import { Memory } from "../../src/memory";

async function contextFilteringDemo() {
  console.log("🔍 Context Filtering Demo");
  console.log("=" .repeat(50));

  // This demo shows how the new context filtering functionality
  // prevents overwhelming context and improves query focus

  console.log("\n💡 New Context Filtering Features:");
  console.log("   1. Filters initial results before agent analysis (> 15 results → 15)");
  console.log("   2. Filters additional results between search hops (prevents duplication)");
  console.log("   3. Final filtering before response generation (> 8 results → 8)");
  console.log("   4. Query-focused prompting for better relevance");

  console.log("\n📋 Example Usage (Updated from your code):");
  console.log(`
// OLD CODE (overwhelming context):
const ci = \`You're a helpful AI assistant...\`;
const result = await memory.chat(qa.question, {
    userId: "sampleuser",
    customInstructions: ci,  // ❌ Old way
    includeContext: true,
    useSearchAgents: true,
    enableMultihop: true,
    maxHops: 20
});

// NEW CODE (focused, filtered context):
const responseInstructions = \`You're a helpful AI assistant. Be **precise**, **concise**, and **objective**.
- Limit responses to **under 10 words**, unless detail is required or requested.
- For **date-related questions**, **deduce exact dates** from context when possible.
- Avoid vague terms like "last year" or "early on" — instead, provide **specific dates** (e.g., "2 Feb, 2002").
- Always aim to match the **ground truth format** in meaning and style.
- Stick to **factual answers**, avoid assumptions or extra commentary.\`;

const reasoningInstructions = \`When analyzing search completeness:
- Focus on finding specific facts that directly answer the query
- For identity questions, search for biographical information, roles, and defining characteristics
- Prioritize concrete details over general discussions
- Stop searching when you have enough specific information to answer the query\`;

const result = await retryWithBackoff(async () => {
    return await memory.chat(qa.question, {
        userId: "sampleuser",
        responseCustomInstructions: responseInstructions,  // ✅ Controls response format
        reasoningCustomInstructions: reasoningInstructions, // ✅ Controls search thoroughness
        includeContext: true,
        useSearchAgents: true,
        enableMultihop: true,
        maxHops: 20
    });
}, 3, 1000);`);

  console.log("\n🎯 Expected Improvements:");
  console.log("   • Better query focus (e.g., 'What is Caroline's identity?' gets identity info, not just advocacy)");
  console.log("   • Reduced context overwhelming (100 results → filtered to most relevant)");
  console.log("   • Faster processing due to less context");
  console.log("   • More precise answers due to better filtering");
  console.log("   • Cleaner agent logs (no more '100 initial results' overwhelming messages)");

  console.log("\n🔄 How Filtering Works:");
  console.log("   1. Initial Search: All results collected");
  console.log("   2. Pre-Agent Filter: If > 15 results, intelligently filter to 15 most relevant");
  console.log("   3. Agent Analysis: Agents work with focused, manageable context");
  console.log("   4. Inter-Hop Filter: Additional results filtered to prevent duplication");
  console.log("   5. Pre-Response Filter: If > 8 results, filter to 8 most query-relevant");
  console.log("   6. Response Generation: LLM works with focused, relevant context");

  console.log("\n📊 Filtering Modes:");
  console.log("   • 'search' mode: Filters for agent analysis (broader, keeps context-building info)");
  console.log("   • 'response' mode: Filters for final answer (stricter, only direct query relevance)");

  console.log("\n🚀 Identity Query Example:");
  console.log("   Query: 'What is Caroline's identity?'");
  console.log("   Before: 'Caroline is involved in LGBTQ advocacy.' (tangential)");
  console.log("   After: 'Caroline is a [specific role/title/identity details]' (direct answer)");

  console.log("\n⚡ Performance Benefits:");
  console.log("   • Reduced LLM token usage");
  console.log("   • Faster processing");
  console.log("   • More accurate responses");
  console.log("   • Better cost efficiency");

  console.log("\n✅ Implementation Complete!");
  console.log("   The context filtering is now automatically applied in:");
  console.log("   • memory.search() method");
  console.log("   • memory.chat() method");
  console.log("   • SearchAgents multihop analysis");
  console.log("   • All response generation");
}

// Run the demo
if (require.main === module) {
  contextFilteringDemo().catch(console.error);
}

export { contextFilteringDemo };