AI API Service (Production-Ready Microservice)

This microservice includes robust API design, prompt engineering, caching, validation, observability, and deployment readiness.

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Overview

AI API is a backend AI microservice built with TypeScript (FastAPI version also supported). It focuses on reliability, scalability, and clean architecture — not just calling LLMs, but doing it correctly, safely, and cost-effectively in production.

This repository showcases:

• Industrial-grade AI model integration
• Prompt engineering and structured responses
• Reliable error handling with retries & backoff
• Caching to reduce LLM cost & latency
• Security, monitoring, scalability, and CI/CD-ready deployment patterns

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Key Features

1. Prompt Engineering & AI Model Integration

• Role-based few-shot prompting
• Structured JSON output enforcement
• Wrapper around OpenAI + future model adapters
• Schema validation for deterministic results

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2. Data Processing & Validation

• Strict input schema validation

• Normalization & sanitization layer

• Ready to extend for:

  • PDF processing
  • Log ingestion
  • Multi-step pipelines

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3. Reliability & Resilience

• Retry w/ exponential backoff
• Redis caching to avoid duplicate LLM calls
• Error-tolerant request flow
• Fully containerized (Docker)
• Supports horizontal scaling (Kubernetes-ready)

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4. Security & Compliance (Partial / Improving)

• Basic safeguards against invalid / unsafe outputs

• Extensible modules for:

  • PII redaction (regex/NER-based)
  • GDPR / HIPAA-friendly data flows
  • OpenAI Moderation API or custom filters

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5. Monitoring & Cost Optimization

Current:

• Request + error logging
• Cached-call optimization
• Payload minimization

Planned:

• Token-level cost tracking
• Prometheus/Grafana metrics dashboard
• Alerts for spikes in usage, cost, or latency

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6. Deployment & Scalability

• Clean microservice architecture (Node.js/TS + optional FastAPI)
• Dockerized for production
• CI/CD-friendly structure (GitHub Actions support)
• Designed for multi-instance scaling & traffic routing

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Feature Roadmap (In Development)

Feature Area	Status
Kubernetes multi-instance scaling	In Progress
Vector DB + RAG workflow (Pinecone / Weaviate)	In Progress
Message queues + DLQ (RabbitMQ / Kafka)	In Progress
PII Redaction	Implementing
Moderation & Compliance	Implementing
Cost dashboards & alerts (Prometheus)	In Progress

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Why This Project Matters

This service mirrors the real responsibilities of a Senior AI Integration Engineer, including:

• Designing structured, safe prompt pipelines
• Integrating multiple LLMs reliably
• Ensuring cost-efficiency at scale
• Handling resilience, fallbacks, backpressure
• Enforcing compliance (PII, moderation, audits)
• Preparing microservices for cloud-native production environments

As more components (RAG, queues, observability) are added, this repository becomes a complete showcase of enterprise-grade AI API engineering.

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Quick Start

git clone https://github.com/ManibalaSinha/devstations.git
cd devstations
npm install
npm run dev          # Start local dev server
npm run build        # Build for production

# Build Docker image
docker build -t devstations .

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Tech Stack

• TypeScript / Node.js
• FastAPI (optional Python implementation)
• Redis (Caching)
• Docker
• OpenAI API
• Prometheus/Grafana (planned)
• Pinecone / Weaviate (planned for RAG)
• Kafka / RabbitMQ (planned)

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About

AI / Python / Backend Engineering Production-grade AI API service focused on prompt engineering, RAG pipelines, caching, monitoring, and secure cloud deployment.

🌐 Project website: https://manibalasinha.github.io/ai-api/