AI integration framework for e-commerce. The project's aim is to provide a tool to automate eCommerce tasks with AI.

Its concept is based on endpoints that can be of type:

• Data source is an endpoint allowing product and product related data access. One of the data sources is PrestaShop API.
• AI assistant is an interface for prompting AI (such as OpenAI's ChatGPT) with purpose of doing a certain operations on product or product related data.

EcommQuery uses a simple self-explaing prompt file format:

# file: product.prompt.txt

@generate_short_descr_of product
Below text is a description of a product:
=== Text Begin ===
{product.descr}
=== Text End ===

Generate short description, return only generated short description in a plain text.

To use the framework:

• configure endpoints
• develop prompt
• develop 'task'
• test and run

The configuration is stored in an INI file:

[ecommquery]
memo = <Explanation of the contents of this configuration.>

# Endpoint for accessing PrestaShop:
[presta_api]
memo = <optional description>
url = https://www.example.com/grocerystore
api_secret_key = TEST06LGUHL19KBQYMK8RNVU45ZK1C1Q

# Endpoint for accessing OpenAI's ChatGPT
[chatgpt]
memo = Chat GPT assistant API
key = DUMMY_KEY-ohsazaejou83d
queries = ~/prompts/

Line queries = ~/prompts/ indicates where prompt files are located. User defines AI prompts in Machine/Model Prompt (*.mp.txt) file, simple example:

@assign_cat_for product
Can you determine categories that fit to the 
following product description?:
=== Description Begin ===
{product.name}
{product.cdescr}
=== Description End ===

Aim to find one or maximum three best categories. Categories are
limited to the names from the list below:
... THE LIST ...
.... .... ....

Format output as JSON array.
@end

# next prompt definitins ...

Content between @assign_cat_for and @end sections is a prompt message.

@assign_cat_for is a prompt name, followed by a class name of the object that is to be datasource for prompt. In this case {product.descr} inserts Product's description into a prompt message.

Let's develop a task that will assign product categories based on AI prompt response:

from ecommquery import *
from ecommquery.core.loader_ini import IniLoader
from ecommquery.exceptions import EcommQueryError

try:
    integr = Integrations()
    integr.addLoaderAndRead(IniLoader('./config.ini'))

    ps = inegr.getService(endpoint='prestashop')
    ai = inegr.getService(endpoint='chatgpt')

    for prod_id in ps.getProductList():
        prod = ps.getProduct(prod_id)
        
        res = ai.sendPrompt('assign_cat_for', [prod])
        
        prod.addCat(json.loads(res))
        ps.commitProduct(the_prod)

    ai.close()
    ps.close()
except EcommQueryError as ecq_err:
    print(ecq_err.message)

Below is the list of supported endpoints and its parameters:

Data Source:

• presta_api– PrestaShop API endpoint
  • url – store or API URL
  • api_secret_key – API key
• allegro_api– Allegro API
  • client_id – client ID
  • client_secret – client secret
  • sandbox – sandbox mode, default: False
• web_scrap– Web scrapping endpoint
  • url – store URL

AI assistant:

• chatgpt- ChatGPT
  • version - model version, default: gpt-3.5-turbo
  • key – ChatGPT key
  • queries – path to directory with prompt files (see example/prompts)

Integrations() object can be used to load more than one configuration file. In such a case, configurations are merged. Look at an example below:

INI file configurations/testing-noe.ini:

# file: configurations/testing-noe.ini'))
[ecommquery]
memo = Testing stores @ Noe

[presta_api]
url = https://noe-test.example.com/grocerystore
api_secret_key = TEST06LGUHL19KBQYMK8RNVU45ZK1C1Q

[presta_api 2]
url = https://noe-test.example.com/electronic-shop
api_secret_key = ...

INI file configurations/testing-sim.ini:

# file: configurations/testing-sim.ini
[ecommquery]
memo = Testing setups @ Sim

[presta_api]
url = https://sim-test.example.com/grocerystore-test01
api_secret_key = ...

