gemini_prompts_summer_goal

🌟 Architectural Vision & System Design

This system was architected to bridge the gap between non-deterministic LLM outputs and the strict reliability requirements of enterprise software. Rather than treating prompts as static strings, this architecture implements PromptOps—treating prompts as versioned, testable, and monitored software artifacts.

The system is designed as a highly performant, asynchronous service that decouples prompt management, execution, and evaluation.

[Client API] ──(FastAPI)──> [Validation Layer (Pydantic)]
                                 │
                                 ├──> [Semantic Cache (Redis)] ──(Hit)──> [Return Cached Output]
                                 │
                                 └──(Miss)──> [Rate Limiter] ──> [Gemini API (Vertex AI)]
                                                                       │
[Evaluation Engine] <──(Async Event)── [PostgreSQL (pgvector)] <───────┘

Core Data & System Flow

• Ingestion / Input: Prompt templates and dynamic variables enter the system via a strictly typed FastAPI gateway. Input payloads are validated against dynamic Pydantic schemas to prevent injection attacks and malformed variables.
• Processing / Logic: Execution is managed via an asynchronous pipeline. If a prompt execution misses the semantic cache, it passes through an adaptive token-bucket rate limiter before calling the Gemini API. The system enforces structured JSON outputs directly at the model