From d289f199c9f1e780dccaf5f44b35a93d3fd0097d Mon Sep 17 00:00:00 2001 From: Enric Tobella Date: Mon, 1 Jun 2026 11:22:09 +0200 Subject: [PATCH 1/2] Update to use pgvector database --- .copier-answers.yml | 3 ++- .github/workflows/test.yml | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.copier-answers.yml b/.copier-answers.yml index 6eec438..e7d0eec 100644 --- a/.copier-answers.yml +++ b/.copier-answers.yml @@ -1,5 +1,5 @@ # Do NOT update manually; changes here will be overwritten by Copier -_commit: v1.42 +_commit: v1.43 _src_path: git+https://github.com/OCA/oca-addons-repo-template additional_ruff_rules: [] convert_readme_fragments_to_markdown: true @@ -16,6 +16,7 @@ odoo_test_flavor: Both odoo_version: 18.0 org_name: Odoo Community Association (OCA) org_slug: OCA +postgres_image: pgvector/pgvector:pg16 rebel_module_groups: [] repo_description: ai repo_name: ai diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 97ed5df..aa6cf01 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -42,7 +42,7 @@ jobs: makepot: "true" services: postgres: - image: postgres:12 + image: pgvector/pgvector:pg16 env: POSTGRES_USER: odoo POSTGRES_PASSWORD: odoo From 6ae96f4180fa5c84e75ae52d430a504c04fbc6ee Mon Sep 17 00:00:00 2001 From: Enric Tobella Date: Mon, 1 Jun 2026 13:27:00 +0200 Subject: [PATCH 2/2] [ADD] field_vector_config --- field_vector_config/README.rst | 145 ++++++ field_vector_config/__init__.py | 2 + field_vector_config/__manifest__.py | 19 + field_vector_config/fields.py | 43 ++ field_vector_config/models/__init__.py | 3 + field_vector_config/models/base.py | 82 +++ .../models/ir_model_field_vector.py | 110 ++++ field_vector_config/models/ir_model_fields.py | 17 + field_vector_config/pyproject.toml | 3 + field_vector_config/readme/CONFIGURE.md | 19 + field_vector_config/readme/CONTEXT.md | 3 + field_vector_config/readme/CONTRIBUTORS.md | 2 + field_vector_config/readme/DESCRIPTION.md | 2 + field_vector_config/readme/USAGE.md | 24 + .../security/ir.model.access.csv | 2 + .../static/description/icon.png | Bin 0 -> 9455 bytes .../static/description/index.html | 489 ++++++++++++++++++ field_vector_config/tests/__init__.py | 2 + field_vector_config/tests/fake_models.py | 15 + .../tests/fake_models_compute.py | 24 + field_vector_config/tests/test_vector.py | 347 +++++++++++++ .../tests/test_vector_compute.py | 97 ++++ .../views/ir_model_field_vector.xml | 91 ++++ test-requirements.txt | 1 + 24 files changed, 1542 insertions(+) create mode 100644 field_vector_config/README.rst create mode 100644 field_vector_config/__init__.py create mode 100644 field_vector_config/__manifest__.py create mode 100644 field_vector_config/fields.py create mode 100644 field_vector_config/models/__init__.py create mode 100644 field_vector_config/models/base.py create mode 100644 field_vector_config/models/ir_model_field_vector.py create mode 100644 field_vector_config/models/ir_model_fields.py create mode 100644 field_vector_config/pyproject.toml create mode 100644 field_vector_config/readme/CONFIGURE.md create mode 100644 field_vector_config/readme/CONTEXT.md create mode 100644 field_vector_config/readme/CONTRIBUTORS.md create mode 100644 field_vector_config/readme/DESCRIPTION.md create mode 100644 field_vector_config/readme/USAGE.md create mode 100644 field_vector_config/security/ir.model.access.csv create mode 100644 field_vector_config/static/description/icon.png create mode 100644 field_vector_config/static/description/index.html create mode 100644 field_vector_config/tests/__init__.py create mode 100644 field_vector_config/tests/fake_models.py create mode 100644 field_vector_config/tests/fake_models_compute.py create mode 100644 field_vector_config/tests/test_vector.py create mode 100644 field_vector_config/tests/test_vector_compute.py create mode 100644 field_vector_config/views/ir_model_field_vector.xml diff --git a/field_vector_config/README.rst b/field_vector_config/README.rst new file mode 100644 index 0000000..8765b09 --- /dev/null +++ b/field_vector_config/README.rst @@ -0,0 +1,145 @@ +================= +Field Vector Fill +================= + +.. + !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + !! This file is generated by oca-gen-addon-readme !! + !! changes will be overwritten. !! + !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + !! source digest: sha256:722ec05a7f6f24bb142697333fdd71c40ae7a2539242bd672c0affe2056d77c6 + !