databend-aiserver

Databend AI Server extends any data warehouse with AI-ready UDFs, seamlessly fusing object storage, embeddings, and SQL pipelines.

UDFs (prefix aiserver_)

• list_stage_files(stage, limit) – enumerate objects from external stages.
• read_pdf(stage, path) – extract PDF text.
• read_docx(stage, path) – extract DOCX text.
• vector_embed_text_1024(model, text) – 1024-dim embeddings (batch-friendly).

Quickstart

uv sync
uv run databend-aiserver --port 8815

Sample SQL

CREATE CONNECTION my_s3_connection
  STORAGE_TYPE = 's3'
  ACCESS_KEY_ID = '<your-access-key-id>'
  SECRET_ACCESS_KEY = '<your-secret-access-key>';

CREATE STAGE docs_stage
  URL='s3://load/files/'
  CONNECTION = (CONNECTION_NAME = 'my_s3_connection');

SELECT * FROM aiserver_list_stage_files(@docs_stage, 50);
SELECT aiserver_read_pdf(@docs_stage, 'reports/q1.pdf');
SELECT aiserver_read_docx(@docs_stage, 'reports/q1.docx');
SELECT aiserver_vector_embed_text_1024('qwen', doc_body) FROM docs_tbl;

Tests

uv run pytest
uv run pytest -m "not slow"

---

Built by the Databend team — engineers who redefine what's possible with data.