Ecosystem of AI Agents

Q2 2025.

This is an intelligent assistant for the admissions committee of the Kazakh-American Free University. It uses a local LLM and RAG system to find relevant answers to user queries.

Below are the main technologies and components that ensure the operation of the university's intelligent agent.

• 1 PostgreSQL database - Used to store embeddings (vectors) of responses split into text chunks;
• 2 Local model Qwen3-Embedding-0.6B - Multilingual model used to generate vector representations (embeddings) of texts;
  • Model context: 32,768 tokens.
• 3 Local model Google/Gemma-4-31B - Multilingual language model with tool_choice support, allowing connection of external tools such as Internet search and the ability to process data from the RAG system;
  • Model context: 7,500 tokens.
• 4 RAG and RAG-Fusion mechanism - Used to combine and rank results from multiple queries and form the most relevant context before generating a response by the model;
• 5 Reranking mechanism using the bge-reranker-v2-m3 model - Used to select the most accurate and relevant answer from multiple found fragments;
• 6 Voice recognition + RVC models - Operates based on the local Vosk model, providing speech-to-text conversion (currently in Russian) and an RVC server for generating human-like voice responses;
• 7 Final result - Passing the final message in the format:
  prompt copy

  {Context}: %Result%.
  {Question}: %Question%.
  {Note}: Give an answer to the applicant.

The agent is available 24/7 on Telegram and helps applicants, parents, and schoolchildren learn everything about admission to our university.

Q2 2025.

This is a library assistant that helps users find books, recommend new items, create tables and compare them, and automatically format bibliographic references according to GOST.

A solution from OpenAI in the form of API access to models is temporarily used. A transition to a local LLM will be made soon. Testing of filling tables on a local LLM is underway.

Below are the main technologies and components that ensure the operation of the university's intelligent agent.

• 1 MySQL database - After the integration of the automated library system KABIS, this agent gained the ability to query the book database to provide answers to user questions;
• 2 Using the RMEL and IPRSmart API - Querying resources to search and retrieve books;
• 3 Model gpt-4o-mini - Multilingual model that uses tool_choice to execute queries to the book database and output results;
  • Model context: 65,536 tokens.
• 4 Final result - The model answers basic questions related to the library. In cases where the user requests any books, the model chooses when to call functions, after which it outputs the final result to the user.

The library assistant is available on the university website and helps students, teachers, and researchers 24/7.

Q4 2025.

This is an AI platform for automated educational course creation, lecture recording with AI avatars and test generation.

Below are the main technologies and components that ensure the operation of the university's intelligent agent.

• 1 Video recording with teacher AI avatars - Built-in system for creating video lessons with realistic AI avatars. Avatars are generated based on photos and audio recordings of real teachers, creating a "live presence" effect and increasing student trust;
• 2 Generation of educational materials - The agent automatically generates lecture texts, presentations, teaching aids and practical assignments for a given discipline. It uses university standard templates and discipline databases;
• 3 Realistic AI avatars - Face generation and speech synchronization models with a high degree of realism (up to 95%) are used. This allows students to perceive content as a live lecture, which increases engagement and trust in the teacher;
• 4 Final result - The agent creates a ready-made multimedia course: includes video lectures, teaching materials, tests and links. The teacher only has to approve and publish the course;
• 5 Test creation - To generate tests in GIFT format and lecture materials, the local model Google/Gemma-4-31B is used
  • Model context: 9,500 tokens.
• 6 Image creation - To generate images, the local model Fooocus and the RTX 5090 32 VRAM graphics card are used.

The AI platform is available 24/7, allowing users to create high-quality videos with virtual avatars, speech synthesis and support for multiple languages, as well as lecture materials and tests at any time.

Q4 2025.

Digital assistant to help international students adapt to the Kazakh-American Free University. Answers questions about accommodation, visa support, academic process and student life.

Below are the main technologies and components that ensure the operation of the university's intelligent agent.

• 1 FastAPI - Web framework for building API and processing user requests;
• 2 Redis - Used for temporary storage of chat history;
• 3 Local model Qwen3-Embedding-0.6B - Creates embeddings and helps the system understand the meaning of queries;
  • Model context: 32,768 tokens.
• 4 Local model Google/Gemma-4-31B - Main language model for generating responses to users;
  • Model context: 7,500 tokens.
• 5 LangChain - Library that ensures interaction between local models and dialogue logic;
• 6 Final result - Personalized response to international students' questions:
  prompt copy

  {Question}: What educational programs are available?
  {Answer}: The following educational programs are available: …
  

The agent is available 24/7 and is hosted on the KAFU AI-Sana platform. Its goal is to facilitate the adaptation process for international students and make communication with the university more convenient and understandable.

Q2 2025.

This is an intelligent assistant for students of the Kazakh-American Free University. It uses a local LLM and RAG system to find relevant answers to user queries.

Below are the main technologies and components that ensure the operation of the university's intelligent agent.

• 1 PostgreSQL database - Used to store embeddings (vectors) of responses split into text chunks;
• 2 Local model Qwen3-Embedding-0.6B - Multilingual model used to generate vector representations (embeddings) of texts;
  • Model context: 32,768 tokens.
• 3 Local model Google/Gemma-4-31B - Multilingual language model with tool_choice support, allowing connection of external tools such as Internet search and the ability to process data from the RAG system;
  • Model context: 7,500 tokens.
• 4 RAG and RAG-Fusion mechanism - Used to combine and rank results from multiple queries and form the most relevant context before generating a response by the model;
• 5 Reranking mechanism using the bge-reranker-v2-m3 model - Used to select the most accurate and relevant answer from multiple found fragments;
• 6 Voice recognition + RVC models - Operates based on the local Vosk model, providing speech-to-text conversion (currently in Russian) and an RVC server for generating human-like voice responses;
• 7 Final result - Passing the final message in the format:
  prompt copy

  {Context}: %Result%.
  {Question}: %Question%.
  {Note}: Give an answer to the student.

The agent is available 24/7 on Telegram and helps students find out the necessary information about our university.