Créer, héberger et exploiter des solutions IA.

Qdrant

Self-hosted vector database for semantic search and RAG — the missing infrastructure layer for your LLM stack.

Code source

Qdrant is an open-source, high-performance vector database written in Rust. It stores vector embeddings — the numerical representations produced by LLMs and embedding models — and retrieves the most similar ones in milliseconds, even across hundreds of millions of vectors.

Deployed on a ServOrbit VPS, Qdrant becomes the memory layer of your AI stack: pair it with Ollama for local inference, LiteLLM for a unified API gateway, and your own application code to build production-grade RAG pipelines, semantic search engines, recommendation systems or AI chatbots that actually remember context — all on infrastructure you control, at a fixed monthly cost.

Fonctionnalités clés

Sub-millisecond approximate nearest-neighbour search over millions of vectors
Payload filters — combine vector similarity with structured filters (category, date, score) in a single query
HNSW indexing + scalar and product quantisation for fast search on small RAM budgets
Collections and named vectors — store multiple embedding types per document in one collection
REST and gRPC APIs — every popular LLM framework (LangChain, LlamaIndex, Haystack) has a native Qdrant connector
Apache 2.0 — fully auditable, no telemetry, air-gap capable

Quand utiliser cette solution ?

1

RAG pipeline memory

Index your documentation, knowledge base or product catalogue as embeddings and retrieve the most relevant chunks before each LLM call. Pair with Ollama on the same VPS for a fully air-gapped, zero-per-token RAG stack.

2

Semantic search

Replace keyword search with meaning-based retrieval. A query for 'fast payment processing' surfaces results about 'instant transactions' even when those exact words never appear — because Qdrant compares intent, not tokens.

3

Recommendation engine

Represent products, articles or users as embedding vectors and find the nearest neighbours. 'Users who liked this' and 'similar products' become a single nearest-neighbour query with optional payload filters for price or category.

Déployer Qdrant sur votre VPS

Guide optimisé pour les VPS Cloud ServOrbit.

01

Order a VPS

Pick a ServOrbit VPS with at least 2 GB RAM (512 MB covers Qdrant itself; the rest is headroom for your embedding model or co-located services). Ubuntu 22.04, Docker pre-installed by the provisioner.

02

Deploy Qdrant

The provisioner runs: docker run -d -p 6333:6333 -p 6334:6334 -v qdrant_storage:/qdrant/storage --name qdrant --restart always qdrant/qdrant:latest. The REST API is immediately available on port 6333; gRPC on 6334.

03

Create a collection and index vectors

Create a collection with the right vector size for your model (e.g. 1536 for OpenAI text-embedding-3-small, 4096 for mxbai-embed-large via Ollama): curl -X PUT http://:6333/collections/my_docs -H 'Content-Type: application/json' -d '{"vectors":{"size":1536,"distance":"Cosine"}}'. Then upsert your embeddings via the REST API or any Qdrant SDK.

04

Query from your application

Install the Qdrant client (pip install qdrant-client or npm install @qdrant/js-client-rest) and query: pass the embedding of the user's question and get back the top-k most similar document chunks. Feed those chunks as context to your LLM for a grounded, hallucination-resistant answer.

05

Secure the instance

Set QDRANT__SERVICE__API_KEY as an environment variable (restart the container with -e QDRANT__SERVICE__API_KEY=) and put Qdrant behind a reverse proxy (Caddy or Nginx) for HTTPS. Never expose port 6333 directly to the internet without authentication.

Questions fréquentes

Créer, héberger et exploiter des solutions IA.

Activez Qdrant sur votre infrastructure.

VPS Cloud dédié — IPv4 incluse, datacenter européen, support inclus. Vos données ne quittent jamais votre serveur.

Besoin d'aide ?

Parcourez notre centre d'aide et notre FAQ, ou écrivez à notre équipe — support en anglais, français et arabe.

ou nous écrire