chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,670 @@
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import json
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import os
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import re
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from abc import ABC, abstractmethod
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from dataclasses import asdict, dataclass
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, Literal, Optional
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from openai import OpenAI
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@dataclass
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class TraceEvent:
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"""Single event in the application trace"""
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event_type: str # "llm_call", "llm_response", "extraction", "classification", "error", "init"
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component: (
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str # "openai_api", "deterministic_extractor", "llm_extractor", "support_agent"
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)
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data: Dict[str, Any]
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class ExtractionMode(Enum):
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"""Extraction modes available"""
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DETERMINISTIC = "deterministic"
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LLM = "llm"
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class BaseExtractor(ABC):
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"""Base class for all extractors"""
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@abstractmethod
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def extract(self, email_content: str, category: str) -> Dict[str, Optional[str]]:
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"""Extract information based on category"""
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pass
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class DeterministicExtractor(BaseExtractor):
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"""Regex and rule-based extraction"""
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def extract(self, email_content: str, category: str) -> Dict[str, Optional[str]]:
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"""Route to appropriate extraction method"""
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extractors = {
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"Bug Report": self._extract_bug_info,
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"Billing": self._extract_billing_info,
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"Feature Request": self._extract_feature_info,
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}
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extractor = extractors.get(category)
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if extractor:
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return extractor(email_content)
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return {}
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def _extract_bug_info(self, email_content: str) -> Dict[str, Optional[str]]:
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"""Extract product version and error code from bug reports"""
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version_pattern = r"version\s*[:\-]?\s*([0-9]+\.[0-9]+(?:\.[0-9]+)?)"
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error_pattern = r"error\s*(?:code\s*)?[:\-]?\s*([A-Z0-9\-_]+)"
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version_match = re.search(version_pattern, email_content, re.IGNORECASE)
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error_match = re.search(error_pattern, email_content, re.IGNORECASE)
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return {
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"product_version": version_match.group(1) if version_match else None,
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"error_code": error_match.group(1) if error_match else None,
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}
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def _extract_billing_info(self, email_content: str) -> Dict[str, Optional[str]]:
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"""Extract invoice number and amount from billing emails"""
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invoice_pattern = r"invoice\s*[#:\-]?\s*([A-Z0-9\-_]+)"
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amount_pattern = r"\$([0-9,]+(?:\.[0-9]{2})?)"
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invoice_match = re.search(invoice_pattern, email_content, re.IGNORECASE)
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amount_match = re.search(amount_pattern, email_content)
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# Clean up amount (remove commas)
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amount = None
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if amount_match:
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amount = amount_match.group(1).replace(",", "")
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return {
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"invoice_number": invoice_match.group(1) if invoice_match else None,
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"amount": amount,
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}
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def _extract_feature_info(self, email_content: str) -> Dict[str, Optional[str]]:
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"""Extract feature request details"""
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# Urgency detection
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urgency_keywords = {
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"urgent": ["urgent", "asap", "immediately", "critical", "emergency"],
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"high": ["important", "soon", "needed", "priority", "essential"],
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"medium": ["would like", "request", "suggest", "consider"],
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"low": ["nice to have", "whenever", "eventually", "someday"],
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}
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urgency_level = "medium" # default
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email_lower = email_content.lower()
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for level, keywords in urgency_keywords.items():
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if any(keyword in email_lower for keyword in keywords):
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urgency_level = level
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break
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# Product area detection
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product_areas = [
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"dashboard",
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"api",
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"mobile",
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"reports",
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"billing",
