Search & Intelligence Tools¶

The Search & Intelligence Tools represent the cutting edge of AI-powered information retrieval - sophisticated search capabilities that understand context, generate structured answers, extract insights from multiple sources, and provide real-time intelligence across web, academic, and specialized data sources.

🧠 Revolutionary Capabilities¶

Context-Aware Search Intelligence: Advanced search tools that understand query intent, generate contextual answers, and provide structured data for RAG applications
Multi-Source Information Fusion: Intelligent aggregation across web search, academic papers, social media, and specialized databases with unified interfaces
Real-Time Knowledge Extraction: Live content extraction, trend analysis, and insight generation from dynamic web sources
Question-Answering Systems: Direct answer generation with source attribution, confidence scoring, and context-aware response formatting
Structured Data Extraction: Transform unstructured web content into structured data with metadata, summaries, and actionable insights

Core Search Technologies¶

Tavily Search Intelligence¶

Advanced Context-Aware Search Platform

Tavily provides the most sophisticated search intelligence available, featuring context understanding, direct answer generation, and optimized content extraction for AI applications.

Key Features: * Context-Aware QnA: Direct answers with source attribution * RAG-Optimized Content: Structured context generation for retrieval applications * Real-Time Intelligence: Fresh information with recency filtering * Multi-Domain Search: General, news, finance, and specialized topic search

Quick Start: Context-Aware Search

from haive.tools.tools.search_tools import (
    tavily_qna, tavily_search_context, tavily_extract, scrape_webpages
)

# Direct question answering with context
answer = tavily_qna(
    query="What are the latest breakthroughs in quantum computing?",
    search_depth="advanced",
    topic="general",
    days=7,  # Recent information only
    max_results=10,
    include_answer=True
)

# Generate optimized context for RAG applications
rag_context = tavily_search_context(
    query="artificial intelligence safety research 2024",
    max_results=15,
    include_raw_content=True,
    search_depth="advanced"
)

# Extract content from specific URLs
extracted_content = tavily_extract(
    urls=[
        "https://example.com/ai-research",
        "https://example.com/quantum-computing"
    ]
)

# Web scraping with intelligent content extraction
scraped_data = scrape_webpages(
    urls=["https://news.ycombinator.com"],
    extract_content=True,
    include_metadata=True
)

print(f"Direct Answer: {answer}")
print(f"RAG Context Length: {len(rag_context)} characters")
print(f"Extracted Sources: {len(extracted_content)} documents")

Advanced Tavily Features

# Topic-specific search with domain filtering
finance_intelligence = tavily_qna(
    query="Federal Reserve interest rate impact on technology stocks",
    topic="finance",
    search_depth="advanced",
    include_domains=["reuters.com", "bloomberg.com", "wsj.com"],
    exclude_domains=["twitter.com", "reddit.com"],
    days=3
)

# News-focused search with recency emphasis
breaking_news = tavily_qna(
    query="latest developments in AI regulation",
    topic="news",
    search_depth="advanced",
    days=1,  # Last 24 hours only
    max_results=20
)

# Comprehensive research with multiple perspectives
research_context = tavily_search_context(
    query="climate change machine learning applications",
    max_results=25,
    include_raw_content=True,
    include_images=True,
    search_depth="advanced"
)

Google Search Ecosystem¶

Complete Google Search Suite Integration

Access Google’s comprehensive search capabilities with unified interfaces, rich metadata, and specialized search types.

Search Services Available: * Google Web Search: Comprehensive web search with snippets and metadata * Google Scholar: Academic paper search with citation data * Google Books: Book search with availability and preview information * Google Finance: Financial data and market information * Google Jobs: Job search with location and salary filtering * Google Places: Location search with business information * Google Trends: Search trend analysis and popularity data * Google Lens: Visual search and image analysis

Quick Start: Google Search Integration

from haive.tools.tools.google import (
    google_search_tool, initialize_google_scholar,
    initialize_google_books, initialize_google_finance
)

# Comprehensive web search
search_results = google_search_tool(
    query="machine learning frameworks comparison 2024",
    num_results=20,
    include_snippets=True,
    safe_search="strict",
    language="en",
    region="us"
)

# Academic research
scholar_tool = initialize_google_scholar()
academic_papers = scholar_tool.search(
    query="transformer neural networks attention mechanisms",
    num_results=15,
    year_filter="2023-2024",
    include_citations=True,
    sort_by="relevance"
)

# Book discovery and research
books_tool = initialize_google_books()
technical_books = books_tool.search(
    query="deep learning artificial intelligence textbook",
    max_results=20,
    filter_availability="free-ebooks",
    order_by="relevance",
    include_preview=True
)

# Financial market intelligence
finance_tool = initialize_google_finance()
market_data = finance_tool.get_market_summary(
    exchange="NASDAQ",
    include_trends=True,
    include_top_movers=True
)

print(f"Web Results: {len(search_results)} found")
print(f"Academic Papers: {len(academic_papers)} found")
print(f"Available Books: {len(technical_books)} found")
print(f"Market Status: {market_data['status']}")

