Building a RAG System from Scratch: Document Q&A Implementation Based on Llama3

Original Author/Maintainer: N3NU Source Platform: GitHub Original Link: https://github.com/N3NU/artificial-intelligence-project-two Publication Time: May 24, 2026

This project is an open-source project that fully demonstrates the RAG tech stack, covering the complete workflow from document loading, chunking, vectorization, retrieval to generation. It implements document Q&A functionality based on Llama3, providing learners with a clear path to build a RAG system.

Since 2023, LLMs (such as GPT-4, Claude, Llama) have shown strong capabilities, but they have the limitation of knowledge cutoff at the time of training data, making them unable to access private or up-to-date information.

Retrieval-Augmented Generation (RAG) technology emerged as a solution. Its core idea is: when a user asks a question, first retrieve relevant information from an external knowledge base, then provide the results as context to the LLM for answer generation, thus solving the knowledge limitation problem of LLMs.

Project Technical Flow: Documents → PDF Loader → Chunking → Embeddings → Chroma Vector DB → Similarity Retrieval → Prompt Construction → Llama3 → Grounded Answer + Citations

1. Document Loading: Process formats like PDF and convert to processable text;
2. Text Chunking: Adopt fixed-length/paragraph/overlap/semantic chunking strategies to balance context and retrieval accuracy;
3. Vectorization: Use Embeddings models to convert text into vectors;
4. Vector Storage: Chroma vector database supports approximate nearest neighbor search;
5. Similarity Retrieval: Convert the question into a vector and search for the most similar document chunks;
6. Prompt Engineering: Integrate retrieval results into prompts to guide model generation;
7. LLM: Choose Llama3, which supports local deployment (advantages in privacy, cost, and customization);
8. Result Output: Generate answers with citations to ensure traceability.

1. Enterprise Internal Knowledge Base: Quickly answer questions about product manuals/technical specifications, improving information access efficiency;
2. Academic Literature Assistant: Locate relevant research, summarize findings, and assist scientific research;
3. Customer Service Automation: Answer customer questions based on product documents, reduce manual pressure, and ensure answer consistency.

1. Retrieval Quality Optimization: Hybrid retrieval (vector + keyword), query rewriting, result reordering;
2. Hallucination Control: Require answers to be based only on context in prompts, lower temperature parameters, post-processing to verify consistency;
3. Long Context Processing: Address retrieval accuracy and multi-chunk reasoning issues under large windows.

Although this project is a practice project, it touches on the core tech stack of AI applications and is a mainstream solution for industrial large model deployment.

RAG is constantly evolving: new paradigms like Multimodal RAG, Agentic RAG, and Graph RAG have emerged, but the core retrieval-generation architecture remains the foundation. Mastering RAG is the key to combining LLMs with private knowledge and a core competency for AI developers.