at what stage of agent pipeline I can implement RAG?

You can implement RAG (Retrieval-Augmented Generation) in the LiveKit agent pipeline by utilizing function calling. According to the LiveKit documentation, you can register function calls with your agent, allowing the model to decide when to call each function. This setup enables you to use RAG by offering the model a function like

retrieve_extra_information

to perform vector searches and provide additional context before generating a response. Would you like more details on how to set this up? Sources: - LiveKit Pipeline Agent Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 If possible can you give full coe

I'm here to help with questions related to Livekit. If you have any specific questions about implementing RAG in Livekit or need guidance on a related topic, please feel free to ask! 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 how to retrieve information from rag in voice pipeline

To retrieve information from RAG in a voice pipeline, you can use function calling to integrate RAG into your LiveKit agent. This involves setting up a function that the agent can call to perform a vector search and retrieve relevant documents. Here's a basic outline of how you can achieve this: 1. Define a Function Context: Create a function context that includes a callable function for retrieving information. This function will be available to the LLM to call when needed. 2. Integrate with VoicePipelineAgent: Pass the function context to the

VoicePipelineAgent

to enable the agent to use the function during its operation. 3. Perform Vector Search: When the function is called, perform a vector search using your RAG setup to retrieve relevant documents or information. Here's a simplified code snippet to illustrate the setup:

from livekit.agents import llm
from livekit.agents.pipeline import VoicePipelineAgent

class AssistantFnc(llm.FunctionContext):
    @llm.ai_callable()
    async def retrieve_extra_information(self, query: str):
        # Implement your vector search logic here
        # For example, query your vector database and return results
        return "Relevant information based on query"

fnc_ctx = AssistantFnc()

pipeline_agent = VoicePipelineAgent(
    vad=vad_instance,
    stt=stt_instance,
    llm=llm_instance,
    tts=tts_instance,
    fnc_ctx=fnc_ctx
)

Would you like more detailed guidance on any specific part of this process? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1728300800.962959 - https://livekit-users.slack.com/archives/C025KM0S1CK/p1732170429.559829 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 When Is this function is called?

If you're looking for further help, please follow-up on one of the other channels available for community support!

@millions-hair-24956 When Is this function is called?

If you're looking for further help, please follow-up on one of the other channels available for community support!