diff --git a/.gitignore b/.gitignore index 03d5cda..55605d3 100644 --- a/.gitignore +++ b/.gitignore @@ -4,12 +4,16 @@ # vector database chroma_* +logs/ tmp/ .DS_Store.env # course content -data/markdown_raw/* -data/markdown_processed/* +data/course_raw/* +data/course_processed/* +# Keep the directories themselves +!data/course_raw/.gitkeep +!data/course_processed/.gitkeep # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..0c058a9 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,36 @@ +# Install pre-commit hooks with: +# prek install +exclude: "|tmp/*|" +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: end-of-file-fixer + - id: mixed-line-ending + - id: trailing-whitespace + - id: check-json + - id: check-yaml + - id: check-added-large-files + args: ["--maxkb=5000"] + + - repo: https://github.com/asottile/pyupgrade + rev: v3.21.2 + hooks: + - id: pyupgrade + + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.14.14 + hooks: + # Run the linter. + - id: ruff-check + types_or: [python, pyi] + args: [--fix] + # Run the formatter. + - id: ruff-format + types_or: [python, pyi] + + - repo: https://github.com/PyCQA/bandit + rev: "1.9.2" + hooks: + - id: bandit diff --git a/README.md b/README.md index 2b21fcc..094f783 100644 --- a/README.md +++ b/README.md @@ -32,54 +32,54 @@ git clone https://github.com/pierrepo/biopyassistant.git cd biopyassistant ``` -### Install [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html). +### Activate the environment -### Create a Conda environment +We use [uv](https://docs.astral.sh/uv/getting-started/installation/) +to manage dependencies and the project environment. -```bash -conda env create -f environment.yml -``` - -### Activate the Conda environment +Sync dependencies: ```bash -conda activate biopyassistantenv +uv sync ``` ### Copy the raw Markdown files of the Python [course](https://github.com/bioinfo-prog/cours-python): ```bash git clone --depth 1 https://github.com/bioinfo-prog/cours-python.git -rm -f data/markdown_raw/*.md -cp cours-python/cours/*.md data/markdown_raw/ +rm -f data/course_raw/*.md +cp cours-python/cours/*.md data/course_raw/ rm -rf cours-python ``` ### Process raw Markdown files ```bash -rm -f data/markdown_processed/*.md -python src/parse_clean_markdown.py --in data/markdown_raw --out data/markdown_processed +rm -f data/course_processed/*.md +uv run src/parse_clean_markdown.py --config data/chapters_and_levels.yaml ``` -In this step, Python comments (`#`) are slighty changed to avoid confusion with Markdown headers (`#`, `##`...) and headers are numbered (from `## Title` to `## 1.1 Title`). Processed Markdown files are stored in `data/markdown_processed` +In this step, Python comments (`#`) are slighty changed to avoid confusion with Markdown headers (`#`, `##`...) and headers are numbered (from `## Title` to `## 1.1 Title`). Processed Markdown files are stored in `data/course_processed` -### Add OpenAI API key +### Add OpenAI and OpenRouter API key -Create an `.env` file with a valid OpenAI API key: +Create an .env file with a valid [OpenAI](https://platform.openai.com/docs/api-reference/authentication) and [OpenRouter](https://openrouter.ai/docs/api/reference/authentication) API key: -```text +```sh OPENAI_API_KEY= +OPENROUTER_API_KEY= ``` - -> Remark: This `.env` file is ignored by git. +> Remark: This .env file is ignored by git. ### Create the vector database ```bash -python src/create_database.py --data-path data/markdown_processed --chroma-path chroma_db +uv run src/create_database.py --course-yaml data/chapters_and_levels.yaml \ + --chroma-path chroma_db \ + --embedding-model text-embedding-3-large \ + --model-provider openai ``` This command will create a Chroma vector database from the processed Markdown files. All files will be split into chunks of 1000 characters with an overlap of 200 characters. @@ -91,21 +91,32 @@ This command will create a Chroma vector database from the processed Markdown fi ```bash -python src/query_chatbot.py --query "Your question here" [--model "model_name"] [--include-metadata] +uv run python src/query_chatbot.py --query "Your question here" \ + --level "user_level" \ + --model "model_name" \ + --provider-llm "provider_name" \ + --include-metadata ``` ### Options -- đŸ€– Model Selection. For instance: `gpt-4o`, `gpt-4-turbo` -- 📝 Include Metadata: Include metadata in the response, such as the sources of the answer. By default, metadata is excluded. +- 📚 **User Level**: Specify the user's Python knowledge level to tailor the chatbot's responses. + Choose between: `beginner`, `intermediate`, `advanced`. +- đŸ€– **Model Selection**: Choose the language model for the query. Examples: `gpt-4o`, `deepseek/deepseek-v3.2`, etc. +- 🌐 **LLM Provider**: Specify the provider of the language model. Choose between: `openai`, `openrouter`. +- 📝 **Include Metadata**: Include metadata in the response, such as the sources of the answer. By default, metadata is excluded. Example: ```bash -python src/query_chatbot.py --query "What is the difference between list and set ?" --model gpt-4-turbo --include-metadata +uv run python src/query_chatbot.py --query "What is the difference between list and set ?" \ + --level "advanced" \ + --model "gpt-4o" \ + --provider-llm "openai" \ + --include-metadata ``` -This command will query the chatbot with the question "What is the difference between list and set ?" using the `gpt-4-turbo` model and include metadata in the response. +This command will query the chatbot for a response to the question "What is the difference between list and set ?" for an intermediate user using the `gpt-4o` model from the `openai` provider. The response will include metadata about the sources of the answer. Output: @@ -124,22 +135,12 @@ For more information, you can refer to the following sources: ## Usage (web interface) -### Streamlit app - ```bash -streamlit run src/streamlit_app.py +uv run streamlit run src/streamlit_app.py ``` This will run the Streamlit app in your web browser. -### Gradio App - - -```bash -python src/gradio_app.py -``` - -This will run the Gradio app in your web browser. A battle mode is available to compare the responses of different models. diff --git a/data/chapters_and_levels.yaml b/data/chapters_and_levels.yaml new file mode 100644 index 0000000..6339af5 --- /dev/null +++ b/data/chapters_and_levels.yaml @@ -0,0 +1,218 @@ +# chapters_and_levels.yaml +# Python course structure for the application. + +# Definition of Markdown file paths for the course content. +# Base directory containing the original course files +raw_course_base_path: "data/course_raw/" +# Base directory to save the cleaned and processed course files +processed_course_base_path: "data/course_processed/" + +chapters: + # List of all available Python chapters and annexes. + # These entries define which Markdown files are ingested into the vector database. + + - id: "01" + title: "Introduction" + file_name: "01_introduction.md" + + - id: "02" + title: "Variables" + file_name: "02_variables.md" + + - id: "03" + title: "Affichage" + file_name: "03_affichage.md" + + - id: "04" + title: "Listes" + file_name: "04_listes.md" + + - id: "05" + title: "Boucles et comparaisons" + file_name: "05_boucles_comparaisons.md" + + - id: "06" + title: "Tests" + file_name: "06_tests.md" + + - id: "07" + title: "Fichiers" + file_name: "07_fichiers.md" + + - id: "08" + title: "Dictionnaires et tuples" + file_name: "08_dictionnaires_tuples.md" + + - id: "09" + title: "Modules" + file_name: "09_modules.md" + + - id: "10" + title: "Fonctions" + file_name: "10_fonctions.md" + + - id: "11" + title: "Plus sur les chaĂźnes de caractĂšres" + file_name: "11_plus_sur_les_chaines_de_caracteres.md" + + - id: "12" + title: "Plus sur les listes" + file_name: "12_plus_sur_les_listes.md" + + - id: "13" + title: "Plus sur les fonctions" + file_name: "13_plus_sur_les_fonctions.md" + + - id: "14" + title: "Conteneurs" + file_name: "14_conteneurs.md" + + - id: "15" + title: "CrĂ©ation de modules" + file_name: "15_creation_modules.md" + + - id: "16" + title: "Bonnes pratiques en programmation Python" + file_name: "16_bonnes_pratiques.md" + + - id: "17" + title: "Expressions rĂ©guliĂšres et parsing" + file_name: "17_expressions_regulieres.md" + + - id: "18" + title: "Jupyter et ses notebooks" + file_name: "18_jupyter.md" + + - id: "19" + title: "Module Biopython" + file_name: "19_module_biopython.md" + + - id: "20" + title: "Module Numpy" + file_name: "20_module_numpy.md" + + - id: "21" + title: "Module Matplotlib" + file_name: "21_module_matplotlib.md" + + - id: "22" + title: "Module Pandas" + file_name: "22_module_pandas.md" + + - id: "23" + title: "Avoir la classe avec les objets" + file_name: "23_avoir_la_classe_avec_les_objets.md" + + - id: "24" + title: "Avoir plus la classe avec les objets" + file_name: "24_avoir_plus_la_classe_avec_les_objets.md" + + - id: "25" + title: "FenĂȘtres graphiques et Tkinter" + file_name: "25_tkinter.md" + + - id: "26" + title: "Remarques complĂ©mentaires" + file_name: "26_remarques_complementaires.md" + + - id: "27" + title: "Mini-projets" + file_name: "27_mini_projets.md" + + - id: "A" + title: "Quelques formats de donnĂ©es en biologies" + file_name: "annexe_A_formats_fichiers.md" + + - id: "B" + title: "Installation de Python" + file_name: "annexe_B_install_python.md" + +levels: + # Definition of student levels. + # Each level references chapters by name to adapt responses to the student's level. + - name: "beginner" + display_name: "DĂ©butant (L2 / L3)" + comment: > + This level is for students who have just started learning Python + and have a basic understanding of programming concepts. + They are likely in their second or third year of university (L2/L3) + and are looking for guidance on fundamental Python topics. + + prompt_path: "prompt_templates/common.txt" + + chapters: + - "01" + - "02" + - "03" + - "04" + - "05" + - "06" + - "07" + - "11" + - "12" + + - name: "intermediate" + display_name: "IntermĂ©diaire (Python 1)" + comment: > + This level is for master`s students with an intermediate Python + knowledge. They have some experience but may need to review + certain fundamentals while deepening their understanding. + + prompt_path: "prompt_templates/common.txt" + + chapters: + - "01" + - "02" + - "03" + - "04" + - "05" + - "06" + - "07" + - "08" + - "09" + - "10" + - "11" + - "12" + - "13" + - "14" + - "15" + - "16" + - "B" + + - name: "advanced" + display_name: "AvancĂ© (Python 2)" + comment: > + This level is for students who have a solid understanding of Python + and are looking to deepen their knowledge with more advanced topics. + They have completed a Python 1 course or have equivalent experience + and are ready to explore more complex concepts. + + prompt_path: "prompt_templates/common.txt" + + chapters: + - "01" + - "02" + - "03" + - "04" + - "05" + - "06" + - "07" + - "08" + - "09" + - "10" + - "11" + - "12" + - "13" + - "14" + - "15" + - "16" + - "17" + - "18" + - "19" + - "20" + - "21" + - "22" + - "23" + - "24" + - "27" + - "B" diff --git a/data/markdown_processed/.gitkeep b/data/course_processed/.gitkeep similarity index 100% rename from data/markdown_processed/.gitkeep rename to data/course_processed/.gitkeep diff --git a/data/markdown_raw/.gitkeep b/data/course_raw/.gitkeep similarity index 100% rename from data/markdown_raw/.gitkeep rename to data/course_raw/.gitkeep diff --git a/data/quiz.json b/data/quiz.json deleted file mode 100644 index 0e30f8a..0000000 --- a/data/quiz.json +++ /dev/null @@ -1,455 +0,0 @@ -{ - "questions": [ - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle est l'origine du nom du langage de programmation Python ?", - "answers": { - "A": "D'un hommage Ă  un serpent.", - "B": "D'un hommage Ă  une sĂ©rie tĂ©lĂ©visĂ©e.", - "C": "Du nom de son crĂ©ateur.", - "D": "D'une abrĂ©viation." - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car le nom *Python* vient d'un hommage Ă  la sĂ©rie tĂ©lĂ©visĂ©e *Monty Python's Flying Circus* dont Guido van Rossum, le crĂ©ateur de Python, est fan. Les autres rĂ©ponses sont incorrectes : A est une fausse information, C est incorrect car le crĂ©ateur s'appelle Guido van Rossum, et D est Ă©galement incorrect car *Python* n'est pas une abrĂ©viation." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quel est l'avantage principal d'un langage interprĂ©tĂ© comme Python ?", - "answers": { - "A": "Il est plus rapide que les langages compilĂ©s.", - "B": "Il permet d'exĂ©cuter le code directement sans compilation prĂ©alable.", - "C": "Il nĂ©cessite moins de mĂ©moire pour fonctionner.", - "D": "Il ne peut pas ĂȘtre utilisĂ© sur plusieurs plateformes." - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car un langage interprĂ©tĂ© comme Python permet d'exĂ©cuter le code directement sans compilation prĂ©alable, contrairement aux langages compilĂ©s comme le C ou le C++. La rĂ©ponse A est incorrecte car les langages interprĂ©tĂ©s sont gĂ©nĂ©ralement plus lents que les langages compilĂ©s. La rĂ©ponse C est incorrecte car le besoin en mĂ©moire dĂ©pend du programme, et non du fait que le langage soit interprĂ©tĂ© ou compilĂ©. La rĂ©ponse D est incorrecte car Python est multiplateforme." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle commande permet de quitter l'interprĂ©teur Python sous Windows ?", - "answers": { - "A": "exit()", - "B": "Ctrl + D", - "C": "Ctrl + Z puis EntrĂ©e", - "D": "quit()" - }, - "correct_answer": "C", - "explanation": "La rĂ©ponse correcte est C car sous Windows, on quitte l'interprĂ©teur Python en pressant simultanĂ©ment les touches Ctrl et Z puis EntrĂ©e. La rĂ©ponse A (exit()) est correcte mais n'est pas spĂ©cifique Ă  Windows, car elle fonctionne aussi sous Linux et Mac OS X. La rĂ©ponse B (Ctrl + D) est incorrecte pour Windows mais correcte pour Linux et Mac OS X. La rĂ©ponse D (quit()) est correcte mais, comme pour exit(), n'est pas spĂ©cifique Ă  Windows." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quel Ă©diteur de texte est recommandĂ© pour les dĂ©butants sous Windows ?", - "answers": { - "A": "Visual Studio Code", - "B": "Sublime Text", - "C": "notepad++", - "D": "gedit" - }, - "correct_answer": "C", - "explanation": "La rĂ©ponse correcte est C car notepad++ est recommandĂ© pour les dĂ©butants sous Windows. Visual Studio Code (A) et Sublime Text (B) sont Ă©galement de trĂšs bons Ă©diteurs de texte mais ne sont pas spĂ©cifiquement recommandĂ©s aux dĂ©butants dans ce cours. La rĂ©ponse D (gedit) est incorrecte car gedit est recommandĂ© pour les utilisateurs de Linux." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle est la commande pour lancer l'interprĂ©teur Python depuis un shell ?", - "answers": { - "A": "run python", - "B": "execute python", - "C": "start python", - "D": "python" - }, - "correct_answer": "D", - "explanation": "La rĂ©ponse correcte est D car la commande pour lancer l'interprĂ©teur Python depuis un shell est simplement 'python'. Les autres rĂ©ponses (A, B, et C) sont incorrectes car elles ne sont pas les commandes reconnues pour lancer l'interprĂ©teur Python." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "Vrai ou Faux", - "question": "Python est un langage de programmation crĂ©Ă© en 1989 par Guido van Rossum.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. Python a bien Ă©tĂ© crĂ©Ă© en 1989 par Guido van Rossum. Il s'est inspirĂ© de la sĂ©rie tĂ©lĂ©visĂ©e Monty Python's Flying Circus pour nommer le langage. La rĂ©ponse B est incorrecte car elle contredit cette information." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "Vrai ou Faux", - "question": "La version 2 de Python est toujours maintenue et est recommandĂ©e pour de nouveaux projets.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. La version 2 de Python est obsolĂšte et n'est plus maintenue. La version 3 est le standard actuel et recommandĂ©e pour tous les nouveaux projets. La rĂ©ponse A est incorrecte car elle contredit cette information." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "Vrai ou Faux", - "question": "Python est un langage compilĂ©, ce qui signifie qu'il doit ĂȘtre transformĂ© en code machine avant d'ĂȘtre exĂ©cutĂ©.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Python est un langage interprĂ©tĂ©, ce qui signifie que le code est lu et exĂ©cutĂ© directement par l'interprĂ©teur Python sans nĂ©cessiter de compilation prĂ©alable. La rĂ©ponse A est incorrecte car elle dĂ©crit le fonctionnement des langages compilĂ©s comme le C ou le C++." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "Vrai ou Faux", - "question": "Miniconda est un logiciel recommandĂ© pour installer Python et gĂ©rer les modules supplĂ©mentaires.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. Miniconda est recommandĂ© pour installer Python et gĂ©rer les modules supplĂ©mentaires nĂ©cessaires, comme NumPy, scipy, matplotlib, pandas, et Biopython. La rĂ©ponse B est incorrecte car elle contredit cette recommandation." - }, - { - "chapter": "Introduction", - "python_level": "DĂ©butant", - "type": "Vrai ou Faux", - "question": "Les Ă©diteurs de texte comme Microsoft Word ou LibreOffice Writer sont adaptĂ©s pour Ă©crire du code Python.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Microsoft Word et LibreOffice Writer sont des traitements de texte et ne sont pas adaptĂ©s pour Ă©crire du code Python. Il est recommandĂ© d'utiliser des Ă©diteurs de texte comme notepad++, BBEdit, CotEditor, gedit, Visual Studio Code, Sublime Text, emacs, vim, ou geany. La rĂ©ponse A est incorrecte car elle suggĂšre l'utilisation de traitements de texte, ce qui n'est pas appropriĂ© pour l'Ă©criture de code." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "Vrai/Faux", - "question": "En Python, la dĂ©claration et l'initialisation d'une variable se font en mĂȘme temps.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A car en Python, lorsque vous dĂ©clarez une variable, vous l'initialisez Ă©galement avec une valeur. Par exemple, l'instruction `x = 2` dĂ©clare et initialise la variable `x` avec la valeur 2. RĂ©pondre 'Faux' serait incorrect car Python ne permet pas de dĂ©clarer une variable sans l'initialiser." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "Vrai/Faux", - "question": "La ligne de code suivante : \n```python\nx = 5\ny = x\n``` signifie que la variable y est Ă©gale Ă  5.