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
385
apps/tenant-backend/app/services/summarization_service.py
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385
apps/tenant-backend/app/services/summarization_service.py
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"""
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GT 2.0 Summarization Service
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Provides AI-powered summarization capabilities for documents and datasets.
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Uses the same pattern as conversation title generation with Llama 3.1 8B.
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"""
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import logging
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import asyncio
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from app.core.postgresql_client import get_postgresql_client
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from app.core.resource_client import ResourceClusterClient
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from app.core.config import get_settings
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logger = logging.getLogger(__name__)
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class SummarizationService:
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"""
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Service for generating AI summaries of documents and datasets.
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Uses the same approach as conversation title generation:
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- Llama 3.1 8B instant model
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- Low temperature for consistency
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- Resource cluster for AI responses
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"""
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def __init__(self, tenant_domain: str, user_id: str):
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self.tenant_domain = tenant_domain
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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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self.settings = get_settings()
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async def generate_document_summary(
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self,
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document_id: str,
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document_content: str,
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document_name: str
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) -> Optional[str]:
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"""
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Generate AI summary for a document using Llama 3.1 8B.
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Args:
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document_id: UUID of the document
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document_content: Full text content of the document
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document_name: Original filename/name of the document
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Returns:
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Generated summary string or None if failed
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"""
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try:
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# Truncate content to first 3000 chars (like conversation title generation)
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content_preview = document_content[:3000]
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# Create summarization prompt
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prompt = f"""Summarize this document '{document_name}' in 2-3 sentences.
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Focus on the main topics, key information, and purpose of the document.
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Document content:
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{content_preview}
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Summary:"""
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logger.info(f"Generating summary for document {document_id} ({document_name})")
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# Call Resource Cluster with same pattern as conversation titles
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summary = await self._call_ai_for_summary(
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prompt=prompt,
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context_type="document",
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max_tokens=150
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)
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if summary:
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# Store summary in database
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await self._store_document_summary(document_id, summary)
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logger.info(f"Generated summary for document {document_id}: {summary[:100]}...")
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return summary
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else:
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logger.warning(f"Failed to generate summary for document {document_id}")
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return None
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except Exception as e:
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logger.error(f"Error generating document summary for {document_id}: {e}")
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return None
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async def generate_dataset_summary(self, dataset_id: str) -> Optional[str]:
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"""
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Generate AI summary for a dataset based on its document summaries.
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Args:
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dataset_id: UUID of the dataset
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Returns:
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Generated dataset summary or None if failed
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"""
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try:
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# Get all document summaries in this dataset
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document_summaries = await self._get_document_summaries_for_dataset(dataset_id)
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if not document_summaries:
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logger.info(f"No document summaries found for dataset {dataset_id}")
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return None
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# Get dataset name for context
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dataset_info = await self._get_dataset_info(dataset_id)
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dataset_name = dataset_info.get('name', 'Unknown Dataset') if dataset_info else 'Unknown Dataset'
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# Combine summaries for LLM context
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combined_summaries = "\n".join([
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f"- {doc['filename']}: {doc['summary']}"
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for doc in document_summaries
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if doc['summary'] # Only include docs that have summaries
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])
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if not combined_summaries.strip():
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logger.info(f"No valid document summaries for dataset {dataset_id}")
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return None
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# Create dataset summarization prompt
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prompt = f"""Based on these document summaries, create a comprehensive 3-4 sentence summary describing what the dataset '{dataset_name}' contains and its purpose:
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Documents in dataset:
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{combined_summaries}
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Dataset summary:"""
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logger.info(f"Generating summary for dataset {dataset_id} ({dataset_name})")
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# Call AI for dataset summary
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summary = await self._call_ai_for_summary(
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prompt=prompt,
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context_type="dataset",
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max_tokens=200
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)
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if summary:
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# Store dataset summary in database
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await self._store_dataset_summary(dataset_id, summary)
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logger.info(f"Generated dataset summary for {dataset_id}: {summary[:100]}...")
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return summary
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else:
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logger.warning(f"Failed to generate summary for dataset {dataset_id}")
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return None
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except Exception as e:
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logger.error(f"Error generating dataset summary for {dataset_id}: {e}")
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return None
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async def update_dataset_summary_on_change(self, dataset_id: str) -> bool:
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"""
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Regenerate dataset summary when documents are added/removed.
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Args:
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dataset_id: UUID of the dataset to update
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Returns:
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True if summary was updated successfully
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"""
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try:
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summary = await self.generate_dataset_summary(dataset_id)
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return summary is not None
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except Exception as e:
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logger.error(f"Error updating dataset summary for {dataset_id}: {e}")
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return False
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async def _call_ai_for_summary(
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self,
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prompt: str,
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context_type: str,
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max_tokens: int = 150
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) -> Optional[str]:
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"""
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Call Resource Cluster for AI summary generation.
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Uses ResourceClusterClient for consistent service discovery.
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Args:
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prompt: The summarization prompt
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context_type: Type of summary (document, dataset)
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max_tokens: Maximum tokens to generate
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Returns:
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Generated summary text or None if failed
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"""
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try:
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# Prepare request payload (same format as conversation service)
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request_data = {
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"model": self.summarization_model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.3, # Lower temperature for consistent summaries
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"max_tokens": max_tokens,
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"top_p": 1.0
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}
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logger.info(f"Calling Resource Cluster for {context_type} summary generation")
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# Use ResourceClusterClient for consistent service discovery and auth
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result = await self.resource_client.call_inference_endpoint(
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tenant_id=self.tenant_domain,
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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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summary = result["choices"][0]["message"]["content"].strip()
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logger.info(f"✅ AI {context_type} summary generated successfully: {summary[:50]}...")