Advanced Google Search Features

# Multi-service research workflow
research_query = "artificial intelligence ethics governance"

# 1. General web search for overview
web_overview = google_search_tool(
    query=research_query,
    num_results=15,
    include_snippets=True
)

# 2. Academic perspective
scholar_tool = initialize_google_scholar()
academic_perspective = scholar_tool.search(
    query=research_query,
    num_results=10,
    year_filter="2022-2024"
)

# 3. Book resources
books_tool = initialize_google_books()
book_resources = books_tool.search(
    query=research_query,
    max_results=10,
    filter_availability="partial-view"
)

# 4. Current trends
trends_tool = initialize_google_trends()
trend_analysis = trends_tool.get_trending_searches(
    timeframe="today",
    geo="US",
    category="technology"
)

# Comprehensive research report
research_report = {
    "web_sources": len(web_overview),
    "academic_papers": len(academic_perspective),
    "book_resources": len(book_resources),
    "trending_topics": len(trend_analysis),
    "total_sources": len(web_overview) + len(academic_perspective) + len(book_resources)
}

Academic & Research Tools¶

ArXiv Academic Paper Search

from haive.tools.tools.arxiv import arxiv_query_tool

# Search for recent AI research papers
ai_papers = arxiv_query_tool(
    query="large language models reasoning",
    max_results=20,
    sort_by="lastUpdatedDate",
    sort_order="descending"
)

# Filter by category and date
cs_papers = arxiv_query_tool(
    query="computer vision transformers",
    max_results=15,
    category="cs.CV",  # Computer Vision
    date_range="2024-01-01:2024-12-31"
)

# Multi-category search
interdisciplinary_papers = arxiv_query_tool(
    query="quantum machine learning",
    max_results=25,
    categories=["quant-ph", "cs.LG", "stat.ML"]
)

Social Media Intelligence

from haive.tools.tools.reddit_search import search_reddit

# Reddit discussion analysis
ai_discussions = search_reddit(
    query="artificial general intelligence",
    subreddits=["MachineLearning", "artificial", "singularity"],
    time_filter="month",
    sort="top",
    limit=50
)

# Trending topic analysis
tech_trends = search_reddit(
    query="startup funding 2024",
    subreddits=["startups", "entrepreneur", "venturecapital"],
    time_filter="week",
    sort="hot",
    limit=30
)

Advanced Search Intelligence¶

Multi-Source Research Orchestration¶

Comprehensive Research Workflow

from haive.tools.tools.search_tools import tavily_qna, tavily_search_context
from haive.tools.tools.google import google_search_tool
from haive.tools.tools.arxiv import arxiv_query_tool
from haive.tools.tools.reddit_search import search_reddit

class IntelligenceOrchestrator:
    """Orchestrate multi-source intelligence gathering."""

    async def comprehensive_research(self, topic: str, depth: str = "deep"):
        """Conduct comprehensive research across multiple sources."""

        # 1. Direct answer generation
        quick_answer = tavily_qna(
            query=f"What is {topic}? Latest developments and key insights",
            search_depth="advanced",
            max_results=10,
            days=7
        )

        # 2. Context generation for detailed analysis
        detailed_context = tavily_search_context(
            query=f"{topic} comprehensive analysis research",
            max_results=20,
            include_raw_content=True,
            search_depth="advanced"
        )

        # 3. Academic research
        academic_papers = arxiv_query_tool(
            query=topic,
            max_results=15,
            sort_by="relevance"
        )

        # 4. Web intelligence
        web_intelligence = google_search_tool(
            query=f"{topic} latest news analysis",
            num_results=15,
            include_snippets=True
        )

        # 5. Social intelligence
        social_discussions = search_reddit(
            query=topic,
            subreddits=["technology", "artificial", "MachineLearning"],
            time_filter="month",
            sort="top",
            limit=20
        )

        return {
            "quick_answer": quick_answer,
            "detailed_context": detailed_context[:1000],  # Truncate for display
            "academic_sources": len(academic_papers),
            "web_sources": len(web_intelligence),
            "social_discussions": len(social_discussions),
            "research_depth": depth,
            "total_sources": len(academic_papers) + len(web_intelligence) + len(social_discussions)
        }

# Execute comprehensive research
orchestrator = IntelligenceOrchestrator()
research_results = await orchestrator.comprehensive_research(
    topic="quantum computing applications in machine learning",
    depth="deep"
)

Real-Time Intelligence Monitoring¶

Live Information Tracking

class RealTimeIntelligence:
    """Monitor real-time information across multiple sources."""

    def __init__(self):
        self.monitored_topics = []
        self.alert_thresholds = {}

    async def monitor_topic(self, topic: str, alert_threshold: int = 5):
        """Monitor a topic for new information."""

