Clothing Search Application with LangChain and Ollama

This project is a Python-based application that uses LangChain, LangChain-Ollama, and the Serper API to analyze an image of a clothing item, describe it, generate a search query, and recommend online stores where similar items can be purchased. The application leverages local vision and text language models (via Ollama) and web search capabilities to provide a seamless clothing search experience.

Features

• Image Analysis: Uses a vision model (llava:13b) to describe clothing items in an uploaded image.
• Search Query Generation: Generates concise search queries (up to three words) based on the image description using a text model (qwen2.5).
• Web Search Integration: Fetches relevant search results from the web using the Serper API.
• Store Recommendations: Recommends up to five online stores with links to purchase similar clothing items.
• Workflow Orchestration: Utilizes LangGraph to manage the multi-step process of image processing, query generation, searching, and recommending.

Prerequisites

Before running the application, ensure you have the following:

• Python 3.8+ installed.
• Ollama installed and running locally with the following models:
  • qwen2.5:latest (text model)
  • llava:13b (vision model)
• A Serper API key for web search functionality.
• A GPU is recommended for faster processing with Ollama models.
• An image file (e.g., red_skirt2.jpg) in the project directory for testing.

Installation

1. Clone the Repository:

   git clone https://github.com/armanjscript/Clothing-Search-Application.git
   cd Clothing-Search-Application