Oleksandr Kozachuk
fbec05dfe9
- Fix infinite retry loop in ai_responder.py that caused test_fix1 to hang - Add missing picture_edit parameter to all AIResponse constructor calls - Set up complete development toolchain with Black, isort, Bandit, and MyPy - Create comprehensive Makefile for development workflows - Add pre-commit hooks with formatting, linting, security, and type checking - Update test mocking to provide contextual responses for different scenarios - Configure all tools for 140 character line length and strict type checking - Add DEVELOPMENT.md with setup instructions and workflow documentation 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
378 lines
15 KiB
Python
378 lines
15 KiB
Python
import json
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import logging
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import os
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import pickle
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import random
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import re
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import time
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from functools import lru_cache, wraps
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from io import BytesIO
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from pathlib import Path
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from pprint import pformat
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from typing import Any, Dict, List, Optional, Tuple, Union
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import multiline
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def pp(*args, **kw):
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if "width" not in kw:
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kw["width"] = 300
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return pformat(*args, **kw)
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@lru_cache(maxsize=300)
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def parse_json(content: str) -> Dict:
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content = content.strip()
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try:
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return json.loads(content)
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except Exception:
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try:
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return multiline.loads(content, multiline=True)
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except Exception as err:
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raise err
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def exponential_backoff(base=2, max_delay=60, factor=1, jitter=0.1, max_attempts=None):
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"""Generate sleep intervals for exponential backoff with jitter.
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Args:
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base: Base of the exponentiation operation
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max_delay: Maximum delay
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factor: Multiplication factor for each increase in backoff
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jitter: Additional randomness range to prevent thundering herd problem
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Yields:
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Delay for backoff as a floating point number.
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"""
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attempt = 0
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while True:
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sleep = min(max_delay, factor * base**attempt)
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jitter_amount = jitter * sleep
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sleep += random.uniform(-jitter_amount, jitter_amount)
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yield sleep
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attempt += 1
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if max_attempts is not None and attempt > max_attempts:
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raise RuntimeError("Max attempts reached in exponential backoff.")
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def async_cache_to_file(filename):
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cache_file = Path(filename)
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cache = None
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if cache_file.exists():
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try:
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with cache_file.open("rb") as fd:
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cache = pickle.load(fd)
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except Exception:
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cache = {}
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def decorator(func):
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@wraps(func)
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async def wrapper(*args, **kwargs):
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if cache is None:
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return await func(*args, **kwargs)
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key = json.dumps((func.__name__, list(args[1:]), kwargs), sort_keys=True)
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if key in cache:
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return cache[key]
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result = await func(*args, **kwargs)
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cache[key] = result
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with cache_file.open("wb") as fd:
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pickle.dump(cache, fd)
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return result
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return wrapper
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return decorator
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def parse_maybe_json(json_string):
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if json_string is None:
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return None
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if isinstance(json_string, (list, dict)):
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return " ".join(map(str, (json_string.values() if isinstance(json_string, dict) else json_string)))
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json_string = str(json_string).strip()
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try:
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parsed_json = parse_json(json_string)
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except Exception:
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for b, e in [("{", "}"), ("[", "]")]:
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if json_string.startswith(b) and json_string.endswith(e):
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return parse_maybe_json(json_string[1:-1])
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return json_string
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if isinstance(parsed_json, str):
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return parsed_json
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if isinstance(parsed_json, (list, dict)):
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return "\n".join(map(str, (parsed_json.values() if isinstance(parsed_json, dict) else parsed_json)))
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return str(parsed_json)
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def same_channel(item1: Dict[str, Any], item2: Dict[str, Any]) -> bool:
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return parse_json(item1["content"]).get("channel") == parse_json(item2["content"]).get("channel")
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class AIMessageBase(object):
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def __init__(self) -> None:
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self.vars: List[str] = []
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def __str__(self) -> str:
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return json.dumps({k: v for k, v in vars(self).items() if k in self.vars})
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class AIMessage(AIMessageBase):
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def __init__(self, user: str, message: str, channel: str = "chat", direct: bool = False, historise_question: bool = True) -> None:
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self.user = user
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self.message = message
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self.urls: Optional[List[str]] = None
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self.channel = channel
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self.direct = direct
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self.historise_question = historise_question
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self.vars = ["user", "message", "channel", "direct", "historise_question"]
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class AIResponse(AIMessageBase):
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def __init__(
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self,
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answer: Optional[str],
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answer_needed: bool,
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channel: Optional[str],
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staff: Optional[str],
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picture: Optional[str],
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picture_edit: bool,
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hack: bool,
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) -> None:
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self.answer = answer
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self.answer_needed = answer_needed
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self.channel = channel
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self.staff = staff
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self.picture = picture
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self.picture_edit = picture_edit
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self.hack = hack
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self.vars = ["answer", "answer_needed", "channel", "staff", "picture", "hack"]
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class AIResponderBase(object):
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def __init__(self, config: Dict[str, Any], channel: Optional[str] = None) -> None:
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super().__init__()
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self.config = config
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self.channel = channel if channel is not None else "system"
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class AIResponder(AIResponderBase):
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def __init__(self, config: Dict[str, Any], channel: Optional[str] = None) -> None:
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super().__init__(config, channel)
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self.history: List[Dict[str, Any]] = []
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self.memory: str = "I am an assistant."
