Awesome-Moviate
Awesome-Moviate is a movie search and recommendation engine demo that combines BM25 keyword search, semantic vector search, and hybrid search using Weaviate as the underlying vector database, serving as a practical example of hybrid retrieval for media content.
About this tool
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
viewsdirectory) for displaying search results and movie details. - Containerized setup with Docker via
docker-compose.ymlto run Weaviate and the app locally. - Python and Node.js environment definitions via
requirements.txtandpackage.jsonfor 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.
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)Healthsearch is an open-source demo application that uses Weaviate as a vector database to retrieve supplement products based on user-written reviews and queries, illustrating real-world semantic product search over vector embeddings.
Bleve is an open-source search library with experimental support for vector search, enabling hybrid search and retrieval in applications.
Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as RAG, recommendation systems, advanced semantic search, anomaly detection, and AI agents. It provides scalable storage and retrieval of vector embeddings with features like filtering, hybrid search, and production‑grade APIs for integrating with machine learning workloads.
Elasticsearch is a distributed search engine supporting various data types, including vectors, and provides scalable vector search capabilities, making it a popular choice for modern AI-powered applications. It can be extended with the k-NN plugin to provide scalable vector search using HNSW and Lucene, enabling hybrid semantic and keyword search capabilities.
Orama is a lightweight search engine that supports vector and hybrid search functionalities, suitable for browser, server, or edge environments.
Solr is a mature open-source search engine that has incorporated vector search capabilities, making it relevant for enterprises looking to implement vector-based search alongside traditional keyword search.