RAG with Pinecone AI Agent > Introduction to RAG with Pinecone AI Agent recipe
Introduction to RAG with Pinecone AI Agent recipe
The Retrieval-Augmented Generation (RAG) with Pinecone AI Agent recipe combines advanced vector search technology with powerful language models to deliver precise, context-aware answers.
When a user submits a request, including both a question and system instructions, the agent first converts this input into a numerical vector using the Gemini text-embedding-004 model. This embedding enables the agent to efficiently search Pinecone’s vector database and retrieve the most relevant knowledge chunks related to the query.
Using the retrieved data as context, the AI agent then generates accurate and contextually rich responses tailored to the user’s needs. This seamless interaction is powered by core components, such as the Pinecone Retriever, which uses the Gemini Embedding model, and the Gemini 2.5 Flash model that facilitates high-quality answer generation. Together, these assets enable the Pinecone Knowledge Agent to seamlessly convert user queries into vector representations, perform intelligent knowledge retrieval, and deliver insightful answers.