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + +.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png + :target: https://odoo-community.org/page/development-status + :alt: Beta +.. |badge2| image:: https://img.shields.io/badge/licence-AGPL--3-blue.png + :target: http://www.gnu.org/licenses/agpl-3.0-standalone.html + :alt: License: AGPL-3 +.. |badge3| image:: https://img.shields.io/badge/github-OCA%2Fai-lightgray.png?logo=github + :target: https://github.com/OCA/ai/tree/18.0/field_vector_config + :alt: OCA/ai +.. |badge4| image:: https://img.shields.io/badge/weblate-Translate%20me-F47D42.png + :target: https://translation.odoo-community.org/projects/ai-18-0/ai-18-0-field_vector_config + :alt: Translate me on Weblate +.. |badge5| image:: https://img.shields.io/badge/runboat-Try%20me-875A7B.png + :target: https://runboat.odoo-community.org/builds?repo=OCA/ai&target_branch=18.0 + :alt: Try me on Runboat + +|badge1| |badge2| |badge3| |badge4| |badge5| + +This module allows to configure vector fields dynamically. Quite +interesting if you want to handle them by using AI. + +**Table of contents** + +.. contents:: + :local: + +Use Cases / Context +=================== + +The original field_vector module implements vector configuration, +however, it is hard to handle how to create and search them. + +With this module, we added the functionality to do it. + +Configuration +============= + +With this module, we can easily add a configuration on a vector to +configure it dynamically on our database. + +.. code:: python + + from odoo.addons.field_vector_config.fields import ComputedVector + + + class ResPartner(models.Model): + _inherit = "res.partner" + + embedding = ComputedVector(string="Embedding") + +With that, we can go to **Settings / Technical / Database Structure** to +add the field manually and configure it. There you can configure the +size of the vector (depends on the method and model), computation +information and so on. + +Important notes: + +- If you make a field that is computed, we recommend to create it in a + pre_init_hook to avoid the creation and allow the user to configure it + properly +- If you change the size of the vector, update the column. It will + update the size and do nothing if it has the proper size. + +Usage +===== + +After the configuration, we can easily compute and search using this +vector. For example: + +.. code:: python + + from odoo.addons.ai_tool.tools import aitool + + + class ProductProduct(models.Model): + + _inherit = "product.product" + + product_vector = ComputedVector( + compute="_compute_product_vector", + store=True, + ) + + @api.depends("name", "description") + def _compute_product_vector(self): + for record in self: + record.product_vector = record._encode_vector("product_vector", f"{record.name}\n{record.description}\n{record.description_purchase}")[0] + + def _find_vector_product(self, value, limit=5): + records = self.search_vector("product_vector", value, limit=limit) + return [{"id": r.id, "name": r.name, "description": r.description} for r in records] + +Bug Tracker +=========== + +Bugs are tracked on `GitHub Issues `_. +In case of trouble, please check there if your issue has already been reported. +If you spotted it first, help us to smash it by providing a detailed and welcomed +`feedback `_. + +Do not contact contributors directly about support or help with technical issues. + +Credits +======= + +Authors +------- + +* Dixmit + +Contributors +------------ + +- `Dixmit `__ + + - Enric Tobella + +Maintainers +----------- + +This module is maintained by the OCA. + +.. image:: https://odoo-community.org/logo.png + :alt: Odoo Community Association + :target: https://odoo-community.org + +OCA, or the Odoo Community Association, is a nonprofit organization whose +mission is to support the collaborative development of Odoo features and +promote its widespread use. + +This module is part of the `OCA/ai `_ project on GitHub. + +You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute. diff --git a/field_vector_config/__init__.py b/field_vector_config/__init__.py new file mode 100644 index 0000000..fedc70d --- /dev/null +++ b/field_vector_config/__init__.py @@ -0,0 +1,2 @@ +from . import models +from . import fields diff --git a/field_vector_config/__manifest__.py