Import EVERYTHING Into Your RAG Agent (Docling & LlamaParse) скачать в хорошем качестве

Import EVERYTHING Into Your RAG Agent (Docling & LlamaParse)

👉 Get our State-of-the-Art n8n RAG Systems and learn how to customize them, in our community https://www.theaiautomators.com/?utm_...    • n8n RAG Masterclass - Build AI Agents + Sy...      • Stop Using RAG for Spreadsheets — Use This...      • Unlock Multimodal RAG Agents in n8n (Image...      • Make your AI Agents 10x Smarter with Graph...   Chapters: 0:00 - Overview 3:11 - Setting Up LlamaParse 5:23 - Integrating LlamaParse with n8n 17:45 - Integrating into RAG Pipeline 19:31 - Introduction to Docling 20:25 - Deploying Docling 29:14 - Mistral OCR In this video, you’ll learn how to import and parse 95+ file types (including docs, slides, spreadsheets, images, and audio) so your AI agents can understand and act on all of it. We compare three approaches: LlamaParse (quick to set up, broad compatibility, rich Markdown), Docling (open-source, self-hosted project by IBM, no external APIs—great for privacy/cost control), and Mistral OCR (PDF-only but fast, affordable, and easy to integrate). You’ll see why OCR + native parsing preserves images, layout, tables, and structure far better than naive n8n nodes. We build an end-to-end ingestion pipeline in n8n: watch files from Google Drive, send to a parser, get back consistent Markdown, then chunk → embed → store in a Supabase vector table using text-embedding-3-small. From there, an agent queries the vector DB for semantically relevant context and answers user questions. You’ll see both webhook and polling strategies for LlamaCloud’s async jobs, plus practical settings that improve results: agentic mode, high-res OCR, adaptive long-table handling, outline/table extraction, and optional HTML tables. We also cover pricing basics (free-tier credits and per-page costs) so you can forecast usage. Next, we spin up Docling with a Render deploy so you can test via a simple UI and hit the API (v1/convert) from n8n. We talk through trade-offs, speed and memory usage vs. cost/privacy, and show a gateway pattern (basic auth + API key) to keep your Docling instance private while exposing a controlled public endpoint. Finally, we revisit Mistral OCR for fast, good-quality PDF parsing, including an option to return extracted images for multimodal RAG. By the end, you’ll have the pieces to: accept nearly any file type, normalize it to Markdown, index it in your vector DB without duplication, enrich with metadata, and chat with the content through an agent, safely and at a cost that fits your use case.