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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()

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2.9.0

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