Chunking in AI - The Secret Sauce You&#re Missing

Hey folks! ?

You know what keeps me up at night? Thinking about how to make our AI systems smarter and more efficient. Today, I want to talk about something that might sound basic but is crucial when building kick-ass AI applications: chunking ✨.

What the heck is chunking anyway? ?

Think of chunking as your AI's way of breaking down a massive buffet of information into manageable, bite-sized portions. Just like how you wouldn't try to stuff an entire pizza in your mouth at once (or maybe you would, no judgment here!), your AI needs to break down large texts into smaller pieces to process them effectively.

This is especially important for what we call RAG (Retrieval-Augmented Generation) models. These bad boys don't just make stuff up - they actually go and fetch real information from external sources. Pretty neat, right?

Why should you care? ?

Look, if you're building anything that deals with text - whether it's a customer support chatbot or a fancy knowledge base search - getting chunking right is the difference between an AI that gives spot-on answers and one that's just... meh.

Too big chunks? Your model misses the point.
Too small chunks? It gets lost in the details.

Let's Get Our Hands Dirty: Real Examples ?

Python Example: Semantic Chunking

First, let's look at a Python example using LangChain for semantic chunking:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader

def semantic_chunk(file_path):
    # Load the document
    loader = TextLoader(file_path)
    document = loader.load()

    # Create a text splitter
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
        separators=["\n\n", "\n", " ", ""]
    )

    # Split the document into chunks
    chunks = text_splitter.split_documents(document)

    return chunks

# Example usage
chunks = semantic_chunk('knowledge_base.txt')
for i, chunk in enumerate(chunks):
    print(f"Chunk {i}: {chunk.page_content[:50]}...")

Node.js and CDK Example: Building a Knowledge Base

Now, let's build something real - a serverless knowledge base using AWS CDK and Node.js! ?

First, the CDK infrastructure (this is where the magic happens):

import * as cdk from 'aws-cdk-lib';
import * as s3 from 'aws-cdk-lib/aws-s3';
import * as lambda from 'aws-cdk-lib/aws-lambda';
import * as opensearch from 'aws-cdk-lib/aws-opensearch';
import * as iam from 'aws-cdk-lib/aws-iam';

export class KnowledgeBaseStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    // S3 bucket to store our documents
    const documentBucket = new s3.Bucket(this, 'DocumentBucket', {
      removalPolicy: cdk.RemovalPolicy.DESTROY,
    });

    // OpenSearch domain for storing our chunks
    const openSearchDomain = new opensearch.Domain(this, 'DocumentSearch', {
      version: opensearch.EngineVersion.OPENSEARCH_2_5,
      capacity: {
        dataNodes: 1,
        dataNodeInstanceType: 't3.small.search',
      },
      ebs: {
        volumeSize: 10,
      },
    });

    // Lambda function for processing documents
    const processorFunction = new lambda.Function(this, 'ProcessorFunction', {
      runtime: lambda.Runtime.NODEJS_18_X,
      handler: 'index.handler',
      code: lambda.Code.fromAsset('lambda'),
      environment: {
        OPENSEARCH_DOMAIN: openSearchDomain.domainEndpoint,
      },
      timeout: cdk.Duration.minutes(5),
    });

    // Grant permissions
    documentBucket.grantRead(processorFunction);
    openSearchDomain.grantWrite(processorFunction);
  }
}

And now, the Lambda function that does the chunking and indexing:

import { S3Event } from 'aws-lambda';
import { S3 } from 'aws-sdk';
import { Client } from '@opensearch-project/opensearch';
import { defaultProvider } from '@aws-sdk/credential-provider-node';
import { AwsSigv4Signer } from '@opensearch-project/opensearch/aws';

const s3 = new S3();
const CHUNK_SIZE = 1000;
const CHUNK_OVERLAP = 200;

// Create OpenSearch client
const client = new Client({
  ...AwsSigv4Signer({
    region: process.env.AWS_REGION,
    service: 'es',
    getCredentials: () => {
      const credentialsProvider = defaultProvider();
      return credentialsProvider();
    },
  }),
  node: `https://${process.env.OPENSEARCH_DOMAIN}`,
});

export const handler = async (event: S3Event) => {
  for (const record of event.Records) {
    const bucket = record.s3.bucket.name;
    const key = decodeURIComponent(record.s3.object.key.replace(/\+/g, ' '));

    // Get the document from S3
    const { Body } = await s3.getObject({ Bucket: bucket, Key: key }).promise();
    const text = Body.toString('utf-8');

    // Chunk the document
    const chunks = chunkText(text);

    // Index chunks in OpenSearch
    for (const [index, chunk] of chunks.entries()) {
      await client.index({
        index: 'knowledge-base',
        body: {
          content: chunk,
          documentKey: key,
          chunkIndex: index,
          timestamp: new Date().toISOString(),
        },
      });
    }
  }
};

function chunkText(text: string): string[] {
  const chunks: string[] = [];
  let start = 0;

  while (start < text.length) {
    const end = Math.min(start + CHUNK_SIZE, text.length);
    let chunk = text.slice(start, end);

    // Try to break at a sentence boundary
    const lastPeriod = chunk.lastIndexOf('.');
    if (lastPeriod !== -1 && lastPeriod !== chunk.length - 1) {
      chunk = chunk.slice(0, lastPeriod + 1);
    }

    chunks.push(chunk);
    start = Math.max(start + chunk.length - CHUNK_OVERLAP, start + 1);
  }

  return chunks;
}

How It All Works Together ?

1. Document Upload: When you upload a document to the S3 bucket, it triggers our Lambda function.
2. Processing: The Lambda function:
   • Retrieves the document from S3
   • Chunks it using our smart chunking algorithm
   • Indexes each chunk in OpenSearch with metadata
3. Retrieval: Later, when your application needs to find information, it can query OpenSearch to find the most relevant chunks.

Here's a quick example of how you might query this knowledge base:

async function queryKnowledgeBase(query: string) {
  const response = await client.search({
    index: 'knowledge-base',
    body: {
      query: {
        multi_match: {
          query: query,
          fields: ['content'],
        },
      },
    },
  });

  return response.body.hits.hits.map(hit => ({
    content: hit._source.content,
    documentKey: hit._source.documentKey,
    score: hit._score,
  }));
}

The AWS Advantage ?️

Using AWS services like S3, Lambda, and OpenSearch gives us:

• Serverless scalability (no servers to manage!)
• Pay-per-use pricing (your wallet will thank you)
• Managed services (less ops work = more coding fun)

Final Thoughts ?

There you have it, folks! A real-world example of how to implement chunking in a serverless knowledge base. The best part? This scales automatically and can handle documents of any size.

Remember, the key to good chunking is:

1. Choose the right chunk size for your use case
2. Consider overlap to maintain context
3. Use natural boundaries when possible (like sentences or paragraphs)

What's your experience with building knowledge bases? Have you tried different chunking strategies? Let me know in the comments below! ?

The above is the detailed content of Chunking in AI - The Secret Sauce You&#re Missing. For more information, please follow other related articles on the PHP Chinese website!