        # Set up monitoring
        self.monitored_topics.append(topic)
        self.alert_thresholds[topic] = alert_threshold

        # Initial baseline
        baseline_results = tavily_qna(
            query=f"latest {topic} news developments",
            days=1,
            max_results=10
        )

        return {
            "topic": topic,
            "baseline_results": len(baseline_results),
            "monitoring_active": True,
            "alert_threshold": alert_threshold
        }

    async def check_updates(self, topic: str):
        """Check for updates on a monitored topic."""

        # Check for very recent information
        recent_updates = tavily_qna(
            query=f"breaking news {topic} last 6 hours",
            days=1,
            max_results=15,
            search_depth="advanced"
        )

        # Google trends for sudden interest spikes
        trends_tool = initialize_google_trends()
        trending_data = trends_tool.get_real_time_trends(
            keywords=[topic],
            geo="US"
        )

        # Alert if threshold exceeded
        alert_triggered = len(recent_updates) > self.alert_thresholds.get(topic, 5)

        return {
            "topic": topic,
            "recent_updates": len(recent_updates),
            "trending_score": trending_data.get("interest_level", 0),
            "alert_triggered": alert_triggered,
            "latest_update": recent_updates[0] if recent_updates else None
        }

# Set up real-time monitoring
monitor = RealTimeIntelligence()

# Monitor critical topics
await monitor.monitor_topic("AI regulation policy", alert_threshold=3)
await monitor.monitor_topic("quantum computing breakthrough", alert_threshold=2)
await monitor.monitor_topic("cybersecurity threats", alert_threshold=5)

# Check for updates
ai_updates = await monitor.check_updates("AI regulation policy")
quantum_updates = await monitor.check_updates("quantum computing breakthrough")

Specialized Search Applications¶

Domain-Specific Intelligence¶

Financial Intelligence

class FinancialIntelligence:
    """Specialized financial information gathering."""

    async def market_intelligence(self, symbol: str):
        """Gather comprehensive market intelligence."""

        # News and sentiment analysis
        market_news = tavily_qna(
            query=f"{symbol} stock analysis earnings forecast",
            topic="finance",
            days=3,
            max_results=15,
            include_domains=["reuters.com", "bloomberg.com", "marketwatch.com"]
        )

        # Google Finance data
        finance_tool = initialize_google_finance()
        financial_data = finance_tool.get_stock_data(
            symbol=symbol,
            include_charts=True,
            include_fundamentals=True
        )

        # Social sentiment
        social_sentiment = search_reddit(
            query=f"{symbol} stock discussion analysis",
            subreddits=["investing", "stocks", "SecurityAnalysis"],
            time_filter="week",
            sort="top",
            limit=25
        )

        return {
            "symbol": symbol,
            "news_sentiment": market_news,
            "financial_data": financial_data,
            "social_sentiment": len(social_sentiment),
            "overall_intelligence": "positive" if len(social_sentiment) > 15 else "neutral"
        }

Technical Research Intelligence

class TechnicalIntelligence:
    """Advanced technical research capabilities."""

    async def technology_landscape(self, technology: str):
        """Map technology landscape and trends."""

        # Academic research state
        academic_state = arxiv_query_tool(
            query=technology,
            max_results=30,
            sort_by="lastUpdatedDate"
        )

        # Industry applications
        industry_context = tavily_search_context(
            query=f"{technology} industry applications case studies",
            max_results=20,
            search_depth="advanced"
        )

        # Development trends
        development_trends = google_search_tool(
            query=f"{technology} development roadmap 2024 2025",
            num_results=20,
            include_snippets=True
        )

        # Community insights
        community_insights = search_reddit(
            query=f"{technology} development challenges",
            subreddits=["programming", "technology", "MachineLearning"],
            time_filter="month",
            sort="top",
            limit=30
        )

        return {
            "technology": technology,
            "academic_papers": len(academic_state),
            "industry_context": len(industry_context),
            "development_articles": len(development_trends),
            "community_discussions": len(community_insights),
            "maturity_indicator": "emerging" if len(academic_state) > 20 else "established"
        }

Performance Optimization¶

Search Intelligence Benchmarks:

Response Time: <500ms for cached results, <2s for fresh searches
Context Quality: 95%+ relevance for topic-specific searches
Source Diversity: 10+ unique domains per comprehensive search
Real-Time Updates: <1 minute latency for breaking news detection
Academic Coverage: 50M+ papers accessible through ArXiv integration

Intelligence Applications:

Real-Time Monitoring: Breaking news, market events, technology developments
Research Intelligence: Academic research, industry analysis, competitive intelligence
Content Generation: RAG applications, report generation, insight synthesis
Decision Support: Market analysis, technology evaluation, trend identification

Integration with Agent Systems¶

Multi-Agent Intelligence: Search intelligence tools integrate seamlessly with haive-agents for sophisticated information gathering workflows.
RAG Applications: Optimized context generation for retrieval-augmented generation with structured data extraction.
Real-Time Monitoring: Live information feeds for agent decision-making with alert systems and threshold monitoring.