Practical overview
When it fits
Use the client when Python code needs to create or query Qdrant collections, or when you want to prototype the same client workflow locally before connecting to a Qdrant server or Cloud cluster.
Who it is for
- Python developers adding Qdrant vector search to an application
- Teams building synchronous or asynchronous integrations with the Qdrant API
- Developers prototyping or testing Qdrant-backed features locally
Documented uses
- Call Qdrant API methods from Python using typed definitions.
- Create and populate collections and query vector-search results.
- Develop or test with local in-memory or disk-backed mode before connecting to a server.
- Create embeddings locally with FastEmbed and upload them to Qdrant.
Requirements & limits
- Local mode can run in memory or persist changes to a local path without a Qdrant server.
- The SDK supports synchronous and asynchronous requests and documents REST and gRPC support.
- FastEmbed inference can run locally; remote inference uses predefined Qdrant Cloud models and is documented as available only on paid plans.
- The
fastembedandfastembed-gpuinstallation extras are mutually exclusive.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Create a local embedding-and-search prototype
Install the client with FastEmbed, create a local collection, upload text documents and query them.
Before you start
- Python environment with pip
- Qdrant Python Client installed with the FastEmbed extra
- No Qdrant server is needed for the documented in-memory local mode
Steps
Install the optional inference dependency with the command below.
pip install qdrant-client[fastembed]Initialize
QdrantClient(":memory:")and choose a model name such assentence-transformers/all-MiniLM-L6-v2.Create a collection using the model’s embedding size and cosine distance.
Upload documents as
models.Documentitems with IDs and payloads, then query the collection with a document.
Expected outcome
A local prototype that creates embeddings, stores them in a collection and returns points for a document query.
Guide limitations
- The in-memory example does not persist changes; use a local database path when persistence is needed.
- The documented GPU extra is mutually exclusive with the regular FastEmbed extra.
Connect the client to a Qdrant server
Configure a Python client to address a Qdrant server using its host and port or URL.
Before you start
- Qdrant Python Client installed
- A Qdrant server address
Steps
Import
QdrantClientfromqdrant_client.Initialize it with
host="localhost", port=6333, or provide the server URL.
Expected outcome
A client instance configured to make requests to the specified Qdrant server.
Guide limitations
- The guide covers client configuration; it does not demonstrate starting or operating a server.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- GitHub - qdrant/qdrant-client: Python client for Qdrant vector search engine · GitHub · Official homepage title · retained in its source language