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.
Use Cases
Vector Pipelines
Ingest data from any data source, convert to embeddings vectors and perform vector operations, in either manual or automatic ways.
Similarity search
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.
Say goodbye to complex MLOps
Chat with us to see the platform live and discover how we can help simplify your ML journey.