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A car `y = x` assigne la valeur de `x` Ă  `y`. Puisque `x` vaut 5, `y` prendra Ă©galement la valeur 5. RĂ©pondre 'Faux' serait incorrect car cela signifierait ne pas comprendre l'opĂ©ration d'affectation." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "Vrai/Faux", - "question": "La variable `y` dans le code suivant est de type float : \n```python\ny = 3.14\n```", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A car `y = 3.14` assigne un nombre dĂ©cimal Ă  `y`, ce qui en fait une variable de type float. RĂ©pondre 'Faux' serait incorrect car cela signifie ne pas reconnaĂźtre le type float." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "Vrai/Faux", - "question": "En Python, il est possible d'utiliser un espace dans le nom d'une variable.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car les noms de variables en Python ne peuvent pas contenir d'espaces. Par exemple, `my variable` est incorrect, mais `my_variable` est correct. RĂ©pondre 'Vrai' serait incorrect car violerait les rĂšgles de nommage des variables en Python." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "Vrai/Faux", - "question": "La fonction `type()` peut ĂȘtre utilisĂ©e pour dĂ©terminer le type d'une variable.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A car la fonction `type()` retourne le type de la variable passĂ©e en argument. Par exemple, `type(3)` retournera ``. RĂ©pondre 'Faux' serait incorrect car cela signifie ne pas connaĂźtre l'utilisation de la fonction `type()`." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Que se passe-t-il lorsque vous exĂ©cutez le code suivant ?\n```python\nx = 5\ny = x\nx = 10\nprint(y)\n```", - "answers": { - "A": "5", - "B": "10", - "C": "Une erreur", - "D": "0" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. Lorsque `y` est assignĂ© Ă  `x`, `y` prend la valeur actuelle de `x`, qui est 5. MĂȘme si `x` est ensuite mis Ă  jour Ă  10, `y` conserve la valeur 5 car les variables sont indĂ©pendantes aprĂšs l'affectation initiale. B est incorrect car `y` ne change pas lorsque `x` est mis Ă  jour. C est incorrect car le code ne produit pas d'erreur. D est incorrect car `y` n'est jamais assignĂ© Ă  0." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quel type de variable est crĂ©Ă© avec l'instruction suivante ?\n```python\nx = 3.14\n```", - "answers": { - "A": "int", - "B": "float", - "C": "str", - "D": "bool" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Le nombre 3.14 est un nombre dĂ©cimal, et en Python, les nombres dĂ©cimaux sont de type float. A est incorrect car int reprĂ©sente des nombres entiers sans partie dĂ©cimale. C est incorrect car str reprĂ©sente des chaĂźnes de caractĂšres. D est incorrect car bool reprĂ©sente des valeurs boolĂ©ennes (True ou False)." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quel est le rĂ©sultat de l'instruction suivante ?\n```python\nx = 2\ny = x + 3\nprint(x * y)\n```", - "answers": { - "A": "10", - "B": "25", - "C": "6", - "D": "5" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. La variable `x` est 2 et `y` est calculĂ©e comme `x + 3`, ce qui donne 5. Le produit `x * y` est donc `2 * 5`, ce qui donne 10. B est incorrect car il suppose une multiplication erronĂ©e. C est incorrect car il ne prend pas en compte la valeur correcte de `y`. D est incorrect car il ne reprĂ©sente aucune des opĂ©rations du code." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quel est le type de la variable `z` aprĂšs l'exĂ©cution du code suivant ?\n```python\nz = 'True'\n```", - "answers": { - "A": "bool", - "B": "str", - "C": "int", - "D": "float" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. MĂȘme si 'True' est une valeur boolĂ©enne en Python, les guillemets simples autour de True signifient que c'est une chaĂźne de caractĂšres (str). A est incorrect car `True` sans guillemets serait un boolĂ©en, mais ici c'est une chaĂźne. C est incorrect car `z` n'est pas un entier. D est incorrect car `z` n'est pas un nombre dĂ©cimal." - }, - { - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle opĂ©ration est correcte pour convertir une chaĂźne de caractĂšres '123' en un entier ?", - "answers": { - "A": "int('123')", - "B": "str(123)", - "C": "float('123')", - "D": "bool('123')" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. La fonction `int()` convertit une chaĂźne de caractĂšres contenant un nombre entier en un entier. B est incorrect car `str(123)` convertit un entier en une chaĂźne de caractĂšres. C est incorrect car `float('123')` convertit la chaĂźne en un nombre dĂ©cimal, pas en un entier. D est incorrect car `bool('123')` convertit la chaĂźne en une valeur boolĂ©enne, ce qui serait True." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "QCM", - "question": "Quelle est la sortie du code suivant ?\n```python\nx = 5\ny = '10'\nprint(x + int(y))\n```", - "answers": { - "A": "510", - "B": "15", - "C": "TypeError", - "D": "5 + 10" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car la variable `y` est initialement une chaĂźne de caractĂšres ('10'). En utilisant `int(y)`, nous convertissons cette chaĂźne de caractĂšres en un entier (10), ce qui permet alors l'addition avec `x` qui est 5. Les autres rĂ©ponses sont incorrectes : A est incorrect car il concatĂšne des chaĂźnes de caractĂšres, C est incorrect car la conversion a Ă©tĂ© faite correctement, et D est incorrect car il ne rĂ©alise pas l'opĂ©ration mais affiche les valeurs sous forme de chaĂźne." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "QCM", - "question": "Quel est le type de la variable `z` aprĂšs l'exĂ©cution du code suivant ?\n```python\nx = 3\nz = x / 2\n```", - "answers": { - "A": "int", - "B": "float", - "C": "str", - "D": "bool" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car l'opĂ©rateur de division `/` en Python renvoie toujours un `float`, mĂȘme si les deux opĂ©randes sont des entiers. Les autres rĂ©ponses sont incorrectes : A est incorrect car le type n'est pas `int`, C est incorrect car ce n'est pas une chaĂźne de caractĂšres, et D est incorrect car ce n'est pas un boolĂ©en." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "QCM", - "question": "Identifiez l'erreur dans le code suivant :\n```python\nx = 10\ny = 20\nz = x + y\nprint('La valeur de z est: ' + z)\n```", - "answers": { - "A": "SyntaxError", - "B": "TypeError", - "C": "NameError", - "D": "Aucune erreur" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Une `TypeError` se produit car nous essayons de concatĂ©ner une chaĂźne de caractĂšres ('La valeur de z est: ') avec un entier (z). Pour corriger cela, z devrait ĂȘtre converti en chaĂźne de caractĂšres en utilisant `str(z)`. Les autres rĂ©ponses sont incorrectes : A est incorrect car il n'y a pas d'erreur de syntaxe, C est incorrect car toutes les variables sont dĂ©finies, et D est incorrect car il y a effectivement une erreur de type." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "QCM", - "question": "Quelle est la sortie du code suivant ?\n```python\nx = 5\nx += 3\nx *= 2\nprint(x)\n```", - "answers": { - "A": "8", - "B": "10", - "C": "16", - "D": "13" - }, - "correct_answer": "C", - "explanation": "La rĂ©ponse correcte est C. Voici le dĂ©roulement du code : initialement, `x` vaut 5. Ensuite, `x += 3` augmente `x` de 3, donc `x` devient 8. Enfin, `x *= 2` multiplie `x` par 2, donc `x` devient 16. Les autres rĂ©ponses sont incorrectes : A est incorrect aprĂšs la premiĂšre opĂ©ration seulement, B est incorrect car il ne tient pas compte de la multiplication, et D est incorrect car il additionne sans multiplier." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "QCM", - "question": "ComplĂ©tez le code pour qu'il affiche 'Python est gĂ©nial!'.\n```python\nmot1 = 'Python'\nmot2 = 'est'\nmot3 = 'gĂ©nial!'\nphrase = ___ + ' ' + ___ + ' ' + ___\nprint(phrase)\n```", - "answers": { - "A": "mot1, mot2, mot3", - "B": "'mot1', 'mot2', 'mot3'", - "C": "mot1 mot2 mot3", - "D": "mot1 + mot2 + mot3" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A car nous devons concatĂ©ner les variables `mot1`, `mot2`, et `mot3` avec des espaces entre elles pour former la phrase 'Python est gĂ©nial!'. Les autres rĂ©ponses sont incorrectes : B est incorrect car cela traiterait les noms de variables comme des chaĂźnes de caractĂšres, C est incorrect car les variables ne sont pas sĂ©parĂ©es par des virgules, et D est incorrect car il manque les espaces entre les mots." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "Vrai ou Faux", - "question": "En Python, la dĂ©claration et l'initialisation d'une variable se font en une seule instruction.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. En Python, lorsqu'on dĂ©clare une variable, on l'initialise Ă©galement en lui assignant une valeur. Par exemple, l'instruction `x = 2` dĂ©clare et initialise la variable `x` avec la valeur 2. Ce n'est pas le cas dans certains autres langages de programmation oĂč la dĂ©claration et l'initialisation sont des Ă©tapes distinctes." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "Vrai ou Faux", - "question": "En Python, une variable de type entier `int` et de type float `float` peuvent ĂȘtre utilisĂ©es ensemble dans une expression arithmĂ©tique et le rĂ©sultat sera de type entier.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Lorsque vous mĂ©langez des entiers et des floats dans une expression arithmĂ©tique en Python, le rĂ©sultat sera de type float. Par exemple, `3 + 2.5` donnera `5.5`, qui est un float. Python convertit automatiquement le rĂ©sultat en float pour prĂ©server la prĂ©cision." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "Vrai ou Faux", - "question": "Le nom d'une variable en Python peut commencer par un chiffre.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. En Python, les noms de variables ne peuvent pas commencer par un chiffre. Ils doivent commencer par une lettre (majuscule ou minuscule) ou un underscore (`_`). Par exemple, `1variable` n'est pas un nom de variable valide, tandis que `variable1` l'est." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "Vrai ou Faux", - "question": "En Python, le symbole `=` est utilisĂ© pour tester l'Ă©galitĂ© entre deux valeurs.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. En Python, le symbole `=` est utilisĂ© pour l'affectation, c'est-Ă -dire pour assigner une valeur Ă  une variable. Pour tester l'Ă©galitĂ© entre deux valeurs, on utilise le symbole `==`. Par exemple, `x = 2` assigne la valeur 2 Ă  la variable `x`, tandis que `x == 2` teste si `x` est Ă©gal Ă  2." - }, - { - "chapter": "Variables", - "python_level": "AvancĂ©", - "type": "Vrai ou Faux", - "question": "Le type d'une variable en Python peut ĂȘtre changĂ© dynamiquement.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. En Python, le type d'une variable est dynamique, ce qui signifie qu'il peut changer au cours de l'exĂ©cution du programme. Par exemple, on peut assigner un entier Ă  une variable `x` (comme dans `x = 2`), puis une chaĂźne de caractĂšres (comme dans `x = 'hello'`). Python gĂšre automatiquement le changement de type." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "AvancĂ©", - "question": "Python a Ă©tĂ© crĂ©Ă© en 1989 par Guido van Rossum.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. Python a en effet Ă©tĂ© crĂ©Ă© en 1989 par Guido van Rossum. Le nom *Python* rend hommage Ă  la sĂ©rie tĂ©lĂ©visĂ©e *Monty Python's Flying Circus* dont G. van Rossum est fan. La premiĂšre version publique de ce langage a Ă©tĂ© publiĂ©e en 1991." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "AvancĂ©", - "question": "Python 2 est encore largement utilisĂ© et maintenu Ă  jour.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Python 2 est obsolĂšte et n'est plus maintenu. Il est recommandĂ© d'utiliser Python 3, plus prĂ©cisĂ©ment la version 3.11 publiĂ©e en octobre 2022." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "AvancĂ©", - "question": "Python est un langage compilĂ©, similaire Ă  C ou C++.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Python est un langage interprĂ©tĂ©, ce qui signifie qu'un script Python n'a pas besoin d'ĂȘtre compilĂ© pour ĂȘtre exĂ©cutĂ©, contrairement aux langages comme le C ou le C++. Cette caractĂ©ristique permet de tester rapidement des commandes et de dĂ©bugger facilement les programmes." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "AvancĂ©", - "question": "Il est possible d'installer Python sur un tĂ©lĂ©phone portable.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La rĂ©ponse correcte est A. Python est multiplateforme et peut ĂȘtre installĂ© sur de nombreux systĂšmes d'exploitation, y compris sur des tĂ©lĂ©phones portables. Cela inclut des systĂšmes comme Android et iOS." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "AvancĂ©", - "question": "Pour Ă©crire du code Python, il est recommandĂ© d'utiliser un traitement de texte comme Microsoft Word.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B. Les traitements de texte comme Microsoft Word ne doivent pas ĂȘtre utilisĂ©s pour Ă©crire du code Python. Il est recommandĂ© d'utiliser des Ă©diteurs de texte comme *notepad++*, *BBEdit*, *CotEditor*, *gedit*, ou des IDE comme *Visual Studio Code*, *Sublime Text*, *emacs*, *vim*, *geany*, etc." - } - ], - "last_modified": "2024-07-15T13:56:58.050476" -} \ No newline at end of file diff --git a/prompt_templates/common.txt b/prompt_templates/common.txt new file mode 100644 index 0000000..8ea07a8 --- /dev/null +++ b/prompt_templates/common.txt @@ -0,0 +1,29 @@ +# Assistant conversationnel contextuel – Programmation Python en biologie + +## RĂŽle + +Tu es un tuteur pĂ©dagogique spĂ©cialisĂ© en Bio-informatique. Ton objectif est d'aider des Ă©tudiants en biologie Ă  maĂźtriser Python en utilisant exclusivement les ressources fournies. + +## Instructions + +1. **Source unique** : RĂ©ponds Ă  la question en te basant **uniquement** sur le texte fourni dans la section "## DOCUMENTS DE RÉFÉRENCE (CONTEXTE)". +2. **HonnĂȘtetĂ© intellectuelle** : Si la rĂ©ponse n'est pas dans le contexte, rĂ©ponds exactement : "DĂ©solĂ©, je suis un assistant pour l'apprentissage de la programmation Python. Je ne suis pas en mesure de rĂ©pondre Ă  cette question." +3. **Style** : Utilise un ton bienveillant et pĂ©dagogique. Vulgarise les concepts informatiques difficiles sans perdre la prĂ©cision biologique. +4. **Format** : Utilise du Markdown pour la clartĂ© (blocs de code pour Python, gras pour les termes clĂ©s). + +## DOCUMENTS DE RÉFÉRENCE (CONTEXTE) + +Voici les seules ressources que tu es autorisĂ© Ă  utiliser pour rĂ©pondre : +{contexte} + +## TÂCHE + +En tenant compte de l'historique suivant : +{chat_history} + +RĂ©ponds de maniĂšre concise et claire Ă  la question suivante de l'Ă©tudiant : +"{question}" + +--- + +**RĂ©ponse attendue :** \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..f5b14cc --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "biopyassistantenv" +version = "0.1.0" +description = "BioPyAssistant environment" +readme = "README.md" +requires-python = "==3.13.*" + +dependencies = [ + "python-dotenv", + "chromadb", + "openai", + "tiktoken", + "langchain", + "langchain-openai", + "langchain-chroma", + "langchain-core", + "langchain-community", + "langchain-text-splitters", + "loguru", + "streamlit", + "click>=8.3.0", + "htbuilder>=0.9.0", + "pydantic-settings>=2.12.0", + "pyperclip>=1.11.0", +] + +[dependency-groups] +dev = [ + "prek>=0.3.0", + "ruff>=0.14.14", +] diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000..436c4ca --- /dev/null +++ b/ruff.toml @@ -0,0 +1,58 @@ +target-version = "py313" +line-length = 88 # Same as Black. + +[format] +docstring-code-format = true +docstring-code-line-length = 72 + +[lint] +preview = true # Allow preview rules. +extend-select = [ + "F", # pyflakes + "E", # Pycodestyle errors + "W", # Pycodestyle warnings + "I", # isort - import ordering + "B", # bugbear + "C90", # McCabe - code complexity metric for functions + "N", # PEP8 Naming + "D", # Pydocstyle - docstring formatting + "UP", # Pyupgrade - upgraded syntax to newer Python versions + "S", # Flake8-bandit – security issues + "BLE", # Flake8-blind-except – flags bare excepts + "FBT", # Flake8-boolean-trap – potential pitfalls with booleans + "A", # Flake8-builtins – misuse of Python built-in names + "COM", # Flake8-commas – trailing/comma issues + "C4", # Flake8-comprehensions – best practices in comprehensions + "EM", # Flake8-errmsg – error message style + "ICN", # Flake8-import-conventions – enforces conventional import aliases + "LOG", # Flake8-logging – proper logging usage + "G", # Flake8-logging-format – logging format string issues + "PT", # Flake8-pytest-style – pytest best practices + "Q", # Flake8-quotes – enforces quote style consistency + "SIM", # Flake8-simplify – code simplification hints + "PD", # Pandas-vet – checks pandas-specific code practices + "NPY", # NumPy-specific rules – ensures NumPy coding standards + "FAST", # FastAPI – FastAPI-specific linting rules + "PERF", # Perflint – performance-related checks + "FURB", # Refurb – rules for code refurbishment + "DOC", # Pydoclint – docstring linting and consistency + "RUF", # Ruff-specific rules – additional Ruff checks +] +ignore = [ + "COM812", # Redundant with ruff formatter. See: https://docs.astral.sh/ruff/rules/missing-trailing-comma/ + "G004", # f-strings are allowed with the loguru module. See https://docs.astral.sh/ruff/rules/logging-f-string/ +] + +# Force numpy-style for docstrings +# https://docs.astral.sh/ruff/faq/#does-ruff-support-numpy-or-google-style-docstrings +[lint.pydocstyle] +convention = "numpy" + +# From https://github.com/astral-sh/ruff/issues/4368 +[lint.extend-per-file-ignores] +"tests/**/*.py" = [ + # at least this three should be fine in tests: + "S101", # asserts allowed in tests... + "ARG", # Unused function args -> fixtures nevertheless are functionally relevant... + "FBT", # Don't care about booleans as positional arguments in tests, e.g. via @pytest.mark.parametrize() +] diff --git a/src/create_database.py b/src/create_database.py index 4da7cea..04705a3 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -1,252 +1,238 @@ """Creates the vectorial Chroma database from Markdown files in the specified directory. -This script loads