Bulk Operations

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Bulk Upload Vectors to a Qdrant Collection

Time: 20 min	Level: Intermediate

Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.

The first important detail about data uploading is that the bottleneck is usually located on the client side, not on the server side. This means that if you are uploading a large dataset, you should prefer a high-performance client library.

Async API

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Build High-Throughput Applications with Qdrant’s Async API

Time: 25 min	Level: Intermediate

Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI have embraced this new paradigm, but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK provides an async client next to its synchronous counterpart.

Databases are often launched as separate services and are accessed via a network. All the interactions with them are IO-bound and can be performed asynchronously so as not to waste time actively waiting for a server response. In Python, this is achieved by using async/await syntax. That lets the interpreter switch to another task while waiting for a response from the server.

Develop & Implement on Qdrant - Vector Search Engine

Bulk Operations

Bulk Upload Vectors to a Qdrant Collection

Async API

Build High-Throughput Applications with Qdrant’s Async API