🔍 Source Information from Helpdesk Ticket #{self.id}
This sale order was created from helpdesk ticket {self.name}
""" # Add original description if available if description: message_body += f"""Original Ticket Description:
{description}
Relevant Chatter Messages:
{chatter_messages}
Attachments: {len(attachments)} file(s) attached to this message
⚠️ {missing_count} product(s) could not be found in the database:
This message will be automatically deleted when the sale order is confirmed.
""" # Special marker for deletion try: # Post the message sale_order.message_post(body=message_body, subject="Products Not Found", body_is_html=True) # Update the sale order flags sale_order.write({ 'missing_product_count': missing_count, 'has_missing_products': True }) _logger.debug(f"Posted unfound products message for sale order {sale_order.id} with {missing_count} products") except Exception as e: _logger.error(f"Error posting unfound products message: {e}") def _get_sale_order_values(self) -> dict: """ Generate sales order values using AI to analyze ticket content, chatter messages, and attachments. The AI will identify products from the content and match them to Odoo products. Returns: dict: Values for creating a sales order including order lines """ self.ensure_one() _logger.debug(f"Generating AI sales order values for ticket {self.id}") # Get the ticket data ticket_data = self._prepare_ai_prompt_data() description = ticket_data.get('ticket_description', '') chatter_messages = ticket_data.get('ticket_messages', '') attachments_info = ticket_data.get('attachments_info', '') attachment_contents = ticket_data.get('attachment_contents', '') # Log the content being analyzed _logger.debug(f"AI Analysis - Content lengths: Description={len(description)}, Chatter={len(chatter_messages)}, Attachments={len(attachment_contents)}") # Get the OpenWebUI provider from company settings company = self.env.company provider = company.openwebui_provider_id if not provider: _logger.error("No OpenWebUI provider configured for company") return {"order_line": []} # Get the OpenWebUI client from the provider ai_client = provider.get_client() # Register the product finding methods as tools registry = ai_client.tool_registry registry.register( self._ai_find_product_id_by_name, non_ai_params=["self"], description="Find a product by its name and return its ID. Input: name (string) - The name of the product to find. Returns the product ID if found, or null if not found." ) registry.register( self._ai_find_product_id_by_code, non_ai_params=["self"], description="Find a product by its code/reference and return its ID. Input: code (string) - The code/reference of the product to find. Returns the product ID if found, or null if not found." ) # Process PDF attachments for analysis attachments_list = [] attachments = self.env['ir.attachment'].search([('res_id', '=', self.id), ('res_model', '=', self._name)]) for attachment in attachments: if attachment.mimetype == 'application/pdf': try: temp_path = f"/tmp/{attachment.name}" shutil.copy(attachment._full_path(attachment.store_fname), temp_path) attachments_list.append(Path(temp_path)) except Exception as e: _logger.error(f"Error processing attachment {attachment.name}: {e}") # Create the prompt for the AI prompt = f"""IMPORTANT: YOU ARE NOT A CONVERSATIONAL ASSISTANT. YOU ARE A DATA EXTRACTION SYSTEM. Your ONLY function is to analyze the provided content and return a structured JSON object so that later it can be used to create a sales order. DO NOT introduce yourself, explain what you can or cannot do, or engage in conversation. ONLY RETURN THE REQUESTED JSON DATA STRUCTURE. TASK: Extract product information and sales order details from the following content: Customer Request: {description} Chatter Messages (IMPORTANT - CAREFULLY ANALYZE THESE FOR PRODUCT INFORMATION): {chatter_messages} Attachments Information: {attachments_info} Attachment Contents (CRITICALLY IMPORTANT - ANALYZE PDF CONTENTS FOR ALL PRODUCT DETAILS): {attachment_contents} WORKFLOW - FOLLOW THESE STEPS EXACTLY: 1. Identify ONLY ACTUAL PRODUCTS mentioned in the content (product names, codes, references) from BOTH chatter messages AND PDF attachments 2. For EACH product identified (from BOTH sources): a. If you find a product code/reference, call _ai_find_product_id_by_code with that code b. If you only have a product name, call _ai_find_product_id_by_name with that name c. If the product is not found in the database, use the display_type: 'line_note' format with this EXACT format: "Unmatched product: Product name (qty: QUANTITY) (ref: REFERENCE if available)" 3. Extract order details (client reference, dates, notes) 4. Construct the JSON response using the product IDs you obtained from tool calls RESPONSE FORMAT: Your response MUST ONLY be a valid JSON object with the following structure: {{ "client_order_ref": "Customer PO number if mentioned", "date_order": "YYYY-MM-DD format if a specific order date is mentioned", "commitment_date": "YYYY-MM-DD format if a delivery date is mentioned", "note": "Any special instructions or notes for the order", "order_line": [ [0, 0, {{ "product_id": NUMERIC_ID_FROM_TOOL_CALL, // Must be an actual ID returned from a tool call "product_uom_qty": QUANTITY, "price_unit": PRICE }}], [0, 0, {{ "display_type": "line_note", "name": "Unmatched product: Product name (qty: QUANTITY) (ref: REFERENCE)", "product_uom_qty": 0.0 }}] ], "unfound_products": [ // IMPORTANT: Include this array with any