Awesome-Moviate

Category: Open-source
Slug: awesome-moviate
Brand: weaviate
Source: https://github.com/weaviate-tutorials/awesome-moviate

Overview

Awesome-Moviate is an open-source demo of a movie search and recommendation engine. It showcases hybrid retrieval for media content by combining:

• BM25 keyword search
• Semantic vector search
• Hybrid search

It uses Weaviate as the underlying vector database.

Features

• Movie search demo UI for exploring movie data and recommendations.
• Hybrid retrieval pipeline that blends:
  • BM25 keyword-based search
  • Vector-based semantic search
  • Combined hybrid search scoring.
• Weaviate integration as the vector database backend.
• Data loading utilities (e.g., add_data.py) to ingest movie datasets into Weaviate.
• Predefined query logic (e.g., queries.js) demonstrating different search modes.
• Web application backend and routing (e.g., index.js) for serving the search experience.
• Frontend views (in the views directory) for displaying search results and movie details.
• Containerized setup with Docker via docker-compose.yml to run Weaviate and the app locally.
• Python and Node.js environment definitions via requirements.txt and package.json for reproducible setup.
• Example media asset (awesome-moviate-demo.gif) demonstrating the application behavior.

Tech Stack

• Weaviate (vector database)
• BM25 keyword search
• Semantic vector search
• Node.js (server, queries, app logic)
• Python (data ingestion scripts)
• Docker & Docker Compose

Pricing

• Not applicable — Awesome-Moviate is an open-source demo project.