Google Gemini search metadata

Using Gemini search metadata in a voice AI application Filipi added support in Pipecat for Google Gemini's `groundingMetadata`. This makes it easy to do things like: - link to URLs that Gemini searched - log searches for use in your evals/observability tools - use specific search result chunks as input for your internal RAG content extraction Links to implementation and example code, below in the thread. Here's a video of Filipi interacting with the simple example in the PR. The search metadata is encapsulated in an `LLMSearchResponseFrame`. You can write a `FrameProcessor` to use the metadata within your @pipecat_ai pipeline, or send the metadata to the client. (Or both.) Here's the PR that implements the `LLMSearchResponseFrame` and related pipeline code: https://github.com/pipecat-ai/pipecat/pull/1030 Here's PR that adds support for sending these frames to the client: https://github.com/pipecat-ai/pipecat/pull/1051 Here's the example that's in the video: https://github.com/pipecat-ai/pipecat/blob/9b61633aa0dbf965195c8abf1f9d75447c6b109a/examples/foundational/31-gemini-grounding-metadata.py