Voice-Driven Research That Lands In Your Docs: From Web to Workspace in One Command

# Modern Minimalist Kitchen Trends (2024)

**Research Date:** March 20, 2024  
**Sources Analyzed:** 8 high-quality design sites

## Key Trends Identified

### 1. Natural Wood Tones (White Oak Dominance)
- **Trend:** White oak cabinetry replacing painted white cabinets
- **Why:** Adds warmth while maintaining minimalist aesthetic
- **Examples:** Integrated grain patterns, slab-front doors
- **Sources:** Architectural Digest, Dwell, Remodelista

### 2. Hidden Storage Systems
- **Trend:** Handleless cabinets and concealed appliances
- **Why:** Maintains clean lines, reduces visual clutter
- **Examples:** Touch-latch doors, panel-ready appliances, pocket doors
- **Key insight:** Form follows function—storage that disappears

### 3. Statement Lighting as Focal Point
- **Trend:** Single dramatic light fixture over island
- **Why:** One accent element in otherwise minimal space
- **Examples:** Sculptural pendants, linear chandeliers, oversized globes
- **Design principle:** One hero piece, everything else restrained

## Synthesis
The current minimalist kitchen movement balances warmth (natural wood) 
with precision (clean lines). Instead of stark white, the trend is 
"warm minimalism"—tactile materials, hidden complexity, one statement 
element. Think Scandinavian hygge meets Japanese wabi-sabi.

## Recommendations
- Specify white oak or walnut over painted finishes
- Budget for high-quality integrated appliances
- Invest in one standout light fixture
- Maximize hidden storage in island and pantry walls

---
*Research curated by voice agent • 8 sources analyzed • 12 mins*

class ResearchCuratorAgent {
  constructor(apiKey, workspaceAPI) {
    this.apiKey = apiKey;
    this.workspaceAPI = workspaceAPI;
    this.searchProvider = new WebSearchProvider();
  }
  
  async handleResearchRequest(voiceQuery) {
    // Step 1: Understand what user wants
    const intent = await this.parseResearchIntent(voiceQuery);
    
    console.log(`[Research] Intent: ${intent.topic}, depth: ${intent.depth}`);
    
    // Step 2: Search the web
    this.speak("I'll search for that and curate the insights for you.");
    
    const searchResults = await this.searchProvider.search({
      query: intent.searchQuery,
      numResults: 20,
      filters: intent.filters
    });
    
    console.log(`[Research] Found ${searchResults.length} sources`);
    
    // Step 3: Filter for quality
    const qualitySources = await this.filterSources(searchResults);
    
    console.log(`[Research] Filtered to ${qualitySources.length} quality sources`);
    
    // Step 4: Extract and synthesize
    const insights = await this.synthesizeInsights(
      qualitySources,
      intent.researchGoal
    );
    
    // Step 5: Structure findings
    const curatedDocument = await this.structureFindings(
      intent.topic,
      insights,
      qualitySources
    );
    
    // Step 6: Write to workspace
    await this.workspaceAPI.createDocument({
      title: intent.topic,
      content: curatedDocument,
      tags: intent.tags
    });
    
    // Step 7: Summarize for user
    const summary = this.generateSummary(insights);
    await this.speak(summary);
    
    return {
      status: "complete",
      insights: insights.length,
      sources: qualitySources.length,
      documentId: curatedDocument.id
    };
  }
  
  async parseResearchIntent(query) {
    // Use OpenAI to understand what user wants
    const response = await openai.chat.completions.create({
      model: "gpt-4",
      messages: [{
        role: "system",
        content: `Parse this research request and extract:
          - topic: what they're researching
          - searchQuery: optimized search query
          - depth: broad or deep
          - researchGoal: what insights they need
          - filters: any constraints (recent, specific sources, etc.)
          - tags: categories for organization`
      }, {
        role: "user",
        content: query
      }],
      response_format: { type: "json_object" }
    });
    
    return JSON.parse(response.choices[0].message.content);
  }
  
  async filterSources(results) {
    // Filter for quality: reputable domains, recent, relevant
    return results.filter(source => {
      // Check domain authority
      if (this.isLowQualityDomain(source.domain)) {
        return false;
      }
      