Markdown files from the specified directory, concatenates their content, -and splits the content into chunks based on headers and word limits. The resulting chunks are saved to a ChromaDB database. +This script loads Markdown files from the specified directory, concatenates their +content, and splits the content into chunks based on headers and word limits. +The resulting chunks are saved to a ChromaDB database. Usage: ====== - python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] + uv run src/create_database.py --course-yaml [course-yaml] + --chroma-path [chroma-path] + --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] + --embedding-model [embedding-model] + --model-provider [model-provider] Arguments: ========== - --data-path : str - The directory containing the processed Markdown files of the python course. + --course-yaml : str + The YAML file containing the course structure, including chapter names, titles, + source Markdown paths, and processed file paths. --chroma-path : str The name of the output path to save the ChromaDB database. --chunk-size : int (optional) The size of the text chunks to be created. Default is 1000. --chunk-overlap : int (optional) The overlap between text chunks. Default is 200. - + --embedding-model : str (optional) + Name of the embedding model to use. + Possible choices : https://openrouter.ai/models?fmt=cards&supported_parameters=structured_outputs&output_modalities=embeddings + Default is "text-embedding-3-large". + --model-provider : str (optional) + Name of the embedding provider to use. + Possible choices are "openrouter" and "openai". + Default is "openai". + Example: ======== - python src/create_database.py --data-path data/markdown_processed --chroma-path chroma_db - -This command will create a Chroma vector database from the processed Markdown files located in the `data/markdown_processed` directory. -The text will be split into chunks of 1000 characters with an overlap of 200 characters. + uv run src/create_database.py --course-yaml data/chapters_and_levels.yaml \ + --chroma-path chroma_db \ + --embedding-model text-embedding-3-large \ + --model-provider openai + +This command will create a Chroma vector database from the processed Markdown files +located in the paths specified in the `data/chapters_and_levels.yaml` file. +The text will be split into chunks of 1000 characters with an overlap of 200 characters +and will be embedded with the model `text-embedding-3-large`. And finally the vector database will be saved to the `chroma_db` directory. """ -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS import os import re -import sys import shutil -import argparse +import sys import unicodedata +from datetime import datetime +from pathlib import Path -from dotenv import load_dotenv +import click +import loguru import tiktoken -from loguru import logger -from langchain_openai import OpenAIEmbeddings -from langchain_core.documents import Document +from dotenv import load_dotenv from langchain_chroma import Chroma -from langchain_community.document_loaders import TextLoader, DirectoryLoader +from langchain_community.document_loaders import TextLoader +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import ( MarkdownHeaderTextSplitter, RecursiveCharacterTextSplitter, ) - - -# CONSTANTS -CHUNK_SIZE = 1000 -CHUNK_OVERLAP = 200 -EMBEDDING_MODEL = "text-embedding-3-large" - - -# FUNCTIONS -def get_args() -> tuple[str, str, int, int]: - """Parse command-line arguments. - - Returns - ------- - data_path, chroma_output_path, chunk_size, chunk_overlap : Tuple[str, str, int, int] - - data_path : str - The directory containing the processed Markdown files of the python course. - - chroma_output_path : str - The name of the output path to save the ChromaDB database. - - chunk_size : int - The size of the text chunks to be created. - - chunk_overlap : int - The overlap between text chunks. - """ - # Create the parser - parser = argparse.ArgumentParser( - description="Create a ChromaDB database from Markdown files in the specified directory." - ) - # Add the arguments - parser.add_argument( - "-d", - "--data-path", - dest="data_path", - help="The directory containing the processed Markdown files of the python course.", - ) - parser.add_argument( - "-c", - "--chroma-path", - dest="chroma_path", - help="The name of the output path to save the ChromaDB database.", - ) - parser.add_argument( - "-s", - "--chunk-size", - dest="chunk_size", - type=int, - default=CHUNK_SIZE, - help="The size of the text chunks to be created.", - ) - parser.add_argument( - "-o", - "--chunk-overlap", - dest="chunk_overlap", - type=int, - default=CHUNK_OVERLAP, - help="The overlap between text chunks.", - ) - # Parse the arguments - args = parser.parse_args() - - # Checks - # db_path should exist - if not os.path.exists(args.data_path): - logger.error(f"The data directory '{args.data_path}' does not exist.") - sys.exit(1) - if args.chunk_size <= 0: - logger.error("The chunk size should be a positive integer.") - sys.exit(1) - if args.chunk_overlap <= 0: - logger.error("The chunk overlap should be a positive integer.") - sys.exit(1) - # chunk_overlap should be less than chunk_size - if args.chunk_overlap >= args.chunk_size: - logger.error( - f"The chunk overlap ({args.chunk_overlap}) should be less than the chunk size ({args.chunk_size})." +from openai import OpenAI + +from logger import create_logger +from parse_clean_markdown import load_chapters_from_yaml + + +class OpenRouterEmbeddings(Embeddings): + """LangChain-compatible embeddings using OpenRouter.""" + + def __init__( + self, + model: str, + api_key: str, + base_url: str = "https://openrouter.ai/api/v1", + logger: "loguru.Logger" = loguru.logger, + ) -> None: + self.model = model + self.api_key = api_key + self.logger = logger + self.client = OpenAI( + api_key=api_key, + base_url=base_url, ) - sys.exit(1) - return ( - args.data_path, - args.chroma_path, - args.chunk_size, - args.chunk_overlap, - ) - - -def load_documents(data_dir: str) -> list[Document]: - """Load Markdown documents, concatenate their content, and extract the name of the Markdown files. + def embed_query(self, text: str) -> list[float]: + """Embed search query. + + Returns + ------- + Embedding for the input query text. + """ + response = self.client.embeddings.create( + model=self.model, + input=text, + encoding_format="float", + ) + return response.data[0].embedding + + def embed_documents( + self, + texts: list[str], + max_retries: int = 3, + ) -> list[list[float]]: + """Embed documents one by one with retries and chunk logging. + + Parameters + ---------- + texts : list[str] + List of input texts to embed. + max_retries : int + Maximum number of retry attempts for a failed embedding. + + Returns + ------- + list[list[float]] + List of embeddings, one per input text. + """ + embeddings: list[list[float]] = [] + + for idx, text in enumerate(texts): + for attempt in range(1, max_retries + 1): + try: + embedding = self.embed_query(text=text) + embeddings.append(embedding) + self.logger.debug( + f"Chunk {idx} embedded successfully on attempt {attempt}." + ) + break # Exit the retry loop on success + except Exception as e: + self.logger.warning( + f"Attempt {attempt} failed for chunk {idx}: {e}" + ) + if attempt == max_retries: + self.logger.error( + f"Failed to embed chunk {idx} after {max_retries} attempts." + ) + raise # Re-raise the exception after max retries + + return embeddings + + +def load_documents( + course_yaml: Path, logger: "loguru.Logger" = loguru.logger +) -> list[Document]: + """Load Markdown files, concatenate their content, and extract filenames. Parameters ---------- - data_dir : str - The directory containing the Markdown files to be processed. + course_yaml : Path + The YAML file containing the course structure, including chapter names, titles, + source Markdown paths, and processed file paths. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- documents : list of Document List of Markdown documents. """ - # Load Markdown documents from the specified directory. - logger.info("Loading Markdown documents...") - loader = DirectoryLoader( - data_dir, glob="*.md", show_progress=True, loader_cls=TextLoader - ) - documents = loader.load() + documents = [] + + # Load chapters and levels from the YAML file + chapters = load_chapters_from_yaml(course_yaml, logger) + + # Iterate through the chapters and load the processed Markdown files as Documents + logger.info("Converting Markdown file to Document...") + for chapter in chapters: + # Get the processed Markdown file path + processed_path = chapter.get("processed_path") + # Check if the processed file path is defined for the chapter + if not processed_path: + logger.warning( + f"No processed_path defined for chapter id={chapter.get('id')} " + f"title={chapter.get('title')}" + ) + continue + + processed_path = Path(processed_path) + # Check if the processed Markdown file exists + if not processed_path.exists(): + logger.warning(f"Processed Markdown file not found: {processed_path}") + continue + # Load the processed Markdown file as a Document + loader = TextLoader(processed_path) + doc = loader.load() + documents.extend(doc) + logger.debug( + f"Converted {processed_path} to Document with " + f"{len(doc[0].page_content)} characters." + ) - # Order the documents by source. + # Order the documents by the file name documents = sorted(documents, key=lambda x: x.metadata.get("source", "")) - logger.success("Markdown document loading complete.\n") - + logger.success( + f"Converted {len(documents)}/{len(chapters)} Markdown documents successfully." + ) return documents -def get_file_names(documents: list[Document]) -> list[str]: - """Extract the file names of the Markdown documents. - - Parameters - ---------- - documents : list of Document - List of Markdown documents. - - Returns - ------- - file_names : list of str - List of file names of the Markdown documents. - """ - logger.info("Extracting file names...") - - file_names = [] - for document in documents: - # Extract the file name from the metadata source - source = document.metadata.get("source", "") - if source: - file_name = source.split("/")[-1].split(".")[ - 0 - ] # Extract the file name without extension - file_names.append(file_name) - - logger.success("Extracted file names successfully.\n") - - return sorted(file_names) - - -def concatenate_content(documents: list[Document]) -> str: - """Concatenate the content of the Markdown documents. +def split_text_into_chunks( + content: str, + chunk_size: int, + chunk_overlap: int, + logger: "loguru.Logger" = loguru.logger, +) -> list[Document]: + """Split concatenated Markdown content into chunks based on headers and word limits. Parameters ---------- - documents : list of Document - List of Markdown documents. - - Returns - ------- - concatenated_content : str - The concatenated content of all the Markdown documents. - """ - logger.info("Concatenating content...") - - concatenated_content = "" - for document in documents: - # Add the document content to the concatenated content - concatenated_content += document.page_content + "\n" - logger.info( - f"There is {len(concatenated_content)} characters in the concatenated content." - ) - - logger.success("Concatenated content successfully.\n") - - return concatenated_content - - -def split_text(content: str, chunk_size: int, chunk_overlap: int) -> list[Document]: - """Split concatenated Markdown content into chunks based on headers and word limits. - - Parameters: - ----------- content : str Concatenated Markdown content to be split into chunks. chunk_size : int The size of the text chunks to be created. chunk_overlap : int The overlap between text chunks. + logger: "loguru.Logger" + Logger for logging messages. - Returns: - -------- + Returns + ------- chunks : list of Document List of text chunks after splitting with content and metadata. format : [{"page_content": str, "metadata": dict}, ...] """ - logger.info("Splitting the documents...") - # create a Markdown header text splitter headers_to_split_on = [ ("#", "chapter_name"), @@ -271,18 +257,14 @@ def split_text(content: str, chunk_size: int, chunk_overlap: int) -> list[Docume # Split the resulting chunks further based on character limits chunks = text_splitter.split_documents(md_header_splits) - logger.success(f"Split documents into {len(chunks)} chunks.\n") - - # tests - # print(chunks[1], end="\n\n") - # print(chunks[100], end="\n\n") - # print(chunks[3000], end="\n\n") - + logger.info(f"Split documents into {len(chunks)} chunks.") return chunks def remove_small_chunks( - chunks: list[Document], min_nb_char: int = 100 + chunks: list[Document], + logger: "loguru.Logger" = loguru.logger, + min_nb_char: int = 100, ) -> list[Document]: """Remove small chunks from the list of text chunks. @@ -292,66 +274,43 @@ def remove_small_chunks( List of text chunks to be filtered. min_nb_char : int Minimum number of characters for a chunk to be kept. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- chunks : list of Document List of text chunks after removing small chunks. """ - logger.info("Removing small chunks...") - logger.info(f"Number of chunks before removing small chunks: {len(chunks)}") - # Remove chunks with less than min_nb_char characters chunks_cleaned = [ chunk for chunk in chunks if len(chunk.page_content) >= min_nb_char ] - - logger.info(f"Number of chunks after removing small chunks: {len(chunks_cleaned)}") - logger.info(f"Number of chunks removed: {len(chunks) - len(chunks_cleaned)}\n") - logger.success("Removed small chunks successfully.\n") - + logger.info( + f"Removed {len(chunks) - len(chunks_cleaned)} small chunks" + f" (less than {min_nb_char} characters)." + ) return chunks_cleaned -def add_index_to_metadata(chunks: list[Document]) -> list[Document]: - """Add an index to the metadata of the text chunks. - - Parameters - ---------- - chunks : list of Document - List of text chunks to which an index is to be added. - - Returns - ------- - chunks : list of Document - List of text chunks with an index added to their metadata. - """ - logger.info("Adding index to metadata...") - - # Add an index to metadata of each chunk - for index, chunk in enumerate(chunks): - chunk.metadata["id"] = index - - logger.success("Added index to metadata successfully.\n") - - return chunks - - -def add_token_number_to_metadata(chunks: list[Document]) -> list[Document]: +def add_token_number_to_metadata( + chunks: list[Document], + logger: "loguru.Logger" = loguru.logger, +) -> list[Document]: """Add the number of tokens to the metadata of the text chunks. Parameters ---------- chunks : list of Document List of text chunks to which the number of tokens is to be added. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- chunks : list of Document List of text chunks with the number of tokens added to their metadata. """ - logger.info("Adding the number of tokens to metadata...") - # Get the encoding for tokenization # for openai embeddings encoding = tiktoken.get_encoding("cl100k_base") @@ -364,54 +323,64 @@ def add_token_number_to_metadata(chunks: list[Document]) -> list[Document]: nb_tokens = len(token) chunk.metadata["nb_tokens"] = nb_tokens - logger.success("Added the number of tokens to metadata successfully.\n") - + logger.info( + f"Total number of characters: \ + {sum(len(chunk.page_content) for chunk in chunks):,}" + ) + count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in chunks) + logger.info(f"Total number of tokens: {count_tokens:,}") return chunks def add_file_names_to_metadata( - chunks: list[Document], file_names: list[str] + chunks: list[Document], file_path: str, logger: "loguru.Logger" = loguru.logger ) -> list[Document]: - """Add file names to the metadata of the text chunks. + """Add file paths to the metadata of the text chunks. Parameters ---------- chunks : list of Document - List of text chunks to which file names are to be added. - file_names : list of str - List of file names of the Markdown documents. + List of text chunks to which file paths are to be added. + file_path : str + File path of the Markdown documents. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- chunks : list of Document - List of text chunks with file names added to their metadata. + List of text chunks with file paths added to their metadata. """ - logger.info("Adding file names to metadata...") + file_name = Path(file_path).name + + # Determine chapter id based on the file name + chapter_id = None + # Match numbered chapters, e.g., "01_intro.md" or "24_avoir_plus_la_classe.md" + match_chapter = re.match(r"(\d+)_", file_name) + if match_chapter: + chapter_id = str(match_chapter.group(1)) + else: + # Match annexes, e.g., "annexe_A.md" + match_annex = re.match(r"annexe[_-]([A-Z0-9]+)", file_name, re.IGNORECASE) + if match_annex: + chapter_id = match_annex.group(1).upper() + + if chapter_id is None: + logger.warning( + f"Could not extract chapter index from file name: {file_name}. " + "No chapter index added to metadata." + ) - # Add file names to metadata of each chunk + # Add metadata to each chunk for chunk in chunks: - # Extract chapter_name from the metadata - chapter_name = chunk.metadata.get("chapter_name", "") - # Get the chapter number or appendix letter - chapter_number = re.match(r"^\d+\s", chapter_name) - appendix_letter = re.search(r"\b[A-Z]", chapter_name) - # Corresponding chapter number or appendix letter with file name - for file_name in file_names: - if chapter_number and file_name.startswith( - f"{chapter_number.group(0).strip().zfill(2)}_" - ): # zfill(2) to pad with zeros - chunk.metadata["file_name"] = file_name - break - elif appendix_letter.group(0) == file_name.split("_")[1]: - chunk.metadata["file_name"] = file_name - break - - logger.success("Added file names to metadata successfully.\n") + chunk.metadata["file_path"] = file_path + chunk.metadata["file_name"] = Path(file_path).name + chunk.metadata["chapter_id"] = chapter_id return chunks -def preprocess_for_url(text: str, is_subsubsection: bool = False) -> str: +def preprocess_for_url(text: str, *, is_subsubsection_name: bool = False) -> str: """Preprocess text for creating URL. Parameters @@ -453,7 +422,7 @@ def preprocess_for_url(text: str, is_subsubsection: bool = False) -> str: processed_text = re.sub(r"[^a-zA-Z]*$", "", processed_text) # Remove the subsubsection number - if is_subsubsection: + if is_subsubsection_name: processed_text = re.sub(r"^[a-zA-Z]?