/chunking

name: chunking
description: Use when splitting extracted text into chunks for LLM context windows or RAG ingestion. Covers chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone `chunk` command.

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Chunking

Use this when feeding documents into an LLM context window or a vector store. Xberg chunks two ways: inline during extraction (chunks land on each document's `chunks` field), or standalone via the `chunk` command for text you already have. Sizing is character-based by default, or token-based when a tokenizer model is supplied.

Inline during extraction

Turn on chunking with `--chunk` and the chunks appear on the structured result under `chunks`:

# 1000-char chunks, 200-char overlap (defaults when --chunk is on)
xberg extract report.pdf --chunk --format json | jq '.chunks | length'

# Explicit size + overlap
xberg extract report.pdf --chunk --chunk-size 1500 --chunk-overlap 300 --format json

Overlap must be smaller than chunk size — the CLI rejects `--chunk-overlap >= --chunk-size`. When you set only `--chunk-overlap` against an existing config, an overlap that exceeds the size is clamped to `chunk_size / 4`.

Standalone `chunk` command

Chunk text you already have, from `--text` or stdin. Output defaults to JSON:

# From a flag
xberg chunk --text "long document text ..." --chunk-size 800 --chunk-overlap 100

# From stdin (pipe extracted content straight in)
xberg extract notes.md | xberg chunk --chunk-size 500 --format json

JSON output carries `chunks` (array of strings), `chunk_count`, the resolved `config` (`max_characters`, `overlap`, `chunker_type`), and `input_size_bytes`. Use `--format text` for a human-readable dump with `--- chunk N ---` separators.

> Note: in the JSON output, `chunker_type` is rendered capitalized (`"Text"`, > `"Markdown"`, `"Yaml"`, `"Semantic"`) because it is emitted via Rust's Debug > formatting, whereas the `--chunker-type` input flag is lowercase > (`text`, `markdown`, `yaml`, `semantic`). Lowercase the value before > comparing if you parse it back.

Chunker types

`--chunker-type` selects the splitting strategy (standalone `chunk` command):

| Type | Behavior | | ---------- | ------------------------------------------------------------------- | | `text` | **Default.** Plain character-window splitting with overlap. | | `markdown` | Markdown-aware — splits on structure (headings, blocks) where possible. | | `yaml` | YAML-aware splitting for structured config/data documents. | | `semantic` | Topic-boundary splitting driven by `--topic-threshold` (0.0–1.0, default 0.75). |

# Markdown-aware chunking keeps headings and blocks intact
xberg chunk --text "$(cat README.md)" --chunker-type markdown

# Semantic chunking — lower threshold = more, smaller topic chunks
xberg chunk --text "$(cat transcript.txt)" --chunker-type semantic --topic-threshold 0.6

Token-based sizing

By default `--chunk-size` counts characters. To size chunks by tokens for a specific model, pass `--chunking-tokenizer` with a HuggingFace tokenizer id. On the `extract` command this implicitly enables chunking. Requires the `chunking-tokenizers` feature (present in the default CLI build).

# Size chunks by GPT-4o tokens during extraction
xberg extract report.pdf --chunking-tokenizer Xenova/gpt-4o --format json

# Or on the standalone command
xberg chunk --text "$(cat doc.txt)" --chunking-tokenizer Xenova/gpt-4o --chunk-size 512

With a tokenizer set, `--chunk-size` is interpreted in tokens, not characters.

Config file alternative

Field names in config files are snake_case under `[chunking]`:

[chunking]
max_characters = 1000
overlap = 200
chunker_type = "markdown"

xberg extract report.pdf --config xberg.toml --format json

> CLI flags map to config fields as `--chunk-size` → `max_characters` and > `--chunk-overlap` → `overlap`. In config files use the snake_case names.

Programmatic access

From Python, enable chunking on the config and read the chunks off the document in the result envelope (`result.results[0].chunks`):

from xberg import ExtractInput, extract, ExtractionConfig, ChunkingConfig

config = ExtractionConfig(
    chunking=ChunkingConfig(max_characters=1000, overlap=200),
)
result = await extract(ExtractInput(uri="report.pdf"), config)
for chunk in result.results[0].chunks or []:
    print(len(chunk.content))

> The public Python `ChunkingConfig` (a dataclass) uses constructor kwargs > `max_characters` / `overlap`; the Rust core struct fields are also > `max_characters` / `overlap`. TOML/JSON config keys are `max_chars` / > `max_overlap` (with `max_characters` / `overlap` accepted as serde aliases), > and dict-form config passed to `ExtractionConfig` likewise accepts the > `max_chars` / `max_overlap` aliases; Node's `ChunkingConfig` interface uses > `maxCharacters` / `overlap`. See `references/python-api.md` and > `references/rust-api.md` in the sibling `xberg` skill.

Picking parameters

• **RAG / vector store** — 500–1000 chars (or 256–512 tokens) with

10–20% overlap. Use `markdown` chunking for docs to keep sections whole.

• **LLM summarization** — larger chunks (1500–4000 chars) with small

overlap; size by tokens to stay under the model window.

• **Topic segmentation** — `semantic` chunker; tune `--topic-threshold`

down for finer splits, up for coarser ones.

Common pitfalls

• **Overlap ≥ size** — rejected on `extract`; clamped to `size / 4` when

only overlap is changed against an existing config.

• **Tokenizer without the feature** — `--chunking-tokenizer` errors if the

CLI w