b/field_vector_config/__manifest__.py new file mode 100644 index 0000000..bf548d1 --- /dev/null +++ b/field_vector_config/__manifest__.py @@ -0,0 +1,19 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +{ + "name": "Field Vector Fill", + "summary": """Autogenerate vectors and add search functions""", + "version": "18.0.1.0.0", + "license": "AGPL-3", + "author": "Dixmit,Odoo Community Association (OCA)", + "website": "https://github.com/OCA/ai", + "depends": [ + "field_vector", + ], + "data": [ + "security/ir.model.access.csv", + "views/ir_model_field_vector.xml", + ], + "demo": [], +} diff --git a/field_vector_config/fields.py b/field_vector_config/fields.py new file mode 100644 index 0000000..2f32de7 --- /dev/null +++ b/field_vector_config/fields.py @@ -0,0 +1,43 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo.addons.field_vector.fields import Vector + + +def split_text_chunks(text, max_words=350, overlap=50): + words = text.split() + chunks = [] + start = 0 + while start < len(words): + chunk = " ".join(words[start : start + max_words]) + chunks.append(chunk) + start += max_words - overlap + return chunks + + +class ComputedVector(Vector): + type = "computed_vector" + + def __init__( + self, + dimensions=10, + **kwargs, + ): + # We set a default dimensions value + super().__init__(dimensions=dimensions, **kwargs) + + @property + def column_type(self): + return ("vector", "vector") + + def vector_dimensions(self, model): + dimensions = ( + model.env["ir.model.fields"] + .sudo() + .search( + [("model", "=", model._name), ("name", "=", self.name)], + limit=1, + ) + .vector_configuration_ids.dimensions + ) + return dimensions or super().vector_dimensions(model) diff --git a/field_vector_config/models/__init__.py b/field_vector_config/models/__init__.py new file mode 100644 index 0000000..23ef101 --- /dev/null +++ b/field_vector_config/models/__init__.py @@ -0,0 +1,3 @@ +from . import ir_model_field_vector +from . import ir_model_fields +from . import base diff --git a/field_vector_config/models/base.py b/field_vector_config/models/base.py new file mode 100644 index 0000000..51a04f8 --- /dev/null +++ b/field_vector_config/models/base.py @@ -0,0 +1,82 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo import api, fields, models +from odoo.exceptions import UserError +from odoo.tools import sql + +from ..fields import ComputedVector + + +class Base(models.AbstractModel): + _inherit = "base" + + @api.model + @api.readonly + @api.returns("self") + def search_vector(self, field_name, data, domain=None, limit=5, minim=0.5): + if domain is None: + domain = [] + if not isinstance(self._fields[field_name], ComputedVector): + raise UserError(f"Field {field_name} is not a computed_vector field") + to_search = self._encode_vector(field_name, data, is_search=True)[0] + query = self._search(domain, limit=limit) + distance = sql.SQL( + "%s <=> %s::vector", + self._field_to_sql(self._table, field_name, query), + to_search, + ) + sql_terms = [ + self._field_to_sql(self._table, "id", "query"), + sql.SQL("%s as distance", distance), + ] + query.order = "distance ASC" + if minim: + query.add_where(sql.SQL("%s < %s", distance, minim)) + self.env.cr.execute(query.select(*sql_terms)) + return self.browse([row[0] for row in self.env.cr.fetchall()]) + + @api.model + @api.readonly + @api.returns("self") + def search_vector_grouped( + self, field_name, data, final_field, domain=None, limit=5, minim=0.5 + ): + if domain is None: + domain = [] + if not isinstance(self._fields[field_name], ComputedVector): + raise UserError(f"Field {field_name} is not a computed_vector field") + if not isinstance(self._fields[final_field], fields.Many2one): + raise UserError(f"Field {final_field} is not a Many2one field") + to_search = self._encode_vector(field_name, data, is_search=True)[0] + query = self._search(domain, limit=limit) + distance = sql.SQL( + "MIN(%s <=> %s::vector)", + self._field_to_sql(self._table, field_name, query), + to_search, + ) + sql_terms = [ + self._field_to_sql(self._table, final_field, "query"), + sql.SQL("%s as distance", distance), + ] + query.order = "distance ASC" + query.groupby = self._field_to_sql(self._table, final_field, query) + if minim: + query.having = sql.SQL("%s < %s", distance, minim) + self.env.cr.execute(query.select(*sql_terms)) + rows = self.env.cr.fetchall() + return self[final_field].browse([row[0] for row in rows]) + + def _encode_vector(self, field_name, data, is_search=False): + config = ( + self.env["ir.model.fields"] + .sudo() + .search( + [("model", "=", self._name), ("name", "=", field_name)], + limit=1, + ) + .vector_configuration_ids + ) + if not config: + raise UserError(f"Field {field_name} is not configured") + return config._encode_vector(data, is_search=is_search) diff --git a/field_vector_config/models/ir_model_field_vector.py b/field_vector_config/models/ir_model_field_vector.py new file mode 100644 index 0000000..4199d9a --- /dev/null +++ b/field_vector_config/models/ir_model_field_vector.py @@ -0,0 +1,110 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo import api, fields, models +from odoo.tools import sql + +try: + import openai +except ImportError: + openai = None + + +class IrModelFieldVector(models.Model): + """ + Model to store vector configuration for ir.model.fields of type computed_vector + """ + + _name = "ir.model.field.vector" + _description = "Ir Model Field Vector" + _rec_name = "field_id" + + field_id = fields.Many2one( + "ir.model.fields", + ondelete="cascade", + required=True, + domain=[ + ("ttype", "=", "computed_vector"), + ("store", "=", True), + ], + ) + dimensions = fields.Integer() + vector_method = fields.Selection( + selection=lambda self: self._get_vector_methods(), + required=True, + ) + search_prefix = fields.Text( + help="""This prefix will be added to the text before encoding it for search. + It can be used to give specific instructions to the embedding model, + like 'Represent this sentence for searching relevant passages: '""" + ) + openai_url = fields.Char( + string="OpenAI URL", default="https://api.openai.com/v1/embeddings" + ) + openai_model = fields.Char(string="OpenAI Model", default="text-embedding-3-small") + openai_api_key = fields.Char(string="OpenAI API Key", groups="base.group_system") + fastembed_model = fields.Char( + string="FastEmbed Model", default="BAAI/bge-small-en-v1.5" + ) + compute = fields.Text(compute="_compute_compute") + + _sql_constraints = [ + ( + "field_id_uniq", + "unique (field_id)", + "A vector field should be associated to a single ir.model.fields record.", + ) + ] + + @api.depends("field_id") + def _compute_compute(self): + for record in self: + if record.field_id: + record.compute = ( + self.env[record.field_id.model] + .sudo() + ._fields[record.field_id.name] + .compute + ) + else: + record.compute = False + + def compute_values(self): + self.ensure_one() + if not self.compute: + return + getattr(self.env[self.field_id.model].search([]), self.compute)() + + def _get_vector_methods(self): + methods = [] + if openai: + methods.append(("openai", "OpenAI")) + return methods + + def update_column(self): + self.ensure_one() + model = self.env[self.field_id.model].browse() + columns = sql.table_columns(self.env.cr, model._table) + column = columns.get(self.field_id.name) + model._fields[self.field_id.name].update_db_column(model, column) + + def _encode_vector(self, data, is_search=False): + """ + It should allways return a list of vectors, even if the input is a single one. + """ + if is_search and self.search_prefix: + data = self.search_prefix + data + return getattr(self, f"_encode_vector_{self.vector_method}")(data) + + def _encode_vector_openai(self, text): + openai_client = openai.OpenAI(**self.sudo()._get_openai_client_parameters()) + response = openai_client.embeddings.create(model=self.openai_model, input=text) + return [embedding.embedding for embedding in response.data] + + def _get_openai_client_parameters(self): + params = {} + if self.openai_url: + params["base_url"] = self.openai_url + if self.openai_api_key: + params["api_key"] = self.openai_api_key + return params diff --git a/field_vector_config/models/ir_model_fields.py b/field_vector_config/models/ir_model_fields.py new file mode 100644 index 0000000..704af0e --- /dev/null +++ b/field_vector_config/models/ir_model_fields.py @@ -0,0 +1,17 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo import fields, models + + +class IrModelFields(models.Model): + _inherit = "ir.model.fields" + + ttype = fields.Selection( + selection_add=[("computed_vector", "Vector with automatic filling")], + ondelete={"computed_vector": "cascade"}, + ) + vector_configuration_ids = fields.One2many( + "ir.model.field.vector", + inverse_name="field_id", + ) diff --git a/field_vector_config/pyproject.toml b/field_vector_config/pyproject.toml new file mode 100644 index 0000000..4231d0c --- /dev/null +++ b/field_vector_config/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["whool"] +build-backend = "whool.buildapi" diff --git a/field_vector_config/readme/CONFIGURE.md b/field_vector_config/readme/CONFIGURE.md new file mode 100644 index 0000000..b94aa1c --- /dev/null +++ b/field_vector_config/readme/CONFIGURE.md @@ -0,0 +1,19 @@ +With this module, we can easily add a configuration on a vector to configure it dynamically on our database. + +```python +from odoo.addons.field_vector_config.fields import ComputedVector + + +class ResPartner(models.Model): + _inherit = "res.partner" + + embedding = ComputedVector(string="Embedding") +``` + +With that, we can go to **Settings / Technical / Database Structure** to add the field manually and configure it. +There you can configure the size of the vector (depends on the method and model), computation information and so on. + +Important notes: + +- If you make a field that is computed, we recommend to create it in a pre_init_hook to avoid the creation and allow the user to configure it properly +- If you change the size of the vector, update the column. It will update the size and do nothing if it has the proper size. diff --git a/field_vector_config/readme/CONTEXT.md b/field_vector_config/readme/CONTEXT.md new file mode 100644 index 0000000..044cc3a --- /dev/null +++ b/field_vector_config/readme/CONTEXT.md @@ -0,0 +1,3 @@ +The original field_vector module implements vector configuration, however, it is hard to handle how to create and search them. + +With this module, we added the functionality to do it. \ No newline at end of file diff --git a/field_vector_config/readme/CONTRIBUTORS.md b/field_vector_config/readme/CONTRIBUTORS.md new file mode 100644 index 0000000..2c066ba --- /dev/null +++ b/field_vector_config/readme/CONTRIBUTORS.md @@ -0,0 +1,2 @@ +- [Dixmit](https://www.dixmit.com) + - Enric Tobella diff --git a/field_vector_config/readme/DESCRIPTION.md b/field_vector_config/readme/DESCRIPTION.md new file mode 100644 index 0000000..41a25c6 --- /dev/null +++ b/field_vector_config/readme/DESCRIPTION.md @@ -0,0 +1,2 @@ +This module allows to configure vector fields dynamically. +Quite interesting if you want to handle them by using AI. diff --git a/field_vector_config/readme/USAGE.md b/field_vector_config/readme/USAGE.md new file mode 100644 index 0000000..67f8989 --- /dev/null +++ b/field_vector_config/readme/USAGE.md @@ -0,0 +1,24 @@ +After the configuration, we can easily compute and search using this vector. For example: + +```python +from odoo.addons.ai_tool.tools import aitool + + +class ProductProduct(models.Model): + + _inherit = "product.product" + + product_vector = ComputedVector( + compute="_compute_product_vector", + store=True, + ) + + @api.depends("name", "description") + def _compute_product_vector(self): + for record in self: + record.product_vector = record._encode_vector("product_vector", f"{record.name}\n{record.description}\n{record.description_purchase}")[0] + + def _find_vector_product(self, value, limit=5): + records = self.search_vector("product_vector", value, limit=limit) + return [{"id": r.id, "name": r.name, "description": r.description} for r in records] +``` \ No newline at end of file diff --git a/field_vector_config/security/ir.model.access.csv b/field_vector_config/security/ir.model.access.csv new file mode 100644 index 0000000..d1bb33b --- /dev/null +++ b/field_vector_config/security/ir.model.access.csv @@ -0,0 +1,2 @@ +id,name,model_id:id,group_id:id,perm_read,perm_write,perm_create,perm_unlink +access_ir_model_field_vector,access_ir_model_field_vector,model_ir_model_field_vector,base.group_user,1,1,1,1 diff --git a/field_vector_config/static/description/icon.png b/field_vector_config/static/description/icon.png new file mode 100644 index 0000000000000000000000000000000000000000..3a0328b516c4980e8e44cdb63fd945757ddd132d GIT binary patch literal 9455 zcmW++2RxMjAAjx~&dlBk9S+%}OXg)AGE&Cb*&}d0jUxM@u(PQx^-s)697TX`ehR4?GS^qbkof1cslKgkU)h65qZ9Oc=ml_0temigYLJfnz{IDzUf>bGs4N!v3=Z3jMq&A#7%rM5eQ#dc?k~! 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+