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return summary
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else:
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logger.error(f"❌ Invalid AI response format for {context_type} summary: {result}")
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return None
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except Exception as e:
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logger.error(f"❌ Error calling Resource Cluster for {context_type} summary: {e}", exc_info=True)
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return None
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async def _store_document_summary(self, document_id: str, summary: str) -> None:
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"""Store document summary in database"""
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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await conn.execute(f"""
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UPDATE {schema_name}.documents
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SET summary = $1,
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summary_generated_at = $2,
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summary_model = $3
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WHERE id = $4
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""", summary, datetime.now(), self.summarization_model, document_id)
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except Exception as e:
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logger.error(f"Error storing document summary for {document_id}: {e}")
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raise
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async def _store_dataset_summary(self, dataset_id: str, summary: str) -> None:
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"""Store dataset summary in database"""
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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await conn.execute(f"""
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UPDATE {schema_name}.datasets
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SET summary = $1,
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summary_generated_at = $2,
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summary_model = $3
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WHERE id = $4
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""", summary, datetime.now(), self.summarization_model, dataset_id)
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except Exception as e:
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logger.error(f"Error storing dataset summary for {dataset_id}: {e}")
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raise
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async def _get_document_summaries_for_dataset(self, dataset_id: str) -> List[Dict[str, Any]]:
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"""Get all document summaries for a dataset"""
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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rows = await conn.fetch(f"""
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SELECT id, filename, original_filename, summary, summary_generated_at
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FROM {schema_name}.documents
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WHERE dataset_id = $1
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AND summary IS NOT NULL
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AND summary != ''
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ORDER BY created_at ASC
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""", dataset_id)
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return [dict(row) for row in rows]
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except Exception as e:
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logger.error(f"Error getting document summaries for dataset {dataset_id}: {e}")
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return []
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async def _get_dataset_info(self, dataset_id: str) -> Optional[Dict[str, Any]]:
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"""Get basic dataset information"""
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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row = await conn.fetchrow(f"""
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SELECT id, name, description
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FROM {schema_name}.datasets
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WHERE id = $1
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""", dataset_id)
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return dict(row) if row else None
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except Exception as e:
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logger.error(f"Error getting dataset info for {dataset_id}: {e}")
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return None
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async def get_datasets_with_summaries(self, user_id: str) -> List[Dict[str, Any]]:
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"""
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Get all user-accessible datasets with their summaries.
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Used for context injection in chat.
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Args:
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user_id: UUID of the user
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Returns:
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List of datasets with summaries
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"""
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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rows = await conn.fetch(f"""
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SELECT id, name, description, summary, summary_generated_at,
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document_count, total_size_bytes
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FROM {schema_name}.datasets
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WHERE (created_by = $1::uuid
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OR access_group IN ('team', 'organization'))
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AND is_active = true
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ORDER BY name ASC
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""", user_id)
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return [dict(row) for row in rows]
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except Exception as e:
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logger.error(f"Error getting datasets with summaries for user {user_id}: {e}")
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return []
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async def get_filtered_datasets_with_summaries(
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self,
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user_id: str,
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allowed_dataset_ids: List[str]
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) -> List[Dict[str, Any]]:
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"""
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Get datasets with summaries filtered by allowed dataset IDs.
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Used for agent-aware context injection in chat.
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Args:
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user_id: User UUID string
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allowed_dataset_ids: List of dataset IDs the agent/user should see
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Returns:
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List of dataset dictionaries with summaries, filtered by allowed IDs
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"""
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if not allowed_dataset_ids:
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logger.info(f"No allowed dataset IDs provided for user {user_id} - returning empty list")
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return []
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try:
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client = await get_postgresql_client()
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async with client.get_connection() as conn:
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schema_name = self.settings.postgres_schema
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# Convert dataset IDs to UUID format for query
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placeholders = ",".join(f"${i+2}::uuid" for i in range(len(allowed_dataset_ids)))
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query = f"""
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SELECT id, name, description, summary, summary_generated_at,
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document_count, total_size_bytes
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FROM {schema_name}.datasets
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WHERE (created_by = $1::uuid
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OR access_group IN ('team', 'organization'))
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AND is_active = true
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AND id = ANY(ARRAY[{placeholders}])
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ORDER BY name ASC
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"""
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params = [user_id] + allowed_dataset_ids
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rows = await conn.fetch(query, *params)
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filtered_datasets = [dict(row) for row in rows]
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logger.info(f"Filtered datasets for user {user_id}: {len(filtered_datasets)} out of {len(allowed_dataset_ids)} requested")
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return filtered_datasets
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except Exception as e:
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logger.error(f"Error getting filtered datasets with summaries for user {user_id}: {e}")
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return []
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# Factory function for dependency injection
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def get_summarization_service(tenant_domain: str, user_id: str) -> SummarizationService:
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"""Factory function to create SummarizationService instance"""
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return SummarizationService(tenant_domain, user_id)
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