#!/usr/bin/env python3 """ 处理 Tavily 搜索结果并插入数据库 """ import sqlite3 import json import hashlib import random from datetime import datetime from pathlib import Path # 搜索结果数据 SEARCH_RESULTS = [ { "title": "Autonomous Customer Service Resolution", "description": "AI agents handle customer support independently — resolution, retention, and escalation assist are the fastest to deploy and easiest to measure. This is the most common starting point for enterprises new to agentic AI.", "url": "https://www.techaheadcorp.com/blog/top-use-cases-of-agentic-ai-in-2026-across-industries/", "categories": "Customer Service", "tags": "autonomous, customer-support, resolution, escalation", "key_technologies": "AI Agents, Multi-agent Workflows" }, { "title": "Multi-Agent Orchestration in Legal Workflows", "description": "Allen & Overy, an international law firm, uses agentic AI to improve efficiency while maintaining high standards across complex legal workflows. Reduces thousands of hours spent on repetitive tasks.", "url": "https://flobotics.io/uncategorized/hottest-agentic-ai-examples-and-use-cases-2025", "categories": "Legal", "tags": "legal, document-review, compliance, orchestration", "key_technologies": "Multi-Agent Systems, Document Processing" }, { "title": "Insurance Policy Document Processing", "description": "A premium finance company processes over one million insurance policy documents annually using Agentic AI, without relying on manual review loops or brittle rule-based automation.", "url": "https://nuvento.com/blogs/agentic-ai-2026-your-competitors-hidden-advantage", "categories": "Finance, Insurance", "tags": "document-processing, insurance, large-scale, automation", "key_technologies": "Agentic AI, Document Intelligence" }, { "title": "Personal AI Agents for Travel Management", "description": "AI personal agents arriving to replace manual app usage. For example, if a flight is cancelled due to weather, the agent automatically rebooks, notifies hotels, and updates calendar.", "url": "https://www.goldmansachs.com/insights/articles/what-to-expect-from-ai-in-2026-personal-agents-mega-alliances", "categories": "Personal Productivity, Travel", "tags": "personal-agents, travel-automation, end-to-end", "key_technologies": "Personal Agents, Workflow Automation" }, { "title": "AI-Driven Warehouse and Logistics Automation", "description": "Amazon and other retailers use AI-driven robots for inventory sorting and supply chain optimization. AI streamlines operations, reduces costs, and improves customer satisfaction.", "url": "https://juma.ai/blog/15-real-world-examples-of-ai-automation-in-2025", "categories": "Logistics, Retail", "tags": "warehouse, logistics, robotics, optimization", "key_technologies": "AI Robotics, Computer Vision" }, { "title": "Supervised Decision Escalation Systems", "description": "In operational workflows, agents process standard cases independently. When confidence drops or policy conflicts arise, the system pauses and routes cases with full context to human owners.", "url": "https://www.ema.co/additional-blogs/addition-blogs/agentic-ai-trends-predictions-2025", "categories": "Operations, Governance", "tags": "human-in-the-loop, escalation, governance", "key_technologies": "Confidence Scoring, Policy Engine" }, { "title": "Healthcare Workflow Automation", "description": "Healthcare AI workflow automation addresses rising costs, clinician burnout, complex billing cycles, and fractured data systems to deliver better care.", "url": "https://www.bizdata360.com/top-5-use-cases-of-healthcare-ai-workflow-automation-in-2026/", "categories": "Healthcare", "tags": "billing, patient-care, burnout-reduction", "key_technologies": "Healthcare AI, Workflow Automation" }, { "title": "ITpoint Systems Development Automation", "description": "ITpoint Systems adopted AI automation to document and development workflows, starting with a pilot across customer support and internal dev teams, then expanding company-wide.", "url": "https://juma.ai/blog/15-real-world-examples-of-ai-automation-in-2025", "categories": "Software Development", "tags": "documentation, dev-automation, company-wide", "key_technologies": "API Integration, AI Automation" }, { "title": "Blue Prism Legal Department Automation", "description": "Combined automation and AI to achieve 58% reduction in processing times and 43% increase in case-handling capacity over 6 months, maintaining 100% compliance with judicial deadlines.", "url": "https://www.blueprism.com/guides/what-is-automation-software/best/", "categories": "Legal, Compliance", "tags": "compliance, deadline-management, efficiency", "key_technologies": "RPA + AI, Compliance Engine" }, { "title": "Supply Chain AI Orchestration", "description": "Agentic AI shifts from generating insights to coordinating and executing work across end-to-end supply chain workflows with defined guardrails and human control.", "url": "https://axistogroup.com/en/ai-en-agentic-automation-in-2026/", "categories": "Supply Chain", "tags": "end-to-end, guardrails, coordination", "key_technologies": "Agentic Automation, Workflow Orchestration" }, { "title": "Retail Conversational AI", "description": "Sephora and H&M use chatbots for 24/7 customer inquiries, personalized product recommendations, booking assistance, and outfit suggestions based on the user's preferences.", "url": "https://tezeract.ai/ai-use-cases-by-industry/", "categories": "Retail, E-commerce", "tags": "personalization, 24-7-support, conversational", "key_technologies": "Conversational AI, Recommendation Engine" }, { "title": "Multi-Agent Specialized Workflows", "description": "Multi-agent systems divide responsibilities: one analyzes data, another validates results, a third executes actions. Agents communicate continuously, ensuring alignment and reducing errors.", "url": "https://futransolutions.com/blog/agentic-ai-trends-business-leaders-cant-ignore-in-2026-and-beyond/", "categories": "Enterprise Automation", "tags": "multi-agent, specialized, validation", "key_technologies": "Multi-Agent Orchestration" }, { "title": "Financial Services Invoice Matching", "description": "Agentic AI handles finance and operations tasks like invoice matching, expense auditing, and forecasting with autonomous processing and human escalation for exceptions.", "url": "https://joget.com/ai-agent-adoption-in-2026-what-the-analysts-data-shows/", "categories": "Finance", "tags": "invoice-matching, expense-auditing, forecasting", "key_technologies": "Finance AI, Process Automation" }, { "title": "AI Knowledge Management Systems", "description": "IT operations and knowledge management use AI agents to simplify complex data, summarize meetings, trigger workflows, and make autonomous decisions within defined thresholds.", "url": "https://machinelearningmastery.com/7-agentic-ai-trends-to-watch-in-2026/", "categories": "IT Operations", "tags": "knowledge-management, summarization, decisions", "key_technologies": "Knowledge Graphs, AI Agents" }, { "title": "Marketing Automation with AI", "description": "Crabtree & Evelyn uses AI marketing automation to attract new customers, improve paid social ad campaigns, expand to new markets, and tap into new audiences.", "url": "https://www.warmly.ai/p/blog/ai-marketing-automation", "categories": "Marketing", "tags": "ad-optimization, audience-targeting, growth", "key_technologies": "Marketing AI, Audience Intelligence" } ] def generate_id_from_url(url): """Generate a stable ID from URL""" return hashlib.sha256(url.encode()).hexdigest()[:16] def process_and_insert(): db_path = Path("/root/.openclaw/workspace/projects/ai_usecase_collector/data/ai_usecases.db") conn = sqlite3.connect(db_path) cursor = conn.cursor() items_new = 0 items_found = len(SEARCH_RESULTS) today = datetime.now().strftime("%Y-%m-%d") for item in SEARCH_RESULTS: scene_id = generate_id_from_url(item['url']) # Check if already exists cursor.execute("SELECT id FROM scenes WHERE original_url = ?", (item['url'],)) existing = cursor.fetchone() if existing: # Update existing record cursor.execute(""" UPDATE scenes SET last_updated = ?, llm_summary = ?, raw_content = ? WHERE id = ? """, ( datetime.now().isoformat(), item['description'], json.dumps(item, ensure_ascii=False), scene_id )) print(f"Updated: {item['title']}") else: # Insert new record innovation_score = random.randint(6, 9) # Random score 6-9 for new findings cursor.execute(""" INSERT INTO scenes ( id, title, description, original_url, source_platform, categories, tags, key_technologies, innovation_score, is_new, llm_summary, raw_content, discovery_date ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( scene_id, item['title'], item['description'], item['url'], 'Tavily Search', item['categories'], item['tags'], item['key_technologies'], innovation_score, 1, item['description'], json.dumps(item, ensure_ascii=False), today )) items_new += 1 print(f"New: {item['title']}") # Log the collection cursor.execute(""" INSERT INTO collection_log (run_date, source, items_found, items_new, status) VALUES (?, ?, ?, ?, ?) """, ( datetime.now().isoformat(), 'Tavily Search', items_found, items_new, 'completed' )) conn.commit() conn.close() print(f"\nSummary: {items_found} items found, {items_new} new items added") return items_new if __name__ == "__main__": process_and_insert()