BGE-M3 Qdrant sample. Hybrid search & reranking

This repository contains a Jupyter notebook that demonstrates how to build an advanced search system using BGE-M3 and Qdrant.

The key feature of this sample is the use of an all-in-one embedding model (BGE-M3) that generates three types of vectors in a single pass:

• Dense vectors: For semantic similarity (1024 dimensions)
• Sparse vectors: For lexical/keyword matching
• ColBERT token vectors: For fine-grained token-level matching

This multi-vector approach provides superior search quality by combining the strengths of different embedding types within a single model.

Requirements

• Python 3.9+
• Docker (for running Qdrant)
• Jupyter Notebook

How It Works

The system operates in the following steps:

1. Data Loading: Products are loaded from a CSV file
2. Text Formatting: Product information is formatted for embedding
3. Embedding Generation: BGE-M3 generates all three embedding types in one pass
4. Vector Database Setup: Qdrant collection is configured for hybrid search
5. Data Indexing: Product data and embeddings are stored in Qdrant
6. Search: Queries go through the same embedding process and retrieve results