bqseine 1.4.0

ETL for BigQuery

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
response = chat([question, context])

In making the call to chat([question, context], history, temperature, mode, model, thinking), where:

• 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

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()