Building an Intelligent Multi-Agent Conversational System for Code Generation, Research, and Topic Modeling
A technology-driven organization aimed to enhance productivity and automate complex user tasks by implementing an AI-powered multi-agent chatbot system. The objective was to build a scalable conversational platform capable of understanding diverse user requests and intelligently routing them to specialized AI agents for accurate execution. The solution was designed to handle use cases such as code generation, technical and non-technical research reporting, and semantic topic modeling through coordinated agent collaboration.
The challenge
A technology-driven organization aimed to enhance productivity and automate complex user tasks by implementing an AI-powered multi-agent chatbot system. The objective was to build a scalable conversational platform capable of understanding diverse user requests and intelligently routing them to specialized AI agents for accurate execution. The solution was designed to handle use cases such as code generation, technical and non-technical research reporting, and semantic topic modeling through coordinated agent collaboration.
Key challenges
- Managing multiple user intents within a single conversational system
- Ensuring efficient task routing to specialized AI agents
- Maintaining response accuracy across technical and non-technical requests
- Difficulty integrating code generation, research, and topic modeling into one framework
- Need for scalable chatbot architecture to support future expansion
Technology used
- Programming Language: Python
- Framework: LangChain for multi-agent orchestration
- AI Models: GPT-4, Gemini Pro, Gemini 1.5, and 3KT for intelligent response generation
- Topic Modeling: BERTopic for semantic topic extraction and analysis
What we built
- Supervisor Agent: Routes user queries to the appropriate specialized agents
- Coder Agent: Generates code based on user requirements and programming tasks
- Research Agent: Produces technical and non-technical research reports
- Semantic Topic Modeler: Extracts meaningful topics from customer reviews using BER Topic
- Response Aggregation: Consolidates outputs and returns contextual responses through the supervisor agent
Objectives
- Build a multi-agent chatbot capable of handling diverse user requests
- Automate code generation, research reporting, and topic extraction tasks
- Improve response quality through intelligent agent orchestration
- Enable scalable and modular chatbot architecture for multiple use cases
- Enhance task efficiency through specialized agent delegation
- Support semantic understanding of customer feedback and content
Outcomes
- Modular and scalable chatbot system for diverse use cases
- Improved efficiency through intelligent task delegation and orchestration
- Unified framework for code generation, research, and semantic topic modeling
- Enhanced response accuracy through specialized AI agents
- Easily extensible architecture for adding future agents and capabilities
- Improved productivity and faster task execution through automation