Integrating Knowledge Base Into Agent

Knowledge bases serve as the core knowledge backbone for AI Agents, delivering accurate and timely domain expertise through Retrieval-Augmented Generation (RAG) technology. This integration significantly enhances an Agent's professional capabilities and response quality.

Relationship between knowledge base and agent

Within the JitAI platform, knowledge base elements establish a tightly integrated collaborative relationship with AI Agents:

• Knowledge provisioning: Knowledge bases transform diverse documents into structured, machine-understandable knowledge, providing Agents with rich knowledge repositories
• Intelligent retrieval: Through vector similarity matching and reranking models, knowledge bases comprehend the semantic intent behind Agent queries and return the most relevant knowledge fragments
• Dynamic enhancement: Agents can query knowledge bases in real-time to obtain the latest business information without requiring model retraining

Technical integration principles

When knowledge bases are integrated, Agents can dynamically acquire relevant knowledge during the reasoning process, enabling:

• Context augmentation: Leverages retrieved knowledge as contextual supplements to improve response accuracy
• Knowledge currency: Obtains the latest document content, circumventing the temporal limitations of model training data
• Domain expertise: Through domain-specific knowledge bases, equips Agents with deep expertise in specialized fields

Integration modes

Knowledge bases support two integration modes:

• Mandatory mode: Agents must query the knowledge base before processing user requests, ensuring every response is grounded in the latest knowledge
• Decision mode: The LLM intelligently determines whether to query the knowledge base, balancing response speed against knowledge accuracy

Using knowledge base in agent

For detailed integration configuration and usage instructions, refer to Integrating Knowledge Base for Retrieval-Augmented Generation (RAG).