""" ACRS (Automated Complaint Resolution System) dispatch logic. Classifies conversations via LLM and POSTs to ACRS API. Extracted from PipelineSession for maintainability. All functions take `p` (pipeline session instance) as first argument to access session state (conversation_log, acrs config, llm, etc.). """ import json from datetime import datetime import requests import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) def dispatch_to_acrs(p): """Classify conversation via LLM and POST to ACRS API. Returns result dict. Args: p: PipelineSession instance. """ if not p.conversation_log: return {'success': False, 'message': 'No conversation to dispatch'} # Step 1: Format conversation text conversation_text = "\n".join( f"[{m['timestamp']}] {m['speaker']}: {m['text']}" for m in p.conversation_log ) # Step 2: Build dynamic classification prompt from dashboard config dept_desc = "" for d in p.acrs_departments: cats = ", ".join(f"ID {c['id']}: {c['name']}" for c in d.get('categories', [])) dept_desc += f" - Department ID {d['id']}: {d['name']}" if cats: dept_desc += f" (Categories: {cats})" dept_desc += "\n" type_desc = "\n".join(f" - Type ID {t['id']}: {t['name']}" for t in p.acrs_types) subcat_desc = "\n".join(f" - Subcategory ID {s['id']}: {s['name']}" for s in p.acrs_subcategories) rating_desc = "\n".join(f" - Rating ID {r['id']}: {r['name']}" for r in p.acrs_ratings) prompt = f"""Analyze this customer service conversation and classify it. Your response MUST be a valid JSON object with these fields: {{ "departmentId": , "typeId": , "categoryId": , "subCategoryId": , "ratingId": , "summary": "<1-2 sentence summary>", "emailSubject": "" }} AVAILABLE OPTIONS: Departments and their categories: {dept_desc if dept_desc else " (none configured)"} Types: {type_desc if type_desc else " (none configured)"} Subcategories: {subcat_desc if subcat_desc else " (none configured)"} Ratings: {rating_desc if rating_desc else " (none configured)"} RULES: - You MUST pick IDs from the options above. Do not invent new IDs. - If no clear match, pick the closest available option. - "summary" should be a concise 1-2 sentence description of the conversation. - "emailSubject" should be a short topic/subject (max 100 chars). - Response must be valid JSON only, no additional text. Conversation to analyze: \"\"\"{conversation_text}\"\"\"""" # Step 3: Call LLM for classification print(f'[ACRS] Classifying conversation ({len(p.conversation_log)} messages)...') try: response = p.llm.chat.completions.create( model=p.llm_model, messages=[{"role": "user", "content": prompt}], temperature=0.1 ) raw = response.choices[0].message.content.strip() if raw.startswith("```json"): raw = raw.replace("```json", "").replace("```", "").strip() elif raw.startswith("```"): raw = raw.replace("```", "").strip() classification = json.loads(raw) print(f'[ACRS] Classification: {json.dumps(classification, indent=2)}') except Exception as e: print(f'[ACRS] Classification error: {e}') return {'success': False, 'message': f'Classification failed: {e}'} # Step 4: Build payload payload = { "spAccount": p.acrs_sp_account or "admin", "incidentDate": datetime.now().strftime('%Y-%m-%d'), "departmentId": classification.get('departmentId'), "typeId": classification.get('typeId'), "categoryId": classification.get('categoryId'), "subCategoryId": classification.get('subCategoryId'), "name": p.session_profile or "Unknown", "originalEmailContent": conversation_text, "noc": classification.get('summary', ''), "emailSubject": classification.get('emailSubject', ''), "ratingId": classification.get('ratingId'), } # Merge any custom fields from dashboard for k, v in p.acrs_custom_fields.items(): if k not in payload: payload[k] = v print(f'[ACRS] Payload: {json.dumps(payload, indent=2)}') # Step 5: POST to ACRS API headers = { "X-API-KEY": p.acrs_api_key, "Content-Type": "application/json", "Accept": "application/json", } try: resp = requests.post( p.acrs_api_url, json=payload, headers=headers, timeout=10, verify=False ) print(f'[ACRS] Response: {resp.status_code}') if resp.status_code in (200, 201): resp_data = {} try: resp_data = resp.json() except Exception: pass case_no = resp_data.get('caseNo', resp_data.get('id', '')) print(f'[ACRS] Dispatch