For search and generative AI projects, the Search & Generative AI Application is composed of a collection of technology-independent components. These can help improve search & generative AI development for repeatable use cases, accelerate project timelines, and reduce overall project costs.
The framework provides a REST service created in Python (GAIA API) which is well documented with Swagger. It has a pipeline architecture optimized for search and generative AI applications which makes it easy to customize functionality to handle complex generative AI requests through the addition or customization of individual stages.
The Search & Gen-AI user interface is the accompanying UI for the framework, made in React and having common functionality found in many search UIs like dynamic fields, facets, filtering, pagination, sorting, highlighting, type ahead, search analytics. etc. It also contains many useful user interface components for generative AI, such as content comparison, semantic search, question-answer, and an assistant (e.g. chat-bot like) interactive dialog interface.
Use Cases
It will flatten the user interface
Computers will be expected to do much more complex, end-to-end tasks
The search box now becomes the single-most critical user interface component
Expectations on language understanding will grow dramatically
Click here to see available Generative AI components
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GAIA although it is a very complete template, it is NOT a final product that solves a specific problem. Its main objective is to speed up the initial process of creating an application. The code base is maintained, enhanced and delivered with multiple out of the box functionality, but it always requires initial configuration and probably customization. |
GAIA API is the new successor of the Enterprise Search UI, an API framework built with Python 3.11+
The key features are:
Truly Engine Agnostic: Add new Non-SQL engines without disrupting the rest of the code
UI Independent: Worked without an UI, and generic enough to adapt one to it
Pipeline Framework: Execute complex process of functional modules, editable on runtime
True Http Endpoints: Enable E2E management of HTTP request for custom process
Built-In Documentation: API via Swagger and configuration data via Pydantic
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