Cookbooks

These cookbooks show practical duckdb-ai workflows. Most of them use the same support_tickets sample table, so you can move from one example to the next without changing context. A few create their own small tables, so that their tests have fixed inputs. The production workflows show how to combine duckdb-ai with DuckDB storage, file, and database extensions.

Each page lists its provider, model and credential prerequisites at the top.

Start with sample data

• Create the sample support tickets table: pasteable setup data with text, metadata, numeric, and timestamp columns.

Production workflows

• Chain several AI steps in SQL: translate, triage and summarize in separate saved steps, skip failed rows downstream, and rerun without paying twice.
• Evaluate a prompt or model before a batch run: compare candidates on a labeled sample for accuracy, per-label errors, confidence thresholds and cost per correct answer.
• Run production batch enrichment from S3 or Parquet: read bounded object-storage inputs, capture row-level failures, and persist outputs.
• Resume a local enrichment job: checkpoint bounded batches in DuckDB and retry failed rows without repeating saved successes.
• Enrich rows from Postgres or MySQL safely: attach source databases read-only, materialize local batches, and write only to reviewed staging targets.
• Write audited AI outputs to lakehouse tables: keep run metadata, successful rows, rejected rows, and usage events together.
• Monitor AI usage, failures, and cost: snapshot ai_usage() for latency, retry, failure, token, and cost reporting.
• Normalize messy documents into structured records: read JSON, Avro, and Excel inputs before extracting typed records.

Try common workflows

• Enrich support tickets with AI text functions: summarize, classify, filter, extract, redact, and translate table columns.
• Compare support tickets with embeddings: rank tickets by semantic similarity, and store embeddings once for repeated searches.
• Store embeddings in Lance for semantic search: persist, index, search, and rerank reusable embeddings.
• Extract typed records from model output: get one typed STRUCT per row with ai_extract_record.
• Turn rows into typed decisions (ai_decide): save choices, scores and probabilities as SQL fields, batch rows with TypeSafe, then filter or export them.
• Route rows by decision confidence: accept confident ai_decide answers and send the rest to a chat-model fallback or a review table.
• Generate read-only SQL over local tables: use local schema context with ai_schema_prompt, ai_sql, and ai_query_data.

Provider setup is covered separately in the provider guides.