Building a RAG-Powered Document Assistant from Scratch

📰 Medium · RAG

Learn to build a RAG-powered document assistant from scratch, leveraging vector databases and cosine similarity for efficient document retrieval and generation.

advanced Published 6 Jul 2026

Action Steps

Build a high-level system architecture for the document assistant using RAG
Configure a vector database like Qdrant for efficient storage and retrieval of document embeddings
Implement token-based chunking with overlap for PDF ingestion and parsing
Compute embeddings locally using a library like Hugging Face Transformers
Set up a FastAPI backend with routes for retrieval, generation, and configuration
Develop a frontend using Next.js and Tailwind UI for user interaction

Who Needs to Know This

This project benefits developers, data scientists, and product managers working on AI-powered document assistants, as it provides a comprehensive guide to building a RAG-powered system from scratch.

Key Insight

💡 RAG-powered document assistants can efficiently retrieve and generate documents using vector databases and cosine similarity.

Key Takeaways

Learn to build a RAG-powered document assistant from scratch, leveraging vector databases and cosine similarity for efficient document retrieval and generation.

Full Article

Title: Building a RAG-Powered Document Assistant from Scratch

URL Source: https://medium.com/@Adekola_Olawale/building-a-rag-powered-document-assistant-from-scratch-6db92a3c082a?source=rss------rag-5

Published Time: 2026-07-06T15:35:30Z

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1. [What is RAG (Retrieval-Augmented Generation)?](https://medium.com/?source=-----6db92a3c082a---------------------------------------#d168 "What is RAG (Retrieval-Augmented Generation)?")
2. [Why Vector Databases and Cosine Similarity?](https://medium.com/?source=-----6db92a3c082a---------------------------------------#19f4 "Why Vector Databases and Cosine Similarity?")
3. [High-Level System Architecture](https://medium.com/?source=-----6db92a3c082a---------------------------------------#a976 "High-Level System Architecture")
4. [PDF Ingestion and Parsing](https://medium.com/?source=-----6db92a3c082a---------------------------------------#6417 "PDF Ingestion and Parsing")
5. [Token-Based Chunking with Overlap](https://medium.com/?source=-----6db92a3c082a---------------------------------------#f9cb "Token-Based Chunking with Overlap")
6. [Computing Embeddings Locally](https://medium.com/?source=-----6db92a3c082a---------------------------------------#6ef0 "Computing Embeddings Locally")
7. [Vector Storage and Qdrant Setup](https://medium.com/?source=-----6db92a3c082a---------------------------------------#b840 "Vector Storage and Qdrant Setup")
8. [Retrieval (Semantic Search)](https://medium.com/?source=-----6db92a3c082a---------------------------------------#3ae3 "Retrieval (Semantic Search)")
9. [LLM Prompting and Generation](https://medium.com/?source=-----6db92a3c082a---------------------------------------#019c "LLM Prompting and Generation")
10. [FastAPI Routes and Configuration](https://medium.com/?source=-----6db92a3c082a---------------------------------------#20e0 "FastAPI Routes and Configuration")
11. [Frontend: Next.js & Tailwind UI](https://medium.com/?source=-----6db92a3c082a---------------------------------------#0ab3 "Frontend: Next.js & Tailwind UI")
12. [Reflecting on Key Choices](https://medium.com/?source=-----6db92a3c082a---------------------------------------#b1d8 "Reflecting on Key Choices")
13. [What Could Go Wrong and How I Handle It](https://medium.com/?source=-----6db92a3c082a---------------------------------------#09e6 "What Could Go Wrong and How I Handle It")
14. [What’s Next / What I’d Do Differently](https://medium.com/?source=-----6db92a3c082a---------------------------------------#9f99 "What’s Next / What I’d Do Differently")

# Building a RAG-Powered Document Assistant from Scratch

## My end-to-end journey building a RAG document assistant usi

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Building a RAG-Powered Document Assistant from Scratch

Key Takeaways

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