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I'm using Gemini Live API for Speech to Speech wit...
# ask-ai
n
I'm using Gemini Live API for Speech to Speech with tool calling for vector search, logs say the vector calling was successful and it shows the results fairly quickly but the agent keeps saying that it doesn't have access to that data although we see in the logs the result of the data fetching
t
Thanks for reaching out, Kevin. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks for reaching out! Could you please clarify if the vector search functionality is part of a custom integration or a specific tool you're using alongside the Gemini Live API? Also, are you hoping the agent will process and respond directly based on the vector search results? Getting a bit more detail will really help me understand your situation better and guide you in the right direction. Sources: Gemini Live API integration guide | LiveKit Docs | Gemini Live API integration guide | LiveKit Docs | Gemini TTS integration guide | LiveKit Docs | Gemini TTS integration guide | LiveKit Docs | Google AI and LiveKit | LiveKit Docs
I'm here to help you work through the issue where vector search results aren't being processed by the agent. Let's take a look at a few steps together: 1. Verify Configuration Parameters: Please double-check that your LLM is set up correctly with all the necessary details like the model name and API key. 2. Check Vector Search Integration: Make sure the vector search feature is properly connected and actually returning the right results. 3. Inspect Data Injection Process: It's important that the vector search results are formatted correctly and injected into the chat context so the LLM can use them when generating responses. 4. Review Tool Usage: If you're using any custom tools, let's confirm they're working as expected. 5. Monitor Logs: Take a look at the logs for any errors related to the RAG lookup or the LLM's response generation — they can give us valuable clues. 6. Update Dependencies: Lastly, ensuring all dependencies, including the LiveKit Agents framework, are up-to-date can help prevent bugs or missing features. Following these steps should guide you toward resolving the issue. If anything feels unclear or you want to dive deeper into any point, please don’t hesitate to reach out—I’m here to support you! Sources: External data and RAG | LiveKit Docs | Gemini Live API integration guide | LiveKit Docs
n
the issue is that the tool outputs are not being injected into the LLM's context at all.
t
I'm here to help you with the issue where tool outputs aren't being injected into the LLM's context. Let's walk through some important steps together: 1. Use the 
on_user_turn_completed
Node: This node is really helpful because it lets you modify the user's message or add extra context (like tool outputs) right before the LLM crafts a response. 2. Call `update_chat_ctx()`: To make sure the injected context sticks around beyond the current turn, don’t forget to explicitly call 
update_chat_ctx()
after you add the tool outputs. 3. Check Context Injection: Double-check that you’re adding the tool outputs to the 
turn_ctx
in the right way. Here’s a quick example:
Copy code
async def on_user_turn_completed(self, turn_ctx, new_message):
    tool_output = await my_tool_function(new_message.text_content())
    turn_ctx.add_message(role="assistant", content=f"Tool result: {tool_output}")
    await self.update_chat_ctx(turn_ctx)
1. Verify Realtime Model Requirements: If you’re working with a realtime model like Gemini Live, just make sure the last message in the context comes from the user—that’s essential for generating a response. 2. Monitor Logs: Keep an eye on your logs for any errors or warnings that might give clues about why the tool outputs aren’t being processed. If any part of this feels unclear or if you’d like a hand implementing these steps, please don’t hesitate to reach out. I’m here to support you! Sources: Docs::Agents | Pipeline nodes and hooks | LiveKit Docs | tests/test_agent_session.py
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