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self.rate_limit_backoff = exponential_backoff()
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self.history_file: Optional[Path] = None
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self.memory_file: Optional[Path] = None
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if "history-directory" in self.config:
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self.history_file = Path(self.config["history-directory"]).expanduser() / f"{self.channel}.dat"
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if self.history_file.exists():
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with open(self.history_file, "rb") as fd:
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self.history = pickle.load(fd)
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self.memory_file = Path(self.config["history-directory"]).expanduser() / f"{self.channel}.memory"
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if self.memory_file.exists():
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with open(self.memory_file, "rb") as fd:
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self.memory = pickle.load(fd)
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logging.info(f"memmory:\n{self.memory}")
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def message(self, message: AIMessage, limit: Optional[int] = None) -> List[Dict[str, Any]]:
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messages = []
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system = self.config.get(self.channel, self.config["system"])
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system = system.replace("{date}", time.strftime("%Y-%m-%d")).replace("{time}", time.strftime("%H:%M:%S"))
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news_feed = self.config.get("news")
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if news_feed and os.path.exists(news_feed):
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with open(news_feed) as fd:
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news_feed = fd.read().strip()
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system = system.replace("{news}", news_feed)
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system = system.replace("{memory}", self.memory)
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messages.append({"role": "system", "content": system})
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if limit is not None:
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while len(self.history) > limit:
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self.shrink_history_by_one()
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for msg in self.history:
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messages.append(msg)
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if not message.urls:
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messages.append({"role": "user", "content": str(message)})
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else:
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content: List[Dict[str, Union[str, Dict[str, str]]]] = [{"type": "text", "text": str(message)}]
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for url in message.urls:
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content.append({"type": "image_url", "image_url": {"url": url}})
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messages.append({"role": "user", "content": content})
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return messages
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async def draw(self, description: str) -> BytesIO:
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if self.config.get("leonardo-token") is not None:
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return await self.draw_leonardo(description)
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return await self.draw_openai(description)
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async def draw_leonardo(self, description: str) -> BytesIO:
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raise NotImplementedError()
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async def draw_openai(self, description: str) -> BytesIO:
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raise NotImplementedError()
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async def post_process(self, message: AIMessage, response: Dict[str, Any]) -> AIResponse:
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for fld in ("answer", "channel", "staff", "picture", "hack"):
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if str(response.get(fld)).strip().lower() in ("none", "", "null", '"none"', '"null"', "'none'", "'null'"):
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response[fld] = None
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for fld in ("answer_needed", "hack", "picture_edit"):
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if str(response.get(fld)).strip().lower() == "true":
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response[fld] = True
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else:
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response[fld] = False
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if response["answer"] is None:
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response["answer_needed"] = False
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else:
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response["answer"] = str(response["answer"])
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response["answer"] = re.sub(r"@\[([^\]]*)\]\([^\)]*\)", r"\1", response["answer"])
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response["answer"] = re.sub(r"\[[^\]]*\]\(([^\)]*)\)", r"\1", response["answer"])
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if message.direct or message.user in message.message:
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response["answer_needed"] = True
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response_message = AIResponse(
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response["answer"],
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response["answer_needed"],
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parse_maybe_json(response["channel"]),
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parse_maybe_json(response["staff"]),
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parse_maybe_json(response["picture"]),
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response["picture_edit"],
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response["hack"],
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)
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if response_message.staff is not None and response_message.answer is not None:
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response_message.answer_needed = True
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if response_message.channel is None:
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response_message.channel = message.channel
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return response_message
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def short_path(self, message: AIMessage, limit: int) -> bool:
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if message.direct or "short-path" not in self.config:
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return False
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for chan_re, user_re in self.config["short-path"]:
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chan_ma = re.match(chan_re, message.channel)
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user_ma = re.match(user_re, message.user)
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if chan_ma and user_ma:
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self.history.append({"role": "user", "content": str(message)})
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while len(self.history) > limit:
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self.shrink_history_by_one()
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if self.history_file is not None:
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with open(self.history_file, "wb") as fd:
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pickle.dump(self.history, fd)
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return True
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return False
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async def chat(self, messages: List[Dict[str, Any]], limit: int) -> Tuple[Optional[Dict[str, Any]], int]:
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raise NotImplementedError()