\d+-?", "", processed_text) # Add a '#' at the beginning @@ -462,30 +431,33 @@ def preprocess_for_url(text: str, is_subsubsection: bool = False) -> str: return processed_text -def add_url_to_metadata(chunks: list[Document]) -> list[Document]: +def add_url_to_metadata( + chunks: list[Document], logger: "loguru.Logger" = loguru.logger +) -> list[Document]: """Add URL to the metadata of the text chunks. Parameters ---------- chunks : list of Document List of text chunks to which URL is to be added. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- chunks : list of Document List of text chunks with URL added to their metadata. """ - logger.info("Adding URL to metadata...") - # Add URL to metadata of each chunk - for chunk in chunks: + for i, chunk in enumerate(chunks, start=1): # Extract file name from the metadata - file_name = chunk.metadata.get("file_name", "") + file_path = chunk.metadata.get("file_name", "") + file_name = Path(file_path).stem # Extract section_id from the metadata if chunk.metadata.get("subsubsection_name", ""): # subsubsection section_id = preprocess_for_url( - chunk.metadata.get("subsubsection_name", ""), True + chunk.metadata.get("subsubsection_name", ""), is_subsubsection_name=True ) elif chunk.metadata.get("subsection_name", ""): # subsection section_id = preprocess_for_url(chunk.metadata.get("subsection_name", "")) @@ -498,79 +470,201 @@ def add_url_to_metadata(chunks: list[Document]) -> list[Document]: chunk.metadata["url"] = ( f"https://python.sdv.u-paris.fr/{file_name}/{section_id}" ) - - logger.success("Added URL to metadata successfully.\n") + logger.debug(f"Chunk {i} URL: {chunk.metadata['url']}") return chunks -def save_to_chroma(chunks: list[Document], chroma_output_path: str) -> None: +def create_embeddings_function( + model_name: str, + provider_name: str, +) -> OpenAIEmbeddings | OpenRouterEmbeddings: + """Create an embeddings function based on the specified model and provider. + + Parameters + ---------- + model_name : str + Name of the embedding model to use. + provider_name : str + Name of the embedding provider to use. + + Returns + ------- + OpenAIEmbeddings | OpenRouterEmbeddings + An instance of the OpenAIEmbeddings or + OpenRouterEmbeddings class initialized + with the specified model and provider. + + Raises + ------ + KeyError + If the API key for the specified provider is not found in environment variables. + """ + # Load the environment variables with LLM api keys + load_dotenv() + try: + # Get the API key and base URL based on the provider + if provider_name == "openrouter": + return OpenRouterEmbeddings( + model=model_name, + api_key=os.environ["OPENROUTER_API_KEY"], + ) + elif provider_name == "openai": + return OpenAIEmbeddings( + model=model_name, api_key=os.getenv("OPENAI_API_KEY") + ) + # Handle the case where the API key is not found in environment variables + except KeyError as e: + msg = f"API key for {provider_name} not found in environment variables." + raise KeyError(msg) from e + + +def save_to_chroma( + chunks: list[Document], + model_name: str, + provider_name: str, + chroma_output_path: Path, + logger: "loguru.Logger" = loguru.logger, +) -> None: """Save text chunks to ChromaDB. Parameters ---------- - chunks : list of str + chunks : list[Document] List of text chunks to save to ChromaDB. - chroma_output_path : str - The name of the output path to save the ChromaDB database. + model_name : str + Name of the embedding model to use. + provider_name : str + Name of the embedding provider to use. + chroma_output_path : Path + Path to the directory where the ChromaDB database will be saved. + logger: "loguru.Logger" + Logger for logging messages. """ - logger.info("Saving to Chroma...") - - # Clear out the database first. - if os.path.exists(chroma_output_path): + logger.info(f"Saving to ChromaDB using embedding model `{model_name}`...") + # Clear the existing database if it exists + if chroma_output_path.exists(): shutil.rmtree(chroma_output_path) - # Create a new DB from the documents and save it to disk - model_embedding = OpenAIEmbeddings(model=EMBEDDING_MODEL) - Chroma.from_documents( - chunks, - model_embedding, - persist_directory=chroma_output_path, - collection_metadata={"hnsw:space": "cosine"}, - ) # distance metric - - logger.success(f"Saved {len(chunks)} chunks to {chroma_output_path}.") + # Create the embeddings function + embeddings_function = create_embeddings_function(model_name, provider_name) + # Generate unique IDs for each chunk + uuids = [str(index) for index in range(len(chunks))] -def generate_data_store() -> None: - """Generates data store by loading, splitting text into chunks, adding metadata and saving the chunks to ChromaDB.""" - # Get command-line arguments. - data_path, chroma_path, chunk_size, chunk_overlap = get_args() - - # Load the environment variables with LLM api keys. - load_dotenv() + # Create a ChromaDB with the embeddings + Chroma.from_documents( + documents=chunks, + ids=uuids, + embedding=embeddings_function, + persist_directory=str(chroma_output_path), + collection_metadata={"hnsw:space": "cosine"}, # distance metric + ) + logger.success(f"Saved {len(chunks)} chunks to {chroma_output_path} successfully!") + + +@click.command() +@click.option( + "--course-yaml", + required=True, + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help=( + "Path to the YAML file defining the course chapters and student levels. " + "The YAML should include chapter names, titles, source Markdown paths, " + "and processed file paths." + ), +) +@click.option( + "--chroma-path", + required=True, + type=click.Path(file_okay=False, dir_okay=True, path_type=Path), + help="Output path to save the ChromaDB database.", +) +@click.option( + "--chunk-size", + default=1000, + type=click.IntRange(min=50), + help="Size of the text chunks to be created.", +) +@click.option( + "--chunk-overlap", + default=200, + type=click.IntRange(min=0), + help="Overlap between text chunks.", +) +@click.option( + "--embedding-model", + default="text-embedding-3-large", + type=str, + help="Name of the embedding model," + "chosen from OpenAI and OpenRouter`s embedding models. ", +) +@click.option( + "--model-provider", + default="openai", + type=click.Choice(["openrouter", "openai"], case_sensitive=False), + help="Name of the embedding provider to use.", +) +def generate_data_store( + course_yaml: Path, + chroma_path: Path, + chunk_size: int, + chunk_overlap: int, + embedding_model: str, + model_provider: str, +) -> None: + """Build a ChromaDB store from chunked text with metadata.""" + # Set-up the logger + log_path = f"logs/{datetime.now().strftime('%Y%m%d')}/create_database.log" + logger = create_logger(log_path) + logger.info("Creating Chroma database...") + + # Validate the CLI arguments + if chunk_overlap >= chunk_size: + logger.error( + f"The chunk overlap ({chunk_overlap}) must be less than " + f" the chunk size ({chunk_size})." + ) + sys.exit(1) + all_chunks = [] # load documents from the specified directory - documents = load_documents(data_path) - - # extract file names from the documents - file_names = get_file_names(documents) - - # concatenate the content of the documents - content = concatenate_content(documents) - - # split text into chunks - chunks = split_text(content, chunk_size, chunk_overlap) - - # remove small chunks - chunks_cleaned = remove_small_chunks(chunks, min_nb_char=100) + documents = load_documents(course_yaml, logger) + logger.info("Processing Documents...") + for doc in documents: + file_name = doc.metadata.get("source", "") + logger.info(f"File path: {file_name}") + # split text of the current document into chunks + chunks = split_text_into_chunks( + doc.page_content, chunk_size, chunk_overlap, logger + ) + # remove small chunks + chunks_cleaned = remove_small_chunks(chunks, logger, min_nb_char=100) - # add index to the metadata - chunks_with_index = add_index_to_metadata(chunks_cleaned) + # add number of tokens to the metadata + chunks_with_tokens = add_token_number_to_metadata(chunks_cleaned, logger) - # add number of tokens to the metadata - chunks_with_tokens = add_token_number_to_metadata(chunks_with_index) + # add file name and chapter id to the chunk metadata + chunks_with_file_name = add_file_names_to_metadata( + chunks_with_tokens, file_name, logger + ) + # add URL to the chunk metadata if available + chunks_with_url = add_url_to_metadata(chunks_with_file_name, logger) - # add file names to the chunks - chunks_with_file_names = add_file_names_to_metadata(chunks_with_tokens, file_names) + all_chunks.extend(chunks_with_url) + logger.debug(f"Example chunk metadata: {all_chunks[-1].metadata}") - # add URL to the chunks - chunks_with_url = add_url_to_metadata(chunks_with_file_names) + logger.info("Summary:") + logger.info(f"Total number of files processed: {len(documents)}") + logger.info(f"Total number of chunks: {len(all_chunks)}") + total_characters = sum(len(chunk.page_content) for chunk in all_chunks) + logger.info(f"Total number of characters: {total_characters:,}") + count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in all_chunks) + logger.info(f"Total number of tokens: {count_tokens:,}") - # save the chunks to ChromaDB - save_to_chroma(chunks_with_url, chroma_path) + # save the embeddings to ChromaDB + save_to_chroma(all_chunks, embedding_model, model_provider, chroma_path, logger) -# MAIN PROGRAM if __name__ == "__main__": generate_data_store() diff --git a/src/create_quiz.py b/src/create_quiz.py deleted file mode 100644 index a7a3d1a..0000000 --- a/src/create_quiz.py +++ /dev/null @@ -1,387 +0,0 @@ -"""Creates Quiz from a chapter of the python course in JSON format using a LLM. - - -Usage: -====== - python src/create_quiz.py --chapter [CHAPTER] --quiz-type [QUIZ_TYPE] --python-level [PYTHON_LEVEL] - -Arguments: -========== - --chapter : str - The name of the chapter of the quiz. It must be one of the chapters of the Python course contained in the data/markdown_processed folder. - Attention: The chapter name must be written in French and with the first letter of each word capitalized. - --quiz-type : str - The type of the quiz (QCM or Vrai/Faux). - --python-level : str - The Python level of the user (DĂ©butant or AvancĂ©). - -Example: -======== - python src/create_quiz.py --chapter Listes --quiz-type QCM --python-level DĂ©butant - -This command will create a quiz of 5 QCM on the chapter "Listes" for a beginner level user. -""" - -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS -import os -import json -import argparse -from typing import Tuple -from datetime import datetime - - -import re -from loguru import logger -from langchain_core.documents import Document -from langchain_core.output_parsers import StrOutputParser -from langchain_openai import ChatOpenAI -from langchain.prompts import ChatPromptTemplate - - -# MODULE IMPORTS -from query_chatbot import calculate_nb_tokens, OPENAI_MODEL_NAME -from create_database import load_documents, get_file_names - - -# CONSTANTS -PROCESSED_DATA_PATH = "data/markdown_processed" - -QUIZ_JSON_PATH = "data/quiz.json" - -GENERATE_QUIZ_PROMPT = """ -Tu es un expert en crĂ©ation de quiz sur Python destinĂ© Ă  des Ă©tudiants. -CrĂ©er un quiz de type {quiz_type} avec 5 questions sur le chapitre suivant : {chapter} pour un Ă©tudiant de niveau {level}. -La question doit ĂȘtre au format markdown et doit porter sur le contenu du chapitre fourni et doit ĂȘtre accompagnĂ©e de rĂ©ponses possibles et d'une explication dĂ©taillĂ©e pour chaque rĂ©ponse. -N'hĂ©sites pas Ă  varier les questions (ex: questions basĂ©s sur le cours, trouver l'erreur dans le code, trouver la sortie du code, complĂ©ter le code, etc.). - -Le format de sortie doit seulement etre un JSON comportant les 5 questions de quiz avec les clĂ©s suivantes : -- type (str): le type du quiz (QCM, Vrai ou Faux) -- chapter (str): le chapitre du quiz -- python_level (str): le niveau Python de l'Ă©tudiant -- question (str): la question posĂ©e -- answers (dict): les rĂ©ponses possibles numĂ©rotĂ©es par ordre alphabĂ©tique (A, B, C, D) -- correct_answer (str): la rĂ©ponse correcte avec la lettre correspondante -- explanation (str): l'explication dĂ©taillĂ©e pour chaque rĂ©ponse possible - -Je veux dans explanation, une explication dĂ©taillĂ©e pour chaque rĂ©ponse possible de maniĂšre Ă  ce que l'Ă©tudiant comprenne pourquoi chaque rĂ©ponse est correcte ou incorrecte. -Le ton doit ĂȘtre pĂ©dagogique et bienveillant. - -Voici un exemple de format de sortie attendue : -{{ - "questions": [ - {{ - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle est la sortie du code suivant : \\n```python\\nx = 5\\ny = 10\\nprint(x + y)\\n```", - "answers": {{ - "A": "10", - "B": "15", - "C": "5", - "D": "50" - }}, - "correct_answer": "B", - "explanation": "La rĂ©ponse correcte est B car x vaut 5 et y vaut 10, donc x + y vaut 15. Les autres rĂ©ponses sont incorrectes car elles ne correspondent pas Ă  la somme de x et y." - }}, - {{ - "chapter": "Variables", - "python_level": "DĂ©butant", - "type": "QCM", - "question": "Quelle structure conditionnelle est utilisĂ©e pour exĂ©cuter un bloc de code si une condition est vraie ?", - "answers": {{ - "A": "for", - "B": "while", - "C": "if", - "D": "def" - }}, - "correct_answer": "C", - "explanation": "La rĂ©ponse correcte est C car la structure conditionnelle if est utilisĂ©e pour exĂ©cuter un bloc de code si une condition est vraie. Les rĂ©ponses A ("for") et B ("while") sont utilisĂ©es pour les boucles afin de rĂ©pĂ©ter un bloc de code plusieurs fois et la rĂ©ponse D ("def") est utilisĂ©e pour dĂ©finir une fonction." - }} - ] -}} - -Voici le contenu du chapitre sur lequel tu dois crĂ©er le quiz : -{chapter_content} -""" - - -# FUNCTIONS -def get_chapters_file_names(documents: list) -> dict: - """Get the names of the chapters. - - Parameters - ---------- - documents : list - The list of documents. - - Returns - ------- - chapters : dict - The chapter names as keys and the chapter file names as values. - """ - logger.info("Getting the names of the chapters...") - chapters = {} - - # Get the names of the chapters - file_names = get_file_names(documents) - - for file_name in file_names: - # Define the pattern to match the chapter name - match_chapter = re.match(r"\d+_(.*)$", file_name) - if match_chapter: - # Get the chapter name and format it - chapter_name = match_chapter.group(1).replace("_", " ").capitalize() - chapters[chapter_name] = file_name - - logger.success("Chapter names with their content files retrieved successfully.\n") - - return chapters - - -def get_args(chapter_names: list[str]) -> Tuple[str, str, str]: - """Get the command line arguments. - - Returns - ------- - Tuple[str, str, str] - The chapter, quiz_type and the python level of the user. - """ - logger.info("Parsing command line arguments") - parser = argparse.ArgumentParser(description="Creates the QCM from a chapter of the python course.") - parser.add_argument("--chapter", type=str, required=True, help="The chapter of the quiz.") - parser.add_argument("--quiz-type", type=str, required=True, help="The type of the quiz.") - parser.add_argument("--python-level", type=str, required=True, help="The Python level of the user.") - args = parser.parse_args() - - logger.info(f"Chapter: {args.chapter}") - logger.info(f"Quiz type: {args.quiz_type}") - logger.info(f"Python level: {args.python_level}") - - # Checks - if args.chapter not in chapter_names: - logger.error(f"The chapter {args.chapter} is not valid. Please choose one of the following chapters: {', '.join(chapter_names)}.") - exit() - if args.quiz_type not in ["QCM", "Vrai/Faux"]: - logger.error(f"The quiz type {args.quiz_type} is not valid. Please choose between 'QCM' and 'Vrai/Faux'.") - exit() - if args.python_level not in ["DĂ©butant", "AvancĂ©"]: - logger.error(f"The Python level {args.python_level} is not valid. Please choose between 'DĂ©butant' and 'AvancĂ©'.") - exit() - logger.success("Command line arguments parsed successfully.\n") - - return args.chapter, args.quiz_type, args.python_level - - -def get_chapter_content(chapters: dict[str, str], chapter_name: str) -> str: - """Get the content of a chapter. - - Parameters - ---------- - chapters : dict[str, str] - Dictionary containing the chapters names and their content. - chapter : str - The chapter of the quiz. - - Returns - ------- - chapter_content : str - The content of the chapter. - """ - logger.info(f"Getting the content of the chapter: {chapter_name}") - - # Get the content of the chapter - chapter_file_name = chapters[chapter_name] + ".md" - chapter_file_path = os.path.join(PROCESSED_DATA_PATH, chapter_file_name) - - with open(chapter_file_path, "r") as file: - chapter_content = file.read() - - # Remove the /n characters from the chapter content - chapter_content = re.sub(r"\n", "", chapter_content) - logger.info(f"Chapter content: {chapter_content[:100]}...") - - logger.success("Chapter content retrieved successfully.\n") - - return chapter_content - - -def create_quiz_json(chapter: str, quiz_type: str, level_python: str, chapter_content: str) -> str: - """Generate a quiz. - - Parameters - ---------- - chapter : str - The chapter of the quiz. - quiz_type : str - The type of the quiz. - level_python : str - The Python level of the user. - chapter_content : str - The content of the chapter. - - Returns - ------- - quiz_json : str - The generated quiz in JSON format. - """ - logger.info("Generating a quiz") - - # Define the model - chat_model = ChatOpenAI(model=OPENAI_MODEL_NAME) - # Define the prompt template - answer_prompt = ChatPromptTemplate.from_template(GENERATE_QUIZ_PROMPT) - - # Input data for the prompt - input_data = { - "quiz_type": quiz_type, - "chapter": chapter, - "level": level_python, - "chapter_content": chapter_content - } - - # Fill the prompt with the input data - filled_prompt = answer_prompt.format(**input_data) - logger.info(f"Filled prompt: {filled_prompt[:100]}...") - nb_tokens_prompt = calculate_nb_tokens(filled_prompt) - logger.info(f"Number of tokens in the prompt: {nb_tokens_prompt}\n") - - # Define the chained prompt - answer_chain = answer_prompt | chat_model | StrOutputParser() - - # Generate the answer - quiz = answer_chain.invoke(input_data) - logger.info(f"Quiz: {quiz}") - logger.success("Quiz generated successfully.