RAG & Base de Connaissances

import { createOrka, OpenAIAdapter, MemoryVectorAdapter } from 'orkajs';
 
const orka = createOrka({
  llm: new OpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY! }),
  vectorDB: new MemoryVectorAdapter(),
  defaults: {
    chunkSize: 500,    // Chunk size in characters
    chunkOverlap: 100, // Overlap between chunks
    topK: 3,           // Number of results by default
  },
});
 
// Create a knowledge base
const result = await orka.knowledge.create({
  name: 'faq',
  source: [
    {
      text: 'Orka JS is a TypeScript framework for building production-ready LLM systems.',
      metadata: { category: 'general' },
    },
    {
      text: 'To install Orka JS, use npm install orkajs.',
      metadata: { category: 'installation' },
    },
    {
      text: 'Orka JS supports Pinecone, Qdrant, Chroma and an in-memory adapter.',
      metadata: { category: 'features' },
    },
  ],
});
 
console.log(`Base created: ${result.documentCount} documents, ${result.chunkCount} chunks`);

// Simple semantic search
const results = await orka.knowledge.search('faq', 'how to install', { topK: 2 });
 
for (const r of results) {
  console.log(`[Score: ${r.score.toFixed(3)}] ${r.content}`);
  console.log(`  Metadata: ${JSON.stringify(r.metadata)}`);
}
 
// Search with metadata filters
const filtered = await orka.knowledge.search('faq', 'features', {
  topK: 5,
  filter: { category: 'features' },
});

// Question with RAG
const answer = await orka.ask({
  knowledge: 'faq',
  question: 'Which vector databases are supported ?',
  includeContext: true,  // Include sources in the response
  systemPrompt: 'You are a technical assistant. Respond concisely.',
  temperature: 0.3,
});
 
console.log(`Answer: ${answer.answer}`);
console.log(`Tokens: ${answer.usage.totalTokens}`);
console.log(`Latency: ${answer.latencyMs}ms`);
 
// Display used sources
if (answer.context) {
  console.log('Sources:');
  for (const source of answer.context) {
    console.log(`  - [${source.score.toFixed(2)}] ${source.content.slice(0, 80)}...`);
  }
}

import { createOrka, OpenAIAdapter, MemoryVectorAdapter } from 'orkajs';
 
async function main() {
  const orka = createOrka({
    llm: new OpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY! }),
    vectorDB: new MemoryVectorAdapter(),
    defaults: { chunkSize: 500, chunkOverlap: 100, topK: 3 },
  });
 
  console.log('📚 Creating the knowledge base......');
 
  await orka.knowledge.create({
    name: 'docs',
    source: [
      { text: 'Orka JS is a TypeScript framework for LLM systems.', metadata: { topic: 'intro' } },
      { text: 'Installation: npm install orkajs', metadata: { topic: 'setup' } },
      { text: 'Supported providers: OpenAI, Anthropic, Mistral, Ollama.', metadata: { topic: 'providers' } },
      { text: 'Vector databases: Pinecone, Qdrant, Chroma, in-memory.', metadata: { topic: 'vectordb' } },
    ],
  });
 
  console.log('🔍 Semantic search...');
  const searchResults = await orka.knowledge.search('docs', 'how to install', { topK: 2 });
 
  for (const r of searchResults) {
    console.log(`  - [${r.score.toFixed(3)}] ${r.content}`);
  }
 
  console.log('\n💬 Question with RAG...');
  const answer = await orka.ask({
    knowledge: 'docs',
    question: 'Which LLM providers are supported?',
    includeContext: true,
  });
 
  console.log(`Answer: ${answer.answer}`);
  console.log(`Tokens: ${answer.usage.totalTokens}, Latency: ${answer.latencyMs}ms`);
 
  if (answer.context) {
    console.log(`Sources used: ${answer.context.length}`);
  }
 
  // Nettoyage
  await orka.knowledge.delete('docs');
}
 
main().catch(console.error);