Articles
Practical Examples

Practical Examples

Building blocks and reference implementations to help you get started with Qdrant. Learn how to use Qdrant to solve real-world problems and build the next generation of AI applications.

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product

Part 5 of the sparse embeddings series. We packaged the entire training pipeline from Parts 1-4 into an open-source CLI and web dashboard that fine-tunes SPLADE models for any product catalog in minutes.

Thierry Damiba

March 09, 2026

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization

Part 4 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Test cross-domain generalization, train a multi-domain model, and decide when to specialize vs generalize.

Thierry Damiba

March 09, 2026

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives

Part 3 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Index products in Qdrant, run retrieval benchmarks, and implement ANCE hard negative mining for a 28% improvement over BM25.

Thierry Damiba

March 09, 2026

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 2: Training SPLADE on Modal

Part 2 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Build a training pipeline on Modal with persistent checkpoints, SpladeLoss, and hyperparameter sweeps.

Thierry Damiba

March 09, 2026

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 1: Why Sparse Embeddings Beat BM25

Part 1 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Learn how sparse embeddings outperform BM25 and dense models for product search, how SPLADE works, and why Qdrant's native sparse vector support matters.

Thierry Damiba

March 09, 2026

Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization

Explore how Qdrant's Binary Quantization can significantly improve the efficiency and performance of OpenAI's Ada-003 embeddings. Learn best practices for real-time search applications.

Nirant Kasliwal

February 21, 2024

Food Discovery Demo

Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.

Kacper Łukawski

September 05, 2023

Semantic Search As You Type

To show off Qdrant's performance, we show how to do a quick search-as-you-type that will come back within a few milliseconds.

Andre Bogus

August 14, 2023

Serverless Semantic Search

Create a serverless semantic search engine using nothing but Qdrant and free cloud services.

Andre Bogus

July 12, 2023

Using LangChain for Question Answering with Qdrant

We combined LangChain, a pre-trained LLM from OpenAI, SentenceTransformers & Qdrant to create a question answering system with just a few lines of code. Learn more!

Kacper Łukawski

January 31, 2023

Question Answering as a Service with Cohere and Qdrant

End-to-end Question Answering system for the biomedical data with SaaS tools: Cohere co.embed API and Qdrant

Kacper Łukawski

November 29, 2022

Q&A with Similarity Learning

A complete guide to building a Q&A system using Quaterion and SentenceTransformers.

George Panchuk

June 28, 2022

Articles

Practical Examples

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 2: Training SPLADE on Modal

Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 1: Why Sparse Embeddings Beat BM25

Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization

Food Discovery Demo

Semantic Search As You Type

Serverless Semantic Search

Using LangChain for Question Answering with Qdrant

Question Answering as a Service with Cohere and Qdrant

Q&A with Similarity Learning