Takeaways
- Classifying user queries into clear categories helps reduce irrelevant matches and improves the accuracy of context retrieved from databases.
- Assigning distinct tasks to different agents allows developers to isolate logic and debug or expand system functions more easily.
- Connecting agent tasks to custom tools enables real-time querying of vector databases based on classification results.
- Using a separate language model instance for each agent role allows you to fine-tune behavior by adjusting parameters such as token count or temperature.
- Borrowing prompt templates from tested implementations helps generate cleaner, more relevant responses without the need for extensive trial and error.
Summary
This technical walkthrough demonstrates how to build a robust Retrieval-Augmented Generation (RAG) chatbot by integrating multiple specialized AI agents using the CrewAI framework and Watsonx.ai LLMs. The solution addresses a common issue in RAG pipelines: retrieving irrelevant context from large VectorDBs. The presenter proposes using dedicated agents for each step—query categorization, document retrieval, and response generation—linked through a sequential crew process.
First, a categorization agent uses a fine-tuned prompt to classify incoming queries into one of three domains: technical, billing, or account. This classification enables targeted retrieval from isolated ChromaDB collections, reducing noisy or semantically similar but irrelevant matches. The categorization output is passed to a retrieval agent equipped with a tool function, which queries the corresponding vector collection using embedded input.
The third agent, responsible for natural language generation, uses both the original query and the retrieved context. A pre-built prompt from Watson Studio's RAG accelerator is interpolated with these inputs to produce a user-facing response. Each agent is powered by a separate instance of the Granite 3.8B model, with tailored configurations to suit their task’s complexity.
The project architecture includes a FastAPI backend, a React-based UI, and an Express server. While the UI is not the focus, the presenter encourages customization using IBM’s Carbon Design System. The final application handles category identification, vector-based retrieval, and prompt-driven generation, showing how multi-agent collaboration can significantly boost RAG pipeline accuracy and maintainability.
