diff --git a/student_test_prep_agent/.gitignore b/student_test_prep_agent/.gitignore new file mode 100644 index 000000000..0a197900e --- /dev/null +++ b/student_test_prep_agent/.gitignore @@ -0,0 +1,174 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc diff --git a/student_test_prep_agent/LICENSE b/student_test_prep_agent/LICENSE new file mode 100644 index 000000000..fad1c3e7a --- /dev/null +++ b/student_test_prep_agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Jyotirmaan Srivastava + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/student_test_prep_agent/README.md b/student_test_prep_agent/README.md new file mode 100644 index 000000000..83c0a3399 --- /dev/null +++ b/student_test_prep_agent/README.md @@ -0,0 +1,208 @@ +# AI-Powered Study Planning Assistant + +An intelligent study planning assistant built using CrewAI that helps students create personalized study plans for their exams. The system uses natural language processing to understand exam requirements and generates comprehensive study plans with schedules, strategies, and learning resources. + +## 🚀 Features + +### 1. Natural Language Input Processing +- Accepts exam information in natural language +- Extracts key details like: + - Subject + - Exam date + - Topics to study + - Time available for preparation + +### 2. Intelligent Study Planning +- Creates day-wise study schedules +- Distributes topics evenly across available days +- Considers topic complexity and importance +- Adapts to different timeframes + +### 3. Study Strategy Generation +- Provides topic-specific study strategies +- Includes memory techniques +- Suggests practice methods +- Recommends review schedules + +### 4. Learning Resource Curation +- Finds relevant YouTube educational videos +- Filters for high-quality educational content +- Provides direct video links with descriptions +- Focuses on reputable educational channels + +### 5. Markdown Output +- Generates well-formatted study plans +- Includes all sections in a readable format +- Easy to print or share digitally +- Compatible with markdown viewers + +## 🛠️ Technical Architecture + +### Core Components + +1. **Input Interpreter Agent** + - Processes natural language input + - Extracts exam information + - Validates and structures the data + +2. **Schedule Builder Agent** + - Creates time-based study plans + - Optimizes topic distribution + - Considers study duration + +3. **Strategy Planner Agent** + - Develops study strategies + - Suggests learning techniques + - Provides memory aids + +4. **YouTube Fetcher Agent** + - Searches for educational videos + - Filters content quality + - Provides direct video links + +5. **Output Formatter Agent** + - Structures the final output + - Creates markdown formatting + - Ensures readability + +### Tools and Technologies + +- **CrewAI**: Framework for orchestrating role-playing AI agents +- **YouTube Data API**: For fetching educational videos +- **Python**: Core programming language +- **Markdown**: Output format +- **Environment Variables**: For API key management + +## 📋 Setup Instructions + +1. **Clone the Repository** + ```bash + git clone https://github.com/yourusername/Exam_Prep_Agent.git + cd Exam_Prep_Agent + ``` + +2. **Set Up Virtual Environment** + ```bash + python -m venv .venv + # On Windows + .venv\Scripts\activate + # On Unix/MacOS + source .venv/bin/activate + ``` + +3. **Install Dependencies** + ```bash + pip install -e . + ``` + +4. **Configure Environment Variables** + Create a `.env` file in the project root: + ``` + YOUTUBE_API_KEY=your_api_key_here + ``` + +## 💻 Usage + +1. **Run the Program** + ```bash + python -m exam_prep.main + ``` + +2. **Enter Exam Information** + Example input: + ``` + I have a test on statistics in 4 days. Topics are probability, distributions, and hypothesis testing. + ``` + +3. **Get Your Study Plan** + - The program will generate a study plan + - Output will be saved as a markdown file + - File will be stored in the `output` directory + +## 📁 Project Structure + +``` +exam_prep/ +├── src/ +│ └── exam_prep/ +│ ├── config/ +│ │ ├── agents.yaml # Agent configurations +│ │ └── tasks.yaml # Task definitions +│ ├── tools/ +│ │ └── youtube_tool.py # YouTube API integration +│ ├── main.py # Entry point +│ └── crew.py # CrewAI implementation +├── output/ # Generated study plans +├── tests/ # Test files +├── .env # Environment variables +└── README.md # Documentation +``` + +## 🔧 Configuration + +### Agent Configuration (agents.yaml) +- Defines agent roles and goals +- Sets up agent behaviors +- Configures tool usage + +### Task Configuration (tasks.yaml) +- Defines task descriptions +- Sets expected outputs +- Links tasks to agents + +## 📄 Output Format + +The generated study plan includes: + +1. **Exam Overview** + - Subject + - Date + - Topics + +2. **Study Schedule** + - Day-by-day breakdown + - Topic distribution + - Time allocation + +3. **Study Strategies** + - Topic-specific approaches + - Learning techniques + - Practice methods + +4. **Learning Resources** + - YouTube video links + - Channel information + - Video descriptions + +## 🔐 Security + +- API keys are stored in `.env` file +- Sensitive files are git-ignored +- No hardcoded credentials + +## 🤝 Contributing + +1. Fork the repository +2. Create a feature branch +3. Commit your changes +4. Push to the branch +5. Create a Pull Request + +## 📄 License + +This project is licensed under the MIT License - see the LICENSE file for details. + +## 🙏 Acknowledgments + +- CrewAI framework +- YouTube Data API +- Python community +- Open source contributors + +## 🔄 Future Improvements + +1. Add more learning resources +2. Implement spaced repetition +3. Add progress tracking +4. Support multiple languages +5. Add mobile interface \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/.gitignore b/student_test_prep_agent/exam_prep/.gitignore new file mode 100644 index 000000000..7279347af --- /dev/null +++ b/student_test_prep_agent/exam_prep/.gitignore @@ -0,0 +1,3 @@ +.env +__pycache__/ +.DS_Store diff --git a/student_test_prep_agent/exam_prep/README.md b/student_test_prep_agent/exam_prep/README.md new file mode 100644 index 000000000..4e629bd95 --- /dev/null +++ b/student_test_prep_agent/exam_prep/README.md @@ -0,0 +1,54 @@ +# ExamPrep Crew + +Welcome to the ExamPrep Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities. + +## Installation + +Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. + +First, if you haven't already, install uv: + +```bash +pip install uv +``` + +Next, navigate to your project directory and install the dependencies: + +(Optional) Lock the dependencies and install them by using the CLI command: +```bash +crewai install +``` +### Customizing + +**Add your `OPENAI_API_KEY` into the `.env` file** + +- Modify `src/exam_prep/config/agents.yaml` to define your agents +- Modify `src/exam_prep/config/tasks.yaml` to define your tasks +- Modify `src/exam_prep/crew.py` to add your own logic, tools and specific args +- Modify `src/exam_prep/main.py` to add custom inputs for your agents and tasks + +## Running the Project + +To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project: + +```bash +$ crewai run +``` + +This command initializes the exam_prep Crew, assembling the agents and assigning them tasks as defined in your configuration. + +This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder. + +## Understanding Your Crew + +The exam_prep Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew. + +## Support + +For support, questions, or feedback regarding the ExamPrep Crew or crewAI. +- Visit our [documentation](https://docs.crewai.com) +- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai) +- [Join our Discord](https://discord.com/invite/X4JWnZnxPb) +- [Chat with our docs](https://chatg.pt/DWjSBZn) + +Let's create wonders together with the power and simplicity of crewAI. diff --git a/student_test_prep_agent/exam_prep/knowledge/user_preference.txt b/student_test_prep_agent/exam_prep/knowledge/user_preference.txt new file mode 100644 index 000000000..dd63a17bf --- /dev/null +++ b/student_test_prep_agent/exam_prep/knowledge/user_preference.txt @@ -0,0 +1,4 @@ +User name is John Doe. +User is an AI Engineer. +User is interested in AI Agents. +User is based in San Francisco, California. diff --git a/student_test_prep_agent/exam_prep/output/study_plan_20250528_112108.md b/student_test_prep_agent/exam_prep/output/study_plan_20250528_112108.md new file mode 100644 index 000000000..68d483153 --- /dev/null +++ b/student_test_prep_agent/exam_prep/output/study_plan_20250528_112108.md @@ -0,0 +1,87 @@ +# Statistics Exam Study Plan (2024-07-29) + +## 1. Exam Overview + +* **Subject:** Statistics (Stats) +* **Exam Date:** 2024-07-29 +* **Topics:** This study plan covers general statistics concepts. **It is crucial to review your class materials, notes, and syllabus for the specific topics covered in your course.** This plan should be used as a template and adapted to your specific needs. + +## 2. Study Schedule + +This schedule incorporates evidence-based learning strategies such as spaced repetition, active recall, interleaving, elaboration, and dual coding. + +### Day 1 (2024-07-26): Foundations - Descriptive Statistics & Probability + +* **Morning:** + * **Measures of Central Tendency:** Understand the meaning, appropriate usage, and calculation of mean, median, and mode. Visualize their representation on a distribution curve. Create examples. + * **Practice:** Work through problems of varying difficulty involving calculating and interpreting these measures. + +* **Afternoon:** + * **Measures of Dispersion:** Focus on range, variance, and standard deviation, their relationship to central tendency, and the impact of outliers. Visualize their representation on a distribution curve. + * **Practice:** Solve problems requiring calculation and interpretation of these measures. + +* **Evening:** + * **Probability Basics:** Review fundamental probability rules (addition, multiplication, complements), conditional probability, and independent events. Use simple scenarios (e.g., cards, dice) to solidify understanding. + * **Spaced Repetition:** Briefly review the morning's topics (central tendency). + +### Day 2 (2024-07-27): Inferential Statistics & Distributions + +* **Morning:** + * **Distributions:** Focus on the normal distribution, its properties, z-scores, and the z-table. Briefly review other relevant distributions (e.g., binomial, t-distribution). Sketch these distributions. + * **Practice:** Work problems involving calculating probabilities using the normal distribution and z-scores. + +* **Afternoon:** + * **Confidence Intervals:** Understand the purpose, interpretation, and construction of confidence intervals. Connect this to the normal distribution visually. + * **Hypothesis Testing:** Grasp the logic of hypothesis testing (null/alternative hypotheses, p-values, significance levels, Type I/II errors). Work through examples. + +* **Evening:** + * **Practice & Review:** Solve mixed problems covering distributions, confidence