Search Action Advanced Customisations

• Dynamic Queries: The query value can be dynamically retrieved from sources such as user input or form inputs.
• Collection-Based Search: You can perform specific searches in separate collections rather than in the same dataset.
• RAG-powered Chatbot: You can create a document-based chatbot using RAG Search.

Technical Risks

• Incorrect Search Type: If the Search Type is selected incorrectly, appropriate results may not be returned.
• Incomplete Query Value: Empty queries may return errors or incomplete data.
• API Definition for RAG: If RAG Search is to be used, the Open AI configuration must be defined in the system.

Search Action enables you to build advanced information search and recommendation systems on the Kuika platform. Thanks to the AI-powered RAG (Retrieval-Augmented Generation) feature, natural language responses can be generated from relevant documents in response to user queries. In addition, Vector Search provides results based on semantic similarities, while methods such as Exact Search support classic, keyword-based information access. These classic methods are used to capture cases where specific words or phrases appear directly in documents. With all these capabilities, you can create smart and contextual search solutions that enrich the user experience.