Recursive Splitter

Unstable API

0.7.0

@project-lakechain/recursive-character-text-splitter

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The recursive character text splitter can be used to split text documents at scale based on a set of delimiters, a maximum chunk size, and a given chunk overlap. This implementation is based on Langchain’s RecursiveCharacterTextSplitter.

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📝 Splitting Text

To use this middleware, you import it in your CDK stack, and connect it to a data source providing text documents, such as the S3 Trigger.

import { RecursiveCharacterTextSplitter } from '@project-lakechain/recursive-character-text-splitter';
import { CacheStorage } from '@project-lakechain/core';

class Stack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string) {
    // The cache storage.
    const cache = new CacheStorage(this, 'Cache');

    // Create the recursive character text splitter.
    const splitter = new RecursiveCharacterTextSplitter.Builder()
      .withScope(this)
      .withIdentifier('Splitter')
      .withCacheStorage(cache)
      .withSource(source) // 👈 Specify a data source
      .build();
  }
}

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Options

You can customize the way that the text splitter will split text documents by specifying a custom delimiter string, a maximum chunk size, and a chunk overlap.

ℹ️ The below example splits text documents based on a maximum chunk size of 4096 characters, and a chunk overlap of 200.

const splitter = new RecursiveCharacterTextSplitter.Builder()
  .withScope(this)
  .withIdentifier('Splitter')
  .withCacheStorage(cache)
  .withSource(source)
  .withChunkSize(4096)
  .withChunkOverlap(200)
  .build();

The default values for this middleware options are documented below.

Option	Default	Description
separators	”\n\n”, “\n”, ” ”, ""	The delimiters used to split text.
chunkSize	4000	The maximum size of each text chunk.
chunkOverlap	200	The characters to overlap between chunks.

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📄 Output

This middleware takes as an input text documents of a given size, and outputs multiple text documents that are the result of the text splitting process. This allows to process each chunk of text in parallel in downstream middlewares.

In addition to producing new text documents, this middleware also associates metadata with each chunk, such as the chunk identifier and order relative to the original document. Below is an example of CloudEvent produced by this middleware.

💁 Click to expand example

{
  "specversion": "1.0",
  "id": "1780d5de-fd6f-4530-98d7-82ebee85ea39",
  "type": "document-created",
  "time": "2023-10-22T13:19:10.657Z",
  "data": {
      "chainId": "6ebf76e4-f70c-440c-98f9-3e3e7eb34c79",
      "source": {
          "url": "s3://bucket/text.txt",
          "type": "text/plain",
          "size": 24536,
          "etag": "1243cbd6cf145453c8b5519a2ada4779"
      },
      "document": {
          "url": "s3://bucket/text.txt",
          "type": "text/plain",
          "size": 24536,
          "etag": "1243cbd6cf145453c8b5519a2ada4779"
      },
      "metadata": {
        "properties": {
          "kind": "text",
          "attrs": {
            "chunk": {
              "id": "4a5b6c7d8e9fd21dacb",
              "order": 0
            }
          }
        }
      },
      "callStack": []
  }
}

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🏗️ Architecture

This middleware runs within a Lambda compute based on the ARM64 architecture, and packages the Langchain library to run the text splitting process.

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🏷️ Properties

Supported Inputs

Mime Type	Description
text/plain	UTF-8 text documents.

Supported Outputs

Mime Type	Description
text/plain	UTF-8 text documents.

Supported Compute Types

Type	Description
CPU	This middleware only supports CPU compute.

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📖 Examples

• Text Splitting Pipeline - Builds a pipeline for splitting text documents using different text splitting algorithms.
• RAG Pipeline - End-to-end RAG pipeline using Amazon Bedrock and Amazon OpenSearch.