🧭 Qdrant Flashcards
📘 Introduction to Qdrant
Qdrant is an open-source vector database. It helps you search embeddings quickly and at scale. As a result, it fits AI search, RAG, and recommendation tasks very well. In this guide, you will find simple flashcards, practical tips, and trusted resources.
To make things easy, we use short sentences and plain language. In addition, we add examples and transitions, so you can follow the ideas step by step.
📚 Qdrant Flashcards
📦 What is Qdrant?
It is a vector database for fast similarity search with embeddings. Therefore, it is a strong choice for modern AI apps.
🧠 What is it used for?
Use it for AI search, recommendations, RAG pipelines, and semantic search. For example, you can rank documents by meaning, not only by keywords.
📐 How are vectors stored?
Vectors are grouped into collections. In addition, each vector can include a payload (metadata) for simple filters and advanced queries.
⚡ Which similarity metrics exist?
Cosine, dot product, and Euclidean distance. As a result, you can match items in the way your model expects.
🧪 Does it support filtering?
Yes. You can combine vector search with payload filters. Consequently, you reduce latency and improve relevance.
🔌 What client options exist?
There are official Python and TypeScript SDKs, plus a REST API. Therefore, you can integrate from almost any stack.
🔄 What is a payload?
It is metadata stored with a vector. For example, you can store language or topic and then filter on it.
🚀 Can it be self-hosted?
Yes. You can run it with Docker or install it directly. However, a managed cloud option is available if you prefer less ops work.
📈 Production-ready?
It supports scale, high availability, and monitoring. Therefore, teams often adopt it for production systems.
🔗 LangChain integration?
Yes. You can use the vector store wrapper to plug into RAG pipelines quickly. In addition, many tutorials show end-to-end builds.
💡 Tips for Better Retrieval
- Dimension discipline: Keep embedding sizes consistent. Consequently, you avoid silent errors.
- Filter first: Apply payload filters to narrow the set. As a result, vector search becomes faster.
- Index choices: Tune HNSW and quantization. For example, raise recall for research and raise speed for production.
- Payload schema: Plan keys like
type,language, andtopic. In addition, document them for your team. - Ingestion: Batch upserts and use caching for hot queries. Therefore, throughput and latency both improve.
🔗 Resources and Further Reading
- Official docs: Qdrant Documentation
- On our site: Uplatz Blog – AI & Data Guides