This example demonstrates how to implement a conversation agent that collects complete LLM responses before sending them to Text-to-Speech (TTS). The implementation provides a simple yet effective approach to ensure smooth and natural speech output.

1. Clone the repository and navigate to the LLM complete chunks example:

   git clone https://github.com/rimelabs/rime-pipecat-agents.git

cd rime-pipecat-agents

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts and experience preferred (super rare at this point).

My own notes from a few months back.

• Survey of LLMS
• Self-attention and transformer networks

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

$ python -m SimpleHTTPServer 8000