\n") - - return quiz - - -def extract_json_from_string(quiz: str) -> str: - """Extract JSON part from a string that contains JSON.""" - try: - start_index = quiz.index('{') - end_index = quiz.rindex('}') + 1 - return quiz[start_index:end_index] - except ValueError: - logger.error("No valid JSON found in the input string.") - raise - - -def validate_quiz_json(quiz: str) -> str: - """Validate the quiz JSON. - - Parameters - ---------- - quiz : str - The quiz generated by the LLM. - - Returns - ------- - quiz_json : str - The validated quiz in JSON format. - """ - logger.info("Validating the quiz JSON...") - - # Extract the JSON content from the quiz - quiz_json = extract_json_from_string(quiz) - - # Validate the JSON content - try: - quiz_data = json.loads(quiz_json) - except json.JSONDecodeError as e: - logger.error(f"The JSON is not well-formed : {e}") - exit() - - # Validate each question - for question in quiz_data["questions"]: - question_type = question.get("type") - answers = question.get("answers", {}) - correct_answer = question.get("correct_answer") - - # Validate the number of answers - if question_type == "QCM" and len(answers) != 4: - logger.error(f"Invalid number of answers for 'QCM' question: {question['question']}") - elif question_type == "Vrai ou Faux" and len(answers) != 2: - logger.error(f"Invalid number of answers for 'Vrai ou Faux' question: {question['question']}") - - # Validate the correct answer is one of the possible answers - if correct_answer not in answers.keys(): - logger.error(f"Invalid correct answer for question: {question['question']}") - - logger.success("Quiz JSON validated successfully.\n") - - return quiz_json - - -def save_to_json(quiz_json: str, file_path: str) -> None: - """Save the quiz in a JSON file. - - Parameters - ---------- - quiz_json : str - The generated quiz in JSON format. - file_path : str - The path of the JSON file. - """ - logger.info(f"Saving the quiz in a JSON file: {file_path}") - - # Convert json into a dictionary - new_quiz_data = json.loads(quiz_json) - - # Add the last modified date to the new quiz data - last_modified = datetime.now().isoformat() - new_quiz_data['last_modified'] = last_modified - - # Load existing quiz data if the file exists - if os.path.exists(file_path): - logger.info("The file already exists. Merging the new quiz data with the existing data.") - with open(file_path, "r", encoding="utf-8") as file: - existing_quiz_data = json.load(file) - # Merge the new quiz data with the existing data - existing_quiz_data["questions"] = existing_quiz_data.get("questions", []) + new_quiz_data.get("questions", []) - # Update the last modified date - existing_quiz_data['last_modified'] = last_modified - # Convert the merged data back to JSON - merged_quiz_json = json.dumps(existing_quiz_data, ensure_ascii=False, indent=4, sort_keys=False) - else: - logger.info("The file does not exist. Saving the new quiz data.") - # If the file does not exist, use the new quiz data - merged_quiz_json = json.dumps(new_quiz_data, ensure_ascii=False, indent=4, sort_keys=False) - - # Save the merged quiz data to the file in UTF-8 encoding - with open(file_path, "w", encoding="utf-8") as file: - file.write(merged_quiz_json) - - logger.success("Quiz saved successfully.\n") - - -# MAIN PROGRAM -if __name__ == "__main__": - # Get chapters information - CHAPTERS = load_documents(PROCESSED_DATA_PATH) - CHAPTERS_DIC = get_chapters_file_names(CHAPTERS) - CHAPTER_NAMES = list(CHAPTERS_DIC.keys()) - - # Get the command line arguments - chapter, quiz_type, level_python = get_args(CHAPTER_NAMES) - - # Get the chapter content - chapter_content = get_chapter_content(CHAPTERS_DIC, chapter) - - # Generate a quiz by LLM - quiz = create_quiz_json(chapter, quiz_type, level_python, chapter_content) - - # Validate the quiz JSON - quiz_json = validate_quiz_json(quiz) - - # Save the quiz in a JSON file - save_to_json(quiz_json, QUIZ_JSON_PATH) - diff --git a/src/gradio_app.py b/src/gradio_app.py deleted file mode 100644 index 936c511..0000000 --- a/src/gradio_app.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Gradio application of BioPyAssistant. - -It allows users to interact with the course in different ways, such as: -- Asking questions to the chatbot. -- Asking questions to the chatbot in a battle mode. -- Answering quizzes about the course. - -Usage: -====== - gradio src/gradio_app.py - -""" - -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRAIRIES IMPORT -import os -import sys - -from dotenv import load_dotenv -import gradio as gr -from loguru import logger - -# Configure the logger -logger.add("biopyassistant.log", rotation="monthly", level="INFO", format="LOG | {time:YYYY-MM-DD HH:mm:ss} | {level} | {message}") -logger.add(sys.stdout, level="INFO") - -# Load the environment variables with LLM api keys. -logger.info(f"Loading API keys...") -load_dotenv() - -# MODULES IMPORT -from gradio_app_chat import create_tab_chatbot -from gradio_app_quiz import create_tab_quiz -from gradio_app_battle import create_tab_battle - - -# CONSTANTS -FLAVICON_RELATIVE_PATH = "data/img/logo_round.ico" -CSS = """ -#correct-answer - {background-color: #46772E} -#wrong-answer - {background-color: #F28C00} -#footer { - position: absolute; - bottom: 0; - transform: scale(0.9); -} - -/* Footer becomes relative for small screens */ -@media only screen and (max-width: 800px) { - #footer { - /* display: none; */ - position: relative; - } -} - -@media only screen and (max-height: 1000px) { - #footer { - /* display: none; */ - position: relative; - } -} -""" - -# Create the Gradio interface -with gr.Blocks( - theme=gr.themes.Default(primary_hue="emerald", secondary_hue="emerald"), - title="BioPyAssistant", - css=CSS, fill_height=True -) as demo: - # Add a title - gr.HTML( - """

🐍 BioPyAssistant 🐍

""" - ) - gr.Markdown(""" - BioPyAssistant est un assistant pĂ©dagogique - pour le [cours de programmation Python](https://python.sdv.u-paris.fr/) pour les biologistes de Patrick Fuchs et Pierre Poulain. - - N'hĂ©site-pas Ă  le solliciter en lui posant tes questions. - """) - # Add a section for asking a question to the chatbot about the course - with gr.Tab("Discuter avec le cours"): - create_tab_chatbot() - - # Add a section for asking a question to the chatbot about the course but in a battle mode - with gr.Tab("Discuter avec le cours (battle)"): - create_tab_battle() - - # Add a section for asking a qcm about a specific chapter - # with gr.Tab("Se tester"): - # create_tab_quiz() - - # Add footer. - with gr.Row(elem_id="footer", equal_height=False): - with gr.Column(scale=1): - gr.HTML("") - with gr.Column(scale=3): - gr.Markdown(""" - [Mentions lĂ©gales](https://u-paris.fr/politique-de-confidentialite/). - Cette application web n'utilise pas de cookie. - Les rĂ©sultats des votes sont collectĂ©s anonymement Ă  des fins de recherche. - - BioPyAssistant a Ă©tĂ© dĂ©veloppĂ© par [Essmay Touami](https://www.linkedin.com/in/essmay-touami/) et [Pierre Poulain](https://www.linkedin.com/in/pierrepo/) dans le cadre du projet pĂ©dagogique [LLM@UPCitĂ©](https://u-paris.fr/aap-innovation-pedagogique-2023-decouvrez-les-projets-laureats/). - Le code source est disponible sur [GitHub](https://github.com/pierrepo/biopyassistant) sous licence BSD 3-clause. - """) - - -# MAIN PROGRAM -if __name__ == "__main__": - # Get the favicon path - FLAVICON_PATH = os.path.abspath(FLAVICON_RELATIVE_PATH) - logger.info(f"Flavicon path: {FLAVICON_PATH}") - - # Launch the Gradio interface - demo.launch( - favicon_path=FLAVICON_PATH, # to add a favicon - server_name="0.0.0.0", # to make the app accessible from other devices - server_port=8080 - ) \ No newline at end of file diff --git a/src/gradio_app_battle.py b/src/gradio_app_battle.py deleted file mode 100644 index d35ef6b..0000000 --- a/src/gradio_app_battle.py +++ /dev/null @@ -1,271 +0,0 @@ -"""This script creates a Gradio tab that allows users to discuss with the course in a battle mode. - -Features: -- Users will ask a question and it will be answered by 2 random different models. -- Users will have to choose wich answer is the best. - -Usage: -====== - gradio src/gradio_app_battle.py -""" - -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS -import os -import random -from typing import Tuple - -import gradio as gr -from loguru import logger -from dotenv import load_dotenv - - -# MODULES IMPORT -from query_chatbot import ( - load_database, - search_similarity_in_database, - get_metadata, - format_chat_history, - contextualize_question, - generate_answer, - add_metadata_to_answer, - format_relevant_chunks, - QUERY_EXAMPLES, - MSGS_QUERY_NOT_RELATED, - CHROMA_PATH, - get_available_llm_models, -) - - -# CONSTANTS -VECTOR_DB = load_database(CHROMA_PATH)[0] -# Load LLM models -LLM_MODELS = get_available_llm_models() -NUM_MODELS = 2 -CHATBOTS = [None] * NUM_MODELS - - - -# FUNCTIONS -def respond(message: str, chat_history1: list, chat_history2: list) -> Tuple[str, list, list, str, str]: - """Respond to the user question. - - Parameters - ---------- - message : str - The user question. - chat_history1 : list - The chat history for the first model. - chat_history2 : list - The chat history for the second model. - Returns - ------- - Tuple[str, list, list] - Empty string to erase the previous question, - the updated chat history for the first model, - the updated chat history for the second model. - """ - logger.info("Responding to the user question...") - - # Randomly choose two models - chosen_models = random.sample(LLM_MODELS, 2) - - for i, model in enumerate(chosen_models): - logger.info(f"Using the model: {model}") - # Choose the appropriate chat history - chat_history = chat_history1 if i == 0 else chat_history2 - # Format the chat history for the model - formatted_chat_history = format_chat_history(chat_history, len_history=10) - # Contextualize the user question with the chat history - chat_context = contextualize_question(chat_history_formatted=formatted_chat_history) - # Search for relevant documents in the database - context = search_similarity_in_database(vector_db=VECTOR_DB, user_query=message) - # If no relevant document was found - if context == []: - logger.info("No relevant documents found in the database.") - logger.success("Returning an automatic response without calling the model.") - # random response betweet responses in MSGS_QUERY_NOT_RELATED - response = random.choice(MSGS_QUERY_NOT_RELATED) - if i == 0: - chat_history1.append((message, response)) - else: - chat_history2.append((message, response)) - - else: - # Format the relevant chunks - context_formatted = format_relevant_chunks(context) - # Get the metadata of the relevant documents - metadata = get_metadata(context) - # Generate the answer - answer = generate_answer( - query=message, - chat_context=chat_context, - relevant_chunks=context_formatted, - model_name=model, - ) - # Add metadata to the answer - final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) - if i == 0: - chat_history1.append((message, final_answer)) - else: - chat_history2.append((message, final_answer)) - - return "", chat_history1, chat_history2, chosen_models[0], chosen_models[1] - - -def clear_chat() -> Tuple[str, list, list]: - """Clear the chat history. - - Parameters - ---------- - chat_history1 : list - The chat history for the first model. - chat_history2 : list - The chat history for the second model. - Returns - ------- - Tuple[str, list, list] - Empty string to erase the previous question, - the updated chat history for the first model, - the updated chat history for the second model. - """ - logger.info("Clearing the chat history...") - fist_conv = [[None, "Bonjour, comment puis-je t'aider ?"]] - return fist_conv, fist_conv - - -def get_vote(button_label: str, model_a: str, model_b: str): - """Get the vote of the user. - - Parameters - ---------- - button_label : str - The label of the button clicked by the user. - model_a : str - The name of the first model. - model_b : str - The name of the second model. - """ - logger.info("Getting the vote of the user...") - if model_a == "" or model_b == "": - logger.warning("Ask a question before voting.") - elif button_label == "âŹ…ïž La rĂ©ponse A est meilleure": - logger.info(f"Model {model_a} vs {model_b}: {model_a}") - elif button_label == "🟰 Les deux rĂ©ponses se valent": - logger.info(f"Model {model_a} vs {model_b}: Tie") - elif button_label == "âžĄïž La rĂ©ponse B est meilleure": - logger.info(f"Model {model_a} vs {model_b}: {model_b}") - elif button_label == "👎 Les deux rĂ©ponses sont mauvaises": - logger.info(f"Model {model_a} vs {model_b}: Both") - else: - logger.error("Invalid button label.") - - -def show_notification() -> None: - """Show a notification to the user.""" - gr.Info( - message=("Merci d'avoir donnĂ© ton avis " - "et contribuĂ© Ă  l'amĂ©lioration de BioPyAssistant !"), - duration=6 - ) - - -def create_tab_battle(): - """Create the interface to discuss with the course in a battle mode.""" - with gr.Blocks( - theme=gr.themes.Default(primary_hue="emerald", secondary_hue="emerald"), - title="BioPyAssistant" - ) as demo: - gr.Markdown(""" - Pose une question, - compare les rĂ©ponses fournies par deux modĂšles de langages tirĂ©s au hasard - et vote pour la meilleure rĂ©ponse. - """) - # Define Chatbots - with gr.Row(): - for i in range(NUM_MODELS): - label = "ModĂšle A" if i == 0 else "ModĂšle B" - with gr.Column(): - CHATBOTS[i] = gr.Chatbot( - label=label, - elem_id="chatbot", - value=[ - [ - None, - "Bonjour, comment puis-je t'aider ?", - ] - ], - bubble_full_width=False, - height=600, - show_copy_button=True, - render=True, - avatar_images=(("data/img/user_avatar.png"), "data/img/chatbot_avatar.png"), - ) - - # Define model components - model_a = gr.Textbox(visible=False) - model_b = gr.Textbox(visible=False) - - # Define the vote buttons - with gr.Row(): - leftvote_btn = gr.Button(value="âŹ…ïž La rĂ©ponse A est meilleure", size="sm", elem_classes="btn-upcite") - tie_btn = gr.Button(value="🟰 Les deux rĂ©ponses se valent", size="sm") - bothbad_btn = gr.Button(value="👎 Les deux rĂ©ponses sont mauvaises", size="sm") - rightvote_btn = gr.Button(value="âžĄïž La rĂ©ponse B est meilleure", size="sm") - - # Define the query textbox - with gr.Row(): - msg = gr.Textbox( - placeholder="Pose-moi une question sur le cours.", - render=True, - show_label=False, - min_width=1200, - ) - # Define the clear button - # clear_btn = gr.ClearButton(value="Effacer l'historique") - - # Define question examples - with gr.Row(): - gr.Examples(examples=QUERY_EXAMPLES, inputs=msg, label="Exemples de questions") - - msg.submit(respond, inputs=[msg, CHATBOTS[0], CHATBOTS[1]], outputs=[msg, CHATBOTS[0], CHATBOTS[1], model_a, model_b]) - - leftvote_btn.click(get_vote, inputs=[leftvote_btn, model_a, model_b]) - tie_btn.click(get_vote, inputs=[tie_btn, model_a, model_b]) - bothbad_btn.click(get_vote, inputs=[bothbad_btn, model_a, model_b]) - rightvote_btn.click(get_vote, inputs=[rightvote_btn, model_a, model_b]) - # clear_btn.click(clear_chat, outputs=[CHATBOTS[0], CHATBOTS[1]]) - - # Show a notification the user as voted. - leftvote_btn.click(show_notification, inputs=None, outputs=None) - tie_btn.click(show_notification, inputs=None, outputs=None) - bothbad_btn.click(show_notification, inputs=None, outputs=None) - rightvote_btn.click(show_notification, inputs=None, outputs=None) - - return demo - - -# MAIN PROGRAM -if __name__ == "__main__": - # Load environment variables with LLM API keys - load_dotenv() - - # Filter the warnings - logger.add("file.log", level="INFO") - - # Create the interface to discuss with the course - demo = create_tab_battle() - - # Launch the Gradio interface - demo.launch( - server_name="0.0.0.0", # to make the app accessible from other devices - inbrowser=True, # to automatically opens a new tab - ) - diff --git a/src/gradio_app_chat.py b/src/gradio_app_chat.py deleted file mode 100644 index 2f3ac13..0000000 --- a/src/gradio_app_chat.py +++ /dev/null @@ -1,190 +0,0 @@ -"""This script creates a Gradio tab that allows users to discuss with the course. - -Features: -- Users can ask questions to the chatbot. -- Users can erase the chat history and start a new conversation. -- Users can erase and rephrase the last question. - -Usage: -====== - gradio src/gradio_app_chat.py -""" - -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS -import random - -from loguru import logger -import gradio as gr - - -# MODULES IMPORT -from query_chatbot import ( - load_database, - search_similarity_in_database, - get_metadata, - format_chat_history, - contextualize_question, - generate_answer, - add_metadata_to_answer, - format_relevant_chunks, - QUERY_EXAMPLES, - MSGS_QUERY_NOT_RELATED, - OPENAI_MODEL_NAME, - CHROMA_PATH, -) - -# CONSTANTS -VECTOR_DB = load_database(CHROMA_PATH)[0] - - -# FUNCTIONS -def respond(message: str, chat_history: list) -> str: - """Respond to the user question. - - Parameters - ---------- - message : str - The user question. - chat_history : list - The chat history. - - Returns - ------- - str - The response to the user question. - """ - logger.info("Responding to the user question...") - - # Format the chat history for the model - formatted_chat_history = format_chat_history(chat_history, len_history=10) - # Contextualize the user question with the chat history - chat_context = contextualize_question(chat_history_formatted=formatted_chat_history) - # Search for relevant documents in the database - context = search_similarity_in_database(vector_db=VECTOR_DB, user_query=message) - # If no relevant document was found - if context == []: - logger.info("No relevant documents found in the database.") - logger.success("Returning an automatic response without calling the model.") - # random response betweet responses in MSGS_QUERY_NOT_RELATED - response = random.choice(MSGS_QUERY_NOT_RELATED) - return response - else: - # Format the relevant chunks - context_formatted = format_relevant_chunks(context) - # Get the metadata of the relevant documents - metadata = get_metadata(context) - # Generate the answer - answer = generate_answer( - query=message, - chat_context=chat_context, - relevant_chunks=context_formatted, - model_name=OPENAI_MODEL_NAME, - ) - # Add