Product Catalog Agent Backend

A backend agentic system for managing product catalog data (offering, specification, characteristic, price) using MongoDB and LangGraph.

Features

• Upsert, filter, and list tools for all product catalog entities
• MongoDB Atlas or local MongoDB support
• LLM agent integration (Ollama, LangGraph)

Prerequisites

• Python 3.10+
• Ollama (for local LLMs)
• MongoDB Atlas account or local MongoDB instance

Setup

1. Install dependencies with uv:

   uv sync

2. Copy the example environment file and edit as needed:

   cp .env.example .env

   • Set MONGODB_URI and MONGODB_DB_NAME for your MongoDB instance (Atlas or local)
   • Set MODEL_NAME for your LLM (default: qwen3:1.7b)

Running the Project

Run:

./run.sh

This script loads environment variables, starts Ollama and the LLM model, and launches the LangGraph dev server.

Notes

• Collections are auto-created in MongoDB: product_offering, product_specification, product_characteristic, product_price
• You can use MongoDB Atlas (cloud) or a local MongoDB instance
• All agent tools are available via the LangGraph agent

Troubleshooting

• Ensure MongoDB is accessible from your machine (check IP whitelist for Atlas)
• If Ollama or the LLM model fails to start, check your model name and Ollama installation

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For more details, see the code and comments in each module.