intervals, and hypothesis testing. Review Day 1 topics using spaced repetition. + +### Day 3 (2024-07-28): Regression, Review, and Exam Strategy + +* **Morning:** + * **Regression Analysis:** Focus on linear regression, correlation, interpreting regression coefficients, and R-squared. Practice interpreting scatterplots and regression output. + * **Targeted Review:** Focus on your weakest areas from the previous two days. + +* **Afternoon:** + * **Holistic Review:** Review all key concepts, formulas, and problem-solving strategies, focusing on connections between concepts. + * **Practice Exam/Sample Questions:** Work through past papers or sample questions under timed conditions, simulating the exam environment. + +* **Evening:** + * **Light Review:** Briefly review key formulas and concepts. Prioritize rest. + * **Exam Preparation:** Organize materials. Visualize a successful exam. + +## 3. Study Strategies (For Each Topic) + +The study schedule incorporates the following strategies: + +* **Spaced Repetition:** Review material at increasing intervals for better retention. +* **Active Recall:** Test yourself frequently to strengthen memory retrieval. +* **Interleaving:** Mix different topics during practice to improve discrimination between concepts. +* **Elaboration:** Connect new information to existing knowledge and explain concepts in your own words. +* **Dual Coding:** Combine visual representations (graphs, diagrams) with verbal explanations. + +## 4. Learning Resources (YouTube and Other Resources) + +Use these YouTube search phrases as a starting point. Remember to vary your sources and practice actively alongside the videos. + +### Day 1: Descriptive Statistics and Probability + +* Search phrases for Measures of Central Tendency, Measures of Dispersion, and Probability Basics (as provided in the original prompt). + +### Day 2: Inferential Statistics and Distributions + +* Search phrases for Distributions, Confidence Intervals, and Hypothesis Testing (as provided in the original prompt). + +### Day 3: Regression Analysis and Review + +* Search phrases for Regression Analysis (as provided in the original prompt). + +**General Tips for YouTube Learning:** + +* Seek reputable sources (university channels, educational organizations, established tutors). +* Check the comments section for clarifying questions and potential errors. +* Use multiple sources for a more comprehensive understanding. +* Practice alongside videos, pausing and working through examples. + +Remember to prioritize sleep, manage stress, and stay hydrated and well-nourished for optimal performance. Adapt this plan to your specific needs and course material. Good luck! \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/output/study_plan_20250528_112520.md b/student_test_prep_agent/exam_prep/output/study_plan_20250528_112520.md new file mode 100644 index 000000000..54954e7ac --- /dev/null +++ b/student_test_prep_agent/exam_prep/output/study_plan_20250528_112520.md @@ -0,0 +1,70 @@ +# Object-Oriented Programming (OOP) Exam Study Plan + +**Exam Date:** May 30th (Assuming preparation starts on May 23rd) + +## 1. Exam Overview + +**Subject:** Object-Oriented Programming (OOP) + +**Date:** May 30th + +**Topics:** This study plan covers core OOP concepts, including: + +* Classes and Objects +* Encapsulation +* Inheritance +* Polymorphism +* Abstraction +* Data Hiding +* Constructors and Destructors (language-specific) + +## 2. Study Schedule + +This schedule utilizes a spaced repetition approach and emphasizes active learning. + +| Day | Date | Topics | +| --- | ---------- | ------------------------------------------- | +| 1 | May 23rd | Classes & Objects, Encapsulation | +| 2 | May 24th | Inheritance | +| 3 | May 25th | Polymorphism | +| 4 | May 26th | Abstraction & Data Hiding | +| 5 | May 27th | Constructors & Destructors (if applicable) | +| 6 | May 28th | Review & Practice | +| 7 | May 29th | Final Review & Rest | + + +**Daily Breakdown:** (See detailed breakdown in the previous response for specific activities and coding examples) + +## 3. Study Strategies + +* **Active Recall:** Test yourself regularly. +* **Spaced Repetition:** Review material at increasing intervals. +* **Hands-on Coding:** Practice implementing OOP concepts in code. +* **Visual Aids:** Use diagrams like UML class diagrams. +* **Seek Clarification:** Ask for help when needed. + + +## 4. Learning Resources + +**Recommended YouTube Search Terms & Channels:** (See previous response for a more detailed breakdown of suggested searches for each topic.) + +General OOP: + +* Search: "Object Oriented Programming explained" +* Channels: freeCodeCamp.org, CS Dojo, The Net Ninja + +Language-Specific Resources: + +* Search: "[OOP Concept] in [Your Programming Language]" (e.g., "Inheritance in Java") + +**Finding Quality Videos:** + +* Check upload dates for recency. +* Look for language-specific tutorials. +* Read comments for community feedback. + +**Example (Illustrative):** + +Suppose you are learning Java. A good search might be: "Inheritance in Java explained." This is more targeted than a generic "Inheritance" search. + +**Key Reminder:** This study plan provides a framework. Adapt it to your learning style and course requirements. The most effective way to learn OOP is by actively writing and experimenting with code. Good luck! \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/output/study_plan_20250528_113434.md b/student_test_prep_agent/exam_prep/output/study_plan_20250528_113434.md new file mode 