[Good First Issue]: Replicate Existing Cookbooks and Create New Examples - Batch 8

[ijindal1]

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For context, see: CONTRIBUTING.md and cookbooks/README.md

Overview

We're looking for contributors to help expand our collection of Jupyter notebooks (cookbooks) that demonstrate various AI and automation tasks using Julep's features. This is a great opportunity for newcomers to get familiar with Julep and contribute to the project!

Objectives

1. Replicate existing cookbooks to ensure they're up-to-date and functioning correctly.
2. Create new cookbooks based on the ideas listed in the cookbooks/README.md file.

Tasks

1. Replicating Existing Cookbooks

• Choose one or more of the existing cookbooks from the list in cookbooks/README.md.
• Follow the instructions in the notebook to run it in Google Colab.
• Verify that all cells execute without errors and produce the expected outputs.
• If you encounter any issues or outdated information, document them for updating.

2. Creating New Cookbooks

• Select an idea from the "Potential Cookbooks for Contributors" section in cookbooks/README.md.
• Create a new Jupyter notebook following the structure of existing cookbooks.
• Implement the chosen use case using Julep's features.
• Ensure your notebook includes:
  • A clear introduction and overview of the task
  • Step-by-step implementation with explanations
  • Proper error handling and user guidance
  • A conclusion summarizing the key learnings

Guidelines for Creating New Cookbooks

1. Naming Convention: Name your notebook following the pattern XX-Descriptive_Name.ipynb, where XX is the next available number in the sequence.

2. Structure:

   • Start with an introduction explaining the purpose of the cookbook.
   • Include a cell for installing the Julep client.
   • Create sections for each major step (e.g., Creating an Agent, Defining a Task, Creating an Execution).
   • Add markdown cells to explain concepts and code.

3. Code Quality:

   • Ensure your code is well-commented and follows PEP 8 style guidelines.
   • Use meaningful variable names.
   • Handle potential errors gracefully.

4. Documentation:

   • Explain each step clearly, assuming the reader is new to Julep.
   • Include links to relevant Julep documentation where appropriate.

5. Testing:

   • Test your notebook thoroughly in Google Colab to ensure it runs without errors.
   • Include sample outputs where relevant.

6. Update README.md:

   • Add your new cookbook to the table in cookbooks/README.md.
   • Include a brief description and mark it as implemented.

How to Contribute

1. Fork the repository.
2. Create a new branch for your cookbook (e.g., cookbook/weather-notification-service).
3. Develop your cookbook following the guidelines above.
4. Test your notebook thoroughly.
5. Commit your changes and push to your fork.
6. Open a pull request with a clear description of your contribution.

Resources

• Julep Documentation
• Existing Cookbooks
• CONTRIBUTING.md

Need Help?

If you have any questions or need assistance, please don't hesitate to ask in the comments of this issue or reach out on our Discord channel.

We're excited to see your contributions and help grow the Julep cookbook collection!

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Long-ass list of ideas

57. AI-Powered Content Generation and Distribution Description: Generates content based on topics, schedules distribution across multiple platforms, and tracks engagement. Features: Tools: dalle, twitter, sendgrid Step Types: Prompt, Tool Call, Evaluate, Schedule Distribution (Set), Send Content, Log
58. Multi-Stage Quality Assurance Workflow Description: Conducts multiple quality assurance checks on products, logs issues, and ensures compliance before release. Features: Tools: api_call, sendgrid Step Types: Tool Call, Evaluate, Conditional Logic, Log Issues, Initiate Compliance Checks, Send Reports
59. Automated Knowledge Base Maintenance Description: Updates and maintains a knowledge base by extracting information from new documents and ensuring consistency. Features: Tools: wikipedia, huggingface Step Types: Tool Call, Evaluate, Conditional Logic, Update Knowledge Base (Set), Log, Return
60. Intelligent Content Personalization Engine Description: Personalizes website content based on user behavior and preferences to enhance user engagement. Features: Tools: web_search, huggingface Step Types: Tool Call, Evaluate, Conditional Logic, Personalize Content (Set), Send Recommendations, Log
61. Automated Feedback Loop for Continuous Improvement Description: Collects user feedback, analyzes it, implements improvements, and notifies stakeholders. Features: Tools: sendgrid, slack, huggingface Step Types: Tool Call, Evaluate, Conditional Logic, Implement Improvements (Set), Send Notifications, Log
62. AI-Driven Market Research and Analysis Description: Conducts market research by gathering data from various sources, analyzing trends, and generating comprehensive reports. Features: Tools: web_search, news, wikipedia Step Types: Tool Call, Evaluate, Conditional Logic, Generate Report (Prompt), Send Report, Log
63. Automated Workflow for Grant Application Processing Description: Processes grant applications by reviewing submissions, evaluating eligibility, and notifying applicants of decisions. Features: Tools: sendgrid, slack, huggingface Step Types: Tool Call, Evaluate, Conditional Logic, Notify Applicants (Send Email), Log
64. Intelligent Scheduling Assistant for Teams Description: Schedules team meetings by finding optimal times, sending invites, and managing calendar conflicts. Features: Tools: google_calendar, slack Step Types: Tool Call, Evaluate, Conditional Logic, Create Event (Set), Send Invitations, Log