🍕 Local RAG — Restaurant Review Analyzer

Retrieve & Analyze Restaurant Reviews Using Ollama Model + LangChain + ChromaDB

This project is a Local Retrieval-Augmented Generation (RAG) system that answers questions about a restaurant based on realistic customer reviews.

It runs completely offline, powered by:

• 🧠 Ollama LLMs
• 🔗 LangChain
• 🗂 ChromaDB
• 📊 CSV-based dataset

Ask any question like:

"What do customers think about the pizza crust?" "What are the top complaints?" "Which pizzas are most praised?"

The model answers using actual retrieved reviews.

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📁 Project Structure

local-rag/
│── main.py                      # RAG pipeline & chatbot loop  
│── vector.py                    # Vector DB creation + retriever  
│── realistic_restaurant_reviews.csv   # Dataset  
│── requirements.txt             # Dependencies  
│── chrome_langchain_db/         # Auto-created vector DB  
│── venv/                        # Virtual environment  

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🖼️ Screenshots

📌 1. Dataset Preview (CSV File)

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📌 2. Project Folder Structure (VS Code)

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📌 3. CLI RAG Chatbot Running

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📌 4. Streamlit RAG Chatbot Running

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🚀 Features

🔹 Local RAG Pipeline

Runs without cloud APIs using Ollama.

🔹 Smart Retrieval

Top 5 relevant reviews fetched using vector similarity.

🔹 Persistent Vector DB

Chroma saves embeddings & reloads instantly.

🔹 Uses Real Reviews

Answers directly grounded in CSV data.

🔹 Lightweight & Fast

Perfect for learning + portfolio projects.

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🧠 How the RAG System Works

1. Load CSV (Pandas)
2. Convert rows → LangChain Documents
3. Generate embeddings using mxbai-embed-large
4. Store vectors in ChromaDB
5. User asks a question
6. Retriever gets top 5 relevant reviews
7. Llama 3.2 generates the final answer

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🛠️ Installation

1️⃣ Install Ollama

https://ollama.com/download

Models required:

ollama pull llama3.2
ollama pull mxbai-embed-large

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2️⃣ Setup Environment

uv venv
source venv/bin/activate    # macOS/Linux
venv\Scripts\activate       # Windows

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3️⃣ Install Python Packages

pip install -r requirements.txt

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▶️ Run the Project

python main.py

You will see:

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Ask your question (q to quit):
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💬 Example Questions

• What do customers think about the service?
• Summarize the positive reviews.
• What complaints are mentioned most?
• Give feedback about thin crust pizza.
• Are there gluten-free options mentioned?

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🧰 Tech Stack

Component	Purpose
🐍 Python	Application logic
🧠 Ollama	Local LLM + embeddings
🔗 LangChain	RAG chain
🗂 ChromaDB	Vector storage
📊 Pandas	CSV processing

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📄 Dataset

Columns included:

• Title
• Date
• Rating
• Review

Used for retrieval + summarization.

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⭐ Future Enhancements

• Streamlit UI version
• Sentiment Analysis
• Multi-query reasoning (Agentic RAG)
• Summary Dashboard

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❤️ If this helped, give it a ⭐ on GitHub!

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