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"user management",
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"analytics",
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"integration",
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"security",
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]
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mentioned_areas = [area for area in product_areas if area in email_lower]
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# Try to extract the main feature request (simple approach)
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feature_keywords = [
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"add",
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"feature",
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"ability",
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"support",
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"implement",
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"create",
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]
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requested_feature = None
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for keyword in feature_keywords:
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pattern = rf"{keyword}\s+(?:a\s+|an\s+|the\s+)?([^.!?]+)"
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match = re.search(pattern, email_content, re.IGNORECASE)
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if match:
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requested_feature = match.group(1).strip()[:100] # Limit length
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break
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return {
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"requested_feature": requested_feature
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or "Feature extraction requires manual review",
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"product_area": mentioned_areas[0] if mentioned_areas else "general",
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"urgency_level": urgency_level,
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}
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class LLMExtractor(BaseExtractor):
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"""LLM-based extraction"""
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def __init__(self, client: OpenAI):
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self.client = client
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def extract(self, email_content: str, category: str) -> Dict[str, Optional[str]]:
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"""Use LLM to extract information"""
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extraction_prompts = {
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"Bug Report": self._get_bug_extraction_prompt,
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"Billing": self._get_billing_extraction_prompt,
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"Feature Request": self._get_feature_extraction_prompt,
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}
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prompt_func = extraction_prompts.get(category)
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if not prompt_func:
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return {}
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prompt = prompt_func(email_content)
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try:
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response = self.client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0,
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max_tokens=200,
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)
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# Parse JSON response
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result = json.loads(
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response.choices[0].message.content.strip()
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if response.choices[0].message.content
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else "{}"
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)
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return result
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except Exception:
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return {}
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def _get_bug_extraction_prompt(self, email_content: str) -> str:
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return f"""
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Extract the following information from this bug report email:
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- product_version: The version number mentioned (e.g., "2.1.4")
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- error_code: Any error code mentioned (e.g., "XYZ-123")
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Email: {email_content}
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Respond with valid JSON only, like:
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{{"product_version": "2.1.4", "error_code": "XYZ-123"}}
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If a field is not found, use null.
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"""
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def _get_billing_extraction_prompt(self, email_content: str) -> str:
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return f"""
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Extract the following information from this billing email:
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- invoice_number: The invoice number (e.g., "INV-2024-001")
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- amount: The dollar amount mentioned (without $ sign, e.g., "299.99")
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Email: {email_content}
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Respond with valid JSON only, like:
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{{"invoice_number": "INV-2024-001", "amount": "299.99"}}
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If a field is not found, use null.
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"""
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def _get_feature_extraction_prompt(self, email_content: str) -> str:
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return f"""
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Extract the following information from this feature request email:
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- requested_feature: Brief description of the main feature requested (max 100 chars)
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- product_area: Which area it relates to (dashboard/api/mobile/reports/billing/user management/analytics/integration/security/general)
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- urgency_level: Urgency level (urgent/high/medium/low)
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Email: {email_content}
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Respond with valid JSON only, like:
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{{"requested_feature": "dark mode for dashboard", "product_area": "dashboard", "urgency_level": "high"}}
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If a field is not found, use appropriate defaults.
