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javis_bot/src/jarvis/reply/prompt_dump.py
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Add Discord-native hybrid front-end for Jarvis (bot + bridge)
Transform isair/jarvis into a Discord-controlled voice assistant running on
the Ubuntu VNC desktop, keeping the mature ~39k-line Python brain intact.

- bot/ (Node + bun, discord.js): /자비스 slash commands (ephemeral),
  voice channel join + voice receive/playback, pluggable VNC screen broadcast
  (selfbot live / noVNC / screenshot)
- bridge/ (Python, Flask): wraps jarvis STT + run_reply_engine + Piper TTS
  behind a thin localhost HTTP API
- .env.example, scripts/ (start_bridge/start_bot/dev), README rewrite,
  docs/language-comparison.md and docs/vnc-xfce-setup.md

Language decision: hybrid (Python brain + Node/bun Discord layer) because
Discord blocks bot video; native screen broadcast only works via a Node
selfbot library.
2026-06-09 14:51:05 +09:00

96 lines
3.0 KiB
Python

"""
Opt-in per-turn prompt dump for the reply engine.
Motivation: PR #232's harness evals cannot reproduce the live confab where
`gemma4:e2b` answers "Tell me about the movie Possessor" with "The movie is
Under the Skin" despite a successful webSearch fetch. To bridge the
harness-vs-field gap, this module writes the exact `messages` array, the
selected tool schema, and the raw LLM response to disk for each turn, so a
user-side reproduction can be replayed verbatim in an eval.
Gated on the env var `JARVIS_DUMP_PROMPTS=1` — off by default because the
dumps contain the full system prompt, memory digest and tool output (likely
PII). Users opt in only when hunting a bug.
Files are written to `~/.local/share/jarvis/prompts/` as per-turn JSON so
each dump is self-contained and easy to `cat` or paste into a test.
"""
from __future__ import annotations
import json
import os
import time
import uuid
from pathlib import Path
from typing import Any, Optional
from ..debug import debug_log
_ENV_VAR = "JARVIS_DUMP_PROMPTS"
def is_enabled() -> bool:
"""Return True when the user has opted in via the env var."""
return os.environ.get(_ENV_VAR, "").strip().lower() in ("1", "true", "yes", "on")
def new_session_id() -> str:
"""A short per-reply identifier so a session's turns sort together on disk."""
return uuid.uuid4().hex[:8]
def _dump_dir() -> Path:
base = Path.home() / ".local" / "share" / "jarvis" / "prompts"
base.mkdir(parents=True, exist_ok=True)
return base
def dump_reply_turn(
*,
session_id: str,
turn: int,
query: str,
model: str,
messages: list,
tools_schema: Optional[list],
use_text_tools: bool,
response: Any = None,
error: Optional[str] = None,
) -> Optional[Path]:
"""Write one turn's full LLM input/output to disk.
Returns the path written, or None when dumping is disabled or failed.
Failure is swallowed — diagnostics must never break the reply loop.
"""
if not is_enabled():
return None
try:
ts = time.strftime("%Y%m%dT%H%M%S")
path = _dump_dir() / f"turn-{ts}-{session_id}-t{turn:02d}.json"
payload = {
"timestamp": time.time(),
"session_id": session_id,
"turn": turn,
"query": query,
"model": model,
"use_text_tools": use_text_tools,
"tools_schema": tools_schema,
"messages": messages,
"response": response,
"error": error,
}
# default=str keeps us safe if something non-serialisable slips in
# (e.g. a bytes field from an upstream response body).
path.write_text(
json.dumps(payload, indent=2, default=str, ensure_ascii=False),
encoding="utf-8",
)
print(f" 📝 Prompt dump: {path}", flush=True)
debug_log(f"Wrote prompt dump to {path}", "planning")
return path
except Exception as exc: # pragma: no cover — diagnostics must not crash the reply loop
debug_log(f"prompt dump failed: {exc}", "planning")
return None