In this example, we’ll build a basic Agentic RAG system using:

VectorStore: Retrieves relevant information from a pre-indexed document database. WebSearch: Fetches up-to-date data from the web when VectorStore lacks the required information. The AI agent dynamically selects the appropriate tool based on the query, showcasing the adaptability and efficiency of agentic RAG.

LLM: gemini-2.0-flash-exp Embedding Model: BAAI/bge-small-en-v1.5

Try it out!