100644 index 000000000..6bc13d14a --- /dev/null +++ b/student_test_prep_agent/exam_prep/output/study_plan_20250528_113434.md @@ -0,0 +1,80 @@ +```markdown +# DSA Exam Study Plan + +## 1. Exam Overview + +**Subject:** DSA (Data Structures and Algorithms) +**Exam Date:** In 3 days (specific date should be added here) +**Topics Covered:** This study plan covers fundamental DSA topics including Arrays, Linked Lists, Stacks, Queues, Searching and Sorting Algorithms, Trees, Graphs, Dynamic Programming, Greedy Algorithms, and Backtracking. + +## 2. Study Schedule + +### Day 1: Foundational Data Structures and Algorithms + +* **Morning:** Review basic data structures: Arrays, Linked Lists (Singly and Doubly), Stacks, Queues. Practice basic operations and implementation in your preferred programming language. +* **Afternoon:** Study basic algorithms: Searching (Linear, Binary), Sorting (Bubble, Insertion, Selection), Recursion. Implement these algorithms and analyze their time and space complexity. +* **Evening:** Solve coding problems related to arrays, linked lists, stacks, queues, searching, and sorting on platforms like LeetCode, HackerRank, or Codewars. Focus on understanding the concepts and applying them practically. + +### Day 2: Trees and Graphs + +* **Morning:** Study Trees: Binary Trees, Binary Search Trees, Tree Traversals (Inorder, Preorder, Postorder). Implement tree operations like insertion, deletion, and searching. +* **Afternoon:** Study Graphs: Graph representations (Adjacency Matrix, Adjacency List), Graph Traversals (Breadth-First Search, Depth-First Search). Implement graph algorithms and understand their applications. +* **Evening:** Solve coding problems related to trees and graphs. Focus on applying different traversal algorithms and understanding their complexities. + +### Day 3: Advanced Algorithms and Revision + +* **Morning:** Study advanced algorithms: Dynamic Programming, Greedy Algorithms, Backtracking. Understand the core concepts and practice implementing basic dynamic programming problems. +* **Afternoon:** Revise all the topics covered in the previous two days. Focus on areas where you feel less confident. Review key concepts, algorithms, and data structures. +* **Evening:** Solve mixed coding problems covering various DSA topics. Practice time management and problem-solving skills under pressure. Take a practice exam or timed coding challenge to simulate exam conditions. Review any remaining weak areas one final time. + + +## 3. Study Strategies + +* **Active Recall:** After studying a topic, try to recall the key concepts without looking at your notes. +* **Spaced Repetition:** Review topics at increasing intervals to reinforce your memory. +* **Practice Problems:** Solve a variety of coding problems to apply your knowledge and identify weak areas. +* **Code Implementation:** Implement data structures and algorithms in your preferred programming language. +* **Time Management:** Practice solving problems under timed conditions to simulate exam pressure. + +## 4. Learning Resources + +### Day 1: Foundational Data Structures and Algorithms + +* **Arrays:** + * [Arrays in Data Structures](https://www.youtube.com/watch?v=EQYg9R7i-40&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=9) (mycodeschool) + * [Data Structures: Arrays](https://www.youtube.com/watch?v=QJNwK2uJySU) (HackerRank) +* **Linked Lists:** + * [Linked List Data Structure](https://www.youtube.com/watch?v=lC-yYCOnN8Y) (Simple Snippets) + * [Data Structures: Linked Lists](https://www.youtube.com/watch?v=njTh_OwMljA) (HackerRank) +* **Stacks & Queues:** + * [Stack and Queue Data Structure](https://www.youtube.com/watch?v=wjI1WNcIntg) (freeCodeCamp.org) + * [Stacks and Queues](https://www.youtube.com/watch?v=L3ud3rXpIxA&list=PLDV1Zeh2NRsB6SWUrDFW2RmDotAfPbeHu&index=8) (WilliamFiset) +* **Searching & Sorting:** + * [Algorithms: Searching and Sorting](https://www.youtube.com/watch?v=rL8X2mlNHPM) (Crash Course Computer Science) + * [Sorting Algorithms Visualized](https://www.youtube.com/watch?v=kPRA0W1kECg) (Timo Bingmann) +* **Recursion:** + * [Recursion crash course](https://www.youtube.com/watch?v=8MRe-P50tts&t=424s) (freeCodeCamp.org) + * [Recursion - Data Structures and Algorithm Tutorials](https://www.youtube.com/watch?v=Mv9NEXX1VHc&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=23) (mycodeschool) + + +### Day 2: Trees and Graphs + +* **Binary Trees & BSTs:** + * [Binary Tree](https://www.youtube.com/watch?v=oSWTXtMglKE) (Abdul Bari) + * [Binary Search Tree](https://www.youtube.com/watch?v=pYT9F8_LFTM&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=28) (mycodeschool) +* **Tree Traversals:** + * [Tree Traversals (Inorder, Preorder and Postorder)](https://www.youtube.com/watch?v=gm8DUjjkmYQ&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=29) (mycodeschool) +* **Graph Representations & Traversals:** + * [Graph Data Structure 1](https://www.youtube.com/watch?v=HDUzBEG1GlA) (Jenny's lectures CS/IT NET&JRF) + * [Graph Algorithms](https://www.youtube.com/watch?v=DgXRjM2sqak&list=PLDV1Zeh2NRsDGO4--qE8yH72HFL1Km93P) (WilliamFiset) + +### Day 3: Advanced Algorithms and Revision + +* **Dynamic Programming:** + * [Dynamic Programming - Learn to Solve Algorithmic Problems](https://www.youtube.com/watch?v=oBt53YbR9Kk) (freeCodeCamp.org) +* **Greedy Algorithms:** + * [Greedy Algorithms](https://www.youtube.com/watch?v=ARvQcqJ_-NY) (GeeksforGeeks) +* **Backtracking:** + * [Backtracking