products that could not be found in the database {{ "name": "Product name", "quantity": QUANTITY, "reference": "REFERENCE if available", "description": "Additional description if available" }} ] }} CRITICAL RULES FOR IDENTIFYING PRODUCTS: 1. A product MUST have AT LEAST ONE of the following to be considered a valid product: - A quantity AND a price - A specific product code/reference - A clearly identifiable product name with quantity 2. DO NOT identify random descriptions, paragraphs, or sections of text as products 3. Be aware that some items may have very long descriptions - this doesn't make them separate products 4. If a product contains subproducts (e.g., a kit or bundle), only identify the MAIN product, not each subproduct 5. If uncertain whether something is a product or just a description, look for quantity and price indicators 6. VERY IMPORTANT: Read product descriptions carefully as they often contain critical information to help identify the correct product 7. For unfound products, capture as much of the description as possible - this helps users identify what the product actually is CRITICAL RULES FOR RESPONSE: 1. You MUST use the provided tools for EVERY valid product mentioned in BOTH chatter messages AND PDF attachments 2. product_id MUST be a numeric ID returned by a tool call, NEVER make up IDs 3. For products not found in the database, use the display_type: 'line_note' format with this EXACT format: "Unmatched product: Product name (qty: QUANTITY) (ref: REFERENCE if available)" 4. Include as much detail as possible for unmatched products including quantity, reference, and description if available 5. Return ONLY valid JSON with no text before or after 6. DO NOT explain what you're doing or respond conversationally 7. DO NOT say you can't create a sales order - your job is ONLY to return the JSON data IMPORTANT: You must invoke the tools directly using function calling, not just output text that looks like a tool call. Use the provided tools via function calling for _ai_find_product_id_by_code and _ai_find_product_id_by_name. """ # Call the AI with tools try: _logger.debug("Sending request to AI for sales order generation") # First, get the AI's analysis with tool calls to find product IDs response = ai_client.chat_with_tools( messages=[ { "role": "system", "content": "You are a data extraction system with access to tools for finding product IDs in an Odoo database. YOU MUST USE THE TOOLS PROVIDED TO ACCURATELY MATCH PRODUCTS PROVIDED TO THE DATABASE. Your ONLY job is to extract product information and return a structured JSON object. DO NOT engage in conversation or explain what you can or cannot do. ONLY return the requested JSON data structure. CRITICALLY IMPORTANT: You MUST identify ONLY ACTUAL PRODUCTS mentioned in BOTH chatter messages AND PDF attachments. A valid product MUST have a quantity AND either a price or product code. DO NOT identify random descriptions or paragraphs as products. VERY IMPORTANT: Read product descriptions carefully as they often contain critical information to help identify the correct product. For ANY product that cannot be found in the database, you MUST include it as a line note with display_type: 'line_note' in your response, and include as much description as possible." }, { "role": "user", "content": prompt } ], tools=["_ai_find_product_id_by_name", "_ai_find_product_id_by_code"], tool_params={}, max_tool_calls=25, files=attachments_list ) _logger.debug(f"Received AI response for ticket {self.id}") except Exception as e: _logger.error(f"Error in AI request: {e}") import traceback _logger.error(f"Traceback: {traceback.format_exc()}") return { "order_line": [], "note": f"AI Error: {str(e)}" } # Process the AI response _logger.debug(f"AI response type: {type(response)}") # If it's a dictionary, use it directly if isinstance(response, dict): _logger.debug("AI returned dictionary response, using directly") response['unfound_products'] = self._extract_unfound_products(response) return response # Handle string responses (extract JSON if possible) if isinstance(response, str): _logger.debug("AI returned text response, attempting to extract JSON") try: # Look for JSON pattern in the text json_pattern = r'```(?:json)?\s*({[\s\S]*?})\s*```' json_matches = re.findall(json_pattern, response) if json_matches: response_data = json.loads(json_matches[0]) response_data['unfound_products'] = self._extract_unfound_products(response_data) return response_data elif response.strip().startswith('{') and response.strip().endswith('}'): response_data = json.loads(response.strip()) response_data['unfound_products'] = self._extract_unfound_products(response_data) return response_data else: _logger.error("Could not extract JSON from text response") return { "order_line": [], "note": f"AI returned invalid format: {response[:200]}..." } except Exception as parse_error: _logger.error(f"Failed to extract JSON from text response: {parse_error}") return { "order_line": [], "note": f"AI parsing error: {str(parse_error)}" } # If we get here, the response is in an unexpected format _logger.error(f"Unexpected response format: {type(response)}") return { "order_line": [], "note": f"AI returned unexpected format: {type(response)}" } def _prepare_ai_prompt_data(self): """ Extract and prepare all relevant data from the helpdesk ticket for AI analysis. This includes ticket description, chatter messages, and attachment contents. Returns: dict: Dictionary containing ticket data