model KnowledgeBase {
  id        String   @id @default(uuid())
  content   String   @db.Text
  metadata  Json?    // { source: "product", id: "...", title: "..." }
  
  // Postgres pgvector support via Prisma Unsupported type
  // Requires: CREATE EXTENSION IF NOT EXISTS vector;
  embedding Unsupported("vector(1536)")?
  
  createdAt DateTime @default(now())
  
  @@map("knowledge_base")
}

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create index for fast cosine similarity search
CREATE INDEX ON knowledge_base 
USING ivfflat (embedding vector_cosine_ops) 
WITH (lists = 100);

// Retriever.js:13-20
let embeddingPipe = null;

async function getEmbeddingPipe() {
    if (!embeddingPipe) {
        console.log("🔌 Loading Embedding Model (BYPASSED FOR RENDER FREE TIER OOM PROTECTION)...");
        // Bypass actual transformer load to save 300MB of RAM
        embeddingPipe = () => { return { toList: () => new Array(1536).fill(0.0) } };
    }
    return embeddingPipe;
}

async function executeSearchKnowledgeBase(query) {
    console.log(`🧠 (OOM Protection) Executing Tool: searchKnowledgeBase for query: "${query}"`);
    return JSON.stringify({ 
        info: "Políticas y RAG desactivado temporalmente por limites de Memoria RAM en servidor Cloud gratuito original. Dile al cliente que te repita la pregunta directa o solicite agendamiento humano si la duda es sobre politicas de envios. No trates de inventar politicas.",
        original_query: query 
    });
}

async function executeSearchKnowledgeBase(query) {
    console.log(`🧠 Executing Tool: searchKnowledgeBase for query: "${query}"`);
    
    // Generate query embedding
    const queryEmbedding = await generateEmbedding(query);
    const embeddingString = `[${queryEmbedding.join(',')}]`;
    
    // Vector similarity search with pgvector
    const results = await prisma.$queryRaw`
        SELECT 
            id,
            content,
            metadata,
            1 - (embedding <=> ${embeddingString}::vector) AS similarity
        FROM knowledge_base
        WHERE embedding IS NOT NULL
        ORDER BY embedding <=> ${embeddingString}::vector
        LIMIT 5
    `;
    
    // Filter by similarity threshold
    const relevantResults = results.filter(r => r.similarity > 0.7);
    
    if (relevantResults.length === 0) {
        return JSON.stringify({ 
            error: "No se encontró información relevante en la base de conocimientos."
        });
    }
    
    return JSON.stringify(relevantResults.map(r => ({
        content: r.content,
        metadata: r.metadata,
        similarity: r.similarity
    })));
}

if (req.method === 'POST') {
    const { content, title, type } = req.body;
    if (!content) return res.status(400).json({ error: 'Content is required' });
    
    // Generate embedding (in production)
    const embedding = await generateEmbedding(content);
    
    const newKnowledge = await prisma.knowledgeBase.create({
        data: {
            content,
            metadata: { title: title || 'Sin Título', type: type || 'Documento' },
            embedding: embedding // Store as vector
        }
    });
    return res.status(201).json({ success: true, id: newKnowledge.id });
}

if (req.method === 'GET') {
    // Fetch knowledge without massive vector embeddings to save bandwidth
    const items = await prisma.$queryRaw`
        SELECT id, content, metadata, "createdAt" 
        FROM knowledge_base 
        ORDER BY "createdAt" DESC
    `;
    return res.status(200).json(items);
}

{
    name: "searchKnowledgeBase",
    description: "Busca en el 'Cerebro RAG' manuales de la empresa, tiempos de envío, costos de envío a ciudades, y políticas generales de la marca.",
    input_schema: {
        type: "object",
        properties: {
            query: {
                type: "string",
                description: "La pregunta o concepto a buscar en la base de políticas (Ej: 'Tiempos de envío Bogotá', 'Manejan contra entrega').",
            }
        },
        required: ["query"],
    },
}

CREATE INDEX ON knowledge_base 
USING ivfflat (embedding vector_cosine_ops) 
WITH (lists = 100);

CREACreate INDEX ON knowledge_base 
USING hnsw (embedding vector_cosine_ops) 
WITH (m = 16, ef_construction = 64);

const systemPrompt = `
Actúas como el Agente Especializado de KAIU Natural Living.

REGLAS DE ORO:
1. ESTRICTAMENTE PROHIBIDO ADIVINAR O ALUCINAR DATOS.
2. Usa searchKnowledgeBase para políticas, tiempos de envío, y costos.
3. NUNCA inventes políticas que no estén en los resultados de las herramientas.
`;

messages.push(new ToolMessage({
    tool_call_id: toolCall.id,
    content: JSON.stringify(relevantResults), // Injected context
    name: 'searchKnowledgeBase'
}));