metadata to the answer - final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) - - return final_answer - - -def vote( - data: gr.LikeData, -) -> None: # TODO: Save the votes in a file to do some statistics - """Display in the logs the vote of the user. - - Parameters - ---------- - data : gr.LikeData - The data containing the vote information. - """ - if data.liked: - logger.info(f"You upvoted this response: {data.value}\n") - else: - logger.info(f"You downvoted this response: {data.value}\n") - - -def get_user_input(data: gr.SelectData) -> str: - """Get the user input. - - Parameters - ---------- - data : gr.SelectData - The data containing the user input. - - Returns - ------- - str - The user input. - """ - logger.info(f"{data.target.label} {data.value}\n") - - return data.value - - -def create_tab_chatbot(): - """Create the interface to discuss with the course.""" - with gr.Blocks( - theme=gr.themes.Default(primary_hue="emerald", secondary_hue="emerald"), - title="BioPyAssistant" - ) as discuss_course_tab: - # Define the query textbox - msg = gr.Textbox( - placeholder="Pose-moi une question sur le cours !", - render=False, - show_label=False, - min_width=200, - ) - # Define the chatbot - bot = gr.Chatbot( - value=[ - [ - None, - "Bonjour, comment puis-je t'aider ?", - ] - ], - bubble_full_width=False, - height=600, - likeable=True, - show_copy_button=True, - render=False, - avatar_images=("data/img/user_avatar.png", "data/img/chatbot_avatar.png"), - ) - # Define the chatbot interface - gr.ChatInterface( - respond, - chatbot=bot, - textbox=msg, - examples=QUERY_EXAMPLES, - cache_examples=False, - submit_btn=None, - retry_btn=None, - undo_btn=None, - clear_btn=None, - fill_width=True, - ) - - # Adding a like/dislike feature - bot.like(vote) - - return discuss_course_tab - - - -# MAIN PROGRAM -if __name__ == "__main__": - # Create the interface to discuss with the course - discuss_course_tab = create_tab_chatbot() - - # Launch the Gradio interface - discuss_course_tab.launch( - server_name="0.0.0.0", # to make the app accessible from other devices - inbrowser=True, # to automatically opens a new tab - ) - diff --git a/src/gradio_app_quiz.py b/src/gradio_app_quiz.py deleted file mode 100644 index c06e400..0000000 --- a/src/gradio_app_quiz.py +++ /dev/null @@ -1,263 +0,0 @@ -""" This script creates a Gradio tab that allows users to take a quiz by selecting various options. - -Features: -- Users can choose a chapter from which to take the quiz. -- Users can select the type of questions: Multiple Choice Questions (MCQ) or True/False. -- Users can specify the difficulty level of the questions. - -The questions for the quiz are retrieved from a JSON file named 'quiz.json' located in the 'data' folder. - -Usage: -====== - gradio src/gradio_app_quiz.py -""" - -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS -import os -import json -import random - -from loguru import logger -import gradio as gr - - -# MODULES IMPORT -from create_database import load_documents -from create_quiz import get_chapters_file_names - - -# CONSTANTS -PROCESSED_DATA_PATH = "data/markdown_processed" -QUIZ_TYPES = [ - "QCM", - "Vrai/Faux" -] - -PATH_QUIZ_JSON = "data/quiz.json" - -CSS = """ -#correct-answer - {background-color: #a3e288} -#wrong-answer - {background-color: #dd553e} -""" - - -# FUNCTIONS -def load_quiz(path_quiz_json: str) -> dict: - """Load the json file that contains the quizzes. - - Parameters - ---------- - path_quiz_json : str - The path to the json file. - - Returns - ------- - dict - The quizzes. - """ - logger.info("Loading the quiz json file...") - try: - full_path = os.path.join(os.getcwd(), path_quiz_json) - with open(full_path, 'r') as file: - quiz_json = json.load(file) - - logger.success("The json file has been loaded.\n") - return quiz_json - except FileNotFoundError: - logger.error("The json file does not exist.") - return None - except json.JSONDecodeError: - logger.error("Error decoding the json file.") - return None - - -def make_quiz(chapter_choice: str, level_choice: str, quiz_type_choice: str): - """Generate a quiz to ask the user. - - Parameters - ---------- - chapter_choice : str - The chapter of the question. - level_choice : str - The level of the user. - quiz_type_choice : str - The type of the quiz. - """ - logger.info("Retrieving a quiz...") - - # Display the quiz parameters - logger.info(f"Chapter: {chapter_choice}") - logger.info(f"Level: {level_choice}") - logger.info(f"Quiz type: {quiz_type_choice}") - - # Load json that contains the quizzes - quiz_json = load_quiz(PATH_QUIZ_JSON) - - # Filter questions based on user choices - filtered_questions = [] - for question in quiz_json["questions"]: - if (question["chapter"] == chapter_choice and - question["python_level"] == level_choice and - question["type"] == quiz_type_choice): - filtered_questions.append(question) - - # Test if there is a quiz for the given parameters - if filtered_questions == []: - logger.error("No quiz found for the given parameters.") - return ( - gr.Markdown("DĂ©solĂ©, aucun quiz n'a Ă©tĂ© trouvĂ© pour les paramĂštres donnĂ©s."), - None, - None, - None, - None, - None, - None, - None, - None - ) - - # Get the proper quiz - QUIZ = random.choice(filtered_questions) - - if quiz_type_choice == "Vrai ou Faux": - return ( - gr.Markdown("## " + QUIZ["question"], visible=True), - None, - None, - None, - None, - gr.Radio( - label="SĂ©lectionnez la proposition correcte :", - choices=["Vrai", "Faux"], - value=None, - visible=True - ), - QUIZ["correct_answer"], - QUIZ["explanation"], - gr.Markdown("", visible=False) - ) - else: - return ( - gr.Markdown(QUIZ["question"], visible=True), - gr.Markdown("## Proposition A\n\n" + QUIZ["answers"]["A"], visible=True), - gr.Markdown("## Proposition B\n\n" + QUIZ["answers"]["B"], visible=True), - gr.Markdown("## Proposition C\n\n" + QUIZ["answers"]["C"], visible=True), - gr.Markdown("## Proposition D\n\n" + QUIZ["answers"]["D"], visible=True), - gr.Radio( - label="SĂ©lectionnez la proposition correcte :", - choices=list(QUIZ["answers"].keys()), - value=None, - visible=True - ), - QUIZ["correct_answer"], - QUIZ["explanation"], - gr.Markdown("", visible=False) - ) - - -def check_answer(quiz_radio, correct_answer, explanation_text): - if quiz_radio == correct_answer: - message = (f"## RĂ©ponse\n\nBravo ! " - f"La rĂ©ponse correct est {correct_answer} \n\n" - f"{explanation_text}") - css_id = "correct-answer" - else: - message = (f"## RĂ©ponse\n\n" - f"DĂ©solĂ©, ce n'est pas la rĂ©ponse {quiz_radio}. " - f"La bonne rĂ©ponse est {correct_answer}.\n\n" - f"{explanation_text}") - css_id = "wrong-answer" - return gr.Radio(visible=False), gr.Markdown(value=message, visible=True, elem_id=css_id) - - -def create_tab_quiz(): - """Create the interface to generate a quiz.""" - with gr.Blocks( - theme=gr.themes.Default(primary_hue="emerald", secondary_hue="emerald"), - title="BioPyAssistant", - css=CSS - ) as quiz_tab: - - gr.Markdown("*SĂ©lectionnez les paramĂštres du quiz*") - # Get the chapter names - CHAPTERS = load_documents(PROCESSED_DATA_PATH) - CHAPTERS_DIC = get_chapters_file_names(CHAPTERS) - CHAPTER_NAMES = list(CHAPTERS_DIC.keys()) - - with gr.Row(): - chapter_choice = gr.Dropdown( - choices=CHAPTER_NAMES, - value=CHAPTER_NAMES[0], - label="Chapitre :") - level_names = ["DĂ©butant", "ConfirmĂ©"] - level_choice = gr.Dropdown( - choices=level_names, - value=level_names[0], - label="Niveau de difficultĂ© :" - ) - - quiz_type_choice = gr.Dropdown( - choices=QUIZ_TYPES, - value=QUIZ_TYPES[0], - label="Type :" - ) - create_quiz_button = gr.Button("GĂ©nĂ©rer un quiz", size="sm") - - with gr.Blocks(): - question = gr.Markdown(visible=False) - if quiz_type_choice == "Vrai ou Faux": - with gr.Row(equal_height=True): - answer_1_md = gr.Markdown(label="RĂ©ponse 1 :", visible=False) - answer_2_md = gr.Markdown(label="RĂ©ponse 2 :", visible=False) - else: - with gr.Row(equal_height=True): - answer_1_md = gr.Markdown(label="RĂ©ponse 1 :", visible=False) - answer_2_md = gr.Markdown(label="RĂ©ponse 2 :", visible=False) - with gr.Row(equal_height=True): - answer_3_md = gr.Markdown(label="RĂ©ponse 3 :", visible=False) - answer_4_md = gr.Markdown(label="RĂ©ponse 4 :", visible=False) - - answer_choice = gr.Radio(visible=False) - explanation_text = gr.Textbox(visible=False) - correct_answer_text = gr.Textbox(visible=False) - explanation_md = gr.Markdown(visible=False) - - - create_quiz_button.click( - fn=make_quiz, - inputs=[chapter_choice, level_choice, quiz_type_choice], - outputs=[ - question, - answer_1_md, answer_2_md, answer_3_md, answer_4_md, - answer_choice, correct_answer_text, explanation_text, explanation_md - ] - ) - - answer_choice.input( - fn=check_answer, - inputs=[answer_choice, correct_answer_text, explanation_text], - outputs=[answer_choice, explanation_md] - ) - - return quiz_tab - - -if __name__ == "__main__": - # Create the Gradio interface for the quiz - quiz_tab = create_tab_quiz() - - # Launch the Gradio interface - quiz_tab.launch( - server_name="0.0.0.0", # to make the app accessible from other devices - inbrowser=True, # to automatically opens a new tab - ) \ No newline at end of file diff --git a/src/logger.py b/src/logger.py new file mode 100644 index 0000000..dd3ce2f --- /dev/null +++ b/src/logger.py @@ -0,0 +1,43 @@ +"""Define logger.""" + +import sys +from pathlib import Path + +import loguru +from loguru import logger + + +def create_logger( + logpath: str | Path | None = None, level: str = "INFO" +) -> "loguru.Logger": + """Create the logger with optional file logging. + + Parameters + ---------- + logpath : str | Path | None, optional + Path to the log file. If None, no file logging is done. + level : str, optional + Logging level. Default is "INFO". + + Returns + ------- + loguru.Logger + Configured logger instance. + """ + # Define log format. + logger_format = ( + "{time:YYYY-MM-DD HH:mm:ss} " + "| {level:<8} " # noqa: RUF027 + "| {message}" + ) + # Remove default logger. + logger.remove() + # Add logger to path (if path is provided). + if logpath: + # Create parent directories. + Path(logpath).parent.mkdir(parents=True, exist_ok=True) + # Add logger to file. + logger.add(logpath, format=logger_format, level="DEBUG", mode="w") + # Add logger to stdout. + logger.add(sys.stdout, format=logger_format, level=level) + return logger diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 46e2f27..dc534d0 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -1,70 +1,147 @@ """Module for processing Markdown files. -This module provides functions for processing Markdown files. -It includes functionality to renumber headers and clean Python comments in Markdown content. +This module provides functions for processing Markdown files. +It includes functionality to renumber headers and clean Python comments +embedded in Markdown content. + +For example: + +Header renumbering: +------------------- +Input: + # Introduction + ## Qu'est-ce que Python ? + +Output: + # 1 Introduction + ## 1.1 Qu'est-ce que Python ? + +Python comment cleaning: +------------------------ +Input: + ```python + # Votre premier commentaire en Python. + print("Hello world!") + + # D'autres commandes plus utiles pourraient suivre. + ``` + +Output: + ```python + #Votre premier commentaire en Python. + print("Hello world!") + + #D'autres commandes plus utiles pourraient suivre. + ``` Usage: ====== - python src/parse_clean_markdown.py --in source_dir --out dest_dir + uv run src/parse_clean_markdown.py --config path/to/chapters_and_levels.yaml Where: - source_dir : str - The source directory containing Markdown files to be processed. - dest_dir : str - The destination directory to save the processed Markdown files. + config : Path + Path to the YAML file defining all chapters and levels. + This YAML file should include the chapter names, titles, and the paths to the + source Markdown files and the destination paths for the processed files. Example: ======== - python src/parse_clean_markdown.py --in data/markdown_raw --out data/markdown_processed + uv run src/parse_clean_markdown.py --config data/chapters_and_levels.yaml -This command will process Markdown files located in the 'data/markdown_raw' directory -and save the processed files to the 'data/markdown_processed' directory. +This command processes Markdown files listed in the YAML file and saves +the cleaned and renumbered files to the paths specified in the YAML file. """ -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" +import re +from datetime import datetime +from pathlib import Path +import click +import loguru +import yaml -# LIBRARY IMPORTS -import os -import re -import argparse -from typing import Union +from logger import create_logger -from loguru import logger +def build_chapter_paths( + file_name: str, + raw_base_path: Path, + processed_base_path: Path, +) -> tuple[Path, Path]: + """Build source and processed file paths for a chapter. -# FUNCTIONS -def get_args() -> tuple[str, str]: - """Get source and destination directories from command line arguments. + Parameters + ---------- + file_name : str + The name of the Markdown file for the chapter (e.g., "01_introduction.md"). + raw_base_path : Path + The base path for raw Markdown files. + processed_base_path : Path + The base path for processed Markdown files. Returns ------- - tuple[str, str] - A tuple containing the source and destination directories. + tuple[Path, Path] + A tuple containing the source path and the processed path for the chapter. """ - parser = argparse.ArgumentParser() - parser.add_argument( - "--in", - dest="source_dir", - required=True, - help="Source directory containing Markdown files.", - ) - parser.add_argument( - "--out", - dest="dest_dir", - required=True, - help="Destination directory to save processed files.", - ) - args = parser.parse_args() - return args.source_dir, args.dest_dir + source_path = raw_base_path / file_name + processed_path = processed_base_path / file_name + return source_path, processed_path -def clean_python_comments(content: str) -> str: +def load_chapters_from_yaml( + yaml_path: Path, logger: "loguru.Logger" = loguru.logger +) -> list[dict]: + """Load chapters from a YAML file and construct paths. + + Parameters + ---------- + yaml_path : Path + Path to the YAML file defining chapters and levels. + logger: "loguru.Logger" + Logger for logging messages. + + Returns + ------- + list[dict] + A list of dictionaries with chapter information, including constructed paths + for source and processed Markdown course files. + """ + logger.info(f"Loading chapters and paths from YAML file: {yaml_path}...") + try: + with yaml_path.open("r", encoding="utf-8") as file: + # Load YAML data + data = yaml.safe_load(file) or {} + # Get chapter information + chapters = data.get("chapters", []) + # Get base paths of raw and processed courses + raw_base_path = Path(data.get("raw_course_base_path", "")) + processed_base_path = Path(data.get("processed_course_base_path", "")) + # Build source and processed paths for each chapter + for chapter in chapters: + file_name = chapter.get("file_name") + if file_name: + chapter["source_path"], chapter["processed_path"] = ( + build_chapter_paths( + file_name, raw_base_path, processed_base_path + ) + ) + else: + logger.warning( + f"No file name defined for chapter: {chapter.get('id')}" + ) + + logger.success(f"Loaded {len(chapters)} chapters successfully.") + return chapters + except FileNotFoundError: + logger.error(f"YAML file not found: {yaml_path}") + return [] + except yaml.YAMLError as e: + logger.error(f"Error parsing YAML file: {e}") + return [] + + +def clean_python_comments(content: str, logger: "loguru.Logger" = loguru.logger) -> str: """Remove spaces between '#' and comments in Python code blocks in Markdown content. Example: @@ -80,11 +157,14 @@ def clean_python_comments(content: str) -> str: ---------- content : str Content of Markdown file. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- str - Markdown content with spaces removed between '#' and comments in Python code blocks. + Markdown content with spaces removed between '#' + and comments in Python code blocks. """ is_python_block = False cleaned_content = [] @@ -93,6 +173,7 @@ def clean_python_comments(content: str) -> str: logger.info("Cleaning comments in Python code blocks...") for line in content.split("\n"): + # Check for the start or end of a Python code block if line.strip().startswith("```python"): is_python_block = True cleaned_content.append(line) @@ -115,20 +196,24 @@ def clean_python_comments(content: str) -> str: f"Number of space lines modified comment lines: {modified_comment_lines}" ) logger.info(f"Number of final content lines: {len(cleaned_content)}") - logger.success(f"Cleaning comments in Python code blocks complete.") + logger.success("Python code block comments cleaned.") return "\n".join(cleaned_content) -def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: +def renumber_headers( + content: str, header_prefix: int | str, logger: "loguru.Logger" = loguru.logger +) -> str: """Renumber headers in Markdown content. Parameters ---------- content : str The Markdown content to renumber. - chapter_number : Union[int, str] - The chapter number to use for renumbering headers. + header_prefix : Union[int, str] + The chapter number or appendix letter to use for renumbering headers. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- @@ -139,12 +224,13 @@ def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: # Regex pattern to match headers with leading "#" and no following "#" header_pattern = r"^(#+)\s+([^#]*)$" # Define default header levels. - # We should have no more than 4 levels of headers. + # We should have no more than 5 levels of headers. headers = { - 1: chapter_number, # Level 1: chapter / annexe. + 1: header_prefix, # Level 1: chapter / appendix. 2: 0, # Level 2: section. 