Local RAG Chatbot: Capabilities & Setup Guide
Praveen Kasam
Certified Agile Leader |Expert Site Reliability Engineering Leader| Founder and host of an SRE Community | SRE Coach | ISAQB certified Architect
?? Capabilities of This RAG System
This Retrieval-Augmented Generation (RAG) Chatbot provides the following functionalities:
? Document Upload & Parsing → Supports PDFs & DOCX files for knowledge extraction.
? Question Answering → Users can ask questions based on uploaded documents.
? Local AI Processing → Runs Mistral/Llama3 models locally via Ollama (no external API calls).
? Vector Search → Stores document embeddings using ChromaDB for fast retrieval.
? Simple Web UI → Built with Flask & Bootstrap, making it easy to use.
?? Preparing the Environment
System Requirements
Install Required Dependencies
Run the following commands to set up your environment:
# Create and activate virtual environment
python3 -m venv rag_env
source rag_env/bin/activate # On Windows use `rag_env\Scripts\activate`
# Install dependencies
pip install flask flask_cors chromadb \
langchain-community langchain-huggingface \
unstructured[all] pypdf python-docx
If using Zsh, escape square brackets:
pip install "unstructured[all]"
?? Step 1: Set Up Flask Backend (app.py)
Create a new file app.py and paste the following code:
This is a Flask-based web application that serves as a Retrieval-Augmented Generation (RAG) chatbot. It allows users to upload PDF and DOCX files, processes them into a vector database (ChromaDB), and enables question-answering through a locally running DeepSeek R1 14B model via Ollama.
1?? Initializing the Flask App
2?? Loading and Processing Documents
3?? Creating a Vector Database
4?? Setting Up the Language Model
5?? Defining Web Routes
The application defines several Flask API routes:
6?? Running the Flask App
领英推荐
?? Step 2: Set Up the Frontend (templates/index.html)
Create a folder templates/, then inside it, create index.html:
Structure of the Web Page
2?? Web Page Title & Metadata
3?? Including Bootstrap for Styling
4?? Main User Interface Elements
?? Document Upload Section
? Question Input Section
?? Answer Display Section
5?? JavaScript Functionalities
6?? Responsive & Modern Design
?? Step 3: Run the Flask App
Start the Flask server:
python app.py
Then, open https://127.0.0.1:5001 in your browser.
?? Summary of Features
? Simple UI → Easy document upload & question input
? Local RAG System → Works offline using Mistral/Llama3
? Vector Database → Uses ChromaDB for fast retrieval
? Flask API → Lightweight & fast
? Bootstrap Styling → Clean, mobile-friendly UI
Now you have a fully functional Local RAG Chatbot with a Web UI! ????
AI for Mittelstand, Software Agency in Frankfurt and India, ? Europportunity, AI jobportal, non-EU candidates #chancenkarte
1 天前We have launched secure AI solution for German SMEs to use own data - fusionAIQ.com
Founder & CEO @ opXon GmbH - Operations Consultants | Next Level Service Management with AI
1 个月Hi Praveen Kasam, using RAG to answer questions about your own data is a really powerful use of AI. I was interested in your guide, but the important part seem to be missing. Or which AI do you prefer to code the flask app?
VP & Head of Service Delivery, Operations and CSR function at Embitel Technologies (India) Pvt. Ltd. (a VW Group company) || Ex Wipro || Ex Aricent || Ex Tata Elxsi
1 个月Hi Praveen, good going. For this to run, you must be needing a high computational power locally, isn’t it ?