---
type: source
title: "Turn ANY File into LLM Knowledge in SECONDS"
description: "Docling, a free open-source Python tool, turns any complex file type (PDF, Word, audio, and more) into clean markdown and RAG-ready chunks, solving the hardest and most important part of a RAG pipeline: data curation."
youtube_id: fg0_0M8kZ8g
url: https://www.youtube.com/watch?v=fg0_0M8kZ8g
slug: turn-any-file-into-llm-knowledge-in-seconds
published: 2025-10-01
duration: "0:21:22"
recency_rank: 66
raw: "../raw/turn-any-file-into-llm-knowledge-in-seconds.md"
tags: [rag, docling, chunking, knowledge-base]
created: 2026-07-21
updated: 2026-07-21
---

Cole argues that the biggest limitation of LLMs is that their knowledge is too general, which is why RAG (retrieval augmented generation) is a perennially important technique for making a model an expert on your own data. But the hardest part of RAG is the curation step: getting messy, non-markdown source files (PDFs with tables and diagrams, Word docs, audio, video) into clean structured text that an LLM and vector database can use. This is the problem Docling solves.

Docling is a free, open-source Python package (pip-installable) that recognizes a file's extension and extracts its content to markdown with almost no code. It handles OCR and object recognition under the hood (with pluggable backends like Tesseract), producing beautiful markdown tables and preserving structure from complex PDFs in well under 30 seconds. For audio it uses an ASR pipeline with OpenAI Whisper Turbo running completely locally (models pulled from Hugging Face), transcribing with timestamps. Beyond extraction, Docling also handles chunking: its hybrid chunking strategy uses an embedding model to decide split boundaries by semantic similarity, keeping related ideas together and never splitting mid-paragraph or mid-list, with a max-token cap per chunk.

The video culminates in a complete RAG AI agent template built on Docling: documents are parsed and hybrid-chunked, chunks are contextualized (headings and subheadings included) plus metadata, embedded, and stored in Postgres with pgvector. A Pydantic AI agent is given a single search-knowledge-base tool that embeds the user query, calls a match_chunks SQL function, and reasons over the retrieved chunks to answer. Cole frames his stack as Crawl4AI for websites and Docling for everything else.

## Concepts covered

- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Knowledge Bases](../concepts/knowledge-bases.md)
- [Structured vs Unstructured Data](../concepts/structured-vs-unstructured-data.md)
- [Chunking](../concepts/chunking.md)
- [Context-Aware Chunking](../concepts/context-aware-chunking.md)
- [Contextual Retrieval](../concepts/contextual-retrieval.md)
- [Vector Embeddings](../concepts/vector-embeddings.md)
- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Agentic RAG](../concepts/agentic-rag.md)

## Entities

- [Docling](../entities/tools/docling.md)
- [OpenAI](../entities/organizations/openai.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [GitHub](../entities/tools/github.md)
- [Crawl4AI](../entities/tools/crawl4ai.md)
- [Dynamous](../entities/organizations/dynamous.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** Why LLM knowledge is too limited and why RAG matters; the hard part is the curate step
- **[0:01:10]** Introduces Docling as the free open-source tool for extracting text from complex file types
- **[0:03:10]** Demos the finished RAG agent answering from an audio file (458% ROI)
- **[0:04:12]** First script: simple PDF text/table extraction to markdown with a DocumentConverter
- **[0:06:19]** Second script: extracting from multiple file types (PDF, Word, markdown) by extension detection
- **[0:08:18]** Working with audio files via FFmpeg + Whisper Turbo ASR pipeline, fully local
- **[0:10:27]** Chunking explained: why you can't dump whole documents into a vector DB
- **[0:11:45]** Hybrid chunking: embedding-model-driven semantic split boundaries
- **[0:15:34]** The grand finale RAG agent template; Docling + Crawl4AI as the full extraction stack
- **[0:18:07]** Pydantic AI agent with a single search_knowledge_base tool over Postgres/pgvector

## Transcript

[Raw transcript](../raw/turn-any-file-into-llm-knowledge-in-seconds.md)