Field Vector Fill

+ + +

Beta License: AGPL-3 OCA/ai Translate me on Weblate Try me on Runboat

+

This module allows to configure vector fields dynamically. Quite +interesting if you want to handle them by using AI.

+

Table of contents

+ +
+

Use Cases / Context

+

The original field_vector module implements vector configuration, +however, it is hard to handle how to create and search them.

+

With this module, we added the functionality to do it.

+
+
+

Configuration

+

With this module, we can easily add a configuration on a vector to +configure it dynamically on our database.

+
+from odoo.addons.field_vector_config.fields import ComputedVector
+
+
+class ResPartner(models.Model):
+    _inherit = "res.partner"
+
+    embedding = ComputedVector(string="Embedding")
+
+

With that, we can go to Settings / Technical / Database Structure to +add the field manually and configure it. There you can configure the +size of the vector (depends on the method and model), computation +information and so on.

+

Important notes:

+
    +
  • If you make a field that is computed, we recommend to create it in a +pre_init_hook to avoid the creation and allow the user to configure it +properly
    • +
  • If you change the size of the vector, update the column. It will +update the size and do nothing if it has the proper size.
    • +
+
+
+

Usage

+

After the configuration, we can easily compute and search using this +vector. For example:

+
+from odoo.addons.ai_tool.tools import aitool
+
+
+class ProductProduct(models.Model):
+
+    _inherit = "product.product"
+
+    product_vector = ComputedVector(
+        compute="_compute_product_vector",
+        store=True,
+    )
+
+    @api.depends("name", "description")
+    def _compute_product_vector(self):
+        for record in self:
+            record.product_vector = record._encode_vector("product_vector", f"{record.name}\n{record.description}\n{record.description_purchase}")[0]
+
+    def _find_vector_product(self, value, limit=5):
+        records = self.search_vector("product_vector", value, limit=limit)
+        return [{"id": r.id, "name": r.name, "description": r.description} for r in records]
+
+
+
+

Bug Tracker

+

Bugs are tracked on GitHub Issues. +In case of trouble, please check there if your issue has already been reported. +If you spotted it first, help us to smash it by providing a detailed and welcomed +feedback.

+

Do not contact contributors directly about support or help with technical issues.

+
+
+

Credits

+
+

Authors

+
    +
  • Dixmit
    • +
+
+
+

Contributors

+ +
+
+

Maintainers

+

This module is maintained by the OCA.

+ +Odoo Community Association + +

OCA, or the Odoo Community Association, is a nonprofit organization whose +mission is to support the collaborative development of Odoo features and +promote its widespread use.

+

This module is part of the OCA/ai project on GitHub.

+

You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute.

