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xtremxpert 2025-02-26 10:44:58 -05:00
parent f615f8c960
commit c507e5d416
6 changed files with 196 additions and 85 deletions
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23
ai_integration/models/ai_provider_instance.py
View file

@ -48,14 +48,20 @@ class BaseAIProviderInstance(models.Model):
api_key = fields.Char(
string='API Key',
help='API key if required by the provider',
invisible=lambda self: self.provider_type == 'ollama'
invisible="[('provider_type', '=', 'ollama')]" # Hide when provider type is ollama
)
@api.onchange('provider_id')
def _onchange_provider_id(self):
if self.provider_id:
self.provider_type = self.provider_id.code
@api.model
def get_default_instance(self):
"""Get the default AI provider instance to use.
Returns:
ai.provider.instance: The default instance to use, or raises UserError if none found
"""
instance = self.env['ai.provider.instance'].search([('active', '=', True)], limit=1)
if not instance:
raise UserError(_('No active AI provider instance found. Please configure one in the settings.'))
return instance
model_ids = fields.One2many(
'ai.model',
@ -76,6 +82,10 @@ class BaseAIProviderInstance(models.Model):
help='Maximum number of retry attempts for failed API calls'
)
@api.model
def _valid_field_parameter(self, field, name):
return name == 'invisible' or super()._valid_field_parameter(field, name)
_sql_constraints = [
('name_uniq',
'unique(name)',
@ -94,4 +104,3 @@ class BaseAIProviderInstance(models.Model):
self.ensure_one()
if self.provider_type == 'none':
raise UserError(_('Please select a provider type'))

3
ai_integration_ollama_api/__manifest__.py
View file

@ -1,6 +1,6 @@
{
'name': 'Ollama Integration',
'version': '1.0',
'version': '1.0.0',
'category': 'Technical',
'summary': 'Integration with Ollama AI models',
'description': """
@ -21,6 +21,7 @@ in your Odoo instance. Features include:
],
'data': [
'data/ai_provider_data.xml',
'data/ai_provider_instance_data.xml',
'views/ollama_stats_views.xml',
'views/ai_provider_instance_views.xml',
'security/ir.model.access.csv',

31
ai_integration_ollama_api/data/ai_provider_instance_data.xml Normal file
View file

@ -0,0 +1,31 @@
<?xml version="1.0" encoding="utf-8"?>
<odoo>
<data noupdate="1">
<!-- Default Ollama Provider Instance -->
<record id="ai_provider_instance_ollama_default" model="ai.provider.instance">
<field name="name">Ollama Local</field>
<field name="provider_type">ollama</field>
<field name="host">http://localhost:11434</field>
<field name="model_name">llama3.2</field>
<field name="temperature">0.7</field>
<field name="top_k">40</field>
<field name="top_p">0.9</field>
<field name="repeat_penalty">1.1</field>
<field name="num_ctx">4096</field>
<field name="num_predict">1024</field>
<field name="min_p">0.05</field>
<field name="repeat_last_n">64</field>
<field name="seed">0</field>
<field name="num_gpu">1</field>
<field name="num_thread">8</field>
<field name="mirostat">0</field>
<field name="mirostat_tau">5.0</field>
<field name="mirostat_eta">0.1</field>
<field name="num_batch">8</field>
<field name="num_keep">0</field>
<field name="tfs_z">1.0</field>
<field name="skip_special_tokens" eval="True"/>
<field name="active" eval="True"/>
</record>
</data>
</odoo>

17
ai_integration_ollama_api/migrations/1.0.0/post-migration.py Normal file
View file

@ -0,0 +1,17 @@
def migrate(cr, version):
# Add num_predict column if it doesn't exist
cr.execute("""
DO $$
BEGIN
IF NOT EXISTS (
SELECT 1
FROM information_schema.columns
WHERE table_name='ai_provider_instance'
AND column_name='num_predict'
) THEN
ALTER TABLE ai_provider_instance
ADD COLUMN num_predict integer DEFAULT 1024;
END IF;
END
$$;
""")