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"""
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class ConfigurableSupportTriageAgent:
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"""Support triage agent with configurable extraction modes"""
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def __init__(
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self,
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api_key: str,
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extractor: Optional[BaseExtractor] = None,
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logdir: str = "logs",
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):
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self.client = OpenAI(api_key=api_key)
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self.traces = []
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self.logdir = logdir
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# Create log directory if it doesn't exist
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os.makedirs(self.logdir, exist_ok=True)
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# If no extractor provided, default to deterministic
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if extractor is None:
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self.extractor = DeterministicExtractor()
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else:
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self.extractor = extractor
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# Store the extractor type for reference
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if isinstance(self.extractor, DeterministicExtractor):
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self.extraction_mode = ExtractionMode.DETERMINISTIC
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elif isinstance(self.extractor, LLMExtractor):
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self.extraction_mode = ExtractionMode.LLM
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else:
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# Custom extractor
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self.extraction_mode = None
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print(
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f"📧 Initialized Support Triage Agent with {self.extraction_mode.value if self.extraction_mode else 'custom'} extraction mode"
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)
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self.traces.append(
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TraceEvent(
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event_type="init",
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component="support_agent",
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data={
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"extraction_mode": (
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self.extraction_mode.value if self.extraction_mode else "custom"
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)
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},
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)
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)
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def set_extractor(self, extractor: BaseExtractor):
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"""Change extractor at runtime"""
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self.extractor = extractor
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# Update extraction mode
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if isinstance(self.extractor, DeterministicExtractor):
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self.extraction_mode = ExtractionMode.DETERMINISTIC
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elif isinstance(self.extractor, LLMExtractor):
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self.extraction_mode = ExtractionMode.LLM
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else:
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self.extraction_mode = None
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print(
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f"🔄 Switched to {self.extraction_mode.value if self.extraction_mode else 'custom'} extraction mode"
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)
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self.traces.append(
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TraceEvent(
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event_type="extractor_change",
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component="support_agent",
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data={
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"new_extractor": type(extractor).__name__,
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"extraction_mode": (
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self.extraction_mode.value if self.extraction_mode else "custom"
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),
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},
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)
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)
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def classify_email(self, email_content: str) -> str:
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"""Classify email into categories using LLM"""
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print("🔍 Step 1: Classifying email category...")
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prompt = f"""
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Classify the following customer email into exactly one of these categories:
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- Billing
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- Bug Report
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- Feature Request
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Email content:
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{email_content}
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Respond with only the category name, nothing else.
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"""
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self.traces.append(
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TraceEvent(
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event_type="llm_call",
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component="openai_api",
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data={
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"operation": "classification",
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"model": "gpt-3.5-turbo",
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"prompt_length": len(prompt),
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"email_length": len(email_content),
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},
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)
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)
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try:
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response = self.client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0,
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max_tokens=10,
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)
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category = (
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response.choices[0].message.content.strip()
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if response.choices[0].message.content
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else "unknown"
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)
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print(f" ➜ Classified as: {category}")
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self.traces.append(
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TraceEvent(
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event_type="llm_response",
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component="openai_api",
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data={
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"operation": "classification",
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"result": category,
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"usage": (
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response.usage.model_dump() if response.usage else None
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),
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},
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)
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)
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return category
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except Exception as e:
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print(" ⚠️ Classification failed, using fallback: Bug Report")
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self.traces.append(
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TraceEvent(
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event_type="error",
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component="openai_api",
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data={"operation": "classification", "error": str(e)},
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)
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)
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return "Bug Report" # Default fallback
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def extract_info(
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self, email_content: str, category: str
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) -> Dict[str, Optional[str]]:
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"""Extract information using configured extractor"""
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print(
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f"⚙️ Step 2: Extracting {category} details using {self.extraction_mode.value if self.extraction_mode else 'custom'} method..."
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)
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self.traces.append(
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TraceEvent(
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event_type="extraction",
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component=type(self.extractor).__name__.lower(),
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data={
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"category": category,
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"email_length": len(email_content),
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"extraction_mode": (
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self.extraction_mode.value if self.extraction_mode else "custom"
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),
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},
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)
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)
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try:
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result = self.extractor.extract(email_content, category)
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# Show extracted fields briefly
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if result:
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extracted_fields = [k for k, v in result.items() if v is not None]
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if extracted_fields:
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print(f" ➜ Extracted: {', '.join(extracted_fields)}")
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else:
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print(" ➜ No specific details extracted")
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self.traces.append(
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TraceEvent(
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event_type="extraction_result",
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component=type(self.extractor).__name__.lower(),
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data={"extracted_fields": list(result.keys()), "result": result},
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)
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)
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return result
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except Exception as e:
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print(f" ⚠️ Extraction failed: {str(e)}")
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self.traces.append(
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TraceEvent(
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event_type="error",
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component=type(self.extractor).__name__.lower(),
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data={"operation": "extraction", "error": str(e)},
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)
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)
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return {}
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def generate_response(self, category: str, extracted_info: Dict[str, Any]) -> str:
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"""Generate response template based on category"""
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print("✍️ Step 3: Generating personalized response...")