+
+
+
+ + diff --git a/field_vector_config/tests/__init__.py b/field_vector_config/tests/__init__.py new file mode 100644 index 0000000..6e5e692 --- /dev/null +++ b/field_vector_config/tests/__init__.py @@ -0,0 +1,2 @@ +from . import test_vector +from . import test_vector_compute diff --git a/field_vector_config/tests/fake_models.py b/field_vector_config/tests/fake_models.py new file mode 100644 index 0000000..126cca6 --- /dev/null +++ b/field_vector_config/tests/fake_models.py @@ -0,0 +1,15 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo import fields, models + +from odoo.addons.field_vector_config.fields import ComputedVector + + +class VectorFakeModel(models.Model): + _name = "vector.fake.model" + _description = "Fake model to test vector configuration" + + name = fields.Char() + partner_id = fields.Many2one("res.partner") + description_vector = ComputedVector(dimensions=10) diff --git a/field_vector_config/tests/fake_models_compute.py b/field_vector_config/tests/fake_models_compute.py new file mode 100644 index 0000000..bcdfb83 --- /dev/null +++ b/field_vector_config/tests/fake_models_compute.py @@ -0,0 +1,24 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from odoo import api, fields, models + +from odoo.addons.field_vector_config.fields import ComputedVector + + +class VectorFakeModel(models.Model): + _name = "vector.fake.model" + _description = "Fake model to test vector configuration" + + name = fields.Char() + partner_id = fields.Many2one("res.partner") + description_vector = ComputedVector( + compute="_compute_description_vector", store=True + ) + + @api.depends("name") + def _compute_description_vector(self): + for record in self: + record.description_vector = record._encode_vector( + "description_vector", record.name or "" + )[0] diff --git a/field_vector_config/tests/test_vector.py b/field_vector_config/tests/test_vector.py new file mode 100644 index 0000000..726bb6e --- /dev/null +++ b/field_vector_config/tests/test_vector.py @@ -0,0 +1,347 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from unittest import mock + +from odoo_test_helper import FakeModelLoader + +from odoo.exceptions import UserError +from odoo.tests import TransactionCase + +from odoo.addons.field_vector.fields import VectorValue + + +class TestVector(TransactionCase): + @classmethod + def setUpClass(cls): + super().setUpClass() + # Load fake models ->/ + cls.loader = FakeModelLoader(cls.env, cls.__module__) + cls.loader.backup_registry() + from .fake_models import VectorFakeModel + + cls.loader.update_registry((VectorFakeModel,)) + + cls.addClassCleanup(cls.loader.restore_registry) + + cls.vector_model = cls.env["vector.fake.model"] + cls.partner_01 = cls.env["res.partner"].create({"name": "Partner 01"}) + cls.partner_02 = cls.env["res.partner"].create({"name": "Partner 02"}) + + def test_vector(self): + vector_field = self.vector_model._fields["description_vector"] + self.assertEqual( + 10, + vector_field.get_current_vector_size( + self.env.cr, self.vector_model._table, vector_field.name + ), + ) + self.assertEqual(10, vector_field.vector_dimensions(self.vector_model)) + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + self.assertEqual( + 10, + vector_field.get_current_vector_size( + self.env.cr, self.vector_model._table, vector_field.name + ), + ) + self.assertEqual(20, vector_field.vector_dimensions(self.vector_model)) + field_config.update_column() + self.assertEqual( + 20, + vector_field.get_current_vector_size( + self.env.cr, self.vector_model._table, vector_field.name + ), + ) + self.assertEqual(20, vector_field.vector_dimensions(self.vector_model)) + + def test_vector_encoding(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + record_01 = self.vector_model.create({"name": "Test"}) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.5] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + record_01.description_vector = record_01._encode_vector( + "description_vector", "MY OWN QUERY" + )[0] + self.assertEqual(record_01.description_vector, VectorValue([0.5] * 20)) + record_02 = self.vector_model.create({"name": "Test"}) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.2] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + record_02.description_vector = record_02._encode_vector( + "description_vector", "MY OWN QUERY" + )[0] + self.assertEqual(record_02.description_vector, VectorValue([0.2] * 20)) + record_03 = self.vector_model.create({"name": "Test"}) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.6] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + record_03.description_vector = record_03._encode_vector( + "description_vector", "MY OWN QUERY" + )[0] + self.assertEqual(record_03.description_vector, VectorValue([0.6] * 20)) + + def test_search(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + record_01 = self.vector_model.create( + { + "name": "Test", + "description_vector": VectorValue([0.2] * 20), + "partner_id": self.partner_01.id, + } + ) + record_02 = self.vector_model.create( + { + "name": "Test", + "description_vector": VectorValue([0.5] * 20), + "partner_id": self.partner_02.id, + } + ) + record_03 = self.vector_model.create( + { + "name": "Test", + "description_vector": VectorValue([0.6] * 20), + "partner_id": self.partner_01.id, + } + ) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.45] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + results = self.vector_model.search_vector( + "description_vector", "MY OWN QUERY" + ) + self.assertEqual(len(results), 3) + self.assertEqual(results[0].id, record_02.id) + self.assertEqual(results[1].id, record_03.id) + self.assertEqual(results[2].id, record_01.id) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.45] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + results = self.vector_model.search_vector( + "description_vector", + "MY OWN QUERY", + domain=[("partner_id", "=", self.partner_01.id)], + ) + self.assertEqual(len(results), 2) + self.assertEqual(results[0].id, record_03.id) + self.assertEqual(results[1].id, record_01.id) + + def test_search_aggregated(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + self.vector_model.create( + [ + { + "name": "Test", + "description_vector": VectorValue([0.2] * 20), + "partner_id": self.partner_01.id, + }, + { + "name": "Test", + "description_vector": VectorValue([0.5] * 20), + "partner_id": self.partner_02.id, + }, + { + "name": "Test", + "description_vector": VectorValue([0.6] * 20), + "partner_id": self.partner_01.id, + }, + ] + ) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.56] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + results = self.vector_model.search_vector_grouped( + "description_vector", "MY OWN QUERY", "partner_id" + ) + self.assertEqual(len(results), 2) + self.assertEqual(results[0].id, self.partner_01.id) + self.assertEqual(results[1].id, self.partner_02.id) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.53] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + results = self.vector_model.search_vector_grouped( + "description_vector", "MY OWN QUERY", "partner_id" + ) + self.assertEqual(len(results), 2) + self.assertEqual(results[0].id, self.partner_02.id) + self.assertEqual(results[1].id, self.partner_01.id) + + def test_aggregated_wrong_field(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + with self.assertRaises(UserError): + self.vector_model.search_vector_grouped( + "description_vector", "MY OWN QUERY", "name" + ) + + def test_aggregated_wrong_base_field(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + with self.assertRaises(UserError): + self.vector_model.search_vector_grouped( + "name", "MY OWN QUERY", "partner_id" + ) + + def test_wrong_base_field(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + with self.assertRaises(UserError): + self.vector_model.search_vector( + "name", + "MY OWN QUERY", + ) + + def test_compute(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + self.assertFalse(field_config.compute) diff --git a/field_vector_config/tests/test_vector_compute.py b/field_vector_config/tests/test_vector_compute.py new file mode 100644 index 0000000..a0a935b --- /dev/null +++ b/field_vector_config/tests/test_vector_compute.py @@ -0,0 +1,97 @@ +# Copyright 2026 Dixmit +# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl). + +from unittest import mock + +from odoo_test_helper import FakeModelLoader + +from odoo.tests import TransactionCase + +from odoo.addons.field_vector.fields import VectorValue + + +class TestVectorCompute(TransactionCase): + @classmethod + def setUpClass(cls): + super().setUpClass() + # Load fake models ->/ + cls.loader = FakeModelLoader(cls.env, cls.__module__) + cls.loader.backup_registry() + from .fake_models_compute import VectorFakeModel + + cls.loader.update_registry((VectorFakeModel,)) + + cls.addClassCleanup(cls.loader.restore_registry) + + cls.vector_model = cls.env["vector.fake.model"] + + def test_compute(self): + field_config = self.env["ir.model.field.vector"].create( + { + "field_id": self.env["ir.model.fields"] + .search( + [ + ("model", "=", "vector.fake.model"), + ("name", "=", "description_vector"), + ], + limit=1, + ) + .id, + "dimensions": 20, + "vector_method": "openai", + } + ) + field_config.update_column() + self.assertTrue(field_config.compute) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.5] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + + mock_openai.return_value.embeddings.create.return_value = mock_response + records = self.env["vector.fake.model"].create( + [ + { + "name": "Test_01", + }, + { + "name": "Test_02", + }, + ] + ) + records.flush_recordset() + self.assertEqual( + VectorValue([0.5] * 20), + self.env["vector.fake.model"] + .search([("name", "=", "Test_01")], limit=1) + .description_vector, + ) + self.assertEqual( + VectorValue([0.5] * 20), + self.env["vector.fake.model"] + .search([("name", "=", "Test_02")], limit=1) + .description_vector, + ) + with mock.patch("openai.OpenAI") as mock_openai: + mock_embedding = mock.MagicMock() + mock_embedding.embedding = [0.3] * 20 + + mock_response = mock.MagicMock() + mock_response.data = [mock_embedding] + mock_openai.return_value.embeddings.create.return_value = mock_response + field_config.compute_values() + records.flush_recordset() + self.assertEqual( + VectorValue([0.3] * 20), + self.env["vector.fake.model"] + .search([("name", "=", "Test_01")], limit=1) + .description_vector, + ) + self.assertEqual( + VectorValue([0.3] * 20), + self.env["vector.fake.model"] + .search([("name", "=", "Test_02")], limit=1) + .description_vector, + ) diff --git a/field_vector_config/views/ir_model_field_vector.xml b/field_vector_config/views/ir_model_field_vector.xml new file mode 100644 index 0000000..b9e921e --- /dev/null +++ b/field_vector_config/views/ir_model_field_vector.xml @@ -0,0 +1,91 @@ + + + + + ir.model.field.vector + +
+
+
+ + + + + + + + + + + + + + + + + + + + + + +
+ + + + + ir.model.field.vector + + + + + + + + + + ir.model.field.vector + + + + + + + + + + Ir Model Field Vector + + ir.model.field.vector + list,form + [] + {} + + + + Field Vector Config + + + + + diff --git a/test-requirements.txt b/test-requirements.txt index 66bc2cb..1949538 100644 --- a/test-requirements.txt +++ b/test-requirements.txt @@ -1 +1,2 @@ odoo_test_helper +openai