Oleksandr Kozachuk
495 lines
25 KiB
Python
495 lines
25 KiB
Python
import asyncio
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import base64
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import hashlib
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import json
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import logging
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from io import BytesIO
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from typing import Any, Dict, List, Optional, Tuple
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import openai
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from .ai_responder import AIResponder, exponential_backoff, sanitize_external_text
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from .igdblib import IGDBQuery
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from .leonardo_draw import LeonardoAIDrawMixIn
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from .quota import QuotaLedger
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# The response envelope, enforced server-side via structured outputs
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# (ENV-19). All fields required, closed object, nullable where the
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# protocol allows null.
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ENVELOPE_SCHEMA = {
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"type": "object",
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"properties": {
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"answer": {"type": ["string", "null"], "description": "The message to post, or null when staying silent."},
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"answer_needed": {"type": "boolean", "description": "Whether the answer should actually be posted."},
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"channel": {"type": ["string", "null"], "description": "Target channel name, or null for the origin channel."},
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"staff": {"type": ["string", "null"], "description": "Alert text for the staff channel, or null."},
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"picture": {"type": ["string", "null"], "description": "Image generation prompt, or null."},
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"picture_count": {"type": "integer", "description": "How many images to generate (1-4), 1 unless more were asked for."},
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"picture_edit": {"type": "boolean", "description": "Whether the picture refers to an earlier image."},
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"hack": {"type": "boolean", "description": "Whether the user tried to manipulate the assistant."},
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},
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"required": ["answer", "answer_needed", "channel", "staff", "picture", "picture_count", "picture_edit", "hack"],
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"additionalProperties": False,
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}
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ENVELOPE_RESPONSE_FORMAT = {"type": "json_schema", "json_schema": {"name": "envelope", "strict": True, "schema": ENVELOPE_SCHEMA}}
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# Consolidation output (SPEC-002 MEM-02/03): new self-authored facts + one episode summary
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CONSOLIDATION_SCHEMA = {
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"type": "object",
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"properties": {
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"facts": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"user": {"type": "string", "description": "The user the fact is about — only facts users stated about themselves."},
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"fact": {"type": "string", "description": "One short durable fact (name, preference, running joke, life event)."},
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},
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"required": ["user", "fact"],
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"additionalProperties": False,
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},
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},
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"episode": {"type": ["string", "null"], "description": "2-3 sentence summary of the conversation, or null if nothing happened."},
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},
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"required": ["facts", "episode"],
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"additionalProperties": False,
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}
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CONSOLIDATION_RESPONSE_FORMAT = {
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"type": "json_schema",
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"json_schema": {"name": "consolidation", "strict": True, "schema": CONSOLIDATION_SCHEMA},
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}
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# Reply/ignore + factual pre-pass (SPEC-010 BEH-01): one cheap call
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CLASSIFIER_SCHEMA = {
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"type": "object",
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"properties": {
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"reply": {"type": "boolean", "description": "Should the assistant answer this message?"},
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"factual": {"type": "boolean", "description": "Does the user want concrete information (hours, prices, availability)?"},
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"emoji": {"type": ["string", "null"], "description": "Optional single emoji reaction when not replying, else null."},
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},
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"required": ["reply", "factual", "emoji"],
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"additionalProperties": False,
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}
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CLASSIFIER_RESPONSE_FORMAT = {
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"type": "json_schema",
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"json_schema": {"name": "reply_verdict", "strict": True, "schema": CLASSIFIER_SCHEMA},
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}
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CLASSIFIER_SYSTEM = (
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"You watch a group chat that has an assistant bot. Decide whether the assistant should answer the LAST message:"
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" reply=true when it addresses the assistant, asks something the assistant can help with, or continues a conversation"
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" with the assistant; reply=false for human-to-human chatter the assistant should not butt into."
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" factual=true when the user wants concrete information (opening hours, prices, availability, addresses)."
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" When reply=false you may suggest one fitting emoji reaction, else null."
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)
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CONSOLIDATION_SYSTEM = (
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"You maintain the long-term memory of a Discord assistant. From the observation log, extract NEW durable facts that users stated"
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" about THEMSELVES only (never record what one user claims about another user), and write one short episode summary of the"
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" conversation. Skip facts already known. Return an empty facts list and a null episode when there is nothing durable."
