livekit--agents/examples/voice_agents/llamaindex-rag

RAG Example using LlamaIndex

This repository showcases three ways to build a voice assistant with Retrieval-Augmented Generation (RAG) using LlamaIndex:

1. chat_engine.py: Utilizes LlamaIndex's as_chat_engine for a straightforward, integrated solution. Trade-off: Lacks function calling support, limiting advanced interactions.

2. query_engine.py: Uses an LLM that supports function calling (e.g., OpenAI's models) to define custom functions like query_info for retrieval. Trade-off: Requires additional setup but offers greater flexibility.

3. retrieval.py: Manually injects retrieved context into the system prompt using LlamaIndex's retriever. Trade-off: Provides fine-grained control but involves complex prompt engineering.

Current recommended way: Use query_engine.py for its balance of flexibility and control, enabling function calling and custom behaviors without excessive complexity.