3: 0, # Level 3: Sub-section 4: 0, # Level 4: Sub-sub-section. + 5: 0, # Level 5: Sub-sub-sub-section. } # Stores the file content with renumbered headers processed_content = [] @@ -153,14 +239,14 @@ def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: if match: header_level = len(match.group(1)) header_text = match.group(2) - # Show errors if we are above level 4 - if header_level > 4: - logger.error("Header level beyond level 4!") + # Show errors if we are above level 5 + if header_level > 5: + logger.error("Header level beyond level 5!") logger.error(line) processed_content.append(line) continue # Increment the appropriate header level, - # if below chapter / annexe level: + # if below chapter / appendix level: if header_level != 1: headers[header_level] += 1 # Reset subsequent levels @@ -175,53 +261,88 @@ def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: return "\n".join(processed_content) -def process_md_files(source_dir: str, dest_dir: str) -> None: - """Process Markdown files in the source directory and save them to the destination directory. +@click.command() +@click.option( + "--config", + "yaml_path", + required=True, + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help="Path to the YAML file defining chapters and levels." + " This YAML file should include the chapter names, titles, and the paths to the " + "source Markdown files and the destination paths for the processed files.", +) +def process_md_files(yaml_path: Path) -> None: + """Process Markdown files listed in a YAML file and save them. Parameters ---------- - source_dir : str - The source directory containing Markdown files. - dest_dir : str - The destination directory to save processed files. + yaml_path : Path + Path to the YAML file defining chapters and their Markdown filenames. """ - logger.info("Processing Markdown files...\n") - if not os.path.exists(dest_dir): - os.makedirs(dest_dir) - - # Get a sorted list of Markdown files in the source directory - markdown_files = sorted([f for f in os.listdir(source_dir) if f.endswith(".md")]) - - for filename in markdown_files: - logger.info(f"Processing file: {filename}") - - # get the path of the source and destination files - source_path = os.path.join(source_dir, filename) - dest_path = os.path.join(dest_dir, filename) - - # Read the content of the source file - with open(source_path, "r", encoding="utf-8") as file: - content = file.read() - - # Clean Python comments - content = clean_python_comments(content) - + # Set up logging + log_path = f"logs/{datetime.now().strftime('%Y%m%d')}/parse_clean_markdown.log" + logger = create_logger(log_path) + logger.info("Starting Markdown processing...") + + # Load chapters from YAML file + chapters = load_chapters_from_yaml(yaml_path, logger) + + # Process each chapter's Markdown file + saved_count = 0 + for i, chapter in enumerate(chapters, start=1): + chapter_name = f"{chapter.get('id')}. {chapter.get('title')}" + logger.info(f"Chapter: {chapter_name} ({i}/{len(chapters)})") + + # Get source file path from YAML + source_path = chapter.get("source_path") + if not source_path: + logger.warning(f"No source file defined for chapter {chapter_name}") + continue + # Check if source file exists + if not source_path.exists(): + logger.warning(f"Markdown file not found: {source_path}") + continue + logger.info(f"Processing file: {source_path.name}") + # Read content + content = source_path.read_text(encoding="utf-8") + # Clean Python-style comments + content = clean_python_comments(content, logger) # Renumber headers - if filename.startswith("annexe"): - annex_character = str(filename.split("_")[1]) - content = renumber_headers(content, annex_character) - if re.match(r"\d{2}_", filename): - chapter_number = int(filename.split("_")[0]) - content = renumber_headers(content, chapter_number) - - # Save the processed content to the destination file - with open(dest_path, "w", encoding="utf-8") as file: - file.write(content) - - logger.success("Markdown files processed successfully.\n") + # Determine if it's an annex or a chapter based on filename + stem = source_path.stem.lower() + # Annex files are named with "annexe" followed by an underscore + letter + # For example: "annexe_a.md", "annexe_b.md", etc. + if stem.startswith("annexe"): + annex_character = stem.split("_")[1] + content = renumber_headers(content, annex_character, logger) + # Chapter files are named with a number followed by an underscore + # For example: "01_introduction.md", "02_variables.md", etc. + elif re.match(r"\d{2}_", source_path.name): + chapter_number = int(stem.split("_")[0]) + content = renumber_headers(content, chapter_number, logger) + + # Get destination file path from YAML + dest_path = chapter.get("processed_path") + if not dest_path: + logger.warning(f"No processed file path defined for chapter {chapter_name}") + continue + # Create output directory if it does not exist + dest_path.parent.mkdir(parents=True, exist_ok=True) + # Save processed content to destination path + try: + dest_path.write_text(content, encoding="utf-8") + logger.info(f"Processed file saved to: {dest_path}") + saved_count += 1 + except PermissionError: + logger.error(f"Permission denied when writing file: {dest_path}") + except OSError as e: + logger.error(f"OS error when writing file {dest_path}: {e}") + + logger.success( + f"Saved {saved_count}/{len(chapters)} processed Markdown files successfully!" + ) # MAIN PROGRAM if __name__ == "__main__": - source_dir, dest_dir = get_args() # Get source and destination directories - process_md_files(source_dir, dest_dir) + process_md_files() diff --git a/src/query_chatbot.py b/src/query_chatbot.py index eceeb84..9f75144 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -1,189 +1,270 @@ """CLI application for searching answers in a vectorial database. -This program allows users to search for answers in a textual database based on a given query text. -It utilizes a similarity search algorithm to find relevant documents in the database and generates -responses to the query using an LLM and the retrieved documents as context. +This program allows users to search for answers in a textual database based on a given +query text. It utilizes a similarity search algorithm to find relevant documents in the +database and generates responses to the query using an LLM and the retrieved documents +as context. Usage: ====== - python src/query_chatbot.py --query "Your question here" [--model "model_name"] - [--include-metadata] - + uv run src/query_chatbot.py --query "Your question here" + --level "user_level" + [--course-yaml "path_to_yaml_file"] + [--model "model_name"] + [--provider-llm "provider_llm_name"] + [--db-path "database_path"] + [--embedding-model "embedding_model"] + [--provider-emb "provider_embeddings_name"] + [--include-metadata] + Arguments: ========== "Your question here" : The query text for which you want to search for answers. + "user_level" : The user level used to adapt model responses. + It can be one of the following: + "beginner", "intermediate", "advanced". + Options: ======== - --model "model_name" : The name or identifier of the model to be used for generating responses. - (Default model: DEFAULT_LLM_MODEL) - - --include-metadata : Optional flag to specify whether to include metadata in the response. - If provided, metadata will be included; otherwise, it will be excluded. - (Default: metadata is excluded) + --course-yaml (Path): + Path to the YAML file defining the course chapters and student levels. + The YAML should include chapter names, titles, source Markdown paths, and + processed file paths. + Default: "data/chapters_and_levels.yaml" + + --model (str): + The name of the model to be used for generating responses. + Default: "gpt-4o" + + --provider-llm (str): + Name of the LLM model provider to use. + It can be either "openai" or "openrouter". + Default: "openai" + + --db-path (str): + File path to the Chroma database containing the context embeddings. + Default: "chroma_db" + + --embedding-model (str): + Name of the embedding model to use. + This should match the embedding model used + to create the Chroma database. + Default: "text-embedding-3-large" + + --provider-emb (str): + Name of the embeddings model provider to use. + It can be either "openai" or "openrouter". + Default: "openai" + + --include-metadata (bool): + Optional flag to specify whether to include metadata in the response. + If provided, metadata will be included; otherwise, it will be excluded. + Default: metadata is excluded Example: ======== - python src/query_chatbot.py --query "D'oĂč vient le nom Python ?" --model "gpt-4o" --include-metadata - -This command will search for answers to the query "Qu'est-ce que Python ?" in the vectorial Chroma database using the "gpt-4o" model. -And it will include metadata in the response. + uv run src/query_chatbot.py --query "D'oĂč vient le nom Python ?" \ + --level "beginner" --model "gpt-4o" \ + --course-yaml "data/chapters_and_levels.yaml" \ + --provider-llm "openai" --db-path "chroma_db" \ + --provider-emb "openai" --embedding-model "text-embedding-3-large" + +This command will search for answers to the query "Qu'est-ce que Python ?" from a +beginner user in the Chroma database located at "data/chroma_db" +using the "text-embedding-3-large" embedding model from the "openai" provider, +The answer will be generated using the "gpt-4o" model from the "openai" provider, +and the response will include metadata from the relevant documents. """ -# METADATA -__authors__ = ("Pierre Poulain", "Essmay Touami") -__contact__ = "pierre.poulain@u-paris.fr" -__copyright__ = "BSD-3 clause" -__date__ = "2024" -__version__ = "1.0.0" - - -# LIBRARY IMPORTS import os -import re -import sys -import random -import argparse -from typing import Tuple, Union, List +import secrets +from datetime import datetime +from pathlib import Path +from time import perf_counter +import click +import loguru import tiktoken +import yaml from dotenv import load_dotenv -from loguru import logger -from langchain_core.documents import Document from langchain_chroma import Chroma +from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser -from langchain_openai import OpenAIEmbeddings, ChatOpenAI -from langchain.schema import AIMessage, HumanMessage -from langchain.prompts import ChatPromptTemplate -from langchain_mistralai.chat_models import ChatMistralAI -from langchain_groq import ChatGroq - - -# CONSTANTS -CHROMA_PATH = "chroma_db" -EMBEDDING_MODEL = "text-embedding-3-large" -OPENAI_MODELS = ["gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"] -GROQ_MODELS = ["llama-3.1-70b-versatile", "llama-3.1-8b-instant"] -MISTRAL_MODELS = ["mistral-large-latest", "open-mixtral-8x7b", "open-mixtral-8x22b"] -DEFAULT_LLM_MODEL = OPENAI_MODELS[0] -OPENAI_MODEL_NAME = OPENAI_MODELS[0] - -PROMPT_TEMPLATE = """ -Tu es un assistant conversationnel pour aider des Ă©tudiants en biologie Ă  apprendre la programmation Python. -Tu dois fournir une rĂ©ponse Ă  la question posĂ©e en te basant strictement sur les extraits de cours donnĂ©s dans le contexte. -Utilise uniquement le contexte suivant pour rĂ©pondre Ă  la question. -La discussion prĂ©cĂ©dente peut t'aider Ă  comprendre le contexte de la question, -mais tu ne dois pas l'utiliser pour rĂ©pondre Ă  la question. - -Discussion prĂ©cĂ©dente : -{chat_history} - -Question : -"{question}" - -Contexte : -"{contexte}" - -RĂ©pond en français Ă  la question posĂ©e de façon claire et concise. -La rĂ©ponse doit ĂȘtre comprĂ©hensible pour des Ă©tudiants dĂ©butants en programmation Python. -Si tu ne connais pas la rĂ©ponse, dis que tu ne sais pas. -Si tu as besoin de plus d'informations, demande-le. -Si tu as besoin de clarifier la question, demande-le aussi. -""" +from langchain_core.prompts import ChatPromptTemplate +from langchain_openai import ChatOpenAI -QUERY_EXAMPLES = [ - "Quelle est la diffĂ©rence entre une liste et un set ?", - "Comment Ă©crire une boucle ?", - "Comment afficher un float avec 2 chiffres avec la virgule ?" -] +from create_database import create_embeddings_function +from logger import create_logger MSGS_QUERY_NOT_RELATED = [ - ("Je suis dĂ©solĂ©, je ne peux pas rĂ©pondre Ă  cette question. " - "Mon domaine d'expertise est la programmation Python. " - "N'hĂ©site pas Ă  me poser des questions liĂ©es Ă  ce sujet, je serai ravi de t'aider."), - ("DĂ©solĂ©, je suis un assistant pour l'apprentissage de la programmation Python. " - "Je ne suis pas en mesure de rĂ©pondre Ă  cette question."), - ("Je ne suis pas sĂ»r de comprendre ta question. " - "Peux-tu la reformuler en utilisant des termes plus simples ?"), + ( + "Je suis dĂ©solĂ©, je ne peux pas rĂ©pondre Ă  cette question. " + "Mon domaine d'expertise est la programmation Python. " + "N'hĂ©site pas Ă  me poser des questions liĂ©es Ă  ce sujet," + "je serai ravi de t'aider." + ), + ( + "DĂ©solĂ©, je suis un assistant pour l'apprentissage de la programmation Python. " + "Je ne suis pas en mesure de rĂ©pondre Ă  cette question." + ), + ( + "Je ne suis pas sĂ»r de pouvoir rĂ©pondre Ă  cette question, car elle ne semble " + "pas ĂȘtre liĂ©e Ă  la programmation Python. Si tu as des questions sur Python, " + "n'hĂ©site pas Ă  me les poser, je serai heureux de t'aider !" + ), +] +MSGS_QUERY_OUT_OF_SCOPE_LEVEL = [ + ( + "Cette question fait rĂ©fĂ©rence Ă  des notions qui ne sont pas encore abordĂ©es " + "dans ce cours." + ), + ( + "Cette notion n'est pas encore abordĂ©e Ă  votre niveau actuel " + "et fait partie de la suite du programme." + ), + ( + "Cette question fait rĂ©fĂ©rence Ă  des notions qui dĂ©passent " + "le cadre du niveau actuel de votre formation." + ), ] -# FUNCTIONS -def get_args() -> Tuple[str, str, bool]: - """Parse the command line arguments. +def get_level_infos( + course_yaml: Path, logger: "loguru.Logger" = loguru.logger +) -> dict[str, dict]: + """ + Load all user level information from a YAML file. + + Parameters + ---------- + course_yaml : Path + Path to the YAML file defining course levels. + logger : loguru.Logger + Logger for logging messages. Returns ------- - Tuple[str, str, bool] - A tuple containing the query, the model name and a flag to include metadata. + dict[str, dict] + Dictionary mapping level name to its info: + { + "name": str, + "display_name": str, + "comment": str, + "prompt_path": str, + "chapters": list[str] + } """ - logger.info("Parsing the command line arguments.") - parser = argparse.ArgumentParser() # Create a parser object - # Add arguments to the parser - parser.add_argument( - "--query", - type=str, - default="", - help="The query text for which you want to search for answers.", - ) - parser.add_argument( - "--model", - type=str, - default=DEFAULT_LLM_MODEL, - help="The name or identifier of the model to be used for generating responses.", - ) - parser.add_argument( - "--include-metadata", - action="store_true", - default=False, - help="Flag to specify whether to include metadata in the response. If provided, metadata will be included.", - ) - # Parse the command line arguments - args = parser.parse_args() - - # Checks - # query is required - if args.query == "": - logger.error("Please provide a query") - sys.exit(1) - - logger.info(f"Query : {args.query}") - logger.info(f"Model name: {args.model}") - logger.info(f"Include metadata: {args.include_metadata}") - logger.success("Command line arguments parsed successfully.\n") + try: + with course_yaml.open("r", encoding="utf-8") as f: + course_data = yaml.safe_load(f) or {} + + levels = course_data.get("levels", []) + if not levels: + logger.warning("No levels defined in YAML file.") + return {} + + level_infos = {} + for level in levels: + name = level.get("name") + if not name: + logger.warning("Level without a name found, skipping.") + continue + + level_infos[name] = { + "name": name, + "display_name": level.get("display_name", ""), + "comment": level.get("comment", ""), + "prompt_path": Path(level.get("prompt_path", "")), + "chapters": [str(ch) for ch in level.get("chapters", [])], + } + return level_infos + + except FileNotFoundError: + logger.error(f"YAML file not found: {course_yaml}") + return {} + except yaml.YAMLError as e: + logger.error(f"Error parsing YAML file: {e}") + return {} + + +def get_user_level_data( + user_level: str, course_yaml: Path, logger: "loguru.Logger" = loguru.logger +) -> dict: + """ + Retrieve user level information, including relevant chapters and prompt file. - return args.query, args.model, args.include_metadata + Parameters + ---------- + user_level : str + The identifier of the user's level (e.g., 'beginner'). + course_yaml : Path + Path to the YAML file defining course levels. + logger : loguru.Logger + Logger for messages. -def get_available_llm_models() -> List[str]: - """List LLM models based on API keys found in the environment variables. - Returns ------- - List[str] - List of available LLM models. + dict + Dictionary containing: + - "chapters": list[str] of chapter IDs relevant to the user level + - "prompt_file": str path to the prompt template + + Raises + ------ + SystemExit + Exits with code 1 if the specified user level is not found in the YAML file. """ - llm_models = [] - if os.getenv("OPENAI_API_KEY"): - llm_models += OPENAI_MODELS - if os.getenv("GROQ_API_KEY"): - llm_models += GROQ_MODELS - if os.getenv("MISTRAL_API_KEY"): - llm_models += MISTRAL_MODELS - return llm_models + # Load all levels + level_infos = get_level_infos(course_yaml, logger) + + # Retrieve the specific user level info + user_info = level_infos.get(user_level) + if not user_info: + available_levels = ", ".join(level_infos.keys()) or "None" + logger.error( + f"Failed to retrieve user level '{user_level}' from YAML. " + f"Available levels: {available_levels}. Exiting." + ) + raise SystemExit(1) + chapters = user_info["chapters"] + prompt_path = user_info["prompt_path"] + + return {"chapters": chapters, "prompt_path": prompt_path} -def load_database(vector_db_path: str) -> Tuple[Chroma, int]: +def load_database( + vector_db_path: str, + embedding_model: str, + provider_embeddings_name: str, + logger: "loguru.Logger" = loguru.logger, +) -> Chroma: """Prepare the vector database. + Parameters + ---------- + vector_db_path : str + The file path to the Chroma database containing the context embeddings. + embedding_model : str + The name of the embedding model to use for loading the database. + provider_embeddings_name : str + The name of the embeddings model provider to use, + needed to create the appropriate + embedding function for loading the database. + logger: "loguru.Logger" + Logger for logging messages. + Returns ------- Chroma: The prepared vector database. - int: The number of chunks in the database. """ - logger.info("Loading the vector database.") - embedding_function = OpenAIEmbeddings( - model=EMBEDDING_MODEL - ) # define the embedding model + logger.info("Loading the vector database...") + logger.debug(f"Vector database path: {vector_db_path}") + logger.debug(f"Embedding model: {embedding_model} from {provider_embeddings_name}") + # Define the embedding function to use for loading the database + embedding_function = create_embeddings_function( + embedding_model, provider_embeddings_name + ) # Load the database from the specified directory vector_db = Chroma( persist_directory=vector_db_path, embedding_function=embedding_function @@ -191,10 +272,8 @@ def load_database(vector_db_path: str) -> Tuple[Chroma, int]: # Count the number of chunks in the database nb_chunks = vector_db._collection.count() logger.info(f"Chunks in the database: {nb_chunks}") - - logger.success("Vector database prepared successfully.