Algorithms](https://www.youtube.com/watch?v=G_UYXzGuqvM) (Computerphile) + +``` \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/output/study_plan_20250528_115827.md b/student_test_prep_agent/exam_prep/output/study_plan_20250528_115827.md new file mode 100644 index 000000000..a3d376871 --- /dev/null +++ b/student_test_prep_agent/exam_prep/output/study_plan_20250528_115827.md @@ -0,0 +1,87 @@ +# 4-Day Study Plan for Statistics + +**Exam Date:** 4 days from today + +## 1. Exam Overview + +**Subject:** Statistics (Stats) + +**Topics Covered:** Descriptive Statistics, Probability, Probability Distributions, Sampling Distributions and Central Limit Theorem, Hypothesis Testing, Confidence Intervals + +## 2. Study Schedule + +This schedule is a template. Adjust the time spent on each topic based on its complexity and your familiarity with it. + +**Day 1: Foundations** + +* **Morning (2-3 hours): Descriptive Statistics:** + * Measures of Central Tendency (mean, median, mode) + * Measures of Variability (range, variance, standard deviation, interquartile range) + * Data Visualization: Histograms, box plots, scatter plots + +* **Afternoon (2-3 hours): Probability:** + * Basic Probability Concepts: Sample space, events, probability rules (addition, multiplication, complements) + * Conditional Probability and Bayes' Theorem + +**Day 2: Distributions and Sampling** + +* **Morning (2-3 hours): Probability Distributions:** + * Discrete Distributions: Binomial, Poisson + * Continuous Distributions: Normal Distribution + +* **Afternoon (2-3 hours): Sampling Distributions and Central Limit Theorem:** + * Sampling Distributions + * Central Limit Theorem + +**Day 3: Inference** + +* **Morning (2-3 hours): Hypothesis Testing:** + * Introduction to Hypothesis Testing + * One-sample and Two-sample t-tests + +* **Afternoon (2-3 hours): Confidence Intervals:** + * Calculating and Interpreting Confidence Intervals for Means + +**Day 4: Review and Practice** + +* **Morning (2-3 hours): Comprehensive Review:** + * Review all key concepts, formulas, and problem-solving strategies. + +* **Afternoon (2-3 hours): Practice Exam:** + * Take a full-length practice exam under timed conditions. + + +## 3. Study Strategies + +For each topic, apply the following strategies: + +* **Active Recall:** Test yourself frequently. +* **Spaced Repetition:** Review material at increasing intervals. +* **Practice Problems:** Work through a variety of problems. +* **Interleaving:** Mix up the order of topics you study. +* **Explain it to someone:** Teaching solidifies your understanding. + +## 4. Learning Resources + +**Day 1: Foundations** + +* **Morning (2-3 hours): Descriptive Statistics:** + * **Measures of Central Tendency (mean, median, mode):** + * [Tutorial 4- Measure Of Central Tendency- Mean, Median And Mode In Hindi by Krish Naik Hindi](https://www.youtube.com/watch?v=X48cZ6DGaSw) + * [Statistics - Mean, Median & Mode for a grouped frequency data by Vectors Academy](https://www.youtube.com/watch?v=RALOqYe9JEI) + * [Education (12 Arts) Educational Statistics(Measures of Central Tendency) by Namita Mukhia by SCHOOL HIGHER SECONDARY](https://www.youtube.com/watch?v=s5a2RH-Rujk) + * **Measures of Variability (range, variance, standard deviation, interquartile range):** + * [Measures of Variability (Range, Standard Deviation, Variance) by Daniel Storage](https://www.youtube.com/watch?v=s7WTQ0H0Acc) + * [Measures of Variability Concept | Measures of Dispersion|Measures of spread|Statistics |Math Dot Com by Math Dot Com](https://www.youtube.com/watch?v=OmdH1WooKXM) + * [MEASURES OF VARIABILITY by CH 04: CEC-UGC 04: Education & Home Science](https://www.youtube.com/watch?v=EU7M9MWRZJc) + * **Data Visualization: Histograms, box plots, scatter plots:** + * [Data Visualization : Scatter Plot Explained with Example in Hindi by 5 Minutes Engineering](https://www.youtube.com/watch?v=aWmHYYQJukk) + * [Science of Data Visualization | Bar, scatter plot, line, histograms, pie, box plots, bubble chart by Prof. Ryan Ahmed](https://www.youtube.com/watch?v=csXmVBw8cdo) + * [Data Visualization : Histogram and its Types Explained in with Example in Hindi by 5 Minutes Engineering](https://www.youtube.com/watch?v=2D5PswC7JBc) + +* **Afternoon (2-3 hours): Probability:** (Resources can be added here) + +**(Add learning resources for the remaining topics following this format)** + + +Good luck with your exam! \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/output/study_plan_20250528_120229.md b/student_test_prep_agent/exam_prep/output/study_plan_20250528_120229.md new file mode 100644 index 000000000..4528f21fa --- /dev/null +++ b/student_test_prep_agent/exam_prep/output/study_plan_20250528_120229.md @@ -0,0 +1,84 @@ +# Deep Learning Exam Study Plan + +## Exam Overview + +* **Subject:** Deep Learning +* **Date:** In 4 days +* **Topics:** Langchain, PyTorch + +## Study Schedule + +| Day | Topic | Activities | +| :-- | :------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| 1 | Langchain | Introduction & Core Concepts: Familiarize yourself with the Langchain framework, explore key components and functionalities, and work through basic examples and tutorials. | +| 2 | PyTorch | Fundamentals & Tensor Operations: Review PyTorch basics and tensor manipulation, practice creating and manipulating tensors, and implement simple neural network operations. | +| 3 | Langchain | Advanced Topics & Applications: Dive into complex Langchain features, explore practical applications and use cases, and work on a small project. | +| 4 | PyTorch | Building & Training Neural