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---
title: Property AI
emoji: 🏠
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "3.44.0"
app_file: app.py
pinned: false
---
# NoBrokerage Chatbot
**AI-powered real estate assistant** for NoBrokerage.com that retrieves property data from a FAISS vectorstore, applies structured filters, and generates grounded summaries and property cards using Groq LLM.
This project is built using **FastAPI**, **LangChain**, **FAISS**, **HuggingFace embeddings**, **Frontend**, and **Groq LLM**, and is ready for **Docker deployment** and **Hugging Face Spaces**.
---
## Table of Contents
- [NoBrokerage Chatbot](#nobrokerage-chatbot)
- [Table of Contents](#table-of-contents)
- [Project Overview](#project-overview)
- [Features](#features)
- [Project Structure](#project-structure)
- [Example Queries the Chatbot Can Handle](#example-queries-the-chatbot-can-handle)
- [How to Run Locally](#how-to-run-locally)
- [1. Clone the Repository](#1-clone-the-repository)
- [2. Create Virtual Environment](#2-create-virtual-environment)
- [3. Install Dependencies](#3-install-dependencies)
- [4. Set Up Environment Variables](#4-set-up-environment-variables)
- [5. Run the FastAPI Server](#5-run-the-fastapi-server)
- [Tech Stack](#tech-stack)
- [Features](#features-1)
- [Deployment Ready](#deployment-ready)
- [👨💻 Author](#-author)
---
## Project Overview
NoBrokerage Chatbot allows users to query property listings by specifying filters like **city, BHK, budget, status, locality**, and returns **summary text** and **cards** with property details.
- **Semantic search**: FAISS vectorstore with HuggingFace embeddings for similarity search.
- **Deterministic filters**: Apply structured metadata filters for city, BHK, budget, status, and locality.
- **LLM summarization**: Groq LLM produces grounded summaries and card outputs strictly from filtered property records.
- **Deployment-ready**: Can run via CLI, FastAPI, Docker, or Hugging Face Spaces.
---
## Features
- Parse natural language queries for:
- Budget (₹, Cr, Lakh)
- BHK
- City
- Property status (Ready to move / Under construction)
- Locality or project
- FAISS similarity search over property embeddings
- Deterministic filtering of search results
- Generate structured JSON output with:
- `summary` (text summary)
- `cards` (detailed property info)
- FastAPI backend with `/chat` endpoint
- Dockerized for easy deployment
- Compatible with Hugging Face Spaces
---
## Project Structure
```bash
NOBROKERAGE/
├── backend/
│ ├── api.py
│
├── data/
├── database/
├── frontend/
├── processed_data/
├── src/
│ └── chatbot.py
├── subha/
├── vectorstore/
│ └── index.faiss
├── .env
├── .gitignore
├── Dockerfile
├── README.md
└── requirements.txt
```
---
## Example Queries the Chatbot Can Handle
The chatbot can intelligently respond to natural language queries like:
- " Find 2BHK apartments in Chembur "
- " 3BHK flat in Pune under ₹1.2 Cr "
- " Under-construction 3BHK in Mumbai "
- " 2bhk flat in pune "
- " 3bhk in Mumbai "
It uses:
- **FAISS** to find the most relevant property documents.
- **LangChain + Groq LLM** (`llama-3.1-8b-instant`) to summarize matching results.
- **Structured filters** for city, budget, BHK, locality, and status.
---
## How to Run Locally
### 1. Clone the Repository
```bash
git clone https://github.com/yourusername/nobrokerage.git
cd nobrokerage
```
### 2. Create Virtual Environment
```bash
python -m venv venv
venv\Scripts\activate # on Windows
# OR
source venv/bin/activate # on Mac/Linux
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Set Up Environment Variables
```bash
GROQ_API_KEY=your_groq_api_key_here
```
### 5. Run the FastAPI Server
```bash
cd backend
uvicorn api:app --reload
```
---
## Tech Stack
- **FastAPI** — Backend API framework
- **Frontend** - index.html, style.css, script.js
- **LangChain** — LLM orchestration
- **Groq LLM (llama-3.1-8b-instant)** — Summarization & reasoning
- **FAISS** — Semantic vector search
- **HuggingFace Sentence Transformer** — Embeddings
- **Docker** — Containerization
- **Python 3.11**
---
## Features
✅ Semantic property search using FAISS
✅ Intelligent summaries and cards generated by Groq LLM
✅ Handles filters like city, budget, BHK, and project status
✅ Ready for Hugging Face Spaces or cloud deployment
✅ Modular architecture (backend + src separation)
---
## Deployment Ready
This backend is designed to work seamlessly with **Docker** and can deploy directly to **Hugging Face Spaces**.
Make sure `vectorstore/` and `.env` are included in your project before building the Docker image.
---
## 👨💻 Author
**Subhakanta Rath**
🎓 MSc AI & ML — IIIT Lucknow
💡 Focused on ML, Data Engineering & Agentic AI Systems
📍 Lucknow, India
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