oracle-ai-developer-hub

Architecture

+--------------------------+        +--------------------------+
|  React UI (frontend/)    | <----> |  FastAPI service         |
|  Vite + Tailwind + FM    |  HTTP  |  /chat /predict /health  |
|                          |        |  /memory/* /observ/*     |
+--------------------------+        +-----------+--------------+
                                                |
                          +---------------------+---------------------+
                          |                                           |
                          v                                           v
              +-----------------------+               +-----------------------+
              |  Agent runtime        |               |  Inference service    |
              |  - Grok 4 (OCI GenAI) |               |  - 92-feature XGBoost |
              |  - 13 tools           |               |  - FeatureRuntime     |
              +-----------+-----------+               +-----------+-----------+
                          |                                       |
                          +-------------------+-------------------+
                                              |
                                              v
              +---------------------------------------------------+
              |  Oracle AI Database (container)                    |
              |  - 49k matches + 47k goals                        |
              |  - PREDICCIONES_FINAL (bulk predictions)          |
              |  - SOCCER_LANGCHAIN_DOCS (LangChain OracleVS)      |
              |  - LangGraph OracleStore step trace               |
              |  - working / episodic / semantic memory           |
              |  - VECTOR(384, FLOAT32) + in-DB ONNX embeddings   |
              |    (ALL_MINILM_L6_V2, 384-dim)                    |
              +---------------------------------------------------+

Components

• soccer_agent/db.py — Oracle connection helpers (get_connection, get_admin_connection).
• soccer_agent/memory/ — Oracle-backed memory and retrieval:
  • working.py — per-session key/value (short-term)
  • episodic.py — past conversation turns, vector-searchable
  • semantic.py — distilled facts (team/decade), vector-searchable
  • langchain_hybrid.py — langchain-oracledb OracleVS table populated from ML prediction rows and football facts; native hybrid search when available, Oracle Text + vector RRF fallback on Oracle AI Database
  • oracle_agent_memory.py — optional oracleagentmemory SDK showcase using the same Oracle connection
  • schema.sql — DDL for agent_sessions, working_memory, episodic_memory, semantic_memory
• soccer_agent/observability/ — langgraph-oracledb step observability:
  • langgraph_steps.py — uses OracleStore to persist ordered per-turn events (turn_start, grounding_retrieved, model_response, tool_call, tool_result, final_response) under namespace ("soccer-agent", "agent-steps", session_id)
• soccer_agent/agent/ — Agent runtime:
  • embeddings.py — Python wrapper around Oracle’s VECTOR_EMBEDDING SQL
  • grok_client.py — HTTPS client for OCI GenAI Inference (bearer token, no OCI SDK)
  • tools.py — 13 tools: seven infrastructure tools (sql_query, hybrid_retrieve, vector_search, predict_match, lookup_prediction, remember, recall) plus six Reto Enseña ML feature tools (get_elo, get_team_form, get_h2h, get_momentum, get_poisson_xg, get_tournament_context) with SQL safety guard
  • loop.py — run_turn: recall, ground, call Grok, dispatch tools, persist memory and LangGraph OracleDB observability steps
• soccer_agent/inference/ — Predictions:
  • bulk.py — Lookup from PREDICCIONES_FINAL for cached rows
  • live.py — On-demand 92-feature inference from models/best_model.pkl
• soccer_agent/api/ — FastAPI + static UI, including GET /observability/{session_id} for step traces.
• soccer_agent/cli/chat.py — Terminal REPL alternative to the web UI.
• .claude/skills/soccer-workshop-setup/ — One-shot scaffolder skill.
• .claude/skills/soccer-agent-toolbelt/ — Runtime tools usable from Claude Code (same surface as the FastAPI agent).
• scripts/ — setup_db.py, init_memory.py, load_onnx_model.py, load_predictions.py, load_langchain_vectors.py, embed_match_facts.py, showcase_oracle_agent_memory.py, verify.py, smoke_test.py.

Why these choices

• In-DB ONNX embeddings (via onnx2oracle) — no external embedding service at runtime; Oracle stores and computes embeddings for the soccer schema. Tokenizer is baked into the ONNX graph so VECTOR_EMBEDDING(MODEL USING :t AS DATA) works on a plain text CLOB.
• LangChain OracleDB vector store after ML inference — once PREDICCIONES_FINAL exists, scripts/load_langchain_vectors.py turns prediction rows and football aggregates into LangChain Document rows in SOCCER_LANGCHAIN_DOCS. This shows the model output becoming retrievable knowledge, not just a one-off prediction table.
• Hybrid retrieval over similarity-only RAG — hybrid_retrieve first tries OracleHybridSearchRetriever/HYBRID VECTOR INDEX for Oracle versions that support it, then falls back to Oracle Text + vector similarity fused with reciprocal rank on Oracle AI Database Free. The runtime is hybrid-first: run_turn injects LangChain OracleVS grounding before chat history and only falls back to semantic-only memory when the hybrid store is unavailable or empty.
• LangGraph OracleDB observability without rewriting the agent — OCI Grok bearer auth still requires the prompt-protocol tool loop, so the runtime is not converted into a LangGraph graph. Instead, langgraph-oracledb OracleStore records each individual agent step in Oracle for durable observability and replay/debugging.
• Bearer-token Grok client — OCI GenAI Inference accepts Authorization: Bearer sk-... directly; the full OCI Python SDK is not needed (and doesn’t fit bearer auth without OCID/fingerprint/key-pair).
• Thirteen tools, narrow surface — same JSON schemas used by the deployed FastAPI agent AND the soccer-agent-toolbelt Claude Code skill. Attendees can prototype questions in Claude Code, then know exactly what Grok will see at runtime, including the hybrid_retrieve tool that showcases Oracle AI Database vector-store hybrid search versus the semantic-only vector_search baseline.
• Static HTML UI — no React/Next build step; loads in any browser; tool-trace is collapsible under each reply for the teaching moment.