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context = f"Category: {category}\nExtracted info: {json.dumps(extracted_info, indent=2)}"
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prompt = f"""
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Generate a professional customer support response template for the following:
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{context}
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The response should:
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- Be polite and professional
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- Acknowledge the specific issue type
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- Include next steps or resolution process
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- Reference any extracted information appropriately
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Keep it concise but helpful.
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"""
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self.traces.append(
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TraceEvent(
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event_type="llm_call",
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component="openai_api",
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data={
|
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"operation": "response_generation",
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"model": "gpt-3.5-turbo",
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"category": category,
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"extracted_fields": list(extracted_info.keys()),
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},
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)
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)
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try:
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response = self.client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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max_tokens=300,
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)
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response_text = (
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response.choices[0].message.content.strip()
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if response.choices[0].message.content
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else ""
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)
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print(" ➜ Response template generated")
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self.traces.append(
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TraceEvent(
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event_type="llm_response",
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component="openai_api",
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data={
|
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"operation": "response_generation",
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"response_length": len(response_text),
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"usage": (
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response.usage.model_dump() if response.usage else None
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),
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},
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)
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)
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return response_text
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except Exception as e:
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print(" ⚠️ Response generation failed, using fallback")
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self.traces.append(
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TraceEvent(
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event_type="error",
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||||
component="openai_api",
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||||
data={"operation": "response_generation", "error": str(e)},
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)
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)
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return "Thank you for contacting support. We will review your request and get back to you soon."
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def export_traces_to_log(
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||||
self, run_id: str, email_content: str, result: Optional[Dict[str, Any]] = None
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):
|
||||
"""Export traces to a log file with run_id"""
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||||
timestamp = datetime.now().isoformat()
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||||
log_filename = (
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||||
f"run_{run_id}_{timestamp.replace(':', '-').replace('.', '-')}.json"
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||||
)
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||||
log_filepath = os.path.join(self.logdir, log_filename)
