Automated PDF to RAG System with Embeddings and Qdrant Search
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This workflow automates the process of extracting data from PDFs, splitting it into manageable chunks, generating embeddings using Mistral AI, and indexing it into Qdrant for efficient semantic search.
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About This Workflow
Overview
This n8n workflow is designed to transform unstructured PDF documents into a structured, searchable knowledge base using a Retrieval Augmented Generation (RAG) approach. It starts by fetching a PDF, extracting its text content, and then intelligently splitting it into sections and chunks. These chunks are then converted into vector embeddings using Mistral AI's cloud service. Finally, the embeddings and associated metadata are stored in a Qdrant vector database, enabling fast and accurate semantic searches. This is particularly useful for building intelligent Q&A systems, knowledge retrieval tools, or any application that requires searching through large volumes of text documents.
Key Features
- Fetches PDF documents from a specified URL.
- Extracts text content from PDF files.
- Splits text into logical chapters, sections, and smaller chunks.
- Generates high-quality vector embeddings using Mistral AI.
- Indexes embeddings and metadata into Qdrant for efficient searching.
- Supports metadata extraction and preservation for context.
How To Use
- Configure the
Get Tax Code Zip Filenode with the URL of your PDF or ZIP file. - Ensure your Mistral Cloud API credentials are set up in n8n.
- Ensure your Qdrant instance is running and accessible, and configure the
QdrantApicredentials. - Adjust the text splitting parameters (
chunkSizeinRecursive Character Text Splitterand chunking logic inContent Chunking @ 50k Chars) as needed for your document structure. - Map your PDF content to the appropriate metadata fields in the
Extract From ChapterandMap To Sectionsnodes. - Run the workflow to process the PDF, generate embeddings, and index them into Qdrant.
- The workflow can then be extended to take user queries, generate embeddings for the query, and perform a search using the
Use Qdrant Search API1node.
Apps Used
Workflow JSON
{
"id": "b670a1bd-17f3-4218-b3bf-4e49c9151b02",
"name": "Automated PDF to RAG System with Embeddings and Qdrant Search",
"nodes": 0,
"category": "AI_Research_RAG_and_Data_Analysis",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: b670a1bd-17f3...
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