Python code:

from ecommquery import *
from ecommquery.core.loader_ini import IniLoader
from ecommquery.exceptions import EcommQueryError

try:
    integr = Integrations()
    integr.addLoaderAndRead(IniLoader('./configurations/testing-noe.ini'))
    integr.addLoaderAndRead(IniLoader('./configurations/testing-sim.ini'))

    # print 
    integr.print()
    
    do_stuff(integr)
except EcommQueryError as ecq_err:
    print(ecq_err.message)

Output of inegr.print() is following:

 INI file: ./configurations/testing-noe.ini (ini:PRODUCTION)
 Id #0
     name: PrestaShop API
     host: https://noe-test.example.com/grocerystore
 Id #1
     name: PrestaShop API
     host: https://noe-test.example.com/electronic-shop
 ==========================================================
 INI file: ./configurations/testing-sim.ini (ini:testing)
 Id #2
     name: PrestaShop API
     host: https://sim-test.example.com/grocerystore-test01
 ====================================================

Store products, manufacturers, taxes, etc. can be accessed via a service object created by endpoint with .getService() method:

def do_stuff(inegr):
    ps = inegr.getService(endpoint='electronic-shop-test02')

    for prod_id in ps.getProductList():
        prod = ps.getProduct(prod_id)
        print(prod.name)
        print(prod.descr)

    the_prod = ps.getProduct('1012')

    the_prod.name('This is product\'s new name')
    # commit changes (update product at the store)
    ps.commitProduct(the_prod)

When processing HTML data, 'sterilization' is a good practice to ensure that there are not any 'meaningless' tags or attributes.

Function HTMLfun.sanitize(html: str) provided by eCommQuery cleans up HTML as follows:

• unwrap (remove tag keeping its content) all elements that are not:

  • h1, h2, h3, h4, h5, h6 - header tags
  • p, br - format tags
  • b, i, strong, em, u - style tags
  • table, tbody, th, tr, td - table tags
  • ul, ol, li - list tags

  Note that div and span elements are also removed (unwraped).

• h1 element is kind of special, only one h1 element should be defined on page. In templates used by eCommerce platforms h1 is usually a product or category name. It is a good idea from an SEO point of view. It means also that when sterilizing description any encounted h1 tags should be shifted to become h2. Argument start_hlevel=2 forces all Header elements to start from h2.

• remove empty style elements, such as <b></b>

• merge consecutive style elements, for instance <b>B</b><b>old</b> merges to <b>Bold</b>. This is to eliminate an over definition. I found that this can exist surprisingly often.

• purge style or class tag arguments if argument: purge_classes = True or purge_styles = True is provided.

When sterilized HTML code is translated to plain text, it looks better (no multiple new lines, no strange spaces). Plain text is a way to communicate with AI, thus machine receives well-formatted text. For instance, while translating unsterilized <b>Co</b><b>conut</b> to text the output is Co conut, thus tag merging is kind important.

Data source for Product:

Data source for Categories:

Data source for Brands:

The fastest way to diagnose issues is to change framework's mode.

The Framework provides the following modes:

• Mode.WORK, normal operation, no output messages, only if print( ... ) function is called from a task.
• Mode.DEBUG, print a lot of detailed messages, including those from modules used by framework (prestapyt, openai).
• Mode.VERBOSE, print only ecommquery related messages (no output messages from modules).
• Mode.PRETEND, 'dry run', do not change the system's state:
  • for Data source any read operation is performed normally, while modificatins are skiped - system prints only message what would be done.
  • for AI assistant prompts are not sent to AI but simply print.

Mode can be passed to Integrations object:

inegr = Integrations( Mode.DEBUG )

in this case the Mode.DEBUG will apply to all endpoints. Mode can be also set only for a certain 'problematic' endpoint:

endpoint = inegr.getService( mode = Mode.DEBUG, endpoint = 'presta_api' )

Modes can be combined:

inegr = Integrations( Mode.VERBOSE | Mode.PRETEND )

In above case framework will pretend that is "doing changes" but also be "verbosable".

prestapyt requires packaging module, but it is not automatically pulled, if you see:

ModuleNotFoundError: No module named 'distutils'

and Python version is >= 3.12, install packaging module, it solves the issue.

• PrestaShop 1.7 API
• PrestaShop 8 API
• Python PrestaShop API access module
• PrestaShop API access Pyton module - Prestapyt
• Docker container with Prestashop tuned for development environment
• OpenAI API reference
• Amazon SP API samples
• Allegro REST API - uwierzytelnianie i autoryzacja
• Allegro REST API - zarządzanie ofertami