iabddlmZddlmZddlmZddlZddlmZddl Z ddl Z GddeZ y))render)APIView)ResponseNstatusc"eZdZdedefdZdZy) PromptViewtextreturnctjdd|tjj}t j |S)Nz ```json|```)flags)resub IGNORECASEstripjsonloads)selfr cleaneds &/var/www/auto_recruiter/arllm/views.pyextract_json_from_llmz PromptView.extract_json_from_llm s2&&TGMMOzz'""cF|j}|jdd}d|d}td|tjd|ddd  }|r:|j |d }t|t |tj St d tj S)Npromptr aRole: Act as an Expert Technical Recruiter and Data Parsing AI. Input Data: I will provide you with the raw text from an EMAIL BODY or PDF or Word, which containing an unstructured Job Description (JD). Your Task: Analyze: Read the text and identify key job details. Ignore email metadata (signatures, "Sent from my iPhone", greetings, and thread headers). Semantic Extraction & Tuning: Interpret Meaning: If the email says, "looking for a rockstar in Python," interpret this as "Expert proficiency in Python" under skills. Standardize: Expand only universal recruitment abbreviations (e.g., "wfh" -> "Remote"). Strictly preserve Client Acronyms, Project Codes, and Proper Nouns exactly as written (e.g., keep "LTA", "JPMC", "DBS" as is; do not expand them). Categorize: Semantically distinguish between what is "Required" (Must have) vs. "Preferred" (Nice to have/Bonus). Refine: Clean up the grammar and tone of the extracted text to be professional and suitable for a formal JD. Audit Transformation: Track every specific change made, including which input headers were mapped to which output keys. Format: Output the data strictly into the specific JSON format defined below. Rules for Extraction: If a field is not mentioned in the email, return "Not Specified". jd_date: Extract the date from the email text if present, otherwise use "Current". job_title : Retain the given title in the JD ai_title : Tuned and Generated Title from the JD job_type: type of Job in the JD example Contract or Employee or etc.. job_id: reference for the JD or JD identifier duration : Duration of the Job requirement in months, if not specified use 24+ months about_company: If not explicitly stated, try to infer the company name/domain from the context, or leave as "Not Specified". no_of_open_positions: If not explicitly stated, give default value as 1 primary_skills: highly important skills required for the JD secondary_skills: technical skills exclude the primary skills domain_requirements: must specified as domain skills in the JD Specific Instruction for "changes" field: You must generate a semicolon-separated string listing THREE types of changes: MAPPING: Which input section was mapped to which JSON key (e.g., "Mapped 'Key Responsibilities' to 'responsibilities'"). CORRECTION: Content fixes (e.g., "Corrected '58 years' to '5-8 Years'"). INFERENCE: Data derived from context (e.g., "Inferred 'LTA' is the Domain/Client"). Output Format: Return ONLY a single valid JSON object. Do not include any conversational text. ```json { "ai_title" : "String", "job_title" : "String", "job_type" : "String", "job_id" : "String", "about_company" : "String", "no_of_open_positions" : "String", "job_summary" : "String", "responsibilities" : "String", "domain_requirements" : "String", "certification_requirements" : "String", "security_clearance_requirements" : "String", "years_of_experience" : "String", "duration" : "String", "onsite_job" : "String", "job_location" : "String", "salary_range" : "String", "required_qualifications" : "String", "preferred_qualifications" : "String", "working_hours" : "String", "benefits" : "String", "requirement_priority" : "String", "search_pattern" : { "job_title": "String", "location": "String", "education": ["String"], "primary_skills": ["String"], "secondary_skills": ["String"], "soft_skills": ["String"], "tools_and_frameworks": ["String"], "salary_range": "String", "keywords": ["String"], "domain_requirements": ["String"] }, "changes": "String", } ``` Input Email Body: {z } zdata: zdeepseek-r1:7bi) num_threadnum_ctx)modelroptionsresponserzError in Prompt response) datagetprintollamageneraterrrHTTP_201_CREATEDHTTP_400_BAD_REQUEST) rrequestargskwargsr" prompt_data prompt_strprompt_response response_datas rpostzPromptView.posts||hhx, HPm QI T x$ //!    66z7RTM - M&2I2IJ J26;V;VWWrN)__name__ __module__ __qualname__strdictrr0rrr r s###$# `Xrr ) django.shortcutsrrest_framework.viewsrrest_framework.responserr%rest_frameworkrrrr r6rrr;s,#), ! fXfXr