Building a RAG-Powered Document Assistant from Scratch

2026-07-06 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

The article details the end-to-end construction of a Retrieval-Augmented Generation (RAG) document assistant capable of processing Nigerian banking or health PDFs and answering natural language questions. The author custom-built the system, avoiding frameworks like LangChain, using FastAPI for the Python backend and Next.js with Tailwind for the frontend. Key components include pypdf for parsing, tiktoken for 500-token chunking with 50-token overlap, sentence-transformers/all-MiniLM-L6-v2 (384-dim) for local embeddings, Qdrant Cloud for vector storage with cosine similarity, and Groq's Llama 3.1 (8B) model for grounded answer generation with a temperature of 0.1. The system provides exact source citations and a polished UI.

Key takeaway

For AI Engineers building custom RAG solutions, prioritize understanding each component's role by implementing them from scratch. Your choice of chunking strategy (e.g., token-based with overlap) and embedding model significantly impacts performance and cost. Ensure your vector database setup supports efficient filtered deletes for idempotent document management. Configure LLM temperature for consistency in fact-based applications to prevent hallucinations.

Key insights

Custom-building RAG systems offers deep architectural understanding and precise control over components like chunking and embedding.

Principles

• Token-based chunking aligns with LLM context windows.
• Fixed-size chunks with overlap prevent context loss.
• Local embedding models reduce cost and latency.

Method

The RAG pipeline involves PDF parsing, token-based chunking, local embedding generation, vector storage in Qdrant, semantic retrieval, and LLM prompting with strict context adherence and source citation.

In practice

• Use tiktoken for LLM-aligned token chunking.
• Implement a payload index in Qdrant for idempotent document re-ingestion.
• Set LLM temperature low (e.g., 0.1) for consistent, fact-based answers.

Topics

• Retrieval-Augmented Generation
• Vector Databases
• Qdrant
• Llama 3.1
• Sentence Transformers
• FastAPI
• Next.js

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.