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You can load any data from your own custom data source by implementing the BaseLoader interface. For example - 
class CustomLoader extends BaseLoader<{ customChunkMetadata: string }> {
    constructor() {
        super('uniqueId');
    }

    async *getChunks() {
        throw new Error('Method not implemented.');
    }
}

​
Customizing the chunk size and overlap

If you want to customize the chunk size or the chunk overlap on an existing loader, all built-in loaders take an additional parameter chunkSize and chunkOverlap in their constructor. For example - 
import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { OpenAiEmbeddings } from '@llm-tools/embedjs-openai';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';
import { DocxLoader } from '@llm-tools/embedjs-loader-msoffice';

const app = await new RAGApplicationBuilder()
.setModel(SIMPLE_MODELS.OPENAI_GPT4_O)
.setEmbeddingModel(new OpenAiEmbeddings())
.setVectorDatabase(new HNSWDb())
.build();

app.addLoader(new DocxLoader({ filePathOrUrl: '...', chunkOverlap: 100, chunkSize: 20 }))
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