56 lines
3.0 KiB
Markdown
56 lines
3.0 KiB
Markdown
## Preparation of the development environment
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- ```
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mkdir todoist-insight && cd todoist-insight
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python3 -m venv venv
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source venv/bin/activate # oppure .\venv\Scripts\activate su Windows
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touch requirements.txt README.md
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mkdir notebooks data src reports
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mkdir data/raw data/processed
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```
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- The activation of the virtual environment has been done with different attempts. At the end, to make it working in VSCode on Windows, I opened a Powershell terminal and executed the corresponding script
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- `.\venv\Scripts\Activate.ps1`
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- This permitted to install `ipykernel` and make it visibale in vscode. The installation was done automatically via VSCode even though it would have worked also with
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- `python -m ipykernel install --user --name=todoist-env --display-name "Python (todoist-env)`
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- Once crated the virtual environment, you have to install the required dependencies with
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- `pip install -r requirements.txt`
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- `uvicorn simple_service:app --reload`
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- ## Structure of the project
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- todoist-insight/
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├── notebooks/
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│ └── 01_analysis_dashboard.ipynb ← Notebook principale
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│
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├── data/
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│ ├── raw/
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│ │ ├── completed_tasks.json ← Export JSON completati
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│ │ └── incomplete_tasks.json ← Export JSON incompleti
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│ └── processed/
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│ └── completed_tasks.csv ← Dataset già trasformato
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│
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├── reports/
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│ ├── ai_analysis.md ← Output analisi GPT su task completati
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│ └── ai_suggestions.md ← Suggerimenti per task aperti
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│
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├── src/
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│ ├── todoist_api.py ← Funzioni per accedere a Todoist
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│ ├── visualization.py ← Grafici (matplotlib/seaborn/plotly)
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│ ├── gpt_analysis.py ← Analisi qualitativa via OpenAI
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│ └── utils.py ← Funzioni ausiliarie (date, parsing, mapping)
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│
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├── .env ← File per chiavi API (non pushare su GitHub)
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├── requirements.txt ← Dipendenze (todoist-api, openai, pandas, ...)
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└── README.md ← Panoramica e guida all’uso
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- ## Improvement of the Palyground project with CrewAI usage
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- {{renderer :mermaid_684d7ac6-4d14-464a-8f71-53a7f313e92e, 3}}
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collapsed:: true
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- ```mermaid
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flowchart LR
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A[Load Task Cache] --> B[AnalyzerAgent]
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B --> C[ClassifierAgent]
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C --> D[AdvisorAgent]
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D --> E[PlannerAgent]
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```
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- #ReadingNotes of
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- [[@Generative AI with LangChain: build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph]]
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- [From Zero to Your First AI Agent in 25 Minutes (No Coding)](https://www.youtube.com/watch?v=EH5jx5qPabU)
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-  |