for AI analysis """ self.ensure_one() _logger.debug(f"Preparing AI prompt data for ticket {self.id}") result = { 'ticket_description': '', 'ticket_messages': '', 'attachments_info': '', 'attachment_contents': '' } # Get ticket description if self.description: result['ticket_description'] = self.description # Get chatter messages messages = [] if self.message_ids: for message in self.message_ids: if message.body and not message.is_internal: # Skip system messages and focus on actual conversation if not message.author_id or message.author_id.name != 'OdooBot': # Format: [Author] on [Date]: [Message] author = message.author_id.name if message.author_id else 'System' date = message.date.strftime('%Y-%m-%d %H:%M') if message.date else '' # Clean HTML from message body body = re.sub(r'<[^>]+>', ' ', message.body) messages.append(f"[{author}] on {date}: {body}") result['ticket_messages'] = '\n\n'.join(messages) # Get attachments attachments = self.env['ir.attachment'].search([('res_id', '=', self.id), ('res_model', '=', self._name)]) attachment_infos = [] attachment_contents = [] for attachment in attachments: # Add attachment metadata attachment_infos.append(f"File: {attachment.name} ({attachment.mimetype}, {attachment.file_size} bytes)") # Add PDF content reference if attachment.mimetype == 'application/pdf': attachment_contents.append(f"IMPORTANT PDF CONTENT: {attachment.name} - The AI must analyze this PDF for ALL product mentions and include ANY products found as either matched products or unmatched line notes") result['attachments_info'] = '\n'.join(attachment_infos) result['attachment_contents'] = '\n\n'.join(attachment_contents) return result def _prepare_order_line_values(self, product, quantity, description=""): """Prepare values for creating a sale order line""" return { 'product_id': product.id, 'product_uom_qty': quantity, 'name': description or product.name, } def _ai_find_product_id_by_name(self, product_name: str) -> int | None: """Find a product by name Args: product_name: The name of the product to find Returns: The ID of the product if found, or None if not found """ return self.env['product.product'].search([ ('name', 'ilike', product_name), ('sale_ok', '=', True) ], limit=1).id def _ai_find_product_id_by_code(self, product_reference: str) -> int | None: """Find a product by code Args: product_reference: The code of the product to find Returns: The ID of the product if found, or None if not found """ return self.env['product.product'].search([ ('default_code', 'ilike', product_reference), ('sale_ok', '=', True) ], limit=1).id def _extract_unfound_products(self, response_data): """ Extract information about products that could not be found in the database. This method analyzes the AI response to identify products that couldn't be matched. Args: response_data: The AI response data (dictionary) Returns: List of dictionaries with information about unfound products """ unfound_products = [] # Check if response has data if not response_data: return unfound_products # First check if there's a dedicated unfound_products array in the response if 'unfound_products' in response_data and isinstance(response_data['unfound_products'], list): _logger.debug(f"Found unfound_products array in AI response with {len(response_data['unfound_products'])} items") for product in response_data['unfound_products']: if isinstance(product, dict): product_info = {} # Extract product information from the unfound_products array if 'name' in product: product_info['name'] = product['name'] if 'quantity' in product: product_info['quantity'] = float(product['quantity']) if 'reference' in product: product_info['reference'] = product['reference'] if product_info and 'name' in product_info: unfound_products.append(product_info) # Also check for line notes in the order lines (for backward compatibility) if 'order_line' in response_data: for line in response_data.get('order_line', []): # Line format is typically [0, 0, {...}] if len(line) >= 3 and isinstance(line[2], dict): line_data = line[2] # Check if this is a line note for an unfound product if line_data.get('display_type') == 'line_note' and 'name' in line_data: name = line_data.get('name', '') # Extract product information from the note if 'Unmatched product:' in name or 'Product not found:' in name: # Parse the product information product_info = {} # Try to extract product name product_name_match = re.search(r'(?:Unmatched product:|Product not found:)\s*([^\(\)\[\],]+)', name) if product_name_match: product_info['name'] = product_name_match.group(1).strip() else: product_info['name'] = name.replace('Unmatched product:', '').replace('Product not found:', '').strip() # Try to extract quantity quantity_match = re.search(r'(?:qty|quantity|Qty|Quantity)[:\s]*(\d+(?:\.\d+)?)', name) if quantity_match: product_info['quantity'] = float(quantity_match.group(1)) # Try to extract reference/code ref_match = re.search(r'(?:ref|reference|code)[:\s]*([\w-]+)', name, re.IGNORECASE) if ref_match: product_info['reference'] = ref_match.group(1) # Any remaining text is considered description if 'description' not in product_info: product_info['description'] = '' product_info['description'] += ' ' + name if product_info['description'] else name # Add to the list of unfound products if product_info: unfound_products.append(product_info) # Return the extracted unfound products _logger.debug(f"Extracted {len(unfound_products)} unfound products from AI response") return unfound_products