Feature Request: Native 429 RetryInfo Parsing & Token-Bucket Pacing for High-Throughput Pipelines #3173

Problem

When processing large streaming datasets (e.g., .jsonl archives) using the google-genai SDK on the Free Tier, developers quickly hit the 5 RPM limit resulting in 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE errors. Currently, developers have to manually implement exponential backoff wrappers that blindly guess the wait time, often breaking high-throughput pipelines.

Proposed Solution

In a recent architecture I built (a deterministic Neuro-Symbolic State Machine wrapping this SDK), I successfully implemented an automated pacing and retry mechanism that:

1. Employs a local $O(1)$ thermodynamic gatekeeper to filter low-entropy logs locally, reducing API calls by ~50%.
2. Dynamically parses the exact wait time from the Google API error message (RetryInfo: "Please retry in X.Xs") rather than guessing the backoff.
3. Implements a Token-Bucket min-interval pacing (e.g., 12.5s for 5 RPM limits) to prevent the 429 error from happening proactively.

Would the team be open to natively integrating a more robust RetryInfo parser and rate-limit pacer directly into the genai.Client retry logic?

Proof of Concept (The Abhidhamma Kernel)

You can review my implementation of this exact fallback/pacing wrapper inside the GeminiPerceptionOrgan class of my Neuro-Symbolic kernel here:
🔗 https://github.com/sunsteelts-ctrl/abhidhamma-neuro-symbolic-kernel

(It acts as a read-only sensory layer, extracting structural causal relationships under strict JSON schemas while seamlessly absorbing 429/503 spikes).

I'd be happy to submit a Pull Request adding an examples/high_throughput_pacing.py to your cookbook if you find this architectural pattern useful for the community.