Store embedding vectors at scale and supercharge your ML & AI applicationsGet Started
The Vector Store provides a secure and efficient solution for data ingestion, transformation into embedding vectors, and storage.of your embedding vectors.
Qwak uses scalable embedding models deployed on the platform to convert data into embedding vectors.
Using a simple Python SDK or a REST API, you can access your entire dataset, to search, upsert or delete vectors. Easily create collections and store vectors of any dimension.
Simplified data ingestion, allows you to effortlessly bring your data into the platform. Use automated, scheduled ingestion jobs to bring data from any database (Snowflake, BigQuery, RedShift, etc.) and convert it to vectors.
Ingest data from any data source, convert to embeddings vectors and perform vector operations, in either manual or automatic ways.
Easily find similarities for applications such as recommendation engines to image retrieval, to deliver personalized and relevant experiences to users while maintaining high performance.
RAG -Retrieval Augmented Generation
Combine retrieval and generation components to fetch up-to-date or context-specific data from an external database, enhancing the performance and accuracy of LLMs and text generation tasks.