Implement the custom endpoint

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import express from 'express';
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import crypto from 'crypto';
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const app = express();
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app.use(express.json());
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// Your knowledge base data (replace with actual database/vector store)
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const documents = [
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  {
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    id: "return-policy-1",
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    content: "Our return policy allows customers to return items within 30 days of purchase for a full refund. Items must be in original condition with tags attached.",
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    category: "returns"
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  },
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  {
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    id: "shipping-info-1", 
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    content: "We offer free shipping on orders over $50. Standard shipping takes 3-5 business days.",
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    category: "shipping"
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  }
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];
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app.post('/kb/search', (req, res) => {
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  try {
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    // Verify webhook secret (recommended)
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    const signature = req.headers['x-vapi-signature'];
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    const secret = process.env.VAPI_WEBHOOK_SECRET;
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    if (signature && secret) {
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      const expectedSignature = crypto
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        .createHmac('sha256', secret)
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        .update(JSON.stringify(req.body))
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        .digest('hex');
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      if (signature !== `sha256=${expectedSignature}`) {
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        return res.status(401).json({ error: 'Invalid signature' });
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      }
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    }
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    const { message } = req.body;
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    if (message.type !== 'knowledge-base-request') {
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      return res.status(400).json({ error: 'Invalid request type' });
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    }
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    // Get the latest user message
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    const userMessages = message.messages.filter(msg => msg.role === 'user');
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    const latestQuery = userMessages[userMessages.length - 1]?.content || '';
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    // Simple keyword-based search (replace with vector search)
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    const relevantDocs = documents
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      .map(doc => ({
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        ...doc,
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        similarity: calculateSimilarity(latestQuery, doc.content)
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      }))
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      .filter(doc => doc.similarity > 0.1)
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      .sort((a, b) => b.similarity - a.similarity)
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      .slice(0, 3);
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    // Return documents for AI processing
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    res.json({
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      documents: relevantDocs.map(doc => ({
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        content: doc.content,
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        similarity: doc.similarity,
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        uuid: doc.id
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      }))
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    });
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  } catch (error) {
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    console.error('Knowledge base search error:', error);
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    res.status(500).json({ error: 'Internal server error' });
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  }
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});
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function calculateSimilarity(query: string, content: string): number {
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  // Simple similarity calculation (replace with proper vector similarity)
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  const queryWords = query.toLowerCase().split(' ');
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  const contentWords = content.toLowerCase().split(' ');
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  const matches = queryWords.filter(word => 
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    contentWords.some(cWord => cWord.includes(word))
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  ).length;
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  return matches / queryWords.length;
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}
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app.listen(3000, () => {
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  console.log('Custom Knowledge Base server running on port 3000');
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});

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import { PineconeClient } from '@pinecone-database/pinecone';
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import OpenAI from 'openai';
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const pinecone = new PineconeClient();
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const openai = new OpenAI();
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app.post('/kb/search', async (req, res) => {
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  try {
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    const { message } = req.body;
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    const latestQuery = getLatestUserMessage(message);
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    // Generate embedding for the query
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    const embedding = await openai.embeddings.create({
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      model: 'text-embedding-ada-002',
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      input: latestQuery
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    });
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    // Search vector database
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    const index = pinecone.Index('knowledge-base');
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    const searchResults = await index.query({
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      vector: embedding.data[0].embedding,
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      topK: 5,
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      includeMetadata: true
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    });
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    // Format response
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    const documents = searchResults.matches.map(match => ({
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      content: match.metadata.content,
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      similarity: match.score,
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      uuid: match.id
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    }));
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    res.json({ documents });
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  } catch (error) {
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    console.error('Vector search error:', error);
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    res.status(500).json({ error: 'Search failed' });
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  }
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});