odoo-connect 0.4.1

Simple RPC client for Odoo

Odoo Connect

A simple library to use Odoo RPC.

Usage

import odoo_connect
odoo = env = odoo_connect.connect(url='http://localhost', username='admin', password='admin')
so = env['sale.order']
so.search_read([('create_uid', '=', 1)], [])

Rationale

OdooRPC or Odoo RPC Client are both more complete and mimic internal Odoo API. Then aio-odoorpc provides an asynchronous API.

This library provides only a simple API for connecting to the server and call methods, so the maintenance should be minimal.

Note that each RPC call is executed in a transaction. So the following code on the server, will add one to every line ordered quantity or fail and do nothing. However, ORM client libraries will perform multiple steps, on a failure, already executed code was committed. You can end with race conditions where some other code sets product_uom_qty to 0 before you increment it.

lines = env['sale.order.line'].search([
	('order_id.name', '=', 'S00001')
])
for line in lines:
	if line.product_uom_qty > 1:
		line.product_uom_qty += 1

A better way of doing something like this is to implement a function on Odoo side and call it. lines.increment_qty([('product_uom_qty', '>', 1)]).

Export and import data

A separate package provides utilities to more easily extract data from Odoo. It also contains utility to get binary data (attachments) and reports.

Since Odoo doesn't accept all kind of values, the format package will help with converting between user-expected values and values returned by Odoo.

The following function will return a table-like (list of lists) structure with the requested data. You can also pass filter names or export names instead of, respectively, domains and fields. Note that this doesn't support groupping.

# Read data as usual
env['sale.order'].search_read_dict([('state', '=', 'sale')], ['name', 'partner_id.name'])
env['sale.order'].read_group([], ['amount_untaxed'], ['partner_id', 'create_date:month'])

# Export data
import odoo_connect.data as odoo_data
so = env['sale.order']
data = odoo_data.export_data(so, [('state', '=', 'sale')], ['name', 'partner_id.name'])
odoo_data.add_url(so, data)

# Import data using Odoo's load() function
odoo_data.load_data(so, data)

# Import data using writes and creates (or another custom method)
for batch in odoo_data.make_batches(data):
	# add ids by querying the model using the 'name' field
	odoo_data.add_fields(so, batch, 'name', ['id'])
	# if you just plan to create(), you can skip adding ids
	odoo_data.load_data(partner, batch, method='write')

Data types

A small module provides functions to translate from JSON values to binary or date values.

Explore

Provides a simple abstraction for querying data with a local cache. It may be easier than executing and parsing a read(). Also, auto-completion for fields is provided in jupyter.

from odoo_connect.explore import explore
sale_order = explore(env['sale.order'])
sale_order = sale_order.search([], limit=1)
sale_order.read()

Development

You can use a vscode container and open this repository inside it. Alternatively, clone and setup the repository manually.

git clone $url
cd odoo-connect
# Install dev libraries
pip install -r requirements.txt
./pre-commit install
# Run some tests
pytest