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async def fix(self, answer: str) -> str:
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raise NotImplementedError()
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async def memory_rewrite(self, memory: str, message_user: str, answer_user: str, question: str, answer: str) -> str:
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raise NotImplementedError()
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async def translate(self, text: str, language: str = "english") -> str:
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raise NotImplementedError()
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def shrink_history_by_one(self, index: int = 0) -> None:
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if index >= len(self.history):
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del self.history[0]
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else:
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current = self.history[index]
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count = sum(1 for item in self.history if same_channel(item, current))
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if count > self.config.get("history-per-channel", 3):
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del self.history[index]
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else:
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self.shrink_history_by_one(index + 1)
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def update_history(self, question: Dict[str, Any], answer: Dict[str, Any], limit: int, historise_question: bool = True) -> None:
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if not isinstance(question["content"], str):
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question["content"] = question["content"][0]["text"]
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if historise_question:
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self.history.append(question)
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self.history.append(answer)
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while len(self.history) > limit:
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self.shrink_history_by_one()
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if self.history_file is not None:
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with open(self.history_file, "wb") as fd:
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pickle.dump(self.history, fd)
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def update_memory(self, memory) -> None:
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if self.memory_file is not None:
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with open(self.memory_file, "wb") as fd:
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pickle.dump(self.memory, fd)
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async def handle_picture(self, response: Dict) -> bool:
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if not isinstance(response.get("picture"), (type(None), str)):
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logging.warning(f"picture key is wrong in response: {pp(response)}")
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return False
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if response.get("picture") is not None:
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response["picture"] = await self.translate(response["picture"])
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return True
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async def memoize(self, message_user: str, answer_user: str, message: str, answer: str) -> None:
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self.memory = await self.memory_rewrite(self.memory, message_user, answer_user, message, answer)
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self.update_memory(self.memory)
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async def memoize_reaction(self, message_user: str, reaction_user: str, operation: str, reaction: str, message: str) -> None:
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quoted_message = message.replace("\n", "\n> ")
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await self.memoize(
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message_user, "assistant", f"\n> {quoted_message}", f"User {reaction_user} has {operation} this raction: {reaction}"
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)
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async def send(self, message: AIMessage) -> AIResponse:
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# Get the history limit from the configuration
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limit = self.config["history-limit"]
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# Check if a short path applies, return an empty AIResponse if it does
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if self.short_path(message, limit):
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return AIResponse(None, False, None, None, None, False, False)
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# Number of retries for sending the message
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retries = 3
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while retries > 0:
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# Get the message queue
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messages = self.message(message, limit)
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logging.info(f"try to send this messages:\n{pp(messages)}")
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# Attempt to send the message to the AI
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answer, limit = await self.chat(messages, limit)
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if answer is None:
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retries -= 1
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continue
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# Attempt to parse the AI's response
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try:
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response = parse_json(answer["content"])
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except Exception as err:
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logging.warning(f"failed to parse the answer: {pp(err)}\n{repr(answer['content'])}")
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answer["content"] = await self.fix(answer["content"])
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# Retry parsing the fixed content
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try:
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response = parse_json(answer["content"])
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except Exception as err:
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logging.error(f"failed to parse the fixed answer: {pp(err)}\n{repr(answer['content'])}")
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retries -= 1
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continue
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if not await self.handle_picture(response):
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retries -= 1
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continue
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# Post-process the message and update the answer's content
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answer_message = await self.post_process(message, response)
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answer["content"] = str(answer_message)
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# Update message history
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self.update_history(messages[-1], answer, limit, message.historise_question)
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logging.info(f"got this answer:\n{str(answer_message)}")
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# Update memory
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if answer_message.answer is not None:
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await self.memoize(message.user, "assistant", message.message, answer_message.answer)
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# Return the updated answer message
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return answer_message
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# Raise an error if the process failed after all retries
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raise RuntimeError("Failed to generate answer after multiple retries")
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