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NodeParser

The NodeParser in LlamaIndex is responsible for splitting Document objects into more manageable Node objects. When you call .fromDocuments(), the NodeParser from the Settings is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.

import { Document, SimpleNodeParser } from "llamaindex";

const nodeParser = new SimpleNodeParser();

Settings.nodeParser = nodeParser;

TextSplitter​

The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.

import { SentenceSplitter } from "llamaindex";

const splitter = new SentenceSplitter({ chunkSize: 1 });

const textSplits = splitter.splitText("Hello World");

MarkdownNodeParser​

The MarkdownNodeParser is a more advanced NodeParser that can handle markdown documents. It will split the markdown into nodes and then parse the nodes into a Document object.

import { MarkdownNodeParser } from "llamaindex";

const nodeParser = new MarkdownNodeParser();

const nodes = nodeParser.getNodesFromDocuments([
  new Document({
    text: `# Main Header
Main content

# Header 2
Header 2 content

## Sub-header
Sub-header content

  `,
  }),
]);

The output metadata will be something like:

[
  TextNode {
    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
    metadata: { 'Header 1': 'Main Header' },
    excludedEmbedMetadataKeys: [],
    excludedLlmMetadataKeys: [],
    relationships: { PARENT: [Array] },
    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
    text: 'Main Header\nMain content',
    textTemplate: '',
    metadataSeparator: '\n'
  },
  TextNode {
    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
    metadata: { 'Header 1': 'Header 2' },
    excludedEmbedMetadataKeys: [],
    excludedLlmMetadataKeys: [],
    relationships: { PARENT: [Array] },
    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
    text: 'Header 2\nHeader 2 content',
    textTemplate: '',
    metadataSeparator: '\n'
  },
  TextNode {
    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
    excludedEmbedMetadataKeys: [],
    excludedLlmMetadataKeys: [],
    relationships: { PARENT: [Array] },
    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
    text: 'Sub-header\nSub-header content',
    textTemplate: '',
    metadataSeparator: '\n'
  }
]

API Reference​