Practical overview
When it fits
Use it to study or adapt a notebook-based hybrid retrieval workflow when you want semantic and lexical matching alongside token-level vectors.
Who it is for
- Developers exploring hybrid semantic and keyword search.
- Teams prototyping product-catalog or document retrieval with vector search.
- RAG builders seeking an example retrieval component for a separate language-model workflow.
Documented uses
- Load product records from a CSV and format their text for embedding.
- Generate dense, sparse and ColBERT token vectors with BGE-M3.
- Configure a Qdrant collection, index records and embeddings, and retrieve matches for embedded queries.
Requirements & limits
The repository is public and lists an Apache-2.0 license. The documented setup requires Python 3.9 or later, Docker for Qdrant, and Jupyter Notebook.
- This is a notebook example rather than a hosted search service or documented API.
- Its example data flow starts with products in a CSV file.
- BGE-M3 produces 1024-dimensional dense vectors as well as sparse and ColBERT token vectors.
- Docker is listed for running Qdrant.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up a Qdrant hybrid-search notebook workflow
Follow the repository’s documented stages for preparing product records and searching their BGE-M3 vectors through Qdrant.
Before you start
- Python 3.9 or later
- Docker for running Qdrant
- Jupyter Notebook
- Product data in a CSV file
Steps
Load product records from a CSV file.
Format product information for embedding.
Generate dense, sparse and ColBERT token vectors with BGE-M3.
Configure a Qdrant collection for hybrid search and index the product data and embeddings.
Embed a query using the same process and retrieve matching records.
Expected outcome
A notebook-based product search workflow that combines semantic, keyword and token-level retrieval representations in Qdrant.
Guide limitations
- The README documents the workflow but does not provide detailed commands or configuration values.
- The repository describes a search example, not a hosted API or a complete downstream RAG application.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- yuniko-software/bge-m3-qdrant-sample · Official homepage title · retained in its source language