Cloud-Native Knowledge Graph Automation to Deliver AI Agents API
summary
Built a cloud-native CI/CD pipeline that continuously ingests CSVs into Neo4j, packages Docker images and on‑prem Podman deployments, and exposes a Quarkus REST proxy via GitHub Actions → Google APIGEE. The result is a conversational agent (C‑GPT) that answers complex, data‑driven queries using Neo4j + Quarkus proxy, LongChain4j/OpenAI/Ollama and Vertex AI agents, returns provenance from git commits, admits “I don’t know” to avoid hallucination, and drives iterative API and graph redesign from LLM feedback with automated OpenAPI docs and weekly short cycles.
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chapters (11)
- 0:00Introduction and motivation
- 3:58Evolution of data delivery methods
- 7:14Building the Knowledge Graph automation
- 13:19Cloud-native API and agent stack
- 19:26AI-driven API and data design
- 24:48Continuous documentation by code
- 26:34Configuring AI agents with tools
- 30:30Technology stack overview
- 32:18Daily habits for knowledge management
- 33:45Knowledge Graph API thought experiments
- 34:05Q&A and closing remarks
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