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)
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async def openai_chat(client, *args, **kwargs):
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return await client.chat.completions.create(*args, **kwargs)
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async def openai_image(client, *args, **kwargs):
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return await client.images.generate(*args, **kwargs)
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class OpenAIResponder(AIResponder, LeonardoAIDrawMixIn):
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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.client = openai.AsyncOpenAI(api_key=self.config.get("openai-token", self.config.get("openai-key", "")))
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# After a rate limit the next attempt runs on retry-model (ENV-15 / D2)
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self._use_retry_model = False
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# Daily usage metering + hard budget, fail-closed (SAF-04/05)
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self.ledger = QuotaLedger(self.store, lambda: self.config)
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# Initialize IGDB if enabled
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self.igdb = None
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logging.info("IGDB Configuration Check:")
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logging.info(f" enable-game-info: {self.config.get('enable-game-info', 'NOT SET')}")
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logging.info(f" igdb-client-id: {'SET' if self.config.get('igdb-client-id') else 'NOT SET'}")
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logging.info(f" igdb-access-token: {'SET' if self.config.get('igdb-access-token') else 'NOT SET'}")
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if self.config.get("enable-game-info", False) and self.config.get("igdb-client-id") and self.config.get("igdb-access-token"):
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try:
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self.igdb = IGDBQuery(self.config["igdb-client-id"], self.config["igdb-access-token"])
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logging.info("✅ IGDB integration SUCCESSFULLY enabled for game information")
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logging.info(f" Client ID: {self.config['igdb-client-id'][:8]}...")
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logging.info(f" Available functions: {len(self.igdb.get_openai_functions())}")
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except Exception as e:
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logging.error(f"❌ Failed to initialize IGDB: {e}")
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self.igdb = None
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else:
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logging.warning("❌ IGDB integration DISABLED - missing configuration or disabled in config")
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async def draw_openai(self, description: str, count: int = 1) -> List[BytesIO]:
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if not self.ledger.budget_ok():
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raise RuntimeError("daily budget exhausted - refusing image call")
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model = self.config.get("image-model", "gpt-image-2")
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kwargs: Dict[str, Any] = {"model": model, "prompt": description, "size": self.config.get("image-size", "1024x1024")}
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if "image-quality" in self.config:
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kwargs["quality"] = self.config["image-quality"]
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if model.startswith("gpt-image"):
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kwargs["n"] = max(1, min(int(count), 4))
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else:
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# legacy models: single image, base64 must be requested (IMG-04)
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kwargs["n"] = 1
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kwargs["response_format"] = "b64_json"
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for _ in range(3):
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try:
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response = await openai_image(self.client, **kwargs)
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buffers = [BytesIO(base64.b64decode(item.b64_json)) for item in response.data]
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self.ledger.add_images(len(buffers))
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logging.info(f"generated {len(buffers)} image(s) on {model} for: {repr(description)}")
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return buffers
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except Exception as err:
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logging.warning(f"Failed to generate image {repr(description)}: {repr(err)}")
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raise RuntimeError(f"Failed to generate image {repr(description)} after multiple retries")
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@staticmethod
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def _last_author(messages: List[Dict[str, Any]]) -> Optional[str]:
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try:
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content = messages[-1]["content"]
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if not isinstance(content, str):
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content = content[0]["text"]
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return str(json.loads(content).get("user")) or None
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except Exception:
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return None
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def _record_usage(self, result: Any) -> None:
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usage = getattr(result, "usage", None)
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prompt_tokens = getattr(usage, "prompt_tokens", None)
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completion_tokens = getattr(usage, "completion_tokens", None)
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if isinstance(prompt_tokens, int) and isinstance(completion_tokens, int):
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self.ledger.add_tokens(prompt_tokens, completion_tokens)
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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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# Safety check for mock objects in tests
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if not isinstance(messages, list) or len(messages) == 0:
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logging.warning("Invalid messages format in chat method")
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return None, limit
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# Hard daily budget, fail-closed (SAF-04)
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if not self.ledger.budget_ok():
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logging.error("daily budget exhausted - refusing model call")
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return None, limit
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try:
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# Clean up any orphaned tool messages from previous conversations
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clean_messages = []
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for i, msg in enumerate(messages):
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if msg.get("role") == "tool":
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# Skip tool messages that don't have a corresponding assistant message with tool_calls
