🚀 An ebook that takes FastAPI from your first endpoint all the way to production-scale AI systems — going deep on LLM/RAG serving, with interview questions at the end of every chapter. Title: FastAPI for AI Engineers: From First Endpoint to Production-Scale AI Systems URL: 🚀 Overview A practical guide (First Edition, 2026, by AI Engineering Insider) for AI engineers serving ML models and LLM/RAG systems in production with Python. It spans 10 chapters and 100 interview questions, woven with real-incident case studies and cost-model sidebars. ❓ Challenges Solved ・Building a model is one skill; serving it safely as a scalable production API is another ・LLM/RAG serving has its own hard parts — streaming, guardrails, cost control The book reframes FastAPI as "the de facto serving layer for AI and ML systems." 💡 Structure & Tech Covered ・Foundations: ASGI/WSGI, Uvicorn, OpenAPI, and Pydantic v2 schema separation and validation ・Implementation: idempotency, meaningful status codes, pagination, and the Router→Service→Repository clean architecture ・DB/Security: SQLAlchemy/SQLModel/Alembic, N+1, pool sizing, JWT, BOLA defense, OWASP API Top 10 ・Async: "never block the event loop," def vs async def, and httpx retries/circuit breakers 🎯 The Core (Ch.9: AI/RAG/LLM) ・Load model weights once in lifespan; offload CPU inference to a thread ・An LLM gateway centralizing auth, prompts, guardrails, and cost metering, with SSE token streaming ・Build RAG with embeddings + a vector DB (start with pgvector); validate output with Pydantic, then retry on failure ・Enforce max_tokens as a "spending limit" via the type system 📊 Highlights ・Practice-first, learning from real incidents (Netflix, Stripe, GitLab, Optus, Air Canada) ・Ch.10 covers Gunicorn+Uvicorn, K8s liveness/readiness, the three pillars of observability (p99 vs p50), and SLO-based alerting #FastAPI# #AIEngineering#

🤝 "LLMs for ambiguity, deterministic agents for hard policy"—a contract-compliance ADK sample that takes role separation seriously. Title: Contract Compliance Pipeline (GoogleCloudPlatform/generative-ai) URL: An auditable demo that uses Google ADK's A2A protocol to coordinate a Python (intake) agent and a Go (validation) agent. Three highlights worth your attention. 🧠 Separating LLM from deterministic work Instead of routing everything through an LLM, ambiguous extraction goes to the LLM/parser side while hard policy enforcement runs in a deterministic Go engine. Rules like "value ≤ $500k" or "term ≤ 5 years" get checked in a repeatable, identical-every-time way. 🔌 Cross-language A2A handoff ADK's RemoteA2aAgent discovers the Go service via its Agent Card (/.well-known/agent.json) and sends a JSON-RPC 2.0 SendMessage. A Python agent (FastAPI:8000) and a Go agent (net/http:8888) collaborate using only a standard protocol. 📜 Auditable policy and artifacts Policies (value cap, term limit, minimum insurance, required exit clause, no unlimited liability, etc.) are passed as custom_policies and swappable per audit. Results are visualized with an execution trace, and the system even auto-generates compliance certificates and parameter sheets. It reads like a template you could lift straight into real regulated/review-heavy systems. #AIAgents# #GoogleADK#

For an AI agent to answer "why did we make that decision?", you need connected memory — not flat chat logs 🕸️ This tool spins the whole thing up in one command. Title: Introducing Create Context Graph URL: 🕸️ Overview Create Context Graph is a Neo4j Labs CLI scaffolding tool that generates a full-stack AI agent app with graph-based memory in a single command. The generated app bundles a FastAPI backend, a Next.js frontend, an AI agent framework, and a Neo4j graph database. ❓ Challenges Solved AI agents are easy to build but still struggle with relationships and causality. ・Flat chat logs and vector stores can't answer structural questions like "why did we decide this?" or "what's blocking this work?" ・In short, agents lacked the sophisticated memory needed to capture relational context 💡 Methodology & How It Works ・It turns data into a "context graph" (a connected knowledge structure), organizing three memory types: chat history, vector content, and reasoning traces ・It uses the POLE+O entity model (Person, Organization, Location, Event, Object) layered with domain-specific types ・When agents decide, the reasoning chain is captured as DecisionTrace nodes with linked TraceStep components, creating queryable provenance ・It supports multiple frameworks (PydanticAI, LangGraph, Claude Agent SDK), 22 built-in domains, Linear/Claude Code/GitHub connectors, real-time reasoning-path visualization, and automatic secret redaction 🌍 Use Cases ・Developers querying issue dependencies and team workflows ・Personal development analytics from Claude Code session history ・Multi-tool correlation combining decisions, commits, and work items Making decision provenance queryable helps with agent explainability, debugging, and cross-team knowledge integration. #GraphRAG# #Neo4j#