This guide covers advanced configurations for the Parse feature to handle a variety of specialized use cases and requirements.
Extended Context: Handling Distant Legends
Layout analysis of a table with it's legend segmented separately.
extended_context. This provides the VLM with the full page image with the cropped segment as context.
VLM parsing results of a table that was segmented separately from it's legend - leveraging extended context.
Table and Picture segments:
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Parse with extended context for tables and pictures
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/construction.pdf",
segment_processing={
"table": {"extended_context": True},
"picture": {"extended_context": True},
},
)
Full-Page VLM: Bypassing Layout Analysis
For documents where layout analysis struggles, or for simple documents where it’s unnecessary, you can bypass layout analysis entirely.
By setting the segmentation_strategy to Page, you can instruct Chunkr to process the entire page with a Vision Language Model (VLM) and generate Markdown directly.
This approach is highly effective for:
- Layout analysis failure: In the rare case that layout analysis struggles with a document’s structure.
- Simple Documents: Tiny, text-only, and uniform documents (e.g., receipts) where layout analysis offers no benefit and simple OCR is sufficient for bounding boxes.
Here’s how to enable it:
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Force full-page VLM processing for Markdown output
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/receipt.pdf",
segmentation_strategy="Page",
)
Disabling Chunking for Non-RAG Workflows
If you’re using Chunkr for data extraction, document analysis, or other non-RAG workflows, you may want to disable chunking entirely.
When chunking is disabled, each chunk in the output will contain exactly one segment.
To disable chunking, set target_length to 0 in the chunk_processing configuration:
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Disable chunking for extraction workflows
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/receipt.pdf",
chunk_processing={
"target_length": 0 # Disables chunking
},
)
Optimizing for speed
The most significant factor affecting processing time is VLM processing. By default, Chunkr uses VLM processing for the following segment types to ensure high-quality data extraction:
- Tables
- Images
- Forms
- Legends
- Formulas
If high-quality data extraction is not critical for certain segment types in your use case, you can disable VLM processing for those segments to significantly improve processing speed.
For example, if your document contains images that are decorative or not essential to extract, you can disable VLM processing for images:
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Disable VLM processing for images to optimize for speed
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/doc.pdf",
segment_processing={
"picture": {"strategy": "Auto"},
},
)
segment_processing configuration. This allows you to balance speed and quality based on your specific requirements.
By default, text segments are processed with OCR which captures the content but loses formatting information. If you need to preserve text styling such as bold, italicization, font colors, and other formatting details, you can enable VLM processing for text segments.
This is useful for use cases like:
- Redlining: Tracking changes and formatting in legal documents
- Document comparison: Identifying styling differences between versions
- Accessibility: Preserving semantic meaning conveyed through formatting
Enabling VLM processing for text segments significantly increases processing time, as text segments are the most common segment type in documents.
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Enable VLM processing for text to capture styling
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/doc.pdf",
segment_processing={
"text": {"strategy": "LLM"},
},
)
Ignoring Segment Types
When you only need specific types of content from your documents, you can ignore certain segment types entirely. This is useful for:
- Focusing on specific content types (e.g., only tables and charts)
- Removing unwanted elements (e.g., headers, footers, page numbers)
- Simplifying output for targeted extraction workflows
For example, if you only want to extract tables and ignore all other content:
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/doc.pdf",
segment_processing={
"text": {"strategy": "Ignore"},
"title": {"strategy": "Ignore"},
"picture": {"strategy": "Ignore"},
"list_item": {"strategy": "Ignore"},
"caption": {"strategy": "Ignore"},
"footnote": {"strategy": "Ignore"},
"formula": {"strategy": "Ignore"},
},
)
import os
from chunkr_ai import Chunkr
client = Chunkr(api_key=os.environ["CHUNKR_API_KEY"])
# Ignore headers and footers
task = client.tasks.parse.create(
file="https://s3.us-east-1.amazonaws.com/chunkr-web/uploads/doc.pdf",
segment_processing={
"page_header": {"strategy": "Ignore"},
"page_footer": {"strategy": "Ignore"},
},
)
