
|
messages = _build_structured_messages(**spec)
|
|
extra_body = _json_response_format(json_mode=spec["json_mode"], json_schema=spec["json_schema"])
|
|
return eff_agent, dict(messages=messages, provider_override=eff_provider, model_override=eff_model,
|
|
profile_override=eff_profile, temperature=temperature, max_tokens=max_tokens,
|
|
timeout=timeout, extra_body=extra_body, task=eff_task)
|
|
|
|
def _finish(
|
|
self, name: str, agent_id: Optional[str], kw: Dict[str, Any], invoked: tuple[str, str, Any],
|
|
purpose: Optional[str], spec: Optional[Dict[str, Any]] = None,
|
|
) -> Any:
|
|
"""Build the result object + audit dict and emit the INFO audit line."""
|
|
real_provider, real_model, response = invoked
|
|
text = _extract_text(response)
|
|
usage = _extract_usage(response)
|
|
eff_task = kw["task"] or ""
|
|
audit: Dict[str, Any] = {"plugin_id": self._plugin_id, "purpose": purpose or "", "profile": kw["profile_override"] or ""}
|
|
fields: Dict[str, Any] = dict(text=text, provider=real_provider, model=real_model, agent_id=agent_id or "default", usage=usage)
|
|
fmt = f"plugin_llm.{name} plugin=%s provider=%s model=%s task=%s purpose=%s "
|
|
log_args = [self._plugin_id, real_provider, real_model, eff_task, purpose or ""]
|
|
cls: Any = PluginLlmCompleteResult
|
|
if spec is not None:
|
|
parsed, content_type = _parse_structured_text(text=text, json_mode=spec["json_mode"], json_schema=spec["json_schema"])
|
|
audit["schema_name"] = spec["schema_name"] or ""
|
|
fields.update(parsed=parsed, content_type=content_type)
|
|
fmt += "content_type=%s "
|
|
log_args.append(content_type)
|
|
cls = PluginLlmStructuredResult
|
|
audit["task"] = eff_task
|
|
logger.info(fmt + "tokens=%d", *log_args, usage.total_tokens)
|
|
return cls(**fields, audit=audit)
|
|
|
|
@staticmethod
|
|
def _host_kwargs(kw: Dict[str, Any]) -> tuple[Dict[str, Any], Optional[Dict[str, str]]]:
|
|
"""Call kwargs → ``call_llm`` kwargs. The auth profile rides in
|
|
``extra_body.metadata.auth_profile``; ``route_info`` is only requested when
|
|
routing through a task slot."""
|
|
merged_extra = dict(kw["extra_body"] or {})
|
|
if kw["profile_override"]:
|
|
merged_extra.setdefault("metadata", {})["auth_profile"] = kw["profile_override"]
|
|
route_info: Optional[Dict[str, str]] = {} if kw["task"] else None
|
|
return dict(task=kw["task"], provider=kw["provider_override"], model=kw["model_override"],
|
|
messages=kw["messages"], temperature=kw["temperature"], max_tokens=kw["max_tokens"],
|
|
timeout=kw["timeout"], extra_body=merged_extra or None, route_info=route_info), route_info
|
|
|
|
@staticmethod
|
|
def _attributed(kw: Dict[str, Any], response: Any, route_info: Optional[Dict[str, str]]) -> tuple[str, str, Any]:
|
|
return (*_resolve_attribution(provider_override=kw["provider_override"], model_override=kw["model_override"],
|
|
response=response, route_info=route_info), response)
|
|
|
|
def _invoke_sync(self, kw: Dict[str, Any]) -> tuple[str, str, Any]:
|
|
"""Host ``call_llm`` (lazy import: circular deps at plugin discovery) →
|
|
``(provider, model, response)``. An injected ``sync_caller`` replaces the
|
|
whole path and receives the call kwargs."""
|
|
if self._sync_caller is not None:
|
|
return self._sync_caller(**kw)
|
|
from agent.auxiliary_client import call_llm
|
|
call_kw, route_info = self._host_kwargs(kw)
|
|
return self._attributed(kw, call_llm(**call_kw), route_info)
|
|
|
|
async def _invoke_async(self, kw: Dict[str, Any]) -> tuple[str, str, Any]:
|
|
"""Async sibling of :meth:`_invoke_sync` (``async_call_llm`` / ``async_caller``)."""
|
|
if self._async_caller is not None:
|
|
return await self._async_caller(**kw)
|
|
from agent.auxiliary_client import async_call_llm
|
|
call_kw, route_info = self._host_kwargs(kw)
|
|
return self._attributed(kw, await async_call_llm(**call_kw), route_info)
|
|
|
|
|
|
def make_plugin_llm_for_test(*, plugin_id: str, policy: _TrustPolicy, sync_caller: Optional[Callable[..., Any]] = None,
|
|
async_caller: Optional[Callable[..., Awaitable[Any]]] = None) -> PluginLlm:
|
|
""":class:`PluginLlm` with an injected policy and caller (no config.yaml, no
|
|
provider). Not part of the public plugin API."""
|
|
return PluginLlm(plugin_id=plugin_id, policy_loader=lambda _pid: policy, sync_caller=sync_caller, async_caller=async_caller)
|
|
|
|
|
|
__all__ = [
|
|
"PluginLlm", "PluginLlmTextInput", "PluginLlmImageInput", "PluginLlmInput", "PluginLlmUsage",
|
|
"PluginLlmCompleteResult", "PluginLlmStructuredResult", "PluginLlmTrustError", "make_plugin_llm_for_test",
|
|
]
|