A demonstration of hybrid search with reranking using Qdrant and BGE-M3 model. A showcase of dense and sparse retrieval combined with ColBERT reranking for optimal search results

Extract Molecular SMILES embeddings from language models pre-trained with various objectives architectures.

Sparse and dense vectors for use in high dimensional vector spaces

Project-aware collection management based on Qdrant, including a Rust MCP, daemon and CLI: hybrid semantic, pattern and full-text (FTS5) search into single or cross-concerns collection. Dedicated collections for knowledge library, LLM behavioral rules, and an LLM scratchpad

COVID-19 Dataset Challenge

AI-Powered Knowledge Retrieval System Using RAG

Build a complete recommendation system using Qdrant’s Universal Query API with dense, sparse, and ColBERT multivectors in one request.

Densim is a library for efficient similarity search and clustering of dense vectors, which are numerical representations of data such as images, text, or audio.