o hW@sddlmZddlmZddlmZmZddlZddlZddl m Z e dde dZ e d Z e d Zejd e e ed d ZejedGdddejZGdddejZGdddeZGdddeZdS))Type)BaseTool) BaseModelFieldN) load_dotenvz7/home/azureuser/microlearn/backend/prompt_opt_dspy/.env) dotenv_path AZURE_API_KEYAZURE_API_HOSTAZURE_API_VERSIONzazure/Csqr-gpt-4o-minig)api_keyapi_base api_version temperature)lmc@sveZdZUdZeZeed<eZ eed<eZ eed<eZ eed<eZ eed<ej ddZeed <d S) DurationEstimatorSignatureuN You are a learning strategist designing microlearning journeys for non-technical professionals. I will provide you with: - The user’s job title - Their experience with the topic - Their current skills - A technical topic they need to learn - The desired proficiency level Instructions: 1. Based on the user’s job title, current skills, and experience **with the topic**, estimate the gap between their current capabilities and the skills needed to reach the desired proficiency level for the given topic. 2. Considering the user is non-technical and can dedicate a maximum of 360 minutes, suggest a suitable total time duration (in minutes, up to 360 minutes) that would be appropriate and realistic for a microlearning journey to bridge this gap. 3. Only output the recommended total time duration in minutes — no explanation or other details. Follow this structured reasoning: 1. **Topic Familiarity (Not Work Experience)**: Estimate the user's starting point based on their self-reported experience with the topic: - **Complete Beginner** → Starting from scratch - **Some Experience** → Aware of basic terms, limited application - **Intermediate** → Comfortable with fundamentals, can apply concepts - **Advanced** → Proficient, seeking mastery or advanced application 2. **Job Context and Learning Bandwidth**: - **Executives / Senior Professionals** → Need only conceptual understanding + business value focus → Limited time, faster grasp of strategic value → Recommend **shorter durations** - **Mid-Level Professionals** → Require **understanding + application** → Moderate time, good motivation → Recommend **efficient durations** - **Junior Professionals** → May need more foundational context → More time available, learning is slower → Recommend **longer durations** 3. **Proficiency Level Goals**: Match effort based on the target outcome: - **No-code** → Focus is on using tools or dashboards without scripting → typically fastest - **Low-code** → Involves configuration or automation with minimal scripting → moderate - **Hands-on Code** → Requires actual coding, debugging, and logic skills → longest duration - **Mixed Approach** → Combination of conceptual, tooling, and coding → variable but usually longer 4. **Topic Complexity Factor**: Use judgment on how inherently complex the topic is. For example: - "APIs" is simpler than "Machine Learning" for most non-technical learners - "Data Visualization" (No-code) may be faster to teach than "Building ETL Pipelines" (Hands-on Code) 5. **Make your estimate logical, realistic, and human-centered.** Output format: [Time Duration in minutes] User Profile: - Job Title: {Job_Title} - Experience with Topic: {Experience} - Current Skills: {Skills} - Target Topic: {topic} - Desired Proficiency Level: {Level} Job_Title ExperienceSkillstopicLevelzBRecommended learning journey duration in minutes (only the number))descduration_minutesN)__name__ __module__ __qualname____doc__dspy InputFieldrstr__annotations__rrrr OutputFieldrr!r!/home/azureuser/microlearn/backend/user_journey_with_openai/agentic_workflow/src/user_journey_service/tools/custom_duration_estimator_tool.pyrs Arcs$eZdZfddZddZZS)DurationEstimatorModulec sttt}tjddddddddd d d d tjd dddddddd d d d tjddddddddd d d d g}ddd}tj|d}|j||d|_ dS)NzData ScientistAdvancedzPython, StatisticszMachine LearningzHands-on coding240)rrrrrrrrrrrzMarketing ManagerzCampaigns, Content Strategyz Data AnalysiszLow-code approach180zProduct ManagerzSome experiencez(Strategy, Cross-functional collaborationzAI for BusinesszMixed approachcSs>zt|j}td|kodkWSWSYdS)Nrih)intrstrip)goldpredtracevalr!r!r"duration_metrics "z9DurationEstimatorModule.__init__..duration_metric)metric)studenttrainset)N) super__init__rPredictrExample with_inputsBootstrapFewShotcompileoptimized_predictor)self predictorr0r- optimizer __class__r!r"r2isH     z DurationEstimatorModule.__init__cCs|j|||||dSN)rrrrr)r8)r9rrrrrr!r!r"forwardszDurationEstimatorModule.forward)rrrr2r? __classcell__r!r!r<r"r#hs 0r#c@sveZdZUdZedddZeed<edddZeed<edddZ eed <edd dZ eed <edd dZ eed <dS)DurationEstimatorInputz'Input schema for DurationEstimatorTool..zUser's job title) descriptionrz&User's experience level with the topicrzUser's current skillsrzThe technical topic to learnrzDesired proficiency levelrN) rrrrrrrrrrrrr!r!r!r"rAs rAc @sXeZdZUdZeed<dZeed<eZe e ed<dededed ed ed ef d d Z dS)DurationEstimatorToolzDuration Estimator ToolnamezEstimates a realistic total duration (in minutes, max 360) for a microlearning journey based on the user's job role, skills, topic familiarity, and target proficiency level.rB args_schemarrrrrreturncCs t}|j|||||d}|jSr>)r#r?r)r9rrrrr estimatorresultr!r!r"_runszDurationEstimatorTool._runN) rrrrDrrrBrArErrrIr!r!r!r"rCs  &rC)typingr crewai.toolsrpydanticrrrosdotenvrgetenv azure_api_keyazure_api_hostazure_api_versionLMr configure SignaturerModuler#rArCr!r!r!r"s,       M<