Networks: Focus on constructing and training neural networks, implement different network architectures, and practice training and evaluating models. | + + +## Study Strategies + +### Langchain - Introduction & Core Concepts (Day 1) + +* **Morning:** + * Introduction (1 hour): Read the official LangChain documentation. + * Key Components (2 hours): Identify and define key components (LLMs, Prompts, Chains, Agents, Memory). Create flashcards. + * Basic Examples (2 hours): Work through introductory tutorials and examples. + +* **Afternoon/Evening:** + * Review (1 hour): Review flashcards. + * Spaced Repetition: Revisit flashcards throughout the evening. + +### PyTorch - Fundamentals & Tensor Operations (Day 2) + +* **Morning:** + * Fundamentals Review (1 hour): Refresh PyTorch basics. + * Tensor Manipulation Practice (2 hours): Solve coding exercises involving tensor operations. + * Simple Operations (2 hours): Implement basic neural network operations. + +* **Afternoon/Evening:** + * Code Review (1 hour): Review and optimize code. + * Practice Problems (1 hour): Solve more practice problems. + +### Langchain - Advanced Topics & Applications (Day 3) + +* **Morning:** + * Advanced Features (2 hours): Deepen understanding of complex LangChain features. + * Practical Applications (2 hours): Research practical use cases. + +* **Afternoon/Evening:** + * Mini-Project (3-4 hours): Start a small project using LangChain. + +### PyTorch - Building & Training Neural Networks (Day 4) + +* **Morning:** + * Network Architectures (2 hours): Focus on different neural network architectures. + * Training and Evaluation (2 hours): Practice training and evaluating models. + +* **Afternoon/Evening:** + * Project Continuation/Model Implementation (3-4 hours): Continue PyTorch project or implement a simple neural network. + * Final Review (1 hour): Review key concepts. + +### General Tips + +* Time Management: Stick to the schedule but be flexible. +* Active Recall: Constantly test yourself. +* Spaced Repetition: Review material at increasing intervals. +* Hands-on Practice: Work through examples and projects. +* Rest and Breaks: Take regular breaks and get enough sleep. + + +## Learning Resources + +### Langchain + +* [LangChain Explained - Complete Tutorial in HINDI | Complete Guide About LangChain | DevDuniya](https://www.youtube.com/watch?v=qTdvPIW_ngc) +* [LangChain Crash Course (10 minutes): Easy-to-Follow Walkthrough of the Most Important Concepts](https://www.youtube.com/watch?v=Y_O-x-itHaU) +* [Master LangChain & Prompt Engineering: Top 4 FREE AI Courses](https://www.youtube.com/watch?v=FXdafunN4gw) + + +### PyTorch + +* [PyTorch for Audio + Music Processing: Course Overview](https://www.youtube.com/watch?v=gp2wZqDoJ1Y) +* [Pytorch vs TensorFlow vs Keras | Which is Better | Deep Learning Frameworks Comparison | Simplilearn](https://www.youtube.com/watch?v=4L86D_fU6sQ) +* [Learn Machine Learning in 3 Months (PyTorch Curriculum)](https://www.youtube.com/watch?v=dS2HYPY7T-4) \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/pyproject.toml b/student_test_prep_agent/exam_prep/pyproject.toml new file mode 100644 index 000000000..53fc43ed9 --- /dev/null +++ b/student_test_prep_agent/exam_prep/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "exam_prep" +version = "0.1.0" +description = "exam_prep using crewAI" +authors = [{ name = "Your Name", email = "you@example.com" }] +requires-python = ">=3.10,<3.13" +dependencies = [ + "crewai[tools]>=0.114.0,<1.0.0", + "google-api-python-client>=2.0.0", + "python-dotenv>=0.19.0" +] + +[project.scripts] +exam_prep = "exam_prep.main:run" +run_crew = "exam_prep.main:run" +train = "exam_prep.main:train" +replay = "exam_prep.main:replay" +test = "exam_prep.main:test" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.crewai] +type = "crew" diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/__init__.py b/student_test_prep_agent/exam_prep/src/exam_prep/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/config/agents.yaml b/student_test_prep_agent/exam_prep/src/exam_prep/config/agents.yaml new file mode 100644 index 000000000..e37c82467 --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/config/agents.yaml @@ -0,0 +1,40 @@ +input_interpreter: + role: > + Exam input interpreter + goal: > + Extract subject, exam date, topics, and other relevant information from the {student_input} + backstory: > + You're a helpful assistant that extracts exam information from the input + +schedule_builder: + role: > + Exam schedule builder + goal: > + create a day-wise study plan that distributes the topics evenly across the days till the exam date + backstory: > + You're a helpful assistant that builds a schedule for the exam + +strategy_planner: + role: > + Exam strategy planner + goal: > + provide practical and effective study strategies for the exam + backstory: > + You're an expert in cognitive psychology and have a deep understanding of how the human mind learns + +youtube_fetcher: + role: > + YouTube Resource Finder + goal: > + Find and provide direct YouTube video links that are highly relevant to each exam topic, focusing on educational content from reputable channels and instructors. For each topic, provide 2-3 of the best video resources with their direct URLs. + backstory: > + You're an expert at finding high-quality educational content on YouTube. You have extensive knowledge of educational channels and can identify the most relevant and helpful videos for specific academic topics. You always provide direct video