All digitalized information is collected in a specialized database, where it is grouped by subject area.
Posted: Sat Feb 22, 2025 9:08 am
The organization has accumulated a significant volume of methodological materials and manuals that require systematization and convenient access for teachers. At the same time, there is a clear request from the center's clients for support and service after the implementation of educational products. Finding a solution To solve the problems of the educational center, the option of creating a smart chatbot was considered, which would help the center’s teacher-clients in planning lessons and providing information.
For the basis of the chatbot, our specialists suggested using the center’s educational and methodological materials with processing according to the following scheme. The materials are broken down into parts that data scientists call chunks. Each such “piece” of text is converted into a numerical vector that reflects its meaning.
When a user enters a text query, it is also converted into a wuhan mobile phone number list numeric vector. The system compares this vector with the database and finds the most relevant "piece" of data. This information is then added to the user's query and fed into the GPT language model. This way, the model gets not only the user's query, but also additional context, allowing it to provide more accurate and useful answers.
The choice of this technology was due to its ability to efficiently process large volumes of data and provide relevant answers to user queries. With its help, it was possible to implement the following chatbot use cases.
For the basis of the chatbot, our specialists suggested using the center’s educational and methodological materials with processing according to the following scheme. The materials are broken down into parts that data scientists call chunks. Each such “piece” of text is converted into a numerical vector that reflects its meaning.
When a user enters a text query, it is also converted into a wuhan mobile phone number list numeric vector. The system compares this vector with the database and finds the most relevant "piece" of data. This information is then added to the user's query and fed into the GPT language model. This way, the model gets not only the user's query, but also additional context, allowing it to provide more accurate and useful answers.
The choice of this technology was due to its ability to efficiently process large volumes of data and provide relevant answers to user queries. With its help, it was possible to implement the following chatbot use cases.