71
ai_integration_ollama_api/models/ai_provider_instance.py
View file

@ -36,16 +36,16 @@ class OllamaAIProviderInstance(models.Model):
else:
# Clear Ollama-specific fields
self.update({
'num_ctx': False,
'temperature': False,
'top_p': False,
'top_k': False,
'repeat_penalty': False,
'repeat_last_n': False,
'num_thread': False,
'num_gpu': False,
'num_batch': False,
'model_name': False,
'num_ctx': False, # Context length
'temperature': False, # Sampling temperature
'top_p': False, # Nucleus sampling threshold
'top_k': False, # Top-k sampling threshold
'repeat_penalty': False, # Penalty for repeated tokens
'repeat_last_n': False, # Number of tokens to consider for repeat penalty
'num_thread': False, # Number of CPU threads to use
'num_gpu': False, # Number of GPUs to use
'num_batch': False, # Batch size for inference
'model_name': False, # Model name/path
})
# Override default host for Ollama
@ -53,34 +53,6 @@ class OllamaAIProviderInstance(models.Model):
default='http://localhost:11434',
help='Ollama server host URL')
@api.onchange('provider_type')
def _onchange_provider_type(self):
"""Handle provider type changes.
When switching to 'ollama':
- Set the provider_id to the Ollama provider
- Set default host if empty
When switching away from 'ollama':
- Clear Ollama-specific fields
"""
if self.provider_type == 'ollama':
# Find and set the Ollama provider
ollama_provider = self.env['ai.provider'].search([('code', '=', 'ollama')], limit=1)
if ollama_provider:
self.provider_id = ollama_provider.id
if not self.host:
self.host = ollama_provider.default_host
else:
# Clear Ollama-specific fields
self.update({
'num_ctx': False, # Context length
'temperature': False, # Sampling temperature
'top_p': False, # Nucleus sampling threshold
'top_k': False, # Top-k sampling threshold
'repeat_penalty': False, # Penalty for repeated tokens
})
def test_connection(self):
"""Test the connection to the Ollama server.
@ -155,16 +127,27 @@ class OllamaAIProviderInstance(models.Model):
existing.write(vals)
else:
self.env['ai.model'].create(vals)
# Invalidate the cache to force reload of related records
self.invalidate_recordset(['model_ids'])
# Return action to reload the view completely
return {
'type': 'ir.actions.client',
'tag': 'display_notification',
'params': {
'type': 'ir.actions.act_window',
'res_model': 'ai.provider.instance',
'res_id': self.id,
'view_mode': 'form',
'target': 'current',
'flags': {
'mode': 'readonly',
'reload': True, # Force reload
},
'context': {'notification': {
'type': 'success',
'title': _('Success'),
'message': _('Successfully synchronized %d models', len(models)),
'sticky': False,
'type': 'success',
}
}}
}
except Exception as e:
raise UserError(_('Model synchronization failed: %s', str(e)))

