mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 15:29:36 +00:00
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
"""
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Competitor Monitor — summarizes competitor positioning from provided data.
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وكيل مراقبة المنافسين — يلخّص وضع المنافسين من البيانات المُقدّمة.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any
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from core.agents.base import BaseAgent
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from core.config.models import Task
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from core.llm.base import Message
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from core.prompts import get_prompt
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from core.utils import generate_id, utcnow
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@dataclass
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class CompetitorSummary:
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id: str
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competitor_name: str
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locale: str
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positioning: str = ""
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pricing_hints: str = ""
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strengths: list[str] = field(default_factory=list)
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weaknesses: list[str] = field(default_factory=list)
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counter_moves: list[str] = field(default_factory=list)
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summary_markdown: str = ""
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created_at: datetime = field(default_factory=utcnow)
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def to_dict(self) -> dict[str, Any]:
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return {
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"id": self.id,
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"competitor_name": self.competitor_name,
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"locale": self.locale,
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"positioning": self.positioning,
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"pricing_hints": self.pricing_hints,
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"strengths": self.strengths,
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"weaknesses": self.weaknesses,
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"counter_moves": self.counter_moves,
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"summary_markdown": self.summary_markdown,
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"created_at": self.created_at.isoformat(),
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}
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class CompetitorMonitorAgent(BaseAgent):
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"""Analyzes competitor info and suggests counter-moves."""
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name = "competitor_monitor"
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async def run(
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self,
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*,
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competitor_name: str,
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competitor_data: str,
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locale: str = "ar",
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**_: Any,
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) -> CompetitorSummary:
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"""Summarize competitor, extract counter-moves."""
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prompt = get_prompt(
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"competitor_summary",
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locale=locale,
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data=f"Competitor: {competitor_name}\n\n{competitor_data}",
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)
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response = await self.router.run(
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task=Task.REASONING,
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messages=[Message(role="user", content=prompt)],
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max_tokens=800,
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temperature=0.3,
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)
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markdown = response.content.strip()
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summary = CompetitorSummary(
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id=generate_id("comp"),
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competitor_name=competitor_name,
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locale=locale,
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summary_markdown=markdown,
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)
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# Best-effort section extraction
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self._populate_sections(summary, markdown)
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self.log.info(
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"competitor_analyzed",
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id=summary.id,
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name=competitor_name,
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n_weaknesses=len(summary.weaknesses),
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)
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return summary
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@staticmethod
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def _populate_sections(summary: CompetitorSummary, markdown: str) -> None:
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"""Parse bullet sections loosely."""
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lines = markdown.splitlines()
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current: list[str] | None = None
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for line in lines:
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s = line.strip()
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lower = s.lower()
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if any(t in lower for t in ("strength", "نقاط القوة", "القوة")):
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current = summary.strengths
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continue
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if any(t in lower for t in ("weakness", "نقاط الضعف", "الضعف")):
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current = summary.weaknesses
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continue
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if any(t in lower for t in ("counter", "مضاد", "الحركات المضادة")):
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current = summary.counter_moves
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continue
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if any(t in lower for t in ("pricing", "السعر", "الأسعار")):
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summary.pricing_hints = s
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current = None
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continue
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if any(t in lower for t in ("position", "التموضع", "الوضع")):
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summary.positioning = s
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current = None
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continue
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if current is not None and (s.startswith("-") or s.startswith("*")):
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current.append(s.lstrip("-* ").strip())
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