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||||
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||||
log_data = {
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"run_id": run_id,
|
||||
"timestamp": timestamp,
|
||||
"email_content": email_content,
|
||||
"result": result,
|
||||
"extraction_mode": (
|
||||
self.extraction_mode.value if self.extraction_mode else "custom"
|
||||
),
|
||||
"traces": [asdict(trace) for trace in self.traces],
|
||||
}
|
||||
|
||||
with open(log_filepath, "w") as f:
|
||||
json.dump(log_data, f, indent=2)
|
||||
|
||||
return log_filepath
|
||||
|
||||
def process_email(
|
||||
self, email_content: str, run_id: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""Main processing function that handles the entire workflow"""
|
||||
|
||||
# Generate run_id if not provided
|
||||
if run_id is None:
|
||||
run_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{hash(email_content) % 10000:04d}"
|
||||
|
||||
print(f"\n🚀 Processing email (Run ID: {run_id})")
|
||||
print(
|
||||
f"📄 Email preview: {email_content[:100]}{'...' if len(email_content) > 100 else ''}"
|
||||
)
|
||||
|
||||
# Reset traces for each new email
|
||||
self.traces = []
|
||||
|
||||
self.traces.append(
|
||||
TraceEvent(
|
||||
event_type="workflow_start",
|
||||
component="support_agent",
|
||||
data={"run_id": run_id, "email_length": len(email_content)},
|
||||
)
|
||||
)
|
||||
|
||||
try:
|
||||
# Step 1: Classify email
|
||||
category = self.classify_email(email_content)
|
||||
|
||||
# Step 2: Extract relevant information based on category
|
||||
extracted_info = self.extract_info(email_content, category)
|
||||
|
||||
# Step 3: Generate response template
|
||||
response_template = self.generate_response(category, extracted_info)
|
||||
|
||||
result = {
|
||||
"category": category,
|
||||
"extracted_info": extracted_info,
|
||||
"response_template": response_template,
|
||||
"extraction_mode": (
|
||||
self.extraction_mode.value if self.extraction_mode else "custom"
|
||||
),
|
||||
}
|
||||
|
||||
print("✅ Workflow completed successfully")
|
||||
print(f"📋 Traces saved to: logs/run_{run_id}_*.json")
|
||||
|
||||
self.traces.append(
|
||||
TraceEvent(
|
||||
event_type="workflow_complete",
|
||||
component="support_agent",
|
||||
data={"run_id": run_id, "success": True},
|
||||
)
|
||||
)
|
||||
|
||||
# Export traces to log file
|
||||
self.export_traces_to_log(run_id, email_content, result)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Workflow failed: {str(e)}")
|
||||
|
||||
self.traces.append(
|
||||
TraceEvent(
|
||||
event_type="error",
|
||||
component="support_agent",
|
||||
data={"operation": "process_email", "error": str(e)},
|
||||
)
|
||||
)
|
||||
|
||||
# Export traces even if processing failed
|
||||
self.export_traces_to_log(run_id, email_content, {})
|
||||
|
||||
# Return minimal result on error
|
||||
return {
|
||||
"category": "Bug Report",
|
||||
"extracted_info": {},
|
||||
"response_template": "Thank you for contacting support. We will review your request and get back to you soon.",
|
||||
"extraction_mode": (
|
||||
self.extraction_mode.value if self.extraction_mode else "custom"
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def default_workflow_client(
|
||||
extractor_type: Literal["deterministic", "llm"] = "deterministic",
|
||||
) -> ConfigurableSupportTriageAgent:
|
||||
"""Create a default workflow client with specified extractor type"""
|
||||
print(f"🔧 Creating workflow client with {extractor_type} extraction...")
|
||||
|
||||
api_key = os.environ.get("OPENAI_API_KEY")
|
||||
|
||||
if extractor_type == "deterministic":
|
||||
extractor = DeterministicExtractor()
|
||||
elif extractor_type == "llm":
|
||||
if api_key is None:
|
||||
raise ValueError(
|
||||
"OPENAI_API_KEY environment variable is required for LLM extractor"
|
||||
)
|
||||
client = OpenAI(api_key=api_key)
|
||||
extractor = LLMExtractor(client)
|
||||
else:
|
||||
raise ValueError(f"Unsupported extractor type: {extractor_type}")
|
||||
|
||||
# Use a default API key if none provided and using deterministic extractor
|
||||
if api_key is None:
|
||||
api_key = "dummy"
|
||||
|
||||
return ConfigurableSupportTriageAgent(
|
||||
api_key=api_key, extractor=extractor, logdir="logs"
|
||||
)
|
||||
|
||||
|
||||
# Example usage and testing
|
||||
def main():
|
||||
# Initialize the agent with different extractors
|
||||
api_key = os.environ.get("OPENAI_API_KEY")
|
||||
if api_key is None:
|
||||
api_key = "dummy"
|
||||
|
||||
# Test emails
|
||||
test_emails = [
|
||||
"Hi, I'm getting error code XYZ-123 when using version 2.1.4 of your software. Please help!",
|
||||
"I need to dispute invoice #INV-2024-001 for 299.99 dollars. The charge seems incorrect.",
|
||||
]
|
||||
|
||||
# Example 1: Using deterministic extractor
|
||||
print("\n=== Using Deterministic Extractor ===")
|
||||
deterministic_extractor = DeterministicExtractor()
|
||||
agent = ConfigurableSupportTriageAgent(
|
||||
api_key=api_key, extractor=deterministic_extractor, logdir="logs"
|
||||
)
|
||||
|
||||
result = agent.process_email(test_emails[0])
|
||||
print(f"Result: {result['response_template']}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user