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.github/workflows/ci.yml

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name: CI
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on:
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push:
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branches: ["main"]
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pull_request:
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install uv
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run: pip install uv
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- name: Sync dependencies
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run: uv sync --all-extras --dev
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- name: Run tests
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env:
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HF_HOME: ${{ github.workspace }}/.hf-cache
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TRANSFORMERS_CACHE: ${{ github.workspace }}/.hf-cache
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run: uv run pytest -m "not slow"

.gitignore

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.venv/
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__pycache__/
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.pytest_cache/
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*.egg-info/
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.hf-cache/

LICENSE

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Apache License
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README.md

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# databend-aiserver
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Blazing analytics, fast search, geo insights, and vector AI—supercharged in a
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new-era Databend warehouse. Databend AI Server fuses object storage and AI so
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all these capabilities are orchestrated with standard SQL.
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## UDFs (prefix `aiserver_`)
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- `list_stage_files(stage, limit)` – enumerate objects from external stages.
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- `read_pdf(stage, path)` – extract PDF text.
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- `read_docx(stage, path)` – extract DOCX text.
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- `vector_embed_text_1024(model, text)` – 1024-dim embeddings (batch-friendly).
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## Quickstart
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```bash
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uv sync
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uv run databend-aiserver --port 8815
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```
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## Sample SQL
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```sql
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-- 1. Register object-storage connection & stage.
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CREATE CONNECTION my_s3_connection
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STORAGE_TYPE = 's3'
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ACCESS_KEY_ID = '<your-access-key-id>'
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SECRET_ACCESS_KEY = '<your-secret-access-key>';
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CREATE STAGE docs_stage
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URL='s3://load/files/'
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CONNECTION = (CONNECTION_NAME = 'my_s3_connection');
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-- 2. Query and analyze directly in Databend SQL.
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SELECT * FROM aiserver_list_stage_files(@docs_stage, 50);
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SELECT aiserver_read_pdf(@docs_stage, 'reports/q1.pdf');
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SELECT aiserver_read_docx(@docs_stage, 'reports/q1.docx');
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SELECT aiserver_vector_embed_text_1024('qwen', doc_body) FROM docs_tbl;
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```
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## Tests
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```bash
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uv run pytest
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uv run pytest -m "not slow"
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```

databend_aiserver/__init__.py

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# Copyright 2025 Databend Labs
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""AI-enhanced Databend UDF server utilities."""
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from .server import create_server
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__all__ = ["create_server"]

databend_aiserver/main.py

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# Copyright 2025 Databend Labs
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Command line entrypoint for the Databend AI UDF server."""
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from __future__ import annotations
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import argparse
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import logging
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import signal
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import sys
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from contextlib import suppress
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from typing import Optional
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from prometheus_client import start_http_server as start_prometheus_server
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from databend_aiserver.server import create_server
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logger = logging.getLogger(__name__)
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def _parse_args(argv: Optional[list[str]] = None) -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Databend AI UDF server")
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parser.add_argument("--host", default="0.0.0.0", help="Bind address for the gRPC server")
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parser.add_argument("--port", type=int, default=8815, help="Port for the gRPC server")
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parser.add_argument(
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"--metrics-port",
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type=int,
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default=None,
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help="Port for Prometheus metrics exporter (disabled when omitted).",
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)
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parser.add_argument(
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"--log-level",
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default="INFO",
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help="Python logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL).",
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)
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return parser.parse_args(argv)
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def _configure_logging(level: str) -> None:
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logging.basicConfig(
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level=getattr(logging, level.upper(), logging.INFO),
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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)
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def main(argv: Optional[list[str]] = None) -> int:
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args = _parse_args(argv)
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_configure_logging(args.log_level)
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if args.metrics_port is not None:
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start_prometheus_server(args.metrics_port)
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logger.info("Prometheus metrics server started on port %s", args.metrics_port)
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server = create_server(host=args.host, port=args.port, metric_port=args.metrics_port)
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logger.info("Starting Databend AI UDF server on %s:%s", args.host, args.port)
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# Handle shutdown gracefully to ensure we stop serving when receiving termination signals.
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stop_event = getattr(server, "stopped", None)
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def _handle_signal(signum, frame): # noqa: ANN001 - signature dictated by signal library
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logger.info("Received signal %s, shutting down.", signum)
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with suppress(Exception):
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server.shutdown()
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if stop_event is not None:
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stop_event.set()
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signal.signal(signal.SIGINT, _handle_signal)
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signal.signal(signal.SIGTERM, _handle_signal)
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try:
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server.serve()
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except KeyboardInterrupt:
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logger.info("Interrupted, stopping server.")
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return 0
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if __name__ == "__main__":
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sys.exit(main())

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