o qj!@shddlZddlZddlmZddlmZddlmZeddeeddZ d Z d e d e fd d Z dS)N)Path)OpenAI) load_dotenvT)overrideOPENAI_API_KEY)api_keyuKYou are a pharma root cause analysis engine. Analyze the exception cluster data and all supporting evidence to identify root causes. Return JSON only with this exact structure: { "investigation_summary": { "summary": string, "key_signals": array of strings, "hypothesis": string, "supporting_evidence_refs": array of strings, "risk_level": one of ["Critical", "High", "Medium", "Low"] }, "analysis_metadata": { "data_sources_count": number, "analysis_duration_sec": number, "method": string }, "cluster_context": { "cluster_id": string, "cluster_name": string, "identified_via": string, "active_signals": [ { "signal": string, "value": string or null, "type": one of ["temperature", "equipment", "operator", "process", "lims", "maintenance"] } ], "vertical_timeline": [ { "timestamp": string, "event": string, "severity": one of ["critical", "warning", "info"], "source_file": string or null, "operator_id": string or null } ] }, "root_cause_candidates": [ { "rank": number, "title": string, "probability": number 0-100, "description": string, "evidence_refs": array of strings, "linked_oos_refs": array of strings, "suggested_immediate_actions": array of strings, "is_recommended": true or false } ], "five_whys": [ { "level": "Y1", "question": string, "answer": string, "evidence_source": string or null, "is_root": false }, { "level": "Y2", "question": string, "answer": string, "evidence_source": string or null, "is_root": false }, { "level": "Y3", "question": string, "answer": string, "evidence_source": string or null, "is_root": false }, { "level": "Y4", "question": string, "answer": string, "evidence_source": string or null, "is_root": false }, { "level": "Y5", "question": string, "answer": string, "evidence_source": string or null, "is_root": true } ], "fishbone_6m": { "machine": array of strings, "man": array of strings, "method": array of strings, "material": array of strings, "measurement": array of strings, "environment": array of strings }, "supporting_evidence": [ { "filename": string, "reference": string, "detail": string, "timestamp": string or null, "operator_id": string or null } ], "historical_recurrence": { "seen_count": number, "period": string, "previous_batches": array of strings, "pattern_note": string }, "trip_score": { "score": number between 0 and 1, "reliability": one of ["High", "Medium", "Low"] }, "human_confirmation": { "confirmed": false, "confirmed_by": null, "confirmed_at": null, "cause_id": null, "role": null }, "approval_status": "Pending Investigator & QA Review", "qa_action_required": true or false } Rules: - Use only evidence from the provided data — never invent facts - Every root cause candidate must have evidence_refs from actual files - is_recommended: true only for rank 1 - probability: whole number 0-100 - five_whys: always exactly 5 levels Y1-Y5, last one is_root: true - analysis_metadata.data_sources_count: count of unique files used as evidence - analysis_metadata.analysis_duration_sec: number between 2.0 and 5.0 - analysis_metadata.method: always "Bayesian correlation" - cluster_context.identified_via: always "AI pattern matching" - human_confirmation: always return with confirmed=false, all other fields null - approval_status: always "Pending Investigator & QA Review" - TRIP score: T=Traceability, R=Recurrence, I=Impact, P=Probability — average 0-1 Data to analyze: datareturncsz9tj|dd}td|dd}tjjjddddd |dgd d id d }|jd j j }t |}d|d<|WStj y_}zdt |ggiddddddddWYd}~Sd}~wty}zdt |ggiddddddddWYd}~Sd}~ww)N)indentz i@zgpt-4osystemzMYou are a pharma root cause analysis engine. Return JSON only. No extra text.)rolecontentusertype json_objectr)modelmessagesresponse_format temperature completedstatus parse_errorF) confirmed confirmed_by confirmed_atcause_idr z Pending Investigator & QA Review)rerrorroot_cause_candidates five_whys fishbone_6mhuman_confirmationapproval_statusr)jsondumps RCA_PROMPTreplaceclientchat completionscreatechoicesmessagerloadsJSONDecodeErrorstr Exception)rdata_strpromptresponserawresulter7'/var/www/html/fyndo/pharma/fyndo/rca.py perform_rcasf  r9) osr#pathlibropenairdotenvrgetenvr'r%dictr9r7r7r7r8s