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if i == 0 or messages[i - 1].get("role") != "assistant" or not messages[i - 1].get("tool_calls"):
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logging.debug(f"Removing orphaned tool message at position {i}")
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continue
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clean_messages.append(msg)
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messages = clean_messages
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last_message_content = messages[-1]["content"]
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if isinstance(last_message_content, str):
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model = self.config["model"]
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elif "model-vision" in self.config:
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model = self.config["model-vision"]
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else:
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messages[-1]["content"] = messages[-1]["content"][0]["text"]
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if self._use_retry_model and "retry-model" in self.config:
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model = self.config["retry-model"]
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except (KeyError, IndexError, TypeError) as e:
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logging.warning(f"Error accessing message content: {e}")
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return None, limit
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try:
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# Prepare function calls if IGDB is enabled
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chat_kwargs = {
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"model": model,
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"messages": messages,
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"response_format": ENVELOPE_RESPONSE_FORMAT,
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}
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author = self._last_author(messages)
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if author:
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# hashed, never the raw Discord name (SAF-10)
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chat_kwargs["safety_identifier"] = "discord-" + hashlib.sha256(author.encode()).hexdigest()[:16]
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if self.igdb and self.config.get("enable-game-info", False):
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try:
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igdb_functions = self.igdb.get_openai_functions()
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if igdb_functions and isinstance(igdb_functions, list):
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chat_kwargs["tools"] = [{"type": "function", "function": func} for func in igdb_functions]
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chat_kwargs["tool_choice"] = "auto"
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# gpt-5.6 rejects tools + reasoning on chat/completions (ENV-21)
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chat_kwargs["reasoning_effort"] = self.config.get("reasoning-effort", "none")
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logging.info(f"🎮 IGDB functions available to AI: {[f['name'] for f in igdb_functions]}")
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logging.debug(f" Full chat_kwargs with tools: {list(chat_kwargs.keys())}")
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except (TypeError, AttributeError) as e:
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logging.warning(f"Error setting up IGDB functions: {e}")
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else:
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logging.debug(
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"🎮 IGDB not available for this request (igdb={}, enabled={})".format(
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self.igdb is not None, self.config.get("enable-game-info", False)
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)
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)
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result = await openai_chat(self.client, **chat_kwargs)
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self._record_usage(result)
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# Handle function calls if present
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message = result.choices[0].message
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# A refusal is a failed attempt, not an answer (ENV-18)
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refusal = getattr(message, "refusal", None)
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if isinstance(refusal, str) and refusal:
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logging.warning(f"model refused: {refusal}")
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return None, limit
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# Log what we received from OpenAI
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logging.debug(f"📨 OpenAI Response: content={bool(message.content)}, has_tool_calls={hasattr(message, 'tool_calls')}")
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if hasattr(message, "tool_calls") and message.tool_calls:
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tool_names = [tc.function.name for tc in message.tool_calls]
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logging.info(f"🔧 OpenAI requested function calls: {tool_names}")
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# Check if we have function/tool calls and IGDB is enabled
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has_tool_calls = (
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hasattr(message, "tool_calls") and message.tool_calls and self.igdb and self.config.get("enable-game-info", False)
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)
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# Clean up any existing tool messages in the history to avoid conflicts
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if has_tool_calls:
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messages = [msg for msg in messages if msg.get("role") != "tool"]
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if has_tool_calls:
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logging.info(f"🎮 Processing {len(message.tool_calls)} IGDB function call(s)...")
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try:
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# Process function calls - serialize tool_calls properly
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tool_calls_data = []
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for tc in message.tool_calls:
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tool_calls_data.append(
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{"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}}
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)
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messages.append({"role": "assistant", "content": message.content or "", "tool_calls": tool_calls_data})
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# Execute function calls
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for tool_call in message.tool_calls:
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function_name = tool_call.function.name
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function_args = json.loads(tool_call.function.arguments)
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logging.info(f"🎮 Executing IGDB function: {function_name} with args: {function_args}")
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# Execute IGDB function
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function_result = await self._execute_igdb_function(function_name, function_args)
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logging.info(f"🎮 IGDB function result: {type(function_result)} - {str(function_result)[:200]}...")
