Summary
This video introduces Vectorize, a tool designed to efficiently build scalable AI pipelines for production-ready applications. It showcases Vectorize Iris, a feature enhancing document parsing capabilities, and the new update that converts unstructured data into high-performance extractable formats seamlessly. Viewers are guided on efficiently building rag apps, setting up pipelines, integrating platforms like OpenAI, and utilizing tools such as Google Vertex AI for effective data processing and extraction testing. The step-by-step process of creating, testing, and refining pipelines using Vectorize is also demonstrated, emphasizing ease of use and speed in building AI applications.
Introduction to Vectorize
Introducing the tool Vectorize designed to solve the problem of building scalable and production-ready AI pipelines quickly and efficiently.
Vectorize Functionality
Exploring what Vectorize does, its unique features, and how it helps Engineers in building AI apps with ease and speed.
Vectorize Iris
A deep dive into Vectorize Iris, a feature that enhances document parsing capabilities to a new level.
Vectorized Update
An overview of the new Vectorize update that converts unstructured data into high-performance extractable text, images, and tables seamlessly.
Building Rag Apps with Vectorize
Guidance on building rag apps efficiently using Vectorize, showcasing how to set up pipelines and applications seamlessly.
Creating Account and Dashboard
Instructions on creating an account, accessing the Vectorize dashboard, and inviting team users to collaborate on pipelines.
Pipeline Editor and Integration
Details on using the pipeline editor, selecting source data, integrating platforms like OpenAI, and building AI pipelines effectively.
Data Source Configuration
Configuring data sources, selecting database types, and integrating tools like Google Vertex AI to process and output data efficiently.
Scheduling and Execution
Explaining how to schedule and trigger data pipelines manually or through automated scheduling for flexibility in data processing.
Rag Pipeline Creation
Step-by-step process of creating a rag pipeline, loading data, backfilling, and uploading sources to the Vector database.
Testing and Sandbox Usage
Utilizing the extraction tester to test documents, upload various data types, and use the rag sandbox for testing and refinement.
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