Skip to main content

ETL for BigQuery

Project description

BQSeine

Bigquery AI agent and data loader

Seine is a data loader that pushes data in a dictionary to BigQuery in relational normalized form. Seine also has functionality to use Gemini to get data insights from Bigquery.

Usage

AI insights

from bqseine.agent import chat
answer, history = chat([question, context], history, temperature, mode, model, thinking)

Arguments:

  • history of type list[types.Content] (types from google.genai)
  • context argument explains to the LLM how the tables in Bigquery and their fields refer to each other and what they mean
  • temperature (set by default to 0.4) is how creative the model is allowed to get with 2 being max
  • mode (set by default to "AUTO") is the tool mode
  • model (set by default to "gemini-2.5-pro") is the AI model to be used
  • thinking (set by default to 1024) how much 'thinking' is the model allowed to do

Return:

  • answer of type str
  • history of type list[types.Content] (types from google.genai)

To run, you will need the following environment variables:

  • GOOGLE_API_KEY, storing your Google AI Studio API key
  • GOOGLE_CLOUD_PROJECT, storing your Google Cloud Project name
  • GOOGLE_APPLICATION_CREDENTIALS, that points to a credential with which you can access Bigquery

Data loading

from bqseine.polyp import sync
sourceData = [
	{
		'item': 'Juice',
		'price': 20.0,
		'stock': [
			{
				'batch': '2025-01-20',
				'qty': 300
			},
			{
				'batch': '2025-02-02',
				'qty': 50
			}
		]
	},
	{
		'item': 'Burger',
		'price': 30.0,
		'stock': [
			{
				'batch': '2025-02-10',
				'qty': 200
			}
		]
	}
]
sync('someGoogleProject', sourceData, 'catalog', 'US')
### The arguments above are:
### sync(<Google project name>, <dict>, <main table name>, <BigQuery region>)*

The above example will generate the following tables in BigQuery:

catalog

seine_id seine_parent_id item price injected
1 0 'Juice' 20.0 now()
2 0 'Burger' 30.0 now()

catalog_stock

seine_id seine_parent_id batch qty injected
1 1 '2025-01-20' 300 now()
2 1 '2025-02-02' 50 now()
3 2 '2025-02-10' 200 now()

Project details


Release history Release notifications | RSS feed

This version

3.9.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bqseine-3.9.0.tar.gz (28.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bqseine-3.9.0-py3-none-any.whl (41.1 kB view details)

Uploaded Python 3

File details

Details for the file bqseine-3.9.0.tar.gz.

File metadata

  • Download URL: bqseine-3.9.0.tar.gz
  • Upload date:
  • Size: 28.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for bqseine-3.9.0.tar.gz
Algorithm Hash digest
SHA256 0c180aa693da9847b818ade73de5353d520c8cf2ffb4f865904f23144cb1ec6b
MD5 fd36da8d92257480ddd8924b2b8709ca
BLAKE2b-256 cf163a57ec14483986608c2fb0578a839aaa0d4f2432936a4dce71131c706a3c

See more details on using hashes here.

File details

Details for the file bqseine-3.9.0-py3-none-any.whl.

File metadata

  • Download URL: bqseine-3.9.0-py3-none-any.whl
  • Upload date:
  • Size: 41.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for bqseine-3.9.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2d2fff87c842ac37e4e1c127dce9af5787ace4c9b397d7c6fda864f0ccd5b515
MD5 f446bbe5a26ad534880b4aa2daf8bc85
BLAKE2b-256 5d3a1ae3ecd1f5537268260cf50bf6b216917eb77900e84ffd57a3e9aa7607d8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page