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messages.append(
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{
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"role": "tool",
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"tool_call_id": tool_call.id,
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# IGDB text is external input — sanitize before prompting (SAF-03)
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"content": (
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sanitize_external_text(json.dumps(function_result), 8000) if function_result else "No results found"
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),
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}
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)
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# Get final response after function execution - remove tools for final call
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final_chat_kwargs = {
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"model": model,
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"messages": messages,
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"response_format": ENVELOPE_RESPONSE_FORMAT,
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}
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logging.debug(f"🔧 Sending final request to OpenAI with {len(messages)} messages (no tools)")
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logging.debug(f"🔧 Last few messages: {messages[-3:] if len(messages) > 3 else messages}")
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final_result = await openai_chat(self.client, **final_chat_kwargs)
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self._record_usage(final_result)
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answer_obj = final_result.choices[0].message
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logging.debug(
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f"🔧 Final OpenAI response: content_length={len(answer_obj.content) if answer_obj.content else 0}, has_tool_calls={hasattr(answer_obj, 'tool_calls') and answer_obj.tool_calls}"
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)
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if answer_obj.content:
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logging.debug(f"🔧 Response preview: {answer_obj.content[:200]}")
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else:
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logging.warning(f"🔧 OpenAI returned NULL content despite {final_result.usage.completion_tokens} completion tokens")
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# If OpenAI returns null content after function calling, use empty string
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if not answer_obj.content and function_result:
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logging.warning("OpenAI returned null after function calling, using empty string")
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answer_obj.content = ""
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except Exception as e:
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# If function calling fails, fall back to regular response
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logging.warning(f"Function calling failed, using regular response: {e}")
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answer_obj = message
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else:
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answer_obj = message
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# Handle null content from OpenAI
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content = answer_obj.content
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if content is None:
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logging.warning("OpenAI returned null content, using empty string")
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content = ""
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answer = {"content": content, "role": answer_obj.role}
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self.rate_limit_backoff = exponential_backoff()
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self._use_retry_model = False
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logging.info(f"generated response {result.usage}: {repr(answer)}")
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return answer, limit
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except openai.BadRequestError as err:
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if "maximum context length is" in str(err) and limit > 4:
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logging.warning(f"context length exceeded, reduce the limit {limit}: {str(err)}")
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limit -= 1
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return None, limit
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raise err
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except openai.RateLimitError as err:
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rate_limit_sleep = next(self.rate_limit_backoff)
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self._use_retry_model = True
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logging.warning(f"got an rate limit error, sleep for {rate_limit_sleep} seconds: {str(err)}")
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await asyncio.sleep(rate_limit_sleep)
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except Exception as err:
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import traceback
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logging.warning(f"failed to generate response: {repr(err)}")
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logging.debug(f"Full traceback: {traceback.format_exc()}")
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return None, limit
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async def classify(self, message: Any, history_tail: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
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"""~100-token reply/factual/emoji verdict on classifier-model (BEH-01/03)."""
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if "classifier-model" not in self.config or not self.ledger.budget_ok():
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return None
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tail = "\n".join(str(entry.get("content", ""))[:300] for entry in history_tail[-6:])
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messages = [
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{"role": "system", "content": CLASSIFIER_SYSTEM},
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{"role": "user", "content": f"Recent chat:\n{tail}\n\nLAST message:\n{str(message)}"},
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]
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try:
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result = await openai_chat(
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self.client, model=self.config["classifier-model"], messages=messages, response_format=CLASSIFIER_RESPONSE_FORMAT
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)
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self._record_usage(result)
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return json.loads(result.choices[0].message.content)
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except Exception as err:
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logging.warning(f"classifier failed - failing open: {repr(err)}")
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return None
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async def consolidate(self, observations: List[Dict[str, Any]], known_facts: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
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"""Batched memory consolidation on memory-model (MEM-02)."""
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if "memory-model" not in self.config or not self.ledger.budget_ok():
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return None
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observation_lines = "\n".join(f"[{obs['kind']}] {obs['user']}: {obs['content']}" for obs in observations)
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known_lines = "\n".join(f"- {fact['user']}: {fact['fact']}" for fact in known_facts) or "(none)"
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messages = [
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{"role": "system", "content": CONSOLIDATION_SYSTEM},
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{"role": "user", "content": f"Known facts:\n{known_lines}\n\nObservation log:\n{observation_lines}"},
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]
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try:
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result = await openai_chat(
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self.client, model=self.config["memory-model"], messages=messages, response_format=CONSOLIDATION_RESPONSE_FORMAT
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)
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self._record_usage(result)
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parsed = json.loads(result.choices[0].message.content)
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logging.info(f"memory consolidation: {len(parsed.get('facts', []))} new facts, episode={bool(parsed.get('episode'))}")
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return parsed
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except Exception as err:
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logging.warning(f"memory consolidation failed: {repr(err)}")
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return None
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async def _execute_igdb_function(self, function_name: str, function_args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""
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Execute IGDB function calls from OpenAI.