links and ensure the content is from reputable educational sources. +output_formatter: + role: > + Output formatter + goal: > + Format the output into a well-structured markdown document with clear sections, headers, and formatting + backstory: > + You're an expert at creating well-formatted markdown documents that are both readable and visually appealing. You know how to use markdown features like headers, lists, tables, and code blocks to create clear and organized study plans. + + diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/config/tasks.yaml b/student_test_prep_agent/exam_prep/src/exam_prep/config/tasks.yaml new file mode 100644 index 000000000..891cafd9c --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/config/tasks.yaml @@ -0,0 +1,51 @@ +extract_exam_info: + description: > + Extract subject, exam date, topics, and other relevant information from the input + expected_output: > + A list of the exam information + agent: input_interpreter + +build_schedule: + description: > + create a day-wise study plan that distributes the topics evenly across the days till the exam date + expected_output: > + A list of the study plan + agent: schedule_builder + +build_strategy: + description: > + provide practical and effective study strategies for the exam + expected_output: > + A list of the study strategies + agent: strategy_planner + +fetch_youtube_videos: + description: > + For each exam topic, use the YouTubeTool to find and provide 2-3 direct YouTube video links that are: + 1. Highly relevant to the specific topic + 2. From reputable educational channels + 3. Appropriate for the academic level + 4. Include the video title, channel name, and direct URL + Format the output as a markdown list with clear topic headers and video details. + expected_output: > + A structured list of YouTube video resources for each topic, including: + - Topic header + - For each video: + * Title + * Channel name + * Direct URL + * Brief description of why it's relevant + agent: youtube_fetcher + +format_output: + description: > + Format the output into a well-structured markdown document with the following sections: + 1. Exam Overview (subject, date, topics) + 2. Study Schedule (day-by-day breakdown) + 3. Study Strategies (for each topic) + 4. Learning Resources (YouTube videos links and other resources) + expected_output: > + A well-formatted markdown document with clear sections, headers, and proper markdown formatting + agent: output_formatter + + diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/crew.py b/student_test_prep_agent/exam_prep/src/exam_prep/crew.py new file mode 100644 index 000000000..a493643db --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/crew.py @@ -0,0 +1,105 @@ +from crewai import Agent, Crew, Process, Task +from crewai.project import CrewBase, agent, crew, task +from dotenv import load_dotenv +from .tools.youtube_tool import YouTubeTool + +load_dotenv() + +# If you want to run a snippet of code before or after the crew starts, +# you can use the @before_kickoff and @after_kickoff decorators +# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators + +@CrewBase +class ExamPrep(): + """ExamPrep crew""" + + # Learn more about YAML configuration files here: + # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended + # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended + agents_config = 'config/agents.yaml' + tasks_config = 'config/tasks.yaml' + + # If you would like to add tools to your agents, you can learn more about it here: + # https://docs.crewai.com/concepts/agents#agent-tools + @agent + def input_interpreter(self) -> Agent: + return Agent( + config=self.agents_config['input_interpreter'], + verbose=True + ) + + @agent + def schedule_builder(self) -> Agent: + return Agent( + config=self.agents_config['schedule_builder'], + verbose=True + ) + + @agent + def strategy_planner(self) -> Agent: + return Agent( + config=self.agents_config['strategy_planner'], + verbose=True + ) + + @agent + def youtube_fetcher(self) -> Agent: + return Agent( + config=self.agents_config['youtube_fetcher'], + tools=[YouTubeTool()], + verbose=True + ) + + @agent + def output_formatter(self) -> Agent: + return Agent( + config=self.agents_config['output_formatter'], + verbose=True + ) + + # To learn more about structured task outputs, + # task dependencies, and task callbacks, check out the documentation: + # https://docs.crewai.com/concepts/tasks#overview-of-a-task + @task + def extract_exam_info(self) -> Task: + return Task( + config=self.tasks_config['extract_exam_info'], + ) + + @task + def build_schedule(self) -> Task: + return Task( + config=self.tasks_config['build_schedule'], + ) + + @task + def build_strategy(self) -> Task: + return Task( + config=self.tasks_config['build_strategy'], + ) + + @task + def fetch_youtube_videos(self) -> Task: + return Task( + config=self.tasks_config['fetch_youtube_videos'], + ) + + @task + def format_output(self) -> Task: + return Task( + config=self.tasks_config['format_output'], + ) + + @crew + def crew(self) -> Crew: + """Creates the ExamPrep crew""" + # To learn how to add knowledge sources to your crew, check out the documentation: + # https://docs.crewai.com/concepts/knowledge#what-is-knowledge + + return Crew( + agents=self.agents, # Automatically created by the @agent decorator + tasks=self.tasks, # Automatically created by the @task decorator + process=Process.sequential, + verbose=True, + # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/ + ) diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/main.py b/student_test_prep_agent/exam_prep/src/exam_prep/main.py new file mode 100644 index 000000000..dee49f506 --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/main.