@millions-hair-24956 does vision work with fireworks llm?

It seems likely that the Fireworks LLM does not support vision capabilities directly. According to the LiveKit documentation, the Fireworks LLM is primarily used for text-based tasks and requires an API key for access. Vision capabilities are not mentioned in the context of Fireworks LLM. Would you like more detail on how to integrate vision capabilities with LiveKit or other LLMs? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 fastest llm that works with vision?

It seems likely that the fastest LLMs for vision tasks are not explicitly detailed in the provided data sources. However, the LiveKit documentation mentions that OpenAI's models, such as GPT-4o, are commonly used for low-latency applications. These models are integrated with LiveKit's framework, which supports vision tasks through plugins like OpenAI's. If you're looking for a specific LLM with vision capabilities, you might consider using OpenAI's models, as they are often optimized for performance and can be integrated with vision tasks using LiveKit's framework. Would you like more detailed information on integrating vision capabilities with these models? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 in voice pipleine agent is maybe there's another way to modify the bot's responses before they're sent.

Yes, in the VoicePipelineAgent, you can modify the bot's responses before they are sent using the

before_llm_cb

callback. This callback allows you to customize the chat context or modify the response before it is processed by the LLM. According to the LiveKit documentation, you can inject additional context or adjust the response by implementing a custom function for this callback. Would you like more detailed guidance on how to implement this callback? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.