136
ai_integration_ollama_api/models/ollama_provider_mixin.py
View file

@ -1,4 +1,10 @@
from odoo import models, fields, api, _
from odoo.exceptions import UserError
import requests
import logging
import json
_logger = logging.getLogger(__name__)
class OllamaProviderMixin(models.AbstractModel):
"""Mixin model that provides Ollama-specific configuration parameters.
@ -27,7 +33,7 @@ class OllamaProviderMixin(models.AbstractModel):
string='Model Name',
help='Name of the Ollama model to use (e.g. llama2, mistral, codellama)',
required=True,
default='llama2')
default='deepseek-r1:32b')
# Context Window Configuration
num_ctx = fields.Integer(
@ -35,7 +41,7 @@ class OllamaProviderMixin(models.AbstractModel):
help='Maximum number of tokens to consider for context. A larger context window allows '
'the model to access more historical information but requires more memory. '
'Range: [0 - 32768].',
default=4096)
default=8192)
# Generation Parameters
temperature = fields.Float(
@ -43,32 +49,14 @@ class OllamaProviderMixin(models.AbstractModel):
help='Controls randomness in the output. Higher values make the output more random, '
'while lower values make it more focused and deterministic. '
'Range: [0.0 - 2.0]',
default=0.8)
default=0.7)
top_p = fields.Float(
string='Top P',
help='Nucleus sampling: only consider the tokens whose cumulative probability exceeds '
'this value. Lower values make the output more focused. '
'Range: [0.0 - 1.0]',
string='Top P (Nucleus Sampling)',
help='Limits the cumulative probability of tokens to sample from. Only the most likely '
'tokens with total probability mass of top_p are considered. '
'Range: [0.0 - 1.0].',
default=0.9)
top_k = fields.Integer(
string='Top K',
help='Only consider the top K tokens for text generation. Lower values make the '
'output more focused. Set to 0 to disable. '
'Range: [0 - 100]',
default=40)
repeat_penalty = fields.Float(
string='Repeat Penalty',
help='Penalty for repeating tokens. Higher values make the output less repetitive. '
'Range: [0.0 - 2.0]',
default=1.1)
# Advanced Configuration
stop_sequences = fields.Char(
string='Stop Sequences',
help='Comma-separated list of sequences where the model should stop generating further tokens.')
top_k = fields.Integer(
string='Top K',
@ -77,13 +65,6 @@ class OllamaProviderMixin(models.AbstractModel):
'Range: [1 - 100].',
default=40)
top_p = fields.Float(
string='Top P (Nucleus Sampling)',
help='Limits the cumulative probability of tokens to sample from. Only the most likely '
'tokens with total probability mass of top_p are considered. '
'Range: [0.0 - 1.0].',
default=0.9)
min_p = fields.Float(
string='Min P',
help='Sets a minimum probability threshold for token selection. Range: [0.0 - 1.0].',
@ -96,10 +77,97 @@ class OllamaProviderMixin(models.AbstractModel):
default=1.1,
digits=(3, 2))
# Advanced Configuration
stop_sequences = fields.Char(
string='Stop Sequences',
help='Comma-separated list of sequences where the model should stop generating further tokens.')
num_predict = fields.Integer(
string='Maximum Tokens',
help='Maximum number of tokens to predict. Set to -1 for unlimited.',
default=2048)
repeat_last_n = fields.Integer(
string='Repeat Last N',
help='Sets the context window for repeat penalty. Range: [0 - 4096]. Default is 64, 0 disables.',
default=64)
default=64
)
def generate_text(self, prompt, **kwargs):
"""Generate text using the Ollama API.
Args:
prompt (str): The prompt to generate text from
**kwargs: Additional parameters to pass to the API
Returns:
str: The generated text
"""
self.ensure_one()
# Prepare the request
url = f"{self.host}/api/generate"
# Build the request data
data = {
'model': self.model_name,
'prompt': prompt,
'stream': False,
'num_ctx': self.num_ctx,
'temperature': self.temperature,
'top_k': self.top_k,
'top_p': self.top_p,
'repeat_penalty': self.repeat_penalty,
'repeat_last_n': self.repeat_last_n,
'num_predict': self.num_predict,
'min_p': self.min_p,
'seed': self.seed,
'num_gpu': self.num_gpu,
'num_thread': self.num_thread,
'mirostat': int(self.mirostat),
'mirostat_tau': self.mirostat_tau,
'mirostat_eta': self.mirostat_eta,
'num_batch': self.num_batch,
'num_keep': self.num_keep,
'tfs_z': self.tfs_z,
'skip_special_tokens': self.skip_special_tokens
}
# Add any additional parameters
if kwargs:
data.update(kwargs)
# Make the request
try:
_logger = logging.getLogger(__name__)
_logger.info("Sending request to Ollama API with data: %s", data)
response = requests.post(url, json=data, timeout=self.timeout)
response.raise_for_status()
# Log the raw response
_logger.info("Raw API response: %s", response.text)
# Parse the response
result = response.json()
response_text = result.get('response', '')
# Si la réponse est une chaîne JSON, la parser
try:
if isinstance(response_text, str):
parsed_response = json.loads(response_text)
_logger.info("Parsed nested JSON response: %s", parsed_response)
return parsed_response
else:
_logger.info("Direct response: %s", response_text)
return response_text
except json.JSONDecodeError:
# Si ce n'est pas du JSON valide, retourner le texte tel quel
_logger.info("Non-JSON response: %s", response_text)
return response_text
except requests.exceptions.RequestException as e:
raise UserError(_('Failed to generate text: %s') % str(e))
# Advanced Generation Parameters
seed = fields.Integer(
@ -174,6 +242,7 @@ class OllamaProviderMixin(models.AbstractModel):
"""Get Ollama-specific options for API calls."""
self.ensure_one()
options = {
'model': self.model_name,
'temperature': self.temperature,
'num_ctx': self.num_ctx,
'num_predict': self.num_predict,
@ -192,6 +261,7 @@ class OllamaProviderMixin(models.AbstractModel):
'num_keep': self.num_keep,
'tfs_z': self.tfs_z,
'skip_special_tokens': self.skip_special_tokens,
'stream': False
}
if self.stop_sequences: