oracle-ai-developer-hub

Soccer analytics agent toolbelt. Gives Claude Code direct access to the Oracle-backed match data, ML predictions, and three-tier memory. Use when answering questions about football matches, building on the soccer agent, or exploring the World Cup dataset.

Bootstrap the soccer analytics agent workshop. Starts the Oracle AI Database Free container, applies schema, loads the FIFA dataset, optionally trains models, populates LangChain OracleVS hybrid retrieval plus semantic memory, applies LangGraph OracleDB observability, and verifies OCI GenAI access. Use when starting the workshop or resetting a stale environment.

Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.

Scaffold an agent system where Oracle AI DB is the *only* state store, composed from the build-paths/skills/ building-block library. Stack — langchain-oracledb + oracle-database-mcp-server + in-DB ONNX embeddings + OCI GenAI Grok 4 + Open WebUI. Three projects — production-feeling NL2SQL+RAG hybrid analyst, self-improving research agent, conversational schema designer. For users who want a real DB-as-only-store agent demo.

Scaffold a small RAG chatbot on Oracle 26ai Free + langchain-oracledb + OCI Generative AI Grok 4 + sentence-transformers MiniLM-L6-v2 (Python-side embeddings, same model intermediate/advanced register inside Oracle) + Open WebUI. Three flavors that share one skeleton — PDF / Markdown / Web. For users new to Oracle who want a polished demo running in an afternoon.

Scaffold a Grok-4 tool-calling agent over an Oracle schema using langchain-oracledb + oracle-database-mcp-server + in-DB ONNX embeddings (registered MiniLM model, no external embedding API) + Open WebUI. For users who've built RAG before and want to rebuild it on the production-feeling Oracle stack.

Top-level router for the build-paths skill set. Asks the user one question (which path?), then dispatches to beginner/, intermediate/, or advanced/. Use when the user wants to scaffold an Oracle-AI-DB project but hasn't picked a difficulty yet.

Scaffold a langchain-oracledb store layer — multi-collection OracleVS wrapper, metadata-as-string monkeypatch, embedder-dim assertion, OracleChatHistory subclass (langchain-oracledb does not ship one). Use when a project needs Oracle as its LangChain vector store and chat-history backend.

Bring up Oracle 26ai Free in Docker, create a project-scoped app user with USERS tablespace + ONNX-friendly grants, and write a `.env` ready for the rest of the build-paths skills. Idempotent — safe to re-invoke. Use when any project needs a fresh local Oracle 26ai instance.

Wire the oracle-database-mcp-server into a Python project so an LLM agent can call list_tables / describe_table / run_sql / vector_search at inference time. Handles install, stdio launch, and LangChain tool conversion via langchain-mcp-adapters. Use whenever a project needs a Grok 4 / GPT-class agent that talks to a live Oracle schema.