RAG-based AI Knowledge Base Assistant: Building a Private Document Q&A System

Project Guide for RAG-based AI Knowledge Base Assistant This project is a Retrieval-Augmented Generation (RAG) chatbot built using LlamaIndex and Google Gemini, designed to provide intelligent Q&A services for private knowledge base documents. Its core goal is to combine document retrieval with generative AI to deliver context-aware answers and support private deployment. The project is maintained by Gauravtech07, and the source code is hosted on GitHub.

Project Background and Overview

• Original Author/Maintainer: Gauravtech07
• Source Platform: GitHub
• Original Link: https://github.com/Gauravtech07/AI-Knowledge-Base-Assistant-RAG-Based-Chatbot-
• Release Time: 2026-06-12

AI Knowledge Base Assistant is a RAG-based intelligent chatbot project that demonstrates how to use modern Large Language Models (LLMs) and vector retrieval technology to build a private knowledge base Q&A system. By combining document retrieval with generative AI, the system can retrieve relevant information from custom knowledge bases when users ask questions and generate context-aware answers.

Technical Architecture and Core Components The project adopts a modular Python architecture with core components including:

1. Document Ingestion Module (ingest.py): Uses LlamaIndex's SimpleDirectoryReader to load documents from the data/files directory and parse them into structured data.
2. Chat Engine Module (chatbot.py): The core of the system, implementing the RAG process:
   • Embedding Model: Google gemini-embedding-001
   • Large Language Model: gemini-2.5-flash
   • Vector Index: VectorStoreIndex to build the document vector library
   • Conversation Mode: context mode supports multi-turn context understanding
3. API Service Layer (main.py): Built with FastAPI to create a RESTful API, providing health check (/) and chat (/chat) endpoints.

Tech Stack and RAG Workflow

• Tech Stack: FastAPI+Uvicorn (Web Framework), LlamaIndex (LLM Data Framework), ChromaDB (Vector Database), Google Generative AI (Gemini Models), PyPDF (PDF Parsing).
• RAG Workflow: Index Building Phase: Load documents → Chunk and convert to vectors (Gemini Embedding) → Store in ChromaDB to build the index. Query Response Phase: Receive user query → Convert to vector → Retrieve relevant document fragments → Input context and question into Gemini to generate an answer.

Application Scenarios and Value

• Application Scenarios: Enterprise knowledge management (internal document Q&A), customer service (automated consultation), education assistance (textbook/paper interaction), compliance review (regulatory clause retrieval).
• Core Advantages: Supports private non-public documents, answers are traceable to sources, knowledge can be updated without retraining, reduces hallucination risks.

Deployment and Usage Steps

1. Configure the GOOGLE_API_KEY environment variable;
2. Place target documents in the data/files directory;
3. Run main.py to start the FastAPI service;
4. Interact with the chatbot via HTTP requests (e.g., the /chat endpoint).

Summary and Future Outlook This project is a clear and easy-to-understand entry-level RAG project, demonstrating how to combine LlamaIndex and Google Gemini to build a document Q&A system, providing a good reference for RAG beginners.

Future expansion directions:

• Support more document formats (Word, Markdown, HTML, etc.);
• Implement streaming responses to enhance user experience;
• Add conversation history management to support multi-turn dialogues;
• Integrate advanced retrieval strategies like hybrid search and reordering;
• Develop a user interface to simplify interaction.