      // Check recency if needed
      if (this.needsRecency && this.isOld(source.publishDate)) {
        return false;
      }
      
      // Check relevance
      if (source.relevanceScore < 0.7) {
        return false;
      }
      
      return true;
    });
  }
  
  async synthesizeInsights(sources, goal) {
    // Use OpenAI to synthesize across sources
    const sourceSummaries = sources.map(s => ({
      url: s.url,
      title: s.title,
      excerpt: s.excerpt
    }));
    
    const response = await openai.chat.completions.create({
      model: "gpt-4",
      messages: [{
        role: "system",
        content: `You are a research analyst. Synthesize insights from 
        these sources. Identify patterns, trends, and key takeaways. 
        Group related findings. Cite sources.`
      }, {
        role: "user",
        content: `Goal: ${goal}\n\nSources:\n${JSON.stringify(sourceSummaries, null, 2)}`
      }]
    });
    
    return this.parseInsights(response.choices[0].message.content);
  }
  
  async structureFindings(topic, insights, sources) {
    // Generate well-structured document
    const sections = [
      this.generateHeader(topic),
      this.generateMetadata(sources),
      this.generateKeyInsights(insights),
      this.generateSynthesis(insights),
      this.generateRecommendations(insights),
      this.generateSourcesList(sources)
    ];
    
    return sections.join('\n\n');
  }
  
  generateKeyInsights(insights) {
    let markdown = "## Key Insights\n\n";
    
    insights.forEach((insight, i) => {
      markdown += `### ${i + 1}. ${insight.title}\n`;
      markdown += `- **Finding:** ${insight.finding}\n`;
      markdown += `- **Why it matters:** ${insight.significance}\n`;
      markdown += `- **Examples:** ${insight.examples.join(', ')}\n`;
      markdown += `- **Sources:** ${insight.sources.join(', ')}\n\n`;
    });
    
    return markdown;
  }
  
  generateSummary(insights) {
    const count = insights.length;
    const topics = insights.map(i => i.title).slice(0, 3).join(', ');
    
    return `Done! I've analyzed the research and identified ${count} key 
    insights: ${topics}. I've written a full breakdown to your workspace 
    with specific findings and recommendations. Want me to go deeper on 
    anything?`;
  }
  
  async speak(text) {
    // Send to OpenAI Realtime API for voice output
    await this.realtimeConnection.sendAudio(text);
  }
}

import openai
from typing import List, Dict
from web_search_provider import WebSearchProvider
from workspace_api import WorkspaceAPI

class ResearchCuratorAgent:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.search_provider = WebSearchProvider()
        self.workspace = WorkspaceAPI()
    
    async def handle_research_request(self, voice_query: str) -> Dict:
        """Main research curation pipeline."""
        
        # Parse intent
        intent = await self.parse_intent(voice_query)
        
        # Search web
        results = await self.search_provider.search(
            query=intent['search_query'],
            num_results=20
        )
        
        # Filter for quality
        quality_sources = self.filter_sources(results)
        
        # Synthesize insights
        insights = await self.synthesize_insights(
            quality_sources,
            intent['research_goal']
        )
        
        # Structure document
        document = self.structure_findings(
            topic=intent['topic'],
            insights=insights,
            sources=quality_sources
        )
        
        # Write to workspace
        doc_id = await self.workspace.create_document(
            title=intent['topic'],
            content=document,
            tags=intent['tags']
        )
        
        return {
            'status': 'complete',
            'insights': len(insights),
            'sources': len(quality_sources),
            'document_id': doc_id
        }
    
    async def parse_intent(self, query: str) -> Dict:
        """Extract research parameters from voice query."""
        
        response = await openai.chat.completions.create(
            model="gpt-4",
            messages=[{
                "role": "system",
                "content": """Parse this research request and extract:
                - topic: what they're researching
                - search_query: optimized search terms
                - depth: 'broad' or 'deep'
                - research_goal: what insights they need
                - filters: any constraints
                - tags: categories for organization"""
            }, {
                "role": "user",
                "content": query
            }],
            response_format={"type": "json_object"}
        )
        
        return response.choices[0].message.parsed
    
    def filter_sources(self, results: List[Dict]) -> List[Dict]:
        """Filter for high-quality, relevant sources."""
        
