Getting Started with Ragify.js

# Using npm
npm install ragify-js
 
# Using yarn
yarn add ragify-js
 
# Using pnpm
pnpm add ragify-js

import { Ragify } from "ragify-js";
import { createEmbeddingProvider } from "ragify-js/factory";
 
// Initialize Ragify
const ragify = new Ragify({
  embeddingProvider: createEmbeddingProvider("openai", "your-openai-api-key"),
  collectionName: "my-documents",
  qdrantApiKey: "your-qdrant-api-key",
});
 
// Initialize the vector store
await ragify.initialize();
 
// Ingest documents
await ragify.ingest({
  text: "Your document text here...",
  metadata: { source: "example" }
});
 
// Query the documents
const results = await ragify.query("Your query here", {
  topK: 5,
  threshold: 0.7
});
 
console.log(results);

# .env file
OPENAI_API_KEY=your-openai-api-key
QDRANT_API_KEY=your-qdrant-api-key

# Ingest documents
npx ragify-js ingest file1.txt file2.txt --collection my-docs
 
# Query documents
npx ragify-js query "Your question here" --collection my-docs --top-k 5

// Simple text document
await ragify.ingest("This is a simple text document");
 
// Document with metadata
await ragify.ingest({
  text: "This is a document with metadata",
  metadata: {
    source: "example",
    author: "John Doe",
    date: "2024-02-20"
  }
});

// Basic query
const results = await ragify.query("What is the capital of France?");
 
// Query with options
const results = await ragify.query("What is the capital of France?", {
  topK: 5,        // Number of results to return
  threshold: 0.7   // Minimum similarity score
});