📄️ Creating AI Agent
Agent (intelligent agent) possesses autonomous decision-making and task execution capabilities, automatically selecting appropriate tools to complete complex business processes based on user input and contextual information.
📄️ Tools in Agent
Tools extend Agent's executable capabilities, enabling it to not only understand and process natural language but also actively call external services, databases, APIs, etc., to implement complex operations such as information querying, data processing, and task execution. By adding appropriate tools to Agent, developers can equip Agent with stronger business processing and automation capabilities to meet diverse application scenario requirements.
📄️ Agent Input and Output
Agent input and output configuration is a key component for implementing intelligent interactions. Through proper configuration of input variables, Agent can receive diverse parameters and contextual information; through defining output result formats, Agent can return structured data for program logic use; through streaming output, you can obtain Agent's runtime status and results in real-time. This article will provide detailed instructions on these configuration methods and usage techniques.
📄️ Using Knowledge Base for Retrieval-Augmented Generation (RAG)
Agent can implement Retrieval-Augmented Generation (RAG) by integrating knowledge bases, enabling it to retrieve relevant information from knowledge bases before generating responses, thereby improving the accuracy and timeliness of answers.
📄️ Implement Single-Task Intelligent Agent with Agent
Coming soon...
📄️ Agent API Exposure
Coming soon...