General Introduction
Vectorize is a data vectorization-focused platform designed to transform unstructured data into optimized vector indexes to support generative AI applications. Through its advanced evaluation engine, Vectorize identifies the most effective data vectorization strategies, helps users rapidly build and deploy real-time RAG (Retrieval-Augmented Generation) pipelines, keeps vector search indexes up-to-date, and seamlessly integrates with existing vector databases.
Function List
- Data vectorization: Transform unstructured data into optimized vector indexes.
- RAG Evaluation Engine: Automatically identify the most efficient vectorization strategies.
- Real-time RAG pipeline: Rapid deployment and maintenance of real-time RAG pipelines.
- Vector search index: Keep the vector search index up-to-date.
- seamless integration: Seamless integration with existing vector databases.
Using Help
Functional operation flow
- Data upload: After logging in, users can upload unstructured data files (e.g., text, images, etc.) through the platform.
- Selection of quantitative strategies: The platform automatically evaluates and recommends the most suitable data-oriented quantitative strategies, or users can manually select them.
- vectorization: Click the "Start Vectorization" button and the platform will automatically process the data and generate the vector index.
- View & Manage: Users can view the index of processed data vectors on the My Data page and manage and download them.
- RAG pipeline deployment: On the "RAG Pipeline" page, users can quickly deploy a real-time RAG pipeline to ensure real-time and accurate data processing.
- Integration and Applications: Through an API interface, users can seamlessly integrate the generated vector indexes into existing AI applications and databases.
Detailed operating instructions
- Data upload: A wide range of file formats are supported, including text (TXT, PDF), images (JPEG, PNG), etc. When uploading, please make sure the file size does not exceed the platform limit.
- Toward Quantitative Strategy Selection: The platform provides a variety of vectorization algorithms, such as TF-IDF, Word2Vec, BERT and so on. Users can choose the appropriate algorithm according to the data type and application requirements.
- vectorization: The processing time depends on the amount of data and the algorithm chosen. After processing, users can download the vector index file or view it directly on the platform.
- RAG pipeline deploymentThe platform provides a one-click deployment feature that allows users to quickly deploy a RAG pipeline by simply selecting a data source and target application.
- API integration: The platform provides detailed API documentation, which allows users to integrate vector indexing into existing AI applications according to the documentation instructions, enabling real-time processing and analysis of data.
