Summary#

I use FastAPI as my primary Python backend framework for AI applications. Both of my recent full-stack projects rely on FastAPI to expose complex AI pipelines (semantic search, batch processing, RAG retrieval, SQL generation) as clean, validated REST APIs consumed by React frontends.

How I Apply This Skill#

• Built a 50+ endpoint FastAPI backend for the Obsidian Notes Pipeline, serving dual processing modes (sync and batch) through a unified interface with Pydantic-validated request/response models
• Refactored a 2,800-line monolithic API into 7 focused router modules with a single centralized connection pool, improving maintainability and eliminating 8 independent database connections
• Integrated a 3-tier Redis caching layer (query results, SQL templates, card specs) in the Text-to-SQL project using an async Redis client with graceful degradation
• Conducted a full OWASP Top 10 security audit on FastAPI endpoints; identified and remediated a path traversal vulnerability (A01) in a static file serving route using pathlib resolution
• Built document ingestion and retrieval endpoints for the RAG Document Assistant, serving 51,000+ chunks across 4 file formats with format-aware viewing through a single URL pattern (/help/custom/{doc_id}/view)
• Exposed a 6-phase pipeline API for the Job Search Agent with on-demand re-enrichment endpoints that trigger Claude Haiku 4.5 analysis in ~2 seconds for $0.0003 per job

Key Strengths#

• Pydantic integration: request validation, response models, and API contracts as first-class Python types
• Router architecture: decomposing large APIs into focused, testable modules
• Async patterns: Uvicorn + async route handlers for I/O-heavy AI workloads (embeddings, LLM calls, vector search)
• Security: CORS configuration, input validation, and endpoint-level vulnerability remediation

Related Projects#

• Obsidian Notes Pipeline
• Text-to-SQL Dashboard - Metabase
• Text-to-SQL Dashboard - Native React Charts
• Job Search Pipeline
• RAG Document Assistant