        quality_domains = {
            'harvard.edu', 'stanford.edu', 'mit.edu',
            'nytimes.com', 'wsj.com', 'economist.com',
            'nature.com', 'science.org', 'arxiv.org',
            # Add domain-specific quality sources
        }
        
        filtered = []
        for source in results:
            domain = self.extract_domain(source['url'])
            
            # Quality checks
            if domain in quality_domains:
                filtered.append(source)
            elif source.get('relevance_score', 0) > 0.8:
                filtered.append(source)
        
        return filtered[:10]  # Top 10 sources
    
    async def synthesize_insights(
        self, 
        sources: List[Dict], 
        goal: str
    ) -> List[Dict]:
        """Synthesize insights across sources."""
        
        source_data = [
            {
                'title': s['title'],
                'excerpt': s['excerpt'],
                'url': s['url']
            }
            for s in sources
        ]
        
        response = await openai.chat.completions.create(
            model="gpt-4",
            messages=[{
                "role": "system",
                "content": """You are a research analyst. Synthesize insights 
                from sources. Identify patterns, trends, and key takeaways. 
                Group related findings. Return structured JSON with insights."""
            }, {
                "role": "user",
                "content": f"Goal: {goal}\n\nSources: {source_data}"
            }],
            response_format={"type": "json_object"}
        )
        
        return response.choices[0].message.parsed['insights']
    
    def structure_findings(
        self, 
        topic: str, 
        insights: List[Dict], 
        sources: List[Dict]
    ) -> str:
        """Generate well-structured markdown document."""
        
        doc = f"# {topic}\n\n"
        doc += f"**Research Date:** {self.get_date()}\n"
        doc += f"**Sources Analyzed:** {len(sources)}\n\n"
        
        doc += "## Key Insights\n\n"
        for i, insight in enumerate(insights, 1):
            doc += f"### {i}. {insight['title']}\n"
            doc += f"- **Finding:** {insight['finding']}\n"
            doc += f"- **Significance:** {insight['significance']}\n"
            doc += f"- **Examples:** {', '.join(insight['examples'])}\n"
            doc += f"- **Sources:** {', '.join(insight['source_refs'])}\n\n"
        
        doc += "## Synthesis\n"
        doc += self.generate_synthesis(insights) + "\n\n"
        
        doc += "## Recommendations\n"
        doc += self.generate_recommendations(insights) + "\n\n"
        
        doc += "## Sources\n"
        for source in sources:
            doc += f"- [{source['title']}]({source['url']})\n"
        
        return doc

# Usage
agent = ResearchCuratorAgent(api_key=os.getenv('OPENAI_API_KEY'))

result = await agent.handle_research_request(
    "Find examples of successful SaaS pricing strategies and analyze what works"
)

print(f"Research complete: {result['insights']} insights from {result['sources']} sources")

// User can refine the research
await agent.speak("Want me to go deeper on any particular trend?");

// User: "Yes, tell me more about the hidden storage trend"

await agent.expandResearch({
  topic: "hidden storage in minimalist kitchens",
  depth: "deep",
  focusArea: "implementation details and costs"
});

function scoreSourceCredibility(source) {
  let score = 0;
  
  // Domain authority
  if (TRUSTED_DOMAINS.includes(source.domain)) score += 30;
  
  // Recency
  const daysOld = (Date.now() - source.publishDate) / (1000 * 60 * 60 * 24);
  if (daysOld < 90) score += 20;
  
  // Author expertise
  if (source.authorExpertise) score += 20;
  
  // Citations
  score += Math.min(source.citationCount / 10, 20);
  
  // Relevance
  score += source.relevanceScore * 10;
  
  return score;
}

// Include images in research
const visualExamples = await searchProvider.searchImages({
  query: "modern minimalist kitchens",
  filters: { quality: "high", license: "creative_commons" }
});

// Add to curated document
document.images = visualExamples.slice(0, 5);

function generateSourceAttribution(sources) {
  return `
## Sources Referenced

${sources.map(s => `
- **[${s.title}](${s.url})**  
  ${s.domain} • Published ${s.publishDate}
`).join('\n')}

*This research was conducted on ${new Date().toISOString().split('T')[0]} 
using web search and AI synthesis. All sources are attributed above.*
`;
}