successful. Case: {case_no}') return {'success': True, 'message': 'Dispatch successful', 'case_no': str(case_no)} else: body = resp.text[:200] print(f'[ACRS] Dispatch failed ({resp.status_code}): {body}') return {'success': False, 'message': f'API returned {resp.status_code}'} except Exception as e: print(f'[ACRS] Request error: {e}') return {'success': False, 'message': f'Request failed: {e}'} def dispatch_callback_to_acrs(p, customer_name, customer_phone, reason): """POST callback request to ACRS API. Department/type/rating from defaults, category/subcategory from LLM. Args: p: PipelineSession instance. customer_name: Customer's name. customer_phone: Customer's phone number. reason: Reason for the callback. """ conversation_text = "\n".join( f"[{m['timestamp']}] {m['speaker']}: {m['text']}" for m in p.conversation_log ) if p.conversation_log else "" # LLM classify category + subcategory from conversation category_id = None subcategory_id = None # Build available options for the selected department's categories dept_cats = [] for d in p.acrs_departments: if d.get('id') == p.acrs_callback_dept_id: dept_cats = d.get('categories', []) break cat_desc = "\n".join(f" - Category ID {c['id']}: {c['name']}" for c in dept_cats) subcat_desc = "\n".join(f" - Subcategory ID {s['id']}: {s['name']}" for s in p.acrs_subcategories) if (dept_cats or p.acrs_subcategories) and (conversation_text or reason): prompt = f"""Classify this callback request into a category and subcategory. Your response MUST be a valid JSON object: {{"categoryId": , "subCategoryId": }} AVAILABLE OPTIONS: Categories: {cat_desc if cat_desc else " (none configured)"} Subcategories: {subcat_desc if subcat_desc else " (none configured)"} RULES: - Pick IDs from the options above only. Do not invent new IDs. - If no clear match, pick the closest available option. - Response must be valid JSON only, no additional text. Callback reason: {reason} Conversation: \"\"\"{conversation_text}\"\"\"""" print(f'[ACRS Callback] Classifying category/subcategory...') try: response = p.llm.chat.completions.create( model=p.llm_model, messages=[{"role": "user", "content": prompt}], temperature=0.1 ) raw = response.choices[0].message.content.strip() if raw.startswith("```json"): raw = raw.replace("```json", "").replace("```", "").strip() elif raw.startswith("```"): raw = raw.replace("```", "").strip() classification = json.loads(raw) category_id = classification.get('categoryId') subcategory_id = classification.get('subCategoryId') print(f'[ACRS Callback] Classification: categoryId={category_id}, subCategoryId={subcategory_id}') except Exception as e: print(f'[ACRS Callback] Classification error (proceeding without): {e}') payload = { "spAccount": p.acrs_sp_account or "admin", "incidentDate": datetime.now().strftime('%Y-%m-%d'), "departmentId": p.acrs_callback_dept_id, "typeId": p.acrs_callback_type_id, "categoryId": category_id, "subCategoryId": subcategory_id, "ratingId": p.acrs_callback_rating_id, "name": customer_name or p.session_profile or "Unknown", "originalEmailContent": conversation_text, "noc": f"Callback: {customer_name} ({customer_phone}). Reason: {reason}", "emailSubject": f"Callback Request: {reason[:80]}", } # Merge custom fields for k, v in p.acrs_custom_fields.items(): if k not in payload: payload[k] = v print(f'[ACRS Callback] Payload: {json.dumps(payload, indent=2)}') headers = { "X-API-KEY": p.acrs_api_key, "Content-Type": "application/json", "Accept": "application/json", } try: resp = requests.post( p.acrs_api_url, json=payload, headers=headers, timeout=10, verify=False ) print(f'[ACRS Callback] Response: {resp.status_code}') if resp.status_code in (200, 201): resp_data = {} try: resp_data = resp.json() except Exception: pass case_no = resp_data.get('caseNo', resp_data.get('id', '')) print(f'[ACRS Callback] Dispatch successful. Case: {case_no}') return {'success': True, 'message': 'Callback dispatch successful', 'case_no': str(case_no)} else: body = resp.text[:200] print(f'[ACRS Callback] Dispatch failed ({resp.status_code}): {body}') return {'success': False, 'message': f'API returned {resp.status_code}'} except Exception as e: print(f'[ACRS Callback] Request error: {e}') return {'success': False, 'message': f'Request failed: {e}'}