Research real estate properties with RAG-backed market analysis

Guide an agent through property search, buyer/renter preference capture, and evidence-backed shortlist notes from structured listing data.

Prerequisites

Python 3.11+, Streamlit, LangChain, OpenAI GPT or Llama model access, Pandas, FastEmbed, DocArrayInMemorySearch or ChromaDB, Poetry

Installation

Use the upstream install or setup path that matches your environment:

• git clone https://github.com/AleksNeStu/ai-real-estate-assistant.git
• docker compose -f deploy/compose/docker-compose.yml up --build
• uv pip install -e ".[dev]" && python -m uvicorn api.main:app --reload --port 8000

Requirements and caveats from upstream:

• 
• Note: The demo uses simulated AI responses for instant exploration. Production deployment requires API keys.
• | Lines of Code | 60,000+ (27K Python + 34K TypeScript) |

Basic usage or getting-started notes:

• Quick Start

• 🚀 Quick Start

• cp deploy/compose/.env.example deploy/compose/.env

• Source: https://github.com/AleksNeStu/ai-real-estate-assistant

• Extracted from upstream docs: https://raw.githubusercontent.com/AleksNeStu/ai-real-estate-assistant/HEAD/README.md

Documentation

• https://github.com/AleksNeStu/ai-real-estate-assistant/blob/main/README.md

Source

• Agent Skill Exchange