hermes-agent/agent/oneshot.py
brooklyn! 211ba9c7d3
feat(agent): one-shot LLM helper + llm.oneshot gateway RPC (#51261)
A "one-shot" is a single stateless model call that runs OUTSIDE any conversation:
it never touches session history, never breaks prompt caching, and returns plain
text. UI surfaces need this for small generative chores — a commit message from a
diff, a rename suggestion, a summary — where an agent turn would pollute the
thread and hand-rolling an LLM call at every call site would be worse.

- `agent/oneshot.py`: `run_oneshot(...)` over the existing auxiliary-client
  plumbing (same path as title generation). Two call shapes: explicit
  instructions/input, or a registered `template` + `variables` (templates own the
  prompt engineering so it stays consistent across CLI/TUI/desktop). Ships a
  `commit_message` template. Model selection inherits the live session via
  `main_runtime`, else the configured aux `task` backend.
- `tui_gateway/server.py`: `llm.oneshot` RPC (long-handler) inheriting the
  session's model when `session_id` resolves.

Stateless by construction — no session mutation, cache untouched.
2026-06-23 08:01:50 +00:00

158 lines
6.1 KiB
Python

"""Shared one-off LLM requests for non-conversational helpers.
A "one-shot" is a single, stateless model call that runs *outside* any
conversation: it never touches a session's history, never breaks prompt
caching, and returns plain text. UI surfaces use it for small generative
chores — a commit message from a diff, a rename suggestion, a summary —
where spinning up an agent turn would be wrong (it would pollute the thread)
and hand-rolling an LLM call at every call site would be worse.
Two ways to call it:
* ``run_oneshot(instructions=..., user_input=...)`` — caller supplies the
full prompt.
* ``run_oneshot(template="commit_message", variables={...})`` — caller
names a registered template and passes its variables; the template owns
the prompt engineering so it stays consistent across CLI/TUI/desktop.
Model selection rides the same auxiliary plumbing as title generation
(:func:`agent.auxiliary_client.call_llm`): pass ``main_runtime`` to inherit
the live session's provider/model, otherwise the configured ``task`` (default
``title_generation``) resolves a cheap/fast backend.
"""
import logging
from typing import Any, Callable, Dict, Optional, Tuple
from agent.auxiliary_client import call_llm, extract_content_or_reasoning
logger = logging.getLogger(__name__)
# A template turns a variables dict into a (instructions, user_input) pair.
# Templates are plain callables (not str.format) so diff/code payloads with
# literal "{" / "}" pass through untouched.
PromptTemplate = Callable[[Dict[str, Any]], Tuple[str, str]]
def _truncate(text: str, limit: int) -> str:
text = text or ""
if len(text) <= limit:
return text
return text[:limit].rstrip() + "\n…(truncated)"
_COMMIT_INSTRUCTIONS = (
"You write git commit messages. Given a diff of staged changes, write ONE "
"concise Conventional Commits message describing what the change does and why.\n"
"Rules:\n"
"- Subject line: type(scope): summary — imperative mood, lower-case, no "
"trailing period, ≤ 72 characters. Types: feat, fix, refactor, perf, docs, "
"test, build, chore, style, ci.\n"
"- Omit the scope if it isn't obvious.\n"
"- Add a short body (wrapped at ~72 cols) ONLY when the change needs "
"explanation; skip it for small/obvious changes.\n"
"- Describe the actual change, never restate the diff line-by-line.\n"
"- Return ONLY the commit message text — no quotes, no markdown fences, no "
"preamble."
)
def _commit_message_template(variables: Dict[str, Any]) -> Tuple[str, str]:
diff = _truncate(str(variables.get("diff") or ""), 12000)
recent = _truncate(str(variables.get("recent_commits") or ""), 1500)
parts = []
if recent.strip():
parts.append(
"Recent commit subjects from this repo (match their style/conventions):\n"
f"{recent}"
)
parts.append("Diff to describe:\n" + (diff or "(no textual diff available)"))
# "Regenerate" must yield something new even on models that decode greedily
# / pin temperature server-side. A trailing nonce isn't enough, so we hand
# back the previous message and require a genuinely different one.
avoid = _truncate(str(variables.get("avoid") or "").strip(), 1000)
if avoid:
parts.append(
"You already proposed the message below and the user wants a "
"different one. Write a NEW message with different wording (and, if "
"reasonable, a different emphasis or scope framing) — do not repeat "
f"it:\n{avoid}"
)
return _COMMIT_INSTRUCTIONS, "\n\n".join(parts)
# Registry of named templates. Add an entry here to give a new surface a
# consistent, reusable prompt without teaching every caller the prompt text.
PROMPT_TEMPLATES: Dict[str, PromptTemplate] = {
"commit_message": _commit_message_template,
}
def render_template(name: str, variables: Optional[Dict[str, Any]] = None) -> Tuple[str, str]:
"""Resolve a registered template into (instructions, user_input).
Raises KeyError if the template name is unknown so callers fail loudly
instead of silently sending an empty prompt.
"""
template = PROMPT_TEMPLATES.get(name)
if template is None:
raise KeyError(f"unknown one-shot template: {name}")
return template(variables or {})
def run_oneshot(
*,
instructions: str = "",
user_input: str = "",
template: Optional[str] = None,
variables: Optional[Dict[str, Any]] = None,
task: str = "title_generation",
max_tokens: int = 1024,
temperature: Optional[float] = 0.3,
timeout: float = 60.0,
main_runtime: Optional[Dict[str, Any]] = None,
) -> str:
"""Run a single stateless LLM request and return its text.
Provide either a registered ``template`` (+ ``variables``) or an explicit
``instructions`` / ``user_input`` pair. Returns the model's text answer,
stripped of surrounding whitespace and any wrapping code fence.
Raises RuntimeError when no LLM provider is configured (surfaced from
:func:`call_llm`) and KeyError for an unknown template name.
"""
if template:
instructions, user_input = render_template(template, variables)
if not (instructions or "").strip() and not (user_input or "").strip():
raise ValueError("run_oneshot requires a template or instructions/user_input")
messages = []
if (instructions or "").strip():
messages.append({"role": "system", "content": instructions})
messages.append({"role": "user", "content": user_input or ""})
response = call_llm(
task=task,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
timeout=timeout,
main_runtime=main_runtime,
)
text = (extract_content_or_reasoning(response) or "").strip()
return _strip_code_fence(text)
def _strip_code_fence(text: str) -> str:
"""Drop a single wrapping ``` fence the model may have added."""
if not text.startswith("```"):
return text
lines = text.splitlines()
if len(lines) >= 2 and lines[0].startswith("```") and lines[-1].strip() == "```":
return "\n".join(lines[1:-1]).strip()
return text