o .lh)y@spddlZddlZddlmZmZddlZddlmZddl m Z ddl m Z ddl mZddlmZddlmZddl m Z dd lmZmZmZmZddlmZddlZddl m Z dd lmZmZmZddlZ  dTd ed edeeeefdeeeefdedede dedefddZdeeeefdeeeefdedefddZdeeeefdeeeefdefddZdeeeefdeeeefdededef ddZded ed!efd"d#Zd$d%Z e!d&krd'Z"d(Z#d)Z$d*d+d,d-d.d/d0d1d2d.d3d4d1d5d.d5d6d7d8d.d9d:d1d3d.d2d;d,dd?d=dd?}|D]5}|d@d!krNdAn |d@d#krWdBndC}|dD|dE|dFd'|dGd)|d@d*dH|dIdJ qCqt|}idK}|D]&}|d|d d }|d}|vrdK|<||7<||7}qdK}|dKkrdLdMdNdOdP}tfdQd |D}||dOd}|dR|dS|d*dT|dU|dV|dWt| dX|dYdZD]$}|dK}|dKkr||d}|d[|d'|d\|d*d]qfd^d/|D}|rX|d_|d2d`D]"}|dd}|da|d db|dd(d)|d*d] q5dc|S)dzJPrepare comprehensive department skills data for admin-level LLM analysis.cSh|]}|dqSskill_idr(.0skillr(r(r) z9_prepare_comprehensive_skills_analysis..zDEPARTMENT OVERVIEW: z total people z#CURRENT DEPARTMENT SKILLS ANALYSIS: skill_countd skill_level skill_typeUnknown skill_namec3|] }|vVqdSNlower)r0techr9r(r) z9_prepare_comprehensive_skills_analysis..)python javascriptjavareactnodezc++zc#z.netzProgramming & Developmentc3r:r;r<)r0devopsr?r(r)r@rA)docker kubernetesawsazuregcprGzci/cdjenkinszDevOps & Cloudc3r:r;r<)r0datar?r(r)r@rA)sqldatabase analyticsrNtableauzpower biexcelzData & Analyticsc3r:r;r<)r0mgmtr?r(r)r@rA)project management leadershipagilescrumteamzManagement & Leadershipc3r:r;r<)r0designr?r(r)r@rA)uiuxr[figma photoshopcreativezDesign & Creativec3r:r;r<)r0businessr?r(r)r@rA)rastrategyanalysis consultingsaleszBusiness & StrategyzOther Technical Skills)namecount percentagelevelFStrong(ModerateLimited • : /z people (.1fz%) at z level - z Coveragecg|] }|dvr|qSr-r(r/current_skill_idsr(r) z:_prepare_comprehensive_skills_analysis..zB SKILL GAPS - AVAILABLE IN ORGANIZATION BUT MISSING IN DEPARTMENT:zTotal Missing Skills: N • ... and more skillsz! SKILLS DISTRIBUTION BY CATEGORY:cs|]}|dVqdS)rgNr(r/r(r(r)r@csr{)rhNr(r/r(r(r)r@r|z z (Avg Coverage: z%):cSs|dS)Nrhr()xr(r(r)sz8_prepare_comprehensive_skills_analysis..T)keyreverserhu🟢u🟡u🔴z  rfrgz%) - riz levelr)Beginner IntermediateAdvancedExpertc3s$|] \}}|d|VqdS)rN)get)r0riweight)level_distributionr(r)r@s"z- STRATEGIC SUMMARY FOR ADMIN DECISION-MAKING:u' • Department Skill Maturity Score: z!% (Higher = More Advanced Skills)u • Current Skills Coverage: z different skillsu! • Potential Skill Expansion: z additional skills availableu • Skill Level Distribution:)rrrrz - z skill instances (z%)cs$g|]}|dddkr|qS)r4r5r(r/)r+r(r)rv(s$u6 ⚠️ CRITICAL COVERAGE GAPS (< 30% of department):u • z: Only  )appendranylenitemssumsortedjoin)rrr+analysis_partsskills_by_categoryr1r4skill_percentager6categorycoverage_statusmissing_skillsskillstotal_people_in_category avg_coverage sorted_skillscoverage_indicatortotal_current_skillstotal_skill_instancesrirgmaturity_score level_weightsweighted_totalrhlow_coverage_skillsr()rurr9r+r)rs    (@          2 rcspdd|Dfdd|D}|sdSdd|ddD}d |}t|dkr6|d t|dd 7}|S) zTPrepare a summary of skills available in organization but not in current department.cSr,r-r(r/r(r(r)r23r3z4_prepare_available_skills_summary..crsr-r(r/rtr(r)rv4rwz5_prepare_available_skills_summary..zAAll organizational skills are already present in this department.cSsg|]}|dqSr?r(r/r(r(r)rv9r3N, z ... and rz)rr)rravailable_skills skill_namesresultr(rtr)!