Knowledge & RAG

const result = await orka.knowledge.create({
  name: 'my-docs',           // Required: unique name for this knowledge base
  source: [...],             // Required: content to index (see formats below)
  chunkSize: 1000,           // Optional: max characters per chunk (default: 1000)
  chunkOverlap: 200,         // Optional: overlap between chunks (default: 200)
  metadata: {                // Optional: metadata attached to all documents
    category: 'support',
    version: '1.0',
  },
});
 
// Returns:
// {
//   name: 'my-docs',
//   documentCount: 5,      // Number of source documents processed
//   chunkCount: 23,        // Total chunks created
// }

// Add new documents to existing knowledge base
const { addedChunks } = await orka.knowledge.add('my-docs', [
  'New document content...',
  'Another new document...',
], {
  chunkSize: 1000,
  chunkOverlap: 200,
  metadata: { addedAt: '2024-01-15' },
});
 
console.log(`Added ${addedChunks} new chunks`);

await orka.knowledge.delete('my-docs');

// Basic RAG query
const answer = await orka.ask('How do I reset my password?', {
  knowledge: 'my-docs',    // Name of your knowledge base
  topK: 5,                 // Number of chunks to retrieve
});
 
// With context inspection (for debugging)
const { answer, context } = await orka.ask('How do I reset my password?', {
  knowledge: 'my-docs',
  topK: 5,
  includeContext: true,    // Returns the chunks used
});
 
console.log('Answer:', answer);
console.log('Sources used:');
for (const chunk of context) {
  console.log(`- [${chunk.score.toFixed(2)}] ${chunk.content.slice(0, 100)}...`);
}

import { Orka } from 'orkajs/core';
import { OpenAIAdapter } from 'orkajs/adapters/openai';
import { MemoryVectorDB } from 'orkajs/adapters/memory';
 
// 1. Initialize Orka with adapters
const orka = new Orka({
  llm: new OpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY }),
  vectorDB: new MemoryVectorDB(),
  defaults: {
    chunkSize: 800,      // Smaller chunks for precise retrieval
    chunkOverlap: 100,
    topK: 3,
  },
});
 
// 2. Create knowledge base from your docs
const { chunkCount } = await orka.knowledge.create({
  name: 'product-docs',
  source: { path: './docs/' },
  metadata: { source: 'official-docs' },
});
console.log(`Indexed ${chunkCount} chunks`);
 
// 3. Add more content later
await orka.knowledge.add('product-docs', [
  { text: 'New feature: Dark mode is now available...', metadata: { type: 'release-note' } },
]);
 
// 4. Query with RAG
const { answer, context } = await orka.ask(
  'How do I enable dark mode?',
  { knowledge: 'product-docs', includeContext: true }
);
 
console.log('Answer:', answer);
console.log('Based on', context.length, 'sources');
 
// 5. Direct search (without LLM)
const results = await orka.knowledge.search('product-docs', 'dark mode', { topK: 5 });
for (const r of results) {
  console.log(`[${r.score.toFixed(2)}] ${r.content.slice(0, 80)}...`);
}