---
type: entity
subtype: tool
title: "Docling"
description: "The open-source Python data-extraction/chunking tool that is the subject of the video summarized and integrated into Archon during the workflow demo."
resource: "https://github.com/docling-project/docling"
tags: [rag, chunking, data-extraction, open-source]
videos: [the-simplest-rag-stack-that-actually-works-complete-guide, the-only-ai-tech-stack-you-need-in-2026, every-rag-strategy-explained-in-13-minutes-no-fluff, super-special-livestream-this-weekend-live-ai-agent-build, should-i-build-my-ai-agents-with-n8n-or-python, docker-just-made-using-mcp-servers-100x-easier-one-click-installs, turn-any-file-into-llm-knowledge-in-seconds]
created: 2026-07-21
updated: 2026-07-21
---

# Docling

Docling is the open-source Python library Cole reaches for whenever the [RAG](../../concepts/rag.md) source is a file rather than a website. As he describes it, "Docling makes it really easy to extract text from PDFs, Word documents, markdown documents, even audio files" ([0:03:34]). It is "a free and open-source tool... to work with all these complex data types so you can properly curate your data no matter how complex it is" ([0:01:10]), and it pairs directly with [Crawl4AI](./crawl4ai.md) in his stack under a simple rule: "If you are working with files, use Docling. If you are working with websites, use Crawl4AI" ([0:13:06]).

## How Cole uses it

Docling's value is that it collapses two hard problems, extraction and [chunking](../../concepts/chunking.md), into a few lines of code. On the extraction side it is format-aware: "under the hood, it recognizes the file extension and it knows what to do to work with those different file types without us having to do that much more in our code" ([0:06:19]), which makes it "helpful to have something out of the box like Docling" for "more complex documents specifically like PDFs with diagrams Excel files" ([0:12:12]).

The feature Cole most consistently praises is its hybrid chunking. "chunking is not an easy problem to solve, but we're able to do this very easily just using what Docling has for us out of the box" ([0:04:30]). He treats hybrid chunking as a form of [context-aware chunking](../../concepts/context-aware-chunking.md): "Docling is a library that I use in Python that makes it very easy to implement hybrid chunking, which is a form of contextaware chunking" ([0:08:09]), and it is "especially useful for the hybrid chunking that they have available" ([0:03:48]). Cleanly chunked text is what makes downstream [vector embeddings](../../concepts/vector-embeddings.md) retrieve well.

Docling sits alongside the rest of Cole's data tooling, "Graphiti for knowledge graphs, Docling for rag" ([0:13:13]), and he has demonstrated integrating it into [Archon](./archon.md) during a workflow build. Because it runs entirely in Python without a hosted service, it fits his [local AI](../../concepts/local-ai.md) preference: "a tool to help with data extraction and chunking for rag" ([0:05:25]) that you own end to end.

## Realizes

- [Chunking](../../concepts/chunking.md) - Splitting source documents into appropriately sized, embeddable segments, the foundational preprocessing step that determines RAG retrieval quality.
- [Context-Aware Chunking](../../concepts/context-aware-chunking.md) - Splitting documents along semantic boundaries and enriching chunks with surrounding context to improve retrieval quality.
- [Document Extraction](../../concepts/document-extraction.md) - Pulling clean raw text out of source files (PDFs, Excel, HTML, JSON) is the real bottleneck in RAG ingestion, solved by branching on each file's MIME type to a format-specific extractor rather than one universal node.

## Contrasts with

- [Crawl4AI](./crawl4ai.md) - The open-source crawling library powering Cole's RAG MCP server, handling llms.txt, sitemaps, and recursive scraping (the website-side counterpart: files use Docling, websites use Crawl4AI).

## Works with

- [Chroma](./chroma.md) - Publisher of the technical report on context rot that Cole cites as the most practical treatment of how input tokens degrade LLM performance.
- [Graphiti](./graphiti.md) - A knowledge-graph tool Cole names as a candidate for a future agentic-RAG-with-knowledge-graphs strategy.
- [Archon](./archon.md) - Cole's free, open-source AI command center and harness builder for AI coding; started as an AI agent that builds other AI agents.

## Related

- [RAG](../../concepts/rag.md) - Grounding an LLM's answers by retrieving relevant documents from an external knowledge base and injecting them into the prompt at query time.
- [Agentic RAG](../../concepts/agentic-rag.md) - Giving an agent retrieval as a tool so it decides when and how to search a knowledge base, picking strategies and issuing follow-up queries instead of a single static lookup.
- [Vector Embeddings](../../concepts/vector-embeddings.md) - Numeric representations of text that enable semantic similarity search, stored and managed in a vector database for retrieval.
- [Local & Self-Hosted AI](../../concepts/local-ai.md) - Running open-weight models on your own hardware or cloud for privacy, cost control, and independence from API providers.

## Sources

- [Turn ANY File into LLM Knowledge in SECONDS](../../sources/turn-any-file-into-llm-knowledge-in-seconds.md) - "[0:01:10] It is a free and open-source tool I'm going to show you how to use today to work with all these complex data types so you can properly curate your data no matter how complex it is."
- [The Simplest RAG Stack That Actually Works (Complete Guide)](../../sources/the-simplest-rag-stack-that-actually-works-complete-guide.md) - "[0:04:30] chunking is not an easy problem to solve, but we're able to do this very easily just using what Docling has for us out of the box."
- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:08:09] Docling is a library that I use in Python that makes it very easy to implement hybrid chunking, which is a form of contextaware chunking."
- [The ONLY AI Tech Stack You Need in 2026](../../sources/the-only-ai-tech-stack-you-need-in-2026.md) - "[0:13:06] If you are working with files, use Docling. If you are working with websites, use Crawl4AI."
- [Turn ANY File into LLM Knowledge in SECONDS](../../sources/turn-any-file-into-llm-knowledge-in-seconds.md) - "[0:06:19] under the hood, it recognizes the file extension and it knows what to do to work with those different file types without us having to do that much more in our code."
- [Super Special Livestream this Weekend! (LIVE AI Agent Build)](../../sources/super-special-livestream-this-weekend-live-ai-agent-build.md) - "[0:03:48] a tool for rag called Docling. And this is especially useful for the hybrid chunking that they have available."
- [Docker Just Made Using MCP Servers 100x Easier (One Click Installs!)](../../sources/docker-just-made-using-mcp-servers-100x-easier-one-click-installs.md) - "[0:05:25] one of the latest videos on my channel where I cover Docling, which is a tool to help with data extraction and chunking for rag."
- [Should I Build My AI Agents with n8n or Python?](../../sources/should-i-build-my-ai-agents-with-n8n-or-python.md) - "[0:13:13] Graphiti for knowledge graphs, Docling for rag. We have Crawl4AI for crawling websites."
