- Dynamic Queries: The query value can be dynamically obtained from sources such as user input or form inputs.
- Collection-Based Search: Specific searches can be performed in separate collections rather than in the same data set.
- Using RAG Search, you can develop a question-answer assistant that works on specific documents or sources. This assistant answers the user's questions based on the given sources.
Technical Risks
- Incorrect Search Type: If the wrong Search Type is selected, appropriate results may not be returned.
- Missing Query Value: Empty queries may return errors or missing data.
- If RAG Search is to be used, a valid OpenAI configuration must be defined in the system.
- If an OpenAI-based model type is to be used, the settings for the relevant model (API Key, model name, etc.) must be specified in the configuration.
Search Action allows 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 to user queries based on relevant documents. 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.
