GT AI OS Community Edition v2.0.33
Security hardening release addressing CodeQL and Dependabot alerts: - Fix stack trace exposure in error responses - Add SSRF protection with DNS resolution checking - Implement proper URL hostname validation (replaces substring matching) - Add centralized path sanitization to prevent path traversal - Fix ReDoS vulnerability in email validation regex - Improve HTML sanitization in validation utilities - Fix capability wildcard matching in auth utilities - Update glob dependency to address CVE - Add CodeQL suppression comments for verified false positives 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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HackWeasel
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apps/tenant-backend/app/services/conversation_summarizer.py
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apps/tenant-backend/app/services/conversation_summarizer.py
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"""
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Conversation Summarization Service for GT 2.0
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Automatically generates meaningful conversation titles using a specialized
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summarization agent with llama-3.1-8b-instant.
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"""
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import json
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import logging
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from typing import List, Optional, Dict, Any
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from app.core.config import get_settings
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from app.core.resource_client import ResourceClusterClient
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logger = logging.getLogger(__name__)
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settings = get_settings()
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class ConversationSummarizer:
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"""Service for generating conversation summaries and titles"""
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def __init__(self, tenant_id: str, user_id: str):
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self.tenant_id = tenant_id
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self.user_id = user_id
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self.resource_client = ResourceClusterClient()
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self.summarization_model = "llama-3.1-8b-instant"
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async def generate_conversation_title(self, messages: List[Dict[str, Any]]) -> str:
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"""
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Generate a concise conversation title based on the conversation content.
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Args:
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messages: List of message dictionaries from the conversation
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Returns:
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Generated conversation title (3-6 words)
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"""
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try:
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# Extract conversation context for summarization
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conversation_text = self._prepare_conversation_context(messages)
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if not conversation_text.strip():
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return "New Conversation"
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# Generate title using specialized summarization prompt
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title = await self._call_summarization_agent(conversation_text)
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# Validate and clean the generated title
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clean_title = self._clean_title(title)
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logger.info(f"Generated conversation title: '{clean_title}' from {len(messages)} messages")
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return clean_title
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except Exception as e:
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logger.error(f"Error generating conversation title: {e}")
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return self._fallback_title(messages)
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def _prepare_conversation_context(self, messages: List[Dict[str, Any]]) -> str:
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"""Prepare conversation context for summarization"""
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if not messages:
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return ""
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# Limit to first few exchanges for title generation
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context_messages = messages[:6] # First 3 user-agent exchanges
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context_parts = []
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for msg in context_messages:
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role = "User" if msg.get("role") == "user" else "Agent"
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# Truncate very long messages for context
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content = msg.get("content", "")
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content = content[:200] + "..." if len(content) > 200 else content
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context_parts.append(f"{role}: {content}")
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return "\n".join(context_parts)
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async def _call_summarization_agent(self, conversation_text: str) -> str:
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"""Call the resource cluster AI inference for summarization"""
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summarization_prompt = f"""You are a conversation title generator. Your job is to create concise, descriptive titles for conversations.
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Given this conversation:
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---
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{conversation_text}
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---
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Generate a title that:
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- Is 3-6 words maximum
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- Captures the main topic or purpose
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- Is clear and descriptive
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- Uses title case
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- Does NOT include quotes or special characters
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Examples of good titles:
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- "Python Code Review"
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- "Database Migration Help"
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- "React Component Design"
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- "System Architecture Discussion"
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Title:"""
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request_data = {
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"messages": [
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{
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"role": "user",
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"content": summarization_prompt
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}
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],
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"model": self.summarization_model,
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"temperature": 0.3, # Lower temperature for consistent, focused titles
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"max_tokens": 20, # Short response for title generation
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"stream": False
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}
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try:
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# Use the resource client instead of direct HTTP calls
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result = await self.resource_client.call_inference_endpoint(
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tenant_id=self.tenant_id,
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user_id=self.user_id,
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endpoint="chat/completions",
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data=request_data
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)
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if result and "choices" in result and len(result["choices"]) > 0:
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title = result["choices"][0]["message"]["content"].strip()
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return title
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else:
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logger.error("Invalid response format from summarization API")
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return ""
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except Exception as e:
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logger.error(f"Error calling summarization agent: {e}")
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return ""
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def _clean_title(self, raw_title: str) -> str:
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"""Clean and validate the generated title"""
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if not raw_title:
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return "New Conversation"
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# Remove quotes, extra whitespace, and special characters
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cleaned = raw_title.strip().strip('"\'').strip()
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# Remove common prefixes that AI might add
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prefixes_to_remove = [
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"Title:", "title:", "TITLE:",
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"Conversation:", "conversation:",
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"Topic:", "topic:",
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"Subject:", "subject:"
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]
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for prefix in prefixes_to_remove:
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if cleaned.startswith(prefix):
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cleaned = cleaned[len(prefix):].strip()
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# Limit length and ensure it's reasonable
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if len(cleaned) > 50:
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cleaned = cleaned[:47] + "..."
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# Ensure it's not empty after cleaning
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if not cleaned or len(cleaned.split()) > 8:
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return "New Conversation"
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return cleaned
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def _fallback_title(self, messages: List[Dict[str, Any]]) -> str:
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"""Generate fallback title when AI summarization fails"""
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if not messages:
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return "New Conversation"
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# Try to use the first user message for context
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first_user_msg = next((msg for msg in messages if msg.get("role") == "user"), None)
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if first_user_msg and first_user_msg.get("content"):
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# Extract first few words from the user's message
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words = first_user_msg["content"].strip().split()[:4]
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if len(words) >= 2:
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fallback = " ".join(words).capitalize()
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# Remove common question words for cleaner titles
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for word in ["How", "What", "Can", "Could", "Please", "Help"]:
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if fallback.startswith(word + " "):
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fallback = fallback[len(word):].strip()
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break
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return fallback if fallback else "New Conversation"
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return "New Conversation"
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async def generate_conversation_title(messages: List[Dict[str, Any]], tenant_id: str, user_id: str) -> str:
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"""
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Convenience function to generate a conversation title.
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Args:
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messages: List of message dictionaries from the conversation
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tenant_id: Tenant identifier
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user_id: User identifier
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Returns:
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Generated conversation title
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"""
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summarizer = ConversationSummarizer(tenant_id, user_id)
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return await summarizer.generate_conversation_title(messages)
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