aidp-ai-sql

# `aidp-ai-sql` — LLM-in-SQL with `ai_generate()` Call an LLM directly inside Spark SQL on AIDP — summarize, classify, extract, or narrate over lakehouse data without leaving SQL. A signature differentiator: most competitor agents can't do this inline. This is a **SQL-helper** skill. Interactive Spark SQL runs through the bundled helper `scripts/aidp_sql.py` (it mints a UPST from the api_key DEFAULT profile, auto-creates a scratch notebook, and returns JSON). No aidp MCP and no AIDP_SESSION required. ## When to use - "Summarize / classify / extract / enrich these rows with AI in SQL." - Generate a grounded narrative over an aggregate (e.g. a finance summary over a spend rollup). ## Signature (model FIRST) ```sql ai_generate('<model>', '<prompt>') ``` e.g. `ai_generate('openai.gpt-5.4', 'Summarize this supplier spend: ...')`. **LIVE-VERIFIED** model-first `(model, prompt)` signature with `openai.gpt-5.4`, `openai.gpt-4o`, and `xai.grok-4`. > **Verify before relying on it (no-fabrication):** confirm the exact signature and the **available model > names** live on the target cluster before treating this as guaranteed — run a trivial > `SELECT ai_generate('<model>', 'hello')` cell first (see smoke test below). Model availability varies > by environment. If a model name fails, list/ask for the correct one rather than guessing. > > **Don't gate on the `/models` REST catalog.** `ai_generate` resolves the model at the Spark engine level, > so it can work even when `aidp-models-catalog`'s `GET /models?modelType=GENERATIVE_AI` returns an empty > list. The smoke test (not the catalog endpoint) is the source of truth for whether `ai_generate` works. ## How to run a cell ```bash python "$PLUGIN_DIR/scripts/aidp_sql.py" \ --region <region> --datalake <DATALAKE_OCID> --workspace <ws> --cluster <cluster-key> \ --code "<python/spark code>" ``` Returns JSON: `{"status":"ok|error","outputs":[...],"spark_job_ids":[...]}`. Exit 0 on success, 1 on cell error. See [scripts/aidp_sql.py](../../scripts/aidp_sql.py) for full flags (`--profile`, `--session-profile`, `--notebook`, `--timeout`). ### Smoke test (do this first) ```bash python "$PLUGIN_DIR/scripts/aidp_sql.py" --region <region> --datalake <ocid> --workspace <ws> --cluster <key> \ --code "spark.sql(\"SELECT ai_generate('openai.gpt-5.4', 'hello')\").show(truncate=False)" ``` ## Workflow (grounded RAG pattern) 1. Ensure cluster RUNNING (cluster-ops via `oci raw-request`; see `references/no-mcp-rest-map.md`). 2. **Ground first:** aggregate/select the rows you want the LLM to reason over (small, relevant set). 3. Embed that grounded context into the prompt and call `ai_generate('<model>', '<grounded prompt>')`. For per-row enrichment, call it as a column expression over a bounded set. 4. Present the generated text alongside the underlying data so the user can verify it. Pass the cell to `--code`: ```python ctx = spark.sql("SELECT ... FROM gold.supplier_spend ...").toPandas().to_string() res = spark.sql(f"SELECT ai_generate('openai.gpt-5.4', 'As a finance analyst, summarize: {ctx}') AS summary") res.show(truncate=False) ``` ## Reliability rules - Always ground the prompt in real query output — don't ask the model to recall data it can't see. - Bound row counts for per-row `ai_generate` (cost + latency). - Show the data behind any AI narrative; never present generated numbers as ground truth without the SQL. ## References - [scripts/aidp_sql.py](../../scripts/aidp_sql.py) — the SQL/notebook-cell executor (bundled helper) - [references/oci-raw-request.md](../../references/oci-raw-request.md) — REST control-plane (clusters, auth) - [references/no-mcp-rest-map.md](../../references/no-mcp-rest-map.md) · pairs with `aidp-analyzing-data`