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +import sys +import warnings +from datetime import datetime +import os + +from exam_prep.crew import ExamPrep + +warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd") + +def get_user_input(): + """ + Get exam information from the user through command line input. + """ + print("\nWelcome to the AI Study Planning Assistant!") + print("Please provide your exam information in natural language.") + print("Example: 'I have a test on statistics in 4 days. Topics are probability, distributions, and hypothesis testing.'") + print("\nEnter your exam information (press Enter twice to finish):") + + lines = [] + while True: + line = input() + if line == "" and lines: # If empty line and we have some input + break + if line: # If line is not empty + lines.append(line) + + return " ".join(lines) + +def run(): + """ + Runs the agentic study assistant with user input and saves output to markdown. + """ + try: + # Get input from user + student_input = get_user_input() + + # Create inputs dictionary + inputs = { + "student_input": student_input + } + + # Run the crew with user input + result = ExamPrep().crew().kickoff(inputs=inputs) + + # Create output directory if it doesn't exist + if not os.path.exists("output"): + os.makedirs("output") + + # Generate filename with timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"study_plan_{timestamp}.md" + + # Save the output to markdown file + with open(f"output/{filename}", "w", encoding="utf-8") as f: + f.write(str(result)) + + print(f"\nStudy plan has been saved to: output/{filename}") + + except Exception as e: + raise Exception(f"An error occurred while running the crew: {e}") + +if __name__ == "__main__": + run() \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/tools/__init__.py b/student_test_prep_agent/exam_prep/src/exam_prep/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/tools/custom_tool.py b/student_test_prep_agent/exam_prep/src/exam_prep/tools/custom_tool.py new file mode 100644 index 000000000..154beae8e --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/tools/custom_tool.py @@ -0,0 +1,19 @@ +from crewai.tools import BaseTool +from typing import Type +from pydantic import BaseModel, Field + + +class MyCustomToolInput(BaseModel): + """Input schema for MyCustomTool.""" + argument: str = Field(..., description="Description of the argument.") + +class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = ( + "Clear description for what this tool is useful for, your agent will need this information to use it." + ) + args_schema: Type[BaseModel] = MyCustomToolInput + + def _run(self, argument: str) -> str: + # Implementation goes here + return "this is an example of a tool output, ignore it and move along." diff --git a/student_test_prep_agent/exam_prep/src/exam_prep/tools/youtube_tool.py b/student_test_prep_agent/exam_prep/src/exam_prep/tools/youtube_tool.py new file mode 100644 index 000000000..91e4e2025 --- /dev/null +++ b/student_test_prep_agent/exam_prep/src/exam_prep/tools/youtube_tool.py @@ -0,0 +1,68 @@ +from typing import List, Dict +from googleapiclient.discovery import build +from googleapiclient.errors import HttpError +import os +from dotenv import load_dotenv +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + +load_dotenv() + +class YouTubeTool(BaseTool): + name: str = "YouTube Search Tool" + description: str = "A tool to search for educational YouTube videos on specific topics" + api_key: str = Field(default_factory=lambda: os.getenv('YOUTUBE_API_KEY')) + youtube: any = None + + def __init__(self): + super().__init__() + self.youtube = build('youtube', 'v3', developerKey=self.api_key) + + def _run(self, topic: str) -> str: + """ + Search for educational videos on a specific topic. + + Args: + topic (str): The topic to search for + + Returns: + str: Formatted string containing video information + """ + try: + # Search for videos + search_response = self.youtube.search().list( + q=f"{topic} tutorial education", + part="snippet", + maxResults=3, + type="video", + relevanceLanguage="en", + videoDuration="medium", # Filter for medium length videos (4-20 minutes) + videoEmbeddable="true", + videoSyndicated="true", + order="relevance" # Sort by relevance + ).execute() + + videos = [] + for item in search_response.get("items", []): + video_info = { + "title": item["snippet"]["title"], + "channel": item["snippet"]["channelTitle"], + "url": f"https://www.youtube.com/watch?v={item['id']['videoId']}", + "description": item["snippet"]["description"] + } + videos.append(video_info) + + # Format the output as a markdown string + output = f"## Videos for {topic}\n\n" + for video in videos: + output += f"### {video['title']}\n" + output += f"- **Channel**: {video['channel']}\n" + output += f"- **URL**: {video['url']}\n" + output += f"- **Description**: {video['description'][:200]}...\n\n" + + return output + + except HttpError as e: + return f"An HTTP error occurred: {str(e)}" + except Exception as e: + return f"An error occurred: {str(e)}" \ No newline at end of file diff --git a/student_test_prep_agent/exam_prep/uv.lock b/student_test_prep_agent/exam_prep/uv.lock new file mode 100644 index 000000000..777f4f723 --- /dev/null +++ b/student_test_prep_agent/exam_prep/uv.lock @@ -0,0 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