QDivZero Retrieval combines a vector database, embeddings, metadata, and semantic search in one layer to prepare and retrieve the information your applications need. Index your knowledge once and use it from RAG, AI agents, recommendation systems, or semantic search engines, without deploying or maintaining your own retrieval infrastructure.
Configure RetrievalRetrievalfor your applications.
Find the relevant information in your data.
QDivZero Retrieval organizes embeddings, metadata, and relationships so your applications can search for and retrieve the information they need. Build semantic search, RAG, recommendation systems, and AI agents without managing a vector database or maintaining your own retrieval infrastructure.
HOW IT WORKS
RETRIEVAL IN ACTION
From words to meaning.
Build semantic search with embeddings to find related documents, products, or content even when the query does not use the same words. QDivZero compares meaning and context through vector search to retrieve the most relevant results.
Searchcomfortable shoes for rainy days
Full query: comfortable shoes for rainy days
rainwaterprooffootwear
Match foundRelated images found
RETRIEVAL IN YOUR APPLICATIONS
Build search, discovery, and recommendations on your data.
Use QDivZero Retrieval to build semantic search, find images and content through visual similarity, or create personalized recommendation systems. Connect your data and use embeddings, metadata, and contextual signals to retrieve the most relevant information for every use case.
Explore the documentationWhat QDivZero Retrieval includes
Integrated embeddings
Generate embeddings for text, images, and other content and use them directly in Retrieval to index, search, and retrieve information within the same workflow.
Managed vector database
Store and organize vectors, indexes, and metadata without deploying or maintaining your own vector database infrastructure.
Semantic search
Find documents, products, or content by meaning and context, even when the query does not use exactly the same words as the indexed information.
Visual search
Find visually similar images, products, and content from a reference image using embeddings and similarity search.
Personalized recommendations
Combine embeddings, metadata, and behavioral signals to recommend relevant products, content, or information based on similarity, context, and each user's interactions.
Filters and metadata
Combine vector search with filters and metadata to narrow results and control which information can be retrieved in each query.
Bulk indexing
Process large volumes of documents, catalogs, or content asynchronously and prepare the data so it is available in Retrieval.
Data updates
Add, modify, or delete information as your data changes and keep vector indexes up to date without rebuilding your entire system.
Retrieval API
Access your indexes and results through an API to integrate search, RAG, AI agents, and recommendation systems directly into your applications.
Frequently asked questions about QDivZero Retrieval
Answers about pricing, vector databases, embeddings, semantic search, RAG, visual search, and recommendation systems for AI applications.
What is Retrieval and what is it used for in an AI application?
Retrieval prepares, indexes, and finds the most relevant information in your data for each query. With QDivZero, you can use it in semantic search, RAG systems, AI agents, and recommendations without deploying or maintaining your own vector database.
How much does QDivZero Retrieval cost?
QDivZero Retrieval is billed according to storage usage, at a per-GB-hour rate listed on the pricing page. Embedding generation is billed at the rate of the model you choose, so the total cost depends on data volume, the model, and how long you keep your indexes active.
How does semantic search with embeddings work?
Semantic search converts the query and indexed content into embeddings to compare meaning and context through vector search. It finds related documents, products, or information even when they do not use exactly the same words.
Does QDivZero Retrieval include a managed vector database?
Yes. QDivZero manages vector storage, indexes, metadata, and result retrieval. You can build applications on a vector database without deploying, scaling, or maintaining that infrastructure separately.
What types of embeddings can I generate with QDivZero Retrieval?
You can generate embeddings for text, images, and other content with compatible models available in QDivZero. You can then use them directly in Retrieval to index data, search by similarity, and retrieve information.
How can I use QDivZero Retrieval to build a RAG system?
Retrieval finds the most relevant fragments in your documents and supplies them as context before the model is invoked. This lets you build RAG systems and AI agents connected to proprietary, current, and controlled information through an API.
How can I build a recommendation system with my own data?
QDivZero Retrieval combines embeddings, metadata, vector similarity, and behavioral signals to relate products, content, or information. You can rank recommendations according to each user's context, interests, and interactions.
How does visual search by image similarity work?
Visual search converts a reference image into embeddings and compares its similarity with indexed images or products. This finds visually related content without relying on exact names, tags, or descriptions.
Can I index, update, and delete large volumes of data?
Yes. You can process documents, catalogs, or content through asynchronous bulk indexing and add, modify, or delete data later. QDivZero keeps vector indexes up to date without requiring you to rebuild the entire system.
How does the Retrieval API integrate with AI models and applications?
The Retrieval API lets you query indexes, apply filters and metadata, and deliver results to your applications. The same indexed knowledge can be reused with different AI models, agents, RAG systems, or search experiences.
What are the benefits of QDivZero Retrieval in production?
QDivZero Retrieval brings embeddings, vectors, indexes, metadata, and search together in a managed layer. It reduces the infrastructure your team operates, simplifies updates, and lets multiple applications reuse the same knowledge through an API.
Do your applications find what they need in your data?
Connect your information and build semantic search, RAG, agents, and recommendation systems on a managed retrieval layer.