ai ??

ai_integration/models/ai_provider_instance.py

@@ -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.model
-    @api.onchange('provider_id')
+    def get_default_instance(self):
-    def _onchange_provider_id(self):
+        """Get the default AI provider instance to use.
-        if self.provider_id:
+        
-            self.provider_type = self.provider_id.code
+        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'))
-

ai_integration_ollama_api/__manifest__.py

@@ -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',

ai_integration_ollama_api/data/ai_provider_instance_data.xml

@@ -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>

ai_integration_ollama_api/migrations/1.0.0/post-migration.py

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

ai_integration_ollama_api/models/ai_provider_instance.py

@@ -36,16 +36,16 @@ class OllamaAIProviderInstance(models.Model):
         else:
             # Clear Ollama-specific fields
             self.update({
-                'num_ctx': False,
+                'num_ctx': False,  # Context length
-                'temperature': False,
+                'temperature': False,  # Sampling temperature
-                'top_p': False,
+                'top_p': False,  # Nucleus sampling threshold
-                'top_k': False,
+                'top_k': False,  # Top-k sampling threshold
-                'repeat_penalty': False,
+                'repeat_penalty': False,  # Penalty for repeated tokens
-                'repeat_last_n': False,
+                'repeat_last_n': False,  # Number of tokens to consider for repeat penalty
-                'num_thread': False,
+                'num_thread': False,  # Number of CPU threads to use
-                'num_gpu': False,
+                'num_gpu': False,  # Number of GPUs to use
-                'num_batch': False,
+                'num_batch': False,  # Batch size for inference
-                'model_name': False,
+                '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',
+                'type': 'ir.actions.act_window',
-                'tag': 'display_notification',
+                'res_model': 'ai.provider.instance',
-                'params': {
+                '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)))

ai_integration_ollama_api/models/ollama_provider_mixin.py

@@ -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',
+        string='Top P (Nucleus Sampling)',
-        help='Nucleus sampling: only consider the tokens whose cumulative probability exceeds '
+        help='Limits the cumulative probability of tokens to sample from. Only the most likely '
-             'this value. Lower values make the output more focused. '
+             'tokens with total probability mass of top_p are considered. '
-             'Range: [0.0 - 1.0]',
+             '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: