Description

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/qdrant/qdrant/raw/master/docs/logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/qdrant/qdrant/raw/master/docs/logo-light.svg"> <img height="100" alt="Qdrant" src="https://github.com/qdrant/qdrant/raw/master/docs/logo.svg"> </picture> </p> <p align="center"> <b>Python Client library for the <a href="https://github.com/qdrant/qdrant">Qdrant</a> vector search engine.</b> </p> <p align=center> <a href="https://pypi.org/project/qdrant-client/"><img src="https://badge.fury.io/py/qdrant-client.svg" alt="PyPI version" height="18"></a> <a href="https://api.qdrant.tech/"><img src="https://img.shields.io/badge/Docs-OpenAPI%203.0-success" alt="OpenAPI Docs"></a> <a href="https://github.com/qdrant/qdrant-client/blob/master/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-success" alt="Apache 2.0 License"></a> <a href="https://qdrant.to/discord"><img src="https://img.shields.io/badge/Discord-Qdrant-5865F2.svg?logo=discord" alt="Discord"></a> <a href="https://qdrant.to/roadmap"><img src="https://img.shields.io/badge/Roadmap-2025-bc1439.svg" alt="Roadmap 2025"></a> </p> # Python Qdrant Client Client library and SDK for the [Qdrant](https://github.com/qdrant/qdrant) vector search engine. Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests. Client allows calls for all [Qdrant API methods](https://api.qdrant.tech/) directly. It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading. See [QuickStart](https://qdrant.tech/documentation/quick-start/#create-collection) for more details! ## Installation ``` pip install qdrant-client ``` ## Features - Type hints for all API methods - Local mode - use same API without running server - REST and gRPC support - Minimal dependencies - Extensive Test Coverage ## Local mode <p align="center"> <!--- https://github.com/qdrant/qdrant-client/raw/master --> <img max-height="180" src="https://github.com/qdrant/qdrant-client/raw/master/docs/images/try-develop-deploy.png" alt="Qdrant"> </p> Python client allows you to run same code in local mode without running Qdrant server. Simply initialize client like this: ```python from qdrant_client import QdrantClient client = QdrantClient(":memory:") # or client = QdrantClient(path="path/to/db") # Persists changes to disk ``` Local mode is useful for development, prototyping and testing. - You can use it to run tests in your CI/CD pipeline. - Run it in Colab or Jupyter Notebook, no extra dependencies required. See an [example](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing) - When you need to scale, simply switch to server mode. ## Connect to Qdrant server To connect to Qdrant server, simply specify host and port: ```python from qdrant_client import QdrantClient client = QdrantClient(host="localhost", port=6333) # or client = QdrantClient(url="http://localhost:6333") ``` You can run Qdrant server locally with docker: ```bash docker run -p 6333:6333 qdrant/qdrant:latest ``` See more launch options in [Qdrant repository](https://github.com/qdrant/qdrant#usage). ## Connect to Qdrant cloud You can register and use [Qdrant Cloud](https://cloud.qdrant.io/) to get a free tier account with 1GB RAM. Once you have your cluster and API key, you can connect to it like this: ```python from qdrant_client import QdrantClient qdrant_client = QdrantClient( url="https://xxxxxx-xxxxx-xxxxx-xxxx-xxxxxxxxx.us-east.aws.cloud.qdrant.io:6333", api_key="<your-api-key>", ) ``` ## Inference API Qdrant Client has Inference API that allows to seamlessly create embeddings and use them in Qdrant. Inference API can be used locally with FastEmbed or remotely with models available in Qdrant Cloud. ### Local Inference with FastEmbed ``` pip install qdrant-client[fastembed] ``` FastEmbed is a library for creating fast vector embeddings on CPU. It is based on ONNX Runtime and allows to run inference both on CPU and GPU. Qdrant Client can use FastEmbed to create embeddings and upload them to Qdrant. This allows to simplify API and make it more intuitive. ```python from qdrant_client import QdrantClient, models # running qdrant in local mode suitable for experiments client = QdrantClient(":memory:") # or QdrantClient(path="path/to/db") for local mode and persistent storage model_name = "sentence-transformers/all-MiniLM-L6-v2" payload = [ {"document": "Qdrant has Langchain integrations", "source": "Langchain-docs", }, {"document": "Qdrant also has Llama Index integrations", "source": "LlamaIndex-docs"}, ] docs = [models.Document(text=data["document"], model=model_name) for data in payload] ids = [42, 2] client.create_collection( "demo_collection", vectors_config=models.Vect