_prepare_available_skills_summary1s  r thresholdc Cst||}g}g}g}dd|D}|D]5} | d|d} | d|kr8|| d| d| | dddq|| d| d| | dddq|D]} | d |vr\|| dqMg} |r| d |D]'} | d | d d | dd|d| ddd|| dd|dd qh|r| d| d d|ddt|dkr| dt|dd|r| d|D]} | d | d d| dd|d| ddd qd| S) zBAnalyze and prioritize skills based on saturation levels and gaps.cSr,r-r(r/r(r(r)r2Kr3z,_analyze_skill_priorities..r4r5r9r7r8)rfrgrhtyper.z4HIGH PRIORITY - Under-represented skills to develop:rorfz : Currently rgrqz (rhrrz %) - Need z more to reach %z/ MEDIUM PRIORITY - Missing skills to introduce:rN ryrzzH LOWER PRIORITY - Well-saturated skills (focus on depth/specialization):rpz%) - Well coveredr)intrrrr) rrr+rthreshold_counthigh_priority_skillslow_priority_skillsrrur1rpriority_analysisr(r(r)_analyze_skill_prioritiesAsN        L   4 r course_namescourse_descriptions course_idscCs@t|}t|}t|}ddt|||D}d|i}|S)NcSsg|] \}}}|||dqS))rf descriptioncourseIdr()r0rfdesccidr(r(r)rvs z.string_course_data_to_json..courses)jsonloadszip)rrrcourse_names_listcourse_descriptions_listcourse_ids_list courses_listrNr(r(r)string_course_data_to_jsonzs    rcCs8|r|stdtddgdd}tgddd}tdd d }|}t|tr*|}nt|trCtd d |Dr>d |}n t|}nt|}||B}||B} ||d} |||d} | | } | | } | j }| j }|dd}|dd}t |}t |}|| dg| di dgd}|S)Nz0Goal/problem and missing skills must be providedrmissing_skills_stra You are a professional learning and development advisor. Given the user's business goal and their skills analysis, output the recommendation report in **STRICT JSON FORMAT**. ### JSON Output Format: { "Goal":{ "goalTitle": "string", "course_recommendations_online": [ { "platform": "Platform name", "course_name": "Course title", "level": "Beginner / Intermediate / Advanced", "why_recommended": "Reason why the course is useful", "relevance_score": integer (0-100), "link": "Course URL or 'Available on [Platform]'" } ] } } **Rules:** - Only return valid JSON, no markdown, no headings. - Use double quotes "" around all keys and string values. - Ensure the JSON is syntactically correct (ready for `json.loads()`). - If a field is missing, use an empty string "" or empty array []. - Only recommend courses with relevance_score more than 75 - Provide 8-10 courses maximum --- ### Inputs: Goal / Problem: {goal} Skills analysis: {missing_skills_str} )input_variablestemplate)rrcourse_data_jsona- You are a professional learning and development advisor. You are given: - A business goal - Analysis of skills - A database of available courses (with name, id, and description) ### Business Goal: {goal} ### Skill analysis: {missing_skills_str} ### Available Courses: {course_data_json} **PRIORITY FRAMEWORK FOR COURSE SELECTION:** 1. **PRIMARY**: Courses that directly support the business goal 2. **SECONDARY**: Courses that address skill gaps identified in the analysis 3. **TERTIARY**: Courses that enhance existing skill levels (beginner to intermediate/advanced) Your task is to: 1. Evaluate each course for alignment with the goal and skills analysis using the priority framework above. 2. Select the most relevant courses (minimum 5) giving highest priority to goal alignment. 