Open the project you will be working on from the Apps screen.
Go to the UI Design module and select the relevant screen.
2. Add Action
Open the + ADD ACTION menu in the Properties panel on the right side.
From the + ADD ACTION menu, add the Searching> Search action according to the trigger event you want (Initial Actions, OnClick, OnBlur, etc.).
3. Configure Action Parameters
Search Type
Specifies the search type. One of the three options must be selected:
Vector Search: Vector Search converts the user's query and documents into multidimensional vectors and finds the closest content based on semantic similarity. With this method, even if the words used in the query do not appear in the document, semantically similar content can be identified.
Example: The query “how to improve customer satisfaction” may return a document titled “ways to improve user experience.”
Exact Vector Search: Exact Vector Search works similarly to Vector Search, but applies stricter threshold values to matches between the query and the document in order to increase accuracy. This ensures that only documents with very close (almost identical) semantic matches are returned as results.
Preferred for search scenarios that require greater precision and low error tolerance.
RAG Search (Retrieval-Augmented Generation): Finds the documents closest to the user's query using vector search and transfers the documents to the artificial intelligence model to generate a response in natural language.
Example: Query: “What is the old name of Izmir?”
Optional Parameter: Session ID
The user can add a Session ID parameter.
When Session ID is added: RAG Search results on Qdrant are associated with the Context information belonging to that session. Thus, questions asked in the same session are answered taking into account the context of previous messages.
When Session ID is not added: Message history is not kept. Each query sent is processed independently and only a response specific to that query is returned.
Both the platform and on-premise have been tested with artificial intelligence models (Qwen 2.5, Mistral, Deepseek, Llama).
The user can enter any text, number, or other type of value for the Session ID; there are no restrictions.
Query
The textual expression to be searched for.
Example: “Where will the technology conference taking place on September 18 in Izmir be held?”
Limit
The maximum number of results to be returned.
Applies only to Vector Search and Exact Vector Search.
If not specified, the default is 5 results.
Collection Name (Optional)
The name of the specific collection to search.
If specified, the search is performed only in this collection.
If not specified, the search is performed in all collections.
