Local RAG Chatbot: Capabilities & Setup Guide

Local RAG Chatbot: Capabilities & Setup Guide

?? 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

  • macOS/Linux/Windows
  • Python 3.8+
  • Virtual Environment (Recommended)

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

  • The application uses Flask as the web framework.
  • A folder named "documents" is created to store uploaded files.


2?? Loading and Processing Documents

  • The app scans the documents folder and processes any PDFs or DOCX files.
  • If a file is a PDF, it is loaded using a PDF processor.
  • If a file is a DOCX, it is loaded using a Word document processor.
  • All document content is then split into chunks to optimize retrieval performance.


3?? Creating a Vector Database

  • The text chunks from documents are converted into vector embeddings.
  • These embeddings are stored inside ChromaDB, a high-performance vector database.
  • The embeddings are created using a sentence-transformer-based model.


4?? Setting Up the Language Model

  • The Ollama framework is used to run a DeepSeek R1 14B model (or Mistral, Llama3).
  • The model is configured to retrieve relevant document chunks when answering questions.


5?? Defining Web Routes

The application defines several Flask API routes:

  • Homepage (/)
  • Upload Endpoint (/upload)
  • Question Answering Endpoint (/ask)


6?? Running the Flask App

  • The application runs on port 5001 and is set to debug mode for easier development.
  • The web app can be started using a standard command.


?? Step 2: Set Up the Frontend (templates/index.html)

Create a folder templates/, then inside it, create index.html:

Structure of the Web Page

  • The page follows a standard HTML5 structure, ensuring compatibility with all modern browsers.
  • The Bootstrap framework is used for styling, improving responsiveness and usability.


2?? Web Page Title & Metadata

  • The page is titled "RAG Chatbot UI".
  • It sets meta viewport settings to ensure a mobile-friendly layout.


3?? Including Bootstrap for Styling

  • The page loads Bootstrap 5.3 from a CDN to provide: Pre-styled buttons, forms, and layout grids. A clean and user-friendly UI.


4?? Main User Interface Elements

?? Document Upload Section

  • Label: "Upload Document (PDF/DOCX)"
  • Input Field: Allows users to select a file.
  • Upload Button: When clicked, triggers the file upload function.

? Question Input Section

  • Label: "Ask a Question"
  • Text Input Box: Users enter their questions here.
  • Ask Button: When clicked, it sends the question to the RAG chatbot.

?? Answer Display Section

  • Heading: "Response"
  • Text Container: Displays the AI-generated answer.


5?? JavaScript Functionalities

  • File Upload Function (uploadFile())
  • Question Submission Function (askQuestion())


6?? Responsive & Modern Design

  • The layout adjusts dynamically for different screen sizes.
  • Uses Bootstrap classes for:Containers (.container mt-5) → Centers content.Primary Buttons (.btn-primary) → Upload button. Success Buttons (.btn-success) → Ask question button.Text Styling (.fw-bold) → Highlights the chatbot’s response.


?? 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! ????

Hrishikesh Lele

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

Andre Joswig

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?

回复
Jayanta Tewari

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 ?

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