The AI-powered 'In app search' functionality in Deskhero utilizes advanced technologies to deliver accurate and relevant search results for rehabilitation centers. Here's how it works:
Use case 1
Data Embeddings and Storage - Each ticket resolution, knowledge base article, and scraped website data are sent to the OpenAI Embeddings API to generate embeddings vectors. These vectors capture the semantic meaning and context of the data.
Use case 2
Vector Database - The generated embeddings vectors are stored in a vector database, allowing for efficient and fast retrieval during searches.
Use case 3
Query Processing and ChatGPT Integration - When a search query is entered through the search bar or a new ticket is created, the query is run against the OpenAI Embeddings API to generate a corresponding embeddings vector. This vector is then used to search the vector database for similar and relevant data.
Use case 4
Use case 4: ChatGPT Answer Generation - The relevant data is passed to ChatGPT along with the original question. ChatGPT utilizes the attached data to generate an answer based on the context and provides it as a search response or suggested reply for tickets.
Use case 5
Use case 5: Seamless Integration - The entire process happens seamlessly within Deskhero, allowing rehabilitation centers to harness the power of AI without any additional complexity or technical expertise.
