Qdrant is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.
project_id
and embedding_model
. You can learn more about how to get your project_id
and API Key from here.
From Qdrant’s side, we need to also define our server URL, in which we will be sending requests to do all sorts of CRUD operations to our vector database. We also need to define our collection name. You can learn more about these concepts from Qdrant’s quick start guide and concepts.
Last but not the least, we also need to get our documents. For simplicity purpose, we define a small list of documents here. But in actual scenarios, this list should be derived from some source (like database or from an API call etc).
A collection is a named set of points (vectors with a payload) among which you can search.If you already have a collection then you can skip this step, otherwise follow the code to create a Qdrant collection. We will be upserting our points in this collection.