3. For each course, return ONLY the following fields: - "course_name": string - "course_id": string - "reason_to_recommend": string (explain how it supports the goal) - "relevance_score": integer (0-100, with goal alignment weighted heavily) Only return JSON Output. class SimpleOfflineCourseRecommendation(BaseModel): ### JSON Output Format: { "goaltitle": "string", "course_recommendations_offline": [ { "course_name": "Course title", "course_id": "Course ID from course_data_json", "why_recommended": "Reason why the course is useful", "relevance_score": integer (0-100), "link": "Course URL or 'Available on [Platform]'" } ] } **Rules:** - Only return valid JSON, no markdown, no headings. - Use double quotes "" around all keys and string values. - Ensure the JSON is syntactically correct (ready for `json.loads()`). - Only include courses with relevance_score more than 75 rrrcss|]}t|tVqdSr;) isinstancer!)r0itemr(r(r)r@ sz.r)rrz```jsonz```course_recommendations_offlineGoalcourse_recommendations_online)rrr) ValueErrorrrr rr!listallrrrrrr)rrcourses_availablecourse_recommendation_prompt2'course_recommendation_prompt_simplifiedrgoal_strronline_course_extraction_chainoffline_course_extraction_chaininput_data_onlineinput_data_offlinerecommendations_offlinerecommendations_onlinerecommendations_offline_data1recommendations_online_data1recommendations_offline_data2recommendations_online_data2recommendations_offline_datarecommendations_online_data final_outputr(r(r))recommend_online_offline_courses_goal_newsX.=         r__main__zOur goal is to improve customer satisfaction scores by 20% in the next 6 months. Please identify training needs that can help frontline employees and support teams contribute to this goal. EngineeringrrPythonr )r.r9r7r4r JavaScriptrrzProject ManagementrSQLrrxrDockerReact)r.r9 KubernetesAWS DevOpsrzNode.js zCI/CDzAgile MethodologyPuru)rrrrrr)zPython for BeginnerszData Science Essentialsz"Machine Learning with Scikit-LearnzDeep Learning with TensorFlowzSales FundamentalszCRM & Customer Engagement)zoLearn the fundamentals of Python programming, including variables, loops, functions, and basic data structures.zqCovers the basics of data analysis, statistics, and visualization using tools like Pandas, NumPy, and Matplotlib.zhBuild and evaluate models using regression, classification, and clustering techniques with Scikit-Learn.zWExplore neural networks, CNNs, and RNNs using TensorFlow to build deep learning models.zMaster the basics of selling, including prospecting, lead qualification, objection handling, and closing techniques to drive revenue growth.zLearn how to effectively use CRM tools to manage leads, track sales pipelines, and build lasting customer relationships through personalized engagement.)123456z<============================================================z(SKILLS ANALYSIS REPORT - ADMIN DASHBOARD zMachine LearningzData AnalyticszBusiness Intelligence)Nr)5rretypingrrpandaspdlangchain_groqrlangchain_ollamarlangchain_core.promptsrlangchain.chainsrlangchain.output_parsersrlangchain_core.pydantic_v1r r r r r rr!rr*rrfloatrrr__name__ sample_goalsample_departmentsample_total_userssample_mapping_skillssample_all_skillsr admin_reportrrrdumpscourse_names_jsoncourse_descriptions_jsoncourse_ids_json course_datarecommend_coursesprintdata_focused_reportrecommend_courses_2r(r(r(r)s             .|" 9 2