Gemini Integration: The Synthesis Engine

async function currentStateOfField(topic: string) {
  // Gemini excels at real-time knowledge grounding
  const currentState = await mcp.invoke('ask-gemini', {
    prompt: `What is the current state of research on ${topic} as of 2025?
             Include:
             - Recent breakthroughs (2024-2025)
             - Active research groups and institutions
             - Upcoming conferences and deadlines
             - Trending methodologies
             - Open problems and challenges`,
    model: 'gemini-2.5-pro' // Uses web grounding
  });

  // Cross-reference with local paper database
  const localPapers = await searchLocalLibrary(topic);

  // Generate report combining real-time and local knowledge
  return mergeKnowledgeSources(currentState, localPapers);
}

async function compareApproaches(papers: Paper[], dimension: string) {
  const comparison = await mcp.invoke('ask-gemini', {
    prompt: `Compare these papers along the dimension: ${dimension}

             For each paper, extract:
             - Approach name
             - Key innovation
             - Performance metrics
             - Limitations

             Then create a comparison table and summary.

             Papers:
             ${papers.map(p => `
               Title: ${p.title}
               Authors: ${p.authors}
               Abstract: ${p.abstract}
               Key Findings: ${p.keyFindings}
             `).join('\n---\n')}`,
    model: 'gemini-2.5-pro'
  });

  return comparison;
}

// Example usage
const transformerPapers = await searchLocalLibrary('transformer architectures');
const comparisonReport = await compareApproaches(
  transformerPapers,
  'attention mechanism efficiency'
);

async function trackFieldEvolution(topic: string, yearRange: [number, number]) {
  const papersByYear = await getPapersByYear(topic, yearRange);

  const evolution = await mcp.invoke('ask-gemini', {
    prompt: `Trace the evolution of research on "${topic}" from ${yearRange[0]} to ${yearRange[1]}.

             For each year, identify:
             - Dominant approaches/paradigms
             - Key papers that shifted the field
             - Emerging trends
             - Discontinued approaches

             Papers grouped by year:
             ${JSON.stringify(papersByYear, null, 2)}

             Provide a narrative timeline and inflection point analysis.`,
    model: 'gemini-2.5-pro'
  });

  return evolution;
}

async function generateContextualCitation(claim: string, context: string) {
  // Find relevant papers
  const relevantPapers = await searchLocalLibrary(claim);

  // Ask Gemini to select best citation and generate context
  const citationAdvice = await mcp.invoke('ask-gemini', {
    prompt: `For this claim: "${claim}"
             In this context: "${context}"

             Available papers: ${JSON.stringify(relevantPapers)}

             Which paper(s) best support this claim?
             Generate:
             1. The formatted in-text citation
             2. A brief parenthetical elaboration if needed
             3. Alternative citations if the claim is contested

             Citation style: APA 7th edition`,
    model: 'gemini-2.5-pro'
  });

  return citationAdvice;
}

{
  "primary_citation": "(Vaswani et al., 2017)",
  "elaboration": "though recent work suggests mixture-of-experts may exceed transformers on specific tasks (Fedus et al., 2022)",
  "alternative_citations": ["(Devlin et al., 2019)", "(Brown et al., 2020)"],
  "confidence": "high"
}

async function progressiveSummarization(papers: Paper[]) {
  const summaries = [];

  // Level 1: Individual paper summaries (parallel)
  for (const paper of papers) {
    const summary = await mcp.invoke('ask-gemini', {
      prompt: `Summarize this paper in 3 sentences:
               Title: ${paper.title}
               Abstract: ${paper.abstract}
               Full text: ${paper.fullText}`,
      model: 'gemini-2.0-flash-exp' // Fast model for bulk work
    });
    summaries.push({ paper: paper.title, summary });
  }

  // Level 2: Theme-based clustering (single call)
  const themes = await mcp.invoke('ask-gemini', {
    prompt: `Given these paper summaries, identify 3-5 major themes:
             ${JSON.stringify(summaries)}`,
    model: 'gemini-2.5-pro'
  });

  // Level 3: Executive summary (single call)
  const executive = await mcp.invoke('ask-gemini', {
    prompt: `Create a 1-paragraph executive summary of research on this topic,
             based on these themes: ${JSON.stringify(themes)}`,
    model: 'gemini-2.5-pro'
  });

  return {
    individual_summaries: summaries,
    themes: themes,
    executive_summary: executive
  };
}

async function summarizeVisualContent(paper: Paper) {
  const figures = await extractFigures(paper.pdfPath);
  const tables = await extractTables(paper.pdfPath);

  const visualSummary = await mcp.invoke('ask-gemini', {
    prompt: `Summarize the key information from these figures and tables:

             Figures:
             ${figures.map(f => `Figure ${f.number}: ${f.caption}`).join('\n')}

             Tables:
             ${tables.map(t => `Table ${t.number}: ${t.caption}\nData: ${JSON.stringify(t.data)}`).join('\n')}

             What are the main findings visualized? Any surprising results?`,
    model: 'gemini-2.5-pro'
  });

  return visualSummary;
}