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ConvApparel Conversational Product Search

FastAPI demo for asking shopping questions against ConvApparel apparel products through Manticore CALL CHAT. User queries go directly to the Manticore chat model; the app exposes only the chat endpoint.

The project entry point is docker-compose.yml. It runs the app and Manticore on the same Compose network, which matters because the app connects to Manticore through the Compose service name manticore.

Dataset

This project uses the google/ConvApparel dataset from Hugging Face: conversations between shoppers and an apparel recommendation assistant. The dataset contains product recommendations for footwear, outerwear, tops, and bottoms.

The preparation script builds a deduplicated product corpus from recommendation items:

  • item_id
  • title
  • description
  • features
  • image_url
  • inferred apparel category

ConvApparel v1 currently yields 82,524 unique product IDs from 175,751 recommendation occurrences.

The repository includes a split SQL dump with product rows and precomputed embedding_vector values:

  • dumps/convapparel_products_with_embeddings.sql.xz.part-*

Restoring this dump is much faster than downloading ConvApparel and waiting for Manticore to calculate 82k embeddings during setup.

Raw downloaded data and locally generated non-embedding SQL are intentionally ignored by git:

  • data/raw/ConvApparel.zip
  • dumps/convapparel_products.sql.gz

Quick Start

Create the environment file and set your OpenRouter key:

cp .env.example .env

Edit .env:

OPENROUTER_API_KEY=

Initialize Manticore from the checked-in precomputed dump and recreate the chat model:

./scripts/init_manticore.sh

Start the API:

docker compose up --build app

Open: http://127.0.0.1:8000

Environment

Set in .env:

OPENROUTER_API_KEY=

The key is passed into the manticore service and used when creating the Manticore chat model.

API

  • POST /api/assistant/chat
    • Body: message, optional conversation_uuid
    • Response includes Manticore response_with_refs when available, plus sources; the UI renders inline reference markers with hover previews and opens the matching product modal on click.

Example:

curl -X POST "http://127.0.0.1:8000/api/assistant/chat" \
  -H "Content-Type: application/json" \
  -d '{"message":"I need waterproof black running shoes for jogging"}'

Manticore Initialization

The Quick Start runs ./scripts/init_manticore.sh once before starting the app. That script starts the manticore service, removes old orphan services, waits for the MySQL protocol, drops any existing convapparel_products table and assistant chat model, restores dumps/convapparel_products_with_embeddings.sql.xz.part-*, and applies dumps/create_chat_model.sql.

Run the initialization script again when you need to reset the convapparel_products table or recreate the assistant chat model. Edit dumps/create_chat_model.sql if the chat model definition needs to change.

Local Python Development

The Compose setup is the supported way to run the full app because app.py connects to Manticore at http://manticore:9308. If you run Uvicorn directly on the host, that service name will not resolve unless you provide an equivalent local hostname or adjust the code/configuration for local development.

For app-only iteration after handling Manticore connectivity:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app:app --reload --port 8000