Add RAG to Agora Conversational AI with Pinecone

Retrieval-Augmented Generation (RAG) offers a solution for enhancing conversational AI by integrating real-time context retrieval from a knowledge base with the reasoning capabilities of large language models (LLMs). This approach addresses common issues such as hallucinated responses and outdated information by retrieving relevant data at query time and presenting it to the model as part of the prompt. The implementation involves embedding documents into vectors, storing them in a database like Pinecone, and retrieving them when needed. While Pinecone's vector search is fast, real-time voice applications require additional optimization for latency and context maintenance. By integrating RAG into Agora's Conversational AI Engine, users can build applications that respond accurately with context from the latest documentation and product information. The setup includes establishing a Node.js backend to handle requests between Agora and OpenAI, incorporating Pinecone for semantic search, and managing context injection for improved response accuracy. The architecture supports scalability and continuous updates, offering a robust foundation for further enhancements like multi-turn conversation support and user-specific personalization.