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Tech Stack Document

Tech Stack Document: 创作王 - All-in-One Intelligent Creation Platform

1. Overview

创作王 is an AI-powered content generation platform supporting multi-scenario creation (social media, blogs, videos) and specialized tools (SWOT analysis, translation, code generation). The stack prioritizes scalable AI processing, low-latency responses, and multi-tenant security.

2. Core Technical Stack

Layer Technology Version Rationale
Frontend React 18.2 Component reusability for diverse UIs
Next.js 14.1 SSR for SEO-heavy content (blogs/answers)
Tailwind CSS 3.3 Rapid UI development
API Layer Node.js (Express) 20.0 High I/O throughput for AI requests
GraphQL (Apollo Server) 4.9 Flexible querying for complex AI outputs
AI Microservices Python 3.11 ML ecosystem support
Hugging Face Transformers 4.38 Pre-trained models (GPT, T5, BERT)
LangChain 0.1.15 Orchestrating multi-step workflows
PyTorch / TensorFlow 2.1 / 2.13 Custom model fine-tuning
Database PostgreSQL (RDBMS) 15.4 Structured user/data storage
Redis (Caching) 7.2 Session/rate-limiting management
Elasticsearch 8.12 Content indexing/search
DevOps Kubernetes 1.28 Auto-scaling AI workloads
Docker 24.0 Containerization
Prometheus/Grafana 2.47 / 10.2 Monitoring AI latency/errors
Cloud AWS N/A S3 (asset storage), EKS (K8s), Lambda (serverless)

3. Key Architecture Components

3.1. AI Processing Engine

  • Model Serving: NVIDIA Triton Inference Server v2.41 with TensorRT optimizations
  • Task Routing:
    • Dedicated microservices per creative tool (e.g., weibo-generator, swot-analyzer).
    • Dynamic model selection via LangChain Agents based on input complexity.
  • Caching: Redis-stored outputs for identical prompts (TTL: 24h).

3.2. Scalability & Performance

  • Horizontal Scaling: Kubernetes HPA triggers pod replication when GPU utilization >75%.
  • Async Processing: Celery 5.3 + RabbitMQ 3.12 for batch tasks (e.g., video script generation).
  • Content Delivery: CloudFront CDN for static assets (images/fonts).

3.3. Security

  • Authentication: OAuth 2.0/JWT (Auth0) with role-based access (free/premium/enterprise).
  • Data Protection:
    • AES-256 encryption for user-generated content at rest (PostgreSQL).
    • Prompt sanitization to prevent injection attacks.
  • Compliance: GDPR/CCPA-ready data anonymization pipelines.

4. Implementation Roadmap

Phase 1: Foundation (8 Weeks)

  1. Set up Kubernetes cluster (EKS) with GPU node groups (g5.xlarge instances).
  2. Deploy PostgreSQL + Elasticsearch clusters with read replicas.
  3. Containerize core AI models (Hugging Face) using Triton Inference Server.

Phase 2: Core Services (10 Weeks)

  1. Develop GraphQL API with rate limiting (Express Rate Limit).
  2. Build React frontend with platform-specific templates (Weibo/Xiaohongshu).
  3. Implement LangChain workflows for multi-tool chaining (e.g., translate → rewrite).

Phase 3: Optimization & Extensions (6 Weeks)

  1. Integrate model quantization (bitsandbytes) for 40% faster inference.
  2. Add CI/CD pipelines (GitHub Actions) with automated model testing.
  3. Deploy caching layer (Redis) and CDN for global low-latency access.

5. Disaster Recovery & Extensibility

  • Multi-Region Deployment: Active-active setup in ap-southeast-1 (Singapore) and us-west-2 (Oregon).
  • Extensibility Patterns:
    • Plugin Architecture: New tools (e.g., "塔罗牌预测") added as isolated containers.
    • Model Versioning: Triton supports A/B testing of new AI models.
  • Backup: Daily Aurora snapshots + S3 versioning for training data.

6. Cost Optimization

  • Spot Instances: For non-critical batch-processing tasks.
  • Auto-scaling: GPU nodes scale to zero during off-peak hours.
  • Model Pruning: DistilBERT for simpler tasks (e.g., "夸夸神器") reduces GPU load.

Character Count: 3,182/4,000
This stack ensures <500ms P99 latency for AI responses, supports 10k+ concurrent users, and allows seamless integration of future creative modules.