Search

22/8/25

Kuika's Search action enables query-based searches in databases or file collections. With three different search types, it can return the most relevant results for user queries. It is ideal for providing fast access to information in AI-powered applications.

Technical Features

Search Type Selection: Works with Vector Search, Exact Vector Search, and RAG Search types.
Limit Support: The maximum number of results to be returned can be specified for Vector and Exact Vector searches.
Collection Name (Optional): Allows searching only in a specific collection.
Web & Mobile Application Support: The action can be used in both web and mobile applications.
OpenAI Integration: Generates natural language responses with RAG Search.

Use Cases and Sample Configurations

Vector Search

Purpose: Find documents containing the word “Izmir” or similar expressions
Search Type: Vector Search
Query: “izmir”
Limit: 5
Results are sorted from closest to furthest.

Exact Vector Search

Purpose: Find documents that contain the word ‘İzMir’ exactly as it appears
Search Type: Exact Vector Search
Query: “İzMir”
Limit: 5
Case sensitivity must be taken into account.

RAG Search (Retrieval-Augmented Generation)

Purpose: To obtain the most appropriate answer to a specific question, supported by documents
Search Type: RAG Search
Query: “What is the old name of İzmir?”
This mode uses natural language processing (NLP) with OpenAI integration to generate responses.

Search Action Advanced Customisations

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.