From 2928cddfd512e77eea1ff2cce17aa119e82906bf Mon Sep 17 00:00:00 2001 From: Nubenetes Bot Date: Sat, 16 May 2026 13:07:33 +0200 Subject: [PATCH] docs: document Zero-Config Dynamic AI Discovery Engine in README and GEMINI.md --- GEMINI.md | 4 ++++ README.md | 29 ++++++++++++++++------------- 2 files changed, 20 insertions(+), 13 deletions(-) diff --git a/GEMINI.md b/GEMINI.md index 10ba3fd0..c62b4b5e 100644 --- a/GEMINI.md +++ b/GEMINI.md @@ -45,6 +45,10 @@ This file contains the accumulated instructions and long-term vision for the aut - **Tracking Stripping**: Systematically remove UTM parameters, social media trackers (X.com, LinkedIn), and URL fragments (`#`). - **Protocol Uniformity**: Standardize on `https://` whenever possible. - **Merge Logic**: Metadata from multiple sources for the same canonical URL MUST be merged, prioritizing the highest star rating and most recent date. +24. **Dynamic AI Model Discovery**: To remain at the cutting edge and ensure system stability, all agents MUST use the dynamic model discovery engine. + - **Live Discovery**: Query the `models.list` API at runtime to identify actually available models for each key. + - **Scoring & Ranking**: Prioritize models using the established 2026 hierarchy (Generation 3.x > 2.x > 1.x; Pro > Flash). + - **Resilient Fallback**: Automatically transition between models and API keys upon encountering 404 (Unsupported) or 429 (Rate Limit) errors. ## 🛠️ Structural Evolution & Navigation ... diff --git a/README.md b/README.md index 6409705d..6223d703 100644 --- a/README.md +++ b/README.md @@ -220,22 +220,25 @@ Nubenetes employs a strategic "Double-Format" protocol to ensure system reliabil - **JSON for AI Communication**: When agents talk to Google Gemini, they utilize **JSON** as the messaging protocol. This ensures rigid data structures and prevents AI formatting errors (like indentation slips) from breaking the processing scripts. - **YAML for Repository Storage**: Once the data is validated, it is serialized into **YAML** for the local database. This provides a clean, human-readable format that is easy to audit via Git diffs and respects the repository's aesthetic standards. -### Dynamic AI Discovery & Intelligence -To minimize API latencies and optimize quality, the system features a **Dynamic Model Discovery Engine**: -1. **Capability-First Discovery**: At the start of each run, the bot queries the Google Model Service to list all available models for the current API keys. -2. **Autonomous Scoring**: Models are ranked using a weighted algorithm: - * **Generation Score**: 3.x > 2.x > 1.x series. - * **Capability Tier**: `Pro` models (Intelligence) are prioritized over `Flash` models (Speed). - * **Stability Factor**: Stable releases are prioritized over experimental/preview versions. -3. **Intelligent Rotation**: If a model returns a 404 (Unsupported) or 429 (Rate Limit), the system automatically falls back to the next best model in the ranked list or rotates the API key. +### Dynamic AI Discovery & Optimization +To eliminate configuration overhead and ensure Nubenetes always utilizes the frontier of AI technology, the system features a **Zero-Config Dynamic Model Discovery Engine**: + +1. **Live Capability Discovery**: At the start of each workflow run, the bot programmatically queries the Google Model Service API to list all models actually available to the provided API keys. +2. **Autonomous Scoring & Ranking**: Models are automatically ranked using a sophisticated 2026-standard weighted algorithm: + * **Generation Tier**: 3.1 > 3.0 > 2.5 > 1.5. + * **Capability Level**: `Pro` models (High Reasoning) are prioritized over `Flash` (High Speed). + * **Production Readiness**: Stable releases are prioritized over experimental or preview versions. +3. **Resilient Failover Protocol**: If a model returns a `404` (Unsupported) or `429` (Rate Limit), the engine immediately rotates to the next best model or switches API keys without interrupting the curation process. ```mermaid graph LR - Start[Workflow Start] --> List[Models.list API] - List --> Score{Scoring Algorithm} - Score -->|Ranked List| Run[Process Tasks] - Run -->|429/404| Fallback[Next Model / Key] - Fallback --> Run + A[Workflow Initiation] --> B[API Model Discovery] + B --> C{Scoring Engine} + C -->|Ranked Queue| D[Task Processing] + D -->|404 / 429| E[Auto-Failover] + E -->|Next Best Model| D + E -->|Key Rotation| D + D -->|Success| F[Inventory Sync] ``` ### Agentic Data Flow