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"""
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logging.info(f"🎮 _execute_igdb_function called: {function_name}")
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if not self.igdb:
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logging.error("🎮 IGDB function called but self.igdb is None!")
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return {"error": "IGDB not available"}
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try:
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if function_name == "search_games":
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query = function_args.get("query", "")
|
|
limit = function_args.get("limit", 5)
|
|
|
|
logging.info(f"🎮 Searching IGDB for: '{query}' (limit: {limit})")
|
|
|
|
if not query:
|
|
logging.warning("🎮 No search query provided to search_games")
|
|
return {"error": "No search query provided"}
|
|
|
|
results = await asyncio.to_thread(self.igdb.search_games, query, limit)
|
|
logging.info(f"🎮 IGDB search returned: {len(results) if results and isinstance(results, list) else 0} results")
|
|
|
|
if results and isinstance(results, list) and len(results) > 0:
|
|
return {"games": results}
|
|
else:
|
|
return {"games": [], "message": f"No games found matching '{query}'"}
|
|
|
|
elif function_name == "get_games_by_release_date":
|
|
year = function_args.get("year")
|
|
month = function_args.get("month")
|
|
platform = function_args.get("platform")
|
|
limit = function_args.get("limit", 10)
|
|
|
|
logging.info(
|
|
f"🎮 Searching IGDB for games releasing in {year}/{month or 'all'} on {platform or 'all platforms'} (limit: {limit})"
|
|
)
|
|
|
|
if not year:
|
|
logging.warning("🎮 No year provided to get_games_by_release_date")
|
|
return {"error": "No year provided"}
|
|
|
|
results = await asyncio.to_thread(self.igdb.get_games_by_release_date, year, month, platform, limit)
|
|
logging.info(
|
|
f"🎮 IGDB release date search returned: {len(results) if results and isinstance(results, list) else 0} results"
|
|
)
|
|
|
|
if results and isinstance(results, list) and len(results) > 0:
|
|
return {"games": results}
|
|
else:
|
|
period = f"{year}/{month}" if month else str(year)
|
|
platform_text = f" on {platform}" if platform else ""
|
|
return {"games": [], "message": f"No games found releasing in {period}{platform_text}"}
|
|
|
|
elif function_name == "get_games_by_platform":
|
|
platform = function_args.get("platform", "")
|
|
genre = function_args.get("genre")
|
|
limit = function_args.get("limit", 10)
|
|
|
|
logging.info(f"🎮 Searching IGDB for games on {platform} {f'in {genre} genre' if genre else ''} (limit: {limit})")
|
|
|
|
if not platform:
|
|
logging.warning("🎮 No platform provided to get_games_by_platform")
|
|
return {"error": "No platform provided"}
|
|
|
|
results = await asyncio.to_thread(self.igdb.get_games_by_platform, platform, genre, limit)
|
|
logging.info(f"🎮 IGDB platform search returned: {len(results) if results and isinstance(results, list) else 0} results")
|
|
|
|
if results and isinstance(results, list) and len(results) > 0:
|
|
return {"games": results}
|
|
else:
|
|
genre_text = f" in {genre} genre" if genre else ""
|
|
return {"games": [], "message": f"No games found for {platform}{genre_text}"}
|
|
|
|
elif function_name == "get_game_details":
|
|
game_id = function_args.get("game_id")
|
|
|
|
logging.info(f"🎮 Getting IGDB details for game ID: {game_id}")
|
|
|
|
if not game_id:
|
|
logging.warning("🎮 No game ID provided to get_game_details")
|
|
return {"error": "No game ID provided"}
|
|
|
|
result = await asyncio.to_thread(self.igdb.get_game_details, game_id)
|
|
logging.info(f"🎮 IGDB game details returned: {bool(result)}")
|
|
|
|
if result:
|
|
return {"game": result}
|
|
else:
|
|
return {"error": f"Game with ID {game_id} not found"}
|
|
else:
|
|
return {"error": f"Unknown function: {function_name}"}
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error executing IGDB function {function_name}: {e}")
|
|
return {"error": f"Failed to execute {function_name}: {str(e)}"}
|