Weaviate for RAG

Introduction

Retrieval-Augmented Generation (RAG) combines the strengths of retrieval systems with generative models to enhance the information generation process. Weaviate is an open-source vector search engine that provides a robust backend for implementing RAG workflows.

What is Weaviate?

Weaviate is a cloud-native, modular, real-time vector search engine. It allows for efficient storage, indexing, and querying of vector embeddings, enabling seamless integration with machine learning models and databases.

Key features include:

• Real-time vector search
• GraphQL API for easy integrations
• Scalability and cloud-native capabilities

Weaviate in RAG

In a RAG architecture, Weaviate serves as the retrieval component, allowing for swift access to relevant information that can augment the generative capabilities of models like GPT-3 or similar.

The standard workflow involves:

1. Embedding input queries and documents to generate vectors.
2. Storing these vectors in Weaviate.
3. Retrieving relevant documents based on vector similarity.
4. Generating responses using the retrieved data.

const { WeaviateClient } = require('weaviate-node-client');

const weaviate = new WeaviateClient({
  scheme: 'http',
  host: 'localhost:8080'
});

// Example to create a schema
const createSchema = async () => {
    await weaviate.schema.createClass({
        class: 'Document',
        properties: [
            { name: 'content', dataType: ['text'] },
            { name: 'embedding', dataType: ['number[]'] }
        ]
    });
};

createSchema();

Setting Up Weaviate

To set up Weaviate for RAG, follow these steps:

1. Install Weaviate using Docker or Kubernetes.
2. Create a schema for your data.
3. Embed your documents and store them in Weaviate.
4. Query Weaviate for relevant documents.

// Example to embed a document
const embedDocument = async (content) => {
    const embedding = await embed(content); // Assume an embed function exists
    await weaviate.data.creator()
        .withClassName('Document')
        .withProperties({
            content: content,
            embedding: embedding
        })
        .do();
};

embedDocument("This is a sample document.");

Best Practices

When using Weaviate for RAG, consider these best practices:

• Regularly update your embeddings to reflect changes in the underlying data.
• Optimize your queries to minimize latency.
• Monitor the performance of your Weaviate instance regularly.

FAQ