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

3.7.0

Download files

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

Source Distribution

bqseine-3.7.0.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-3.7.0-py3-none-any.whl (41.1 kB view details)

Uploaded Python 3

File details

Details for the file bqseine-3.7.0.tar.gz.

File metadata

  • Download URL: bqseine-3.7.0.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-3.7.0.tar.gz
Algorithm Hash digest
SHA256 bdd7c28353403c17ccfdf84172514cc6073eea5a82fc2fd0dea8ec4363fefbaa
MD5 5f03ed8543cff423f1a7b58d4318e3b1
BLAKE2b-256 677fe0f9e63e9bc2c6d797a015cb298cfffc0609bcd78324383920df8009ef5a

See more details on using hashes here.

File details

Details for the file bqseine-3.7.0-py3-none-any.whl.

File metadata

  • Download URL: bqseine-3.7.0-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-3.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ea3d5e7dd081f65a9034465caf187cafff8b495cc75fcd3b43f49571e503cdb3
MD5 e2020bf478cc4e6f503532ecbaa68501
BLAKE2b-256 0e8f312853c567b4d5983707f9b096abddaedaab66046bb17e0f55e78903d7e1

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