\n") - - return vector_db, nb_chunks + logger.success("Vector database prepared successfully.") + return vector_db def search_similarity_in_database( @@ -202,8 +281,8 @@ def search_similarity_in_database( user_query: str, nb_chunks: int = 3, score_threshold: float = 0.35, - logger_flag: bool = True, -) -> List[Document]: + logger: "loguru.Logger" = loguru.logger, +) -> list[Document]: """Search for relevant documents in the database based on the query text. Parameters @@ -216,161 +295,42 @@ def search_similarity_in_database( The number of top matching documents to retrieve. score_threshold : float The relevance score threshold for filtering the results. - logger_flag : bool - Flag to indicate whether to log the search results. + logger : "loguru.Logger" + Logger for logging messages. Returns ------- relevant_chunks : list List of relevant documents found in the database. """ - if logger_flag: - logger.info("Searching for relevant documents in the database...") - + logger.info("Searching for relevant documents in the database...") # Define the retriever retriever = vector_db.as_retriever( search_type="similarity_score_threshold", - search_kwargs={"k": nb_chunks, "score_threshold": score_threshold}, + search_kwargs={ + "k": nb_chunks, + "score_threshold": score_threshold, + }, ) - - # Perform a similarity search with relevance scores + # Perform a similarity search relevant_chunks = retriever.invoke(user_query) - if logger_flag: - # Display information about the relevant chunks - for chunk in relevant_chunks: - logger.info(f"Chunk ID: {chunk.metadata['id']}") - logger.info(f"Number of tokens: {chunk.metadata['nb_tokens']}") - logger.info(f"Content: {chunk.page_content[:20]}...\n") - - logger.success("Search completed successfully.\n") - - return relevant_chunks - - -def format_relevant_chunks(relevant_chunks: list) -> str: - """Format the relevant documents for the OpenAI model. - - Parameters - ---------- - relevant_chunks : list - List of relevant documents from the database. - - Returns - ------- - formatted_chunks : str - The formatted relevant documents. - """ - logger.info("Formatting the relevant documents.") - formatted_chunks = "" - + # Display information about the relevant chunks for chunk in relevant_chunks: - formatted_chunks += f"Chunk ID: {chunk.metadata['id']}\n" - formatted_chunks += f"Content: {chunk.page_content}\n" - formatted_chunks += f"Source: {chunk.metadata}\n\n" - - logger.success("Relevant documents formatted successfully.\n") - - return formatted_chunks - - -def format_chat_history( - chat_history: list[Tuple[str, str]] = [], len_history: int = 10 -) -> List[Union[HumanMessage, AIMessage]]: - """Format the chat history for the promt template. - - Parameters - ---------- - chat_history : list[tuple[str, str]], optional - The chat history to format, by default []. - len_history : int, optional - The number of chat history entries to consider, by default 10. - - Returns - ------- - list[Union[HumanMessage, AIMessage]] - The formatted chat history. - """ - logger.info("Formatting the chat history...") - formatted_history = [] - # Define the pattern to identify and remove metadata - metadata_pattern = re.compile( - r"Pour plus d\'informations, consultez les sources suivantes :.*$", re.DOTALL + logger.debug(f"Chunk ID: {chunk.id}") + logger.debug(f"Chapter name: {chunk.metadata['chapter_name']}") + logger.debug(f"URL: {chunk.metadata['url']}") + logger.debug(f"Number of tokens: {chunk.metadata['nb_tokens']}") + logger.debug("Chunk content:") + for line in chunk.page_content.splitlines(): + # Split into sentences for better readability + for sentence in line.split(". "): + logger.debug(f"{sentence}") + logger.debug("--------------------------------------") + logger.success( + f"Retrieval completed with {len(relevant_chunks)} relevant chunks found." ) - # if chat history is not empty - if len(chat_history) > 0: - for human, ai in chat_history[-len_history:]: - # Append the human and AI messages to the formatted history - if human != None: - formatted_history.append(HumanMessage(content=human)) - else: - formatted_history.append(HumanMessage(content="")) - logger.info(f"Human message: {human}") - - # Remove metadata from AI message - cleaned_ai = re.sub(metadata_pattern, "", ai).strip() - formatted_history.append(AIMessage(content=cleaned_ai)) - logger.info(f"AI message (cleaned): {cleaned_ai}") - - logger.success( - f"Chat history formatted successfully with {len(formatted_history)} entries.\n" - ) - return formatted_history - - else: # if chat history is empty - logger.info("Chat history is empty.") - logger.success("Chat history formatted successfully with 0 entries.\n") - return chat_history - - -def contextualize_question(chat_history_formatted: list[Union[HumanMessage, AIMessage]] -) -> str: - """Add context to the user query using the chat history. - - Parameters - ---------- - chat_history_formatted : list[Union[HumanMessage, AIMessage]] - The formatted chat history. - - Returns - ------- - chat_context : str - The contextualized user query. - """ - logger.info("Contextualizing the user query...") - chat_context = "" - - # Iterate over the formatted chat history and append to the context - for i, message in enumerate(chat_history_formatted): - if isinstance(message, HumanMessage): - chat_context += f"Question {i // 2 + 1}: {message.content}\n" - elif isinstance(message, AIMessage): - chat_context += f"RĂ©ponse {i // 2 + 1}: {message.content}\n" - - logger.info(f"Chat context: {chat_context}") - logger.success("Contextualized query constructed successfully.\n") - - return chat_context - - -def get_metadata(relevant_chunks: list) -> list[dict]: - """Get the metadata of the top matching documents. - - Parameters - ---------- - relevant_chunks : list - List of top matching documents and their metadata. - - Returns - ------- - metadatas : list - List of metadata dictionaries for the top matching documents. - """ - logger.info("Extracting metadata of the top matching documents.") - metadatas = [doc.metadata for doc in relevant_chunks] - logger.success("Metadata extracted successfully.\n") - - return metadatas + return relevant_chunks def calculate_nb_tokens(text: str) -> int: @@ -394,73 +354,128 @@ def calculate_nb_tokens(text: str) -> int: def generate_answer( - query: str, chat_context: str, relevant_chunks: list, model_name: str, logger_flag: bool = True -) -> str: + query: str, + user_level: str, + provider_llm_name: str, + chat_history: str | None, + relevant_chunks: list, + level_relevant_chapter_ids: list[str], + model_name: str, + prompt_path: Path, + logger: "loguru.Logger", +) -> tuple[str, int, int]: """Generate an answer to the user query. Parameters ---------- query : str The user query. - chat_context : str - The contextualized chat history. + user_level : str + The user level, used for logging purposes. + provider_llm_name : str + The name of the LLM model provider to use. + chat_history : str | None + The contextualized chat history, for IU interface users, + or None for non-IU users. relevant_chunks : list List of relevant documents from the database. + level_relevant_chapter_ids : list[str] + List of chapter IDs relevant to the user level, used for logging purposes. model_name : str The name of the OpenAI model to use for generating the answer. - logger_flag : bool, optional - Flag to indicate whether to log the output, by default True. + prompt_path : Path + The file path to the text file containing the prompt template. + logger : loguru.Logger + Logger for logging messages. Returns ------- answer : str The answer generated by the model. + nb_tokens_prompt : int + The number of tokens in the prompt. + nb_tokens_answer : int + The number of tokens in the answer. """ - if logger_flag: - logger.info("Generating an answer to the user query...") - + # Format the relevant documents for the model + context = "\n\n".join( + [ + f"Document {chunk.id} : {chunk.page_content}" + for i, chunk in enumerate(relevant_chunks) + ] + ) + # Load environment variables + load_dotenv() # Define the model - if model_name in MISTRAL_MODELS: - chat_model = ChatMistralAI(model=model_name) - elif model_name in GROQ_MODELS: - chat_model = ChatGroq(model=model_name) - elif model_name in OPENAI_MODELS: - chat_model = ChatOpenAI(model=model_name) + if provider_llm_name == "openai": + api_key = os.getenv("OPENAI_API_KEY") + chat_model = ChatOpenAI(model=model_name, api_key=api_key) + # For OpenRouter, we don't have a specific class in LangChain, + # but we can use the ChatOpenAI class with the appropriate base URL and API key. + # Doc: https://openrouter.ai/docs/guides/community/langchain + elif provider_llm_name == "openrouter": + api_key = os.getenv("OPENROUTER_API_KEY") + chat_model = ChatOpenAI( + model=model_name, api_key=api_key, base_url="https://openrouter.ai/api/v1" + ) + # Retrieve the prompt template + prompt_template_content = Path(prompt_path).read_text(encoding="utf-8") # Define the prompt template - answer_prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) + answer_prompt = ChatPromptTemplate.from_template(prompt_template_content) # Define the chained prompt answer_chain = answer_prompt | chat_model | StrOutputParser() # Input data for the prompt input_data = { - "contexte": relevant_chunks, + "contexte": context, "question": query, - "chat_history": chat_context, + "chat_history": chat_history, } - if logger_flag: - # Fill the prompt with the input data - filled_prompt = answer_prompt.format(**input_data) - logger.info(f"Filled prompt: {filled_prompt}") - nb_tokens_prompt = calculate_nb_tokens(filled_prompt) - logger.info(f"Number of tokens in the prompt: {nb_tokens_prompt}\n") # Generate the answer answer = answer_chain.invoke(input_data) - if logger_flag: - logger.success("Answer generated from LLM successfully.\n") - - return answer + logger.debug(f"Question: {query}") + logger.debug(f"Prompt path: {prompt_path}") + logger.debug(f"LLM model used: {model_name} from {provider_llm_name}") + logger.debug("Answer generated by the LLM:") + for line in answer.splitlines(): + for sentence in line.split(". "): + logger.debug(f"{sentence}") + # Fill the prompt with the input data + filled_prompt = answer_prompt.format(**input_data) + nb_tokens_prompt = calculate_nb_tokens(filled_prompt) + # Calculate the number of tokens in the answer + nb_tokens_answer = calculate_nb_tokens(answer) + + # Formate the answer if relevant chunks are found but not relevant to the user level + for chunk in relevant_chunks: + if chunk.metadata["chapter_id"] not in level_relevant_chapter_ids: + logger.warning( + "This question seems to be related to the course " + "but not relevant to the user level." + ) + logger.debug( + f"Chapter `{chunk.metadata['chapter_id']}` " + f" not in the list of level-relevant chapters for {user_level}: " + f"{level_relevant_chapter_ids}" + ) + warning_sentence = secrets.choice(MSGS_QUERY_OUT_OF_SCOPE_LEVEL) + answer = f"{warning_sentence}\n\n{answer}" + logger.debug(f"Final answer: {answer.replace('\n\n', ' ')}") + break + + return answer, nb_tokens_prompt, nb_tokens_answer def add_metadata_to_answer( - answer_from_model, metadatas: list[dict], iu: bool = False + answer_from_model, relevant_chunks: list[Document], *, iu: bool = False ) -> str: """Add metadata to the response. Parameters ---------- - answer : str + answer_from_model : str The response text predicted by the AI model. - metadatas : list - List of metadata dictionaries for the top matching documents. + relevant_chunks : list[Document] + List of relevant document chunks with their metadata. iu : bool Flag to specify interface user or not. @@ -469,11 +484,12 @@ def add_metadata_to_answer( str The answer with added metadata. """ - logger.info("Adding metadata to the response...") - # Generate sources string sources_set = set() # Use a set to store unique sources + # Extract metadata for each relevant chunk + metadatas = [doc.metadata for doc in relevant_chunks] + for metadata in metadatas: file_name = metadata["file_name"] # get the file name chapter_name = metadata["chapter_name"] # get the chapter name @@ -523,86 +539,209 @@ def add_metadata_to_answer( sources_list = list(sources_set) # cast to join into a string sources_text = "\n- ".join(sources_list) sources_string = ( - f"Pour plus d'informations, je t'invite Ă  consulter les rubriques suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n- {sources_text}" + "Pour plus d'informations, je t'invite Ă  consulter les rubriques " + "suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n" + f"- {sources_text}" ) # Add the sources to the response response_with_metadata = f"{answer_from_model}\n\n{sources_string}" - logger.info(f"Answer with metadata: {response_with_metadata}") - logger.success("Metadata added to the response successfully.\n") - return response_with_metadata -def display_answer(user_query: str, final_response: Union[str, dict]) -> None: +def display_answer( + user_query: str, + answer: str, + start_time: float, + logger: "loguru.Logger", + nb_tokens_prompt: int, + nb_tokens_answer: int, +) -> None: """Display the results. Parameters ---------- user_query : str The query from the user. - final_response : str + answer : str The response text with added metadata. + start_time : float + The start time of the process, used to calculate elapsed time. + logger : loguru.Logger + Logger for logging messages. + nb_tokens_prompt : int + The number of tokens in the prompt, used for logging usage statistics. + nb_tokens_answer : int + The number of tokens in the answer, used for logging usage statistics. """ - logger.info("Displaying the results.") - - print("\n\n") - print("Question:") - print(f"{user_query}\n") - print("Reponse:") - print(f"{final_response}") - print("\n\n") - - logger.success("Results displayed successfully.") + print("\n\nQuestion:") + print(f"{user_query}\n\n") + print("RĂ©ponse:") + print(f"{answer}\n\n") + + logger.debug("Usage statistics:") + logger.debug(f"Number of tokens in the prompt: {nb_tokens_prompt}") + logger.debug(f"Number of tokens in the answer: {nb_tokens_answer}") + logger.debug(f"Total number of tokens: {nb_tokens_prompt + nb_tokens_answer}") + + elapsed_time = perf_counter() - start_time + logger.success( + f"Query chatbot completed successfully in {elapsed_time:.2f} seconds!" + ) -def interrogate_model() -> None: +@click.command() +@click.option( + "--query", + "user_query", + type=str, + required=True, + help="The query text for which you want to search for answers.", +) +@click.option( + "--level", + "user_level", + type=click.Choice( + ["beginner", "intermediate", "advanced"], + case_sensitive=False, + ), + required=True, + help="User level used to adapt model responses.", +) +@click.option( + "--course-yaml", + "course_yaml", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + default=Path("data/chapters_and_levels.yaml"), + help=( + "Path to the YAML file defining the course chapters and student levels. " + "The YAML should include chapter names, titles, source Markdown paths, " + "and processed file paths." + ), +) +@click.option( + "--model", + "model_name", + type=str, + default="gpt-4o", + show_default=True, + help="The name or identifier of the model to be used for generating responses.", +) +@click.option( + "--provider-llm", + "provider_llm_name", + type=click.Choice( + ["openai", "openrouter"], + case_sensitive=False, + ), + default="openai", + help="Name of the LLM model provider to use.", +) +@click.option( + "--db-path", + "database_path", + type=click.Path(exists=True, file_okay=False, dir_okay=True), + default="chroma_db", + show_default=True, + help="File path to the Chroma database containing the context embeddings.", +) +@click.option( + "--embedding-model", + "embedding_model", + default="text-embedding-3-large", + type=str, + help="Name of the embedding model to use." + "This should match the embedding model used to create the Chroma database.", +) +@click.option( + "--provider-emb", + "provider_embeddings_name", + type=click.Choice( + ["openai", "openrouter"], + case_sensitive=False, + ), + default="openai", + help="Name of the embeddings model provider to use.", +) +@click.option( + "--include-metadata", + is_flag=True, + default=False, + help="Include metadata in the response if this flag is provided.", +) +def interrogate_model( + user_query: str, + user_level: str, + course_yaml: Path, + model_name: str, + provider_llm_name: str, + database_path: str, + embedding_model: str, + provider_embeddings_name: str, + *, + include_metadata: bool, +) -> None: """Interrogate the AI model to search for answers in a vector database.""" - # Load the environment variables - load_dotenv() - # Load the query text from the command line arguments - user_query, model_name, include_metadata = get_args() - - # Check required model is available: - LLM_MODELS = get_available_llm_models - if model_name not in LLM_MODELS: - logger.error(f"Model {model_name} is not available.") - sys.exit(1) + start_time = perf_counter() + # Set-up the logger + log_path = ( + f"logs/{datetime.now().strftime('%Y%m%d')}/" + f"query_chatbot_{datetime.now().strftime('%H:%M:%S')}.log" + ) + logger = create_logger(log_path) + logger.info("Starting the command line interface for querying the chatbot...") + # USER LEVEL INFORMATION RETRIEVAL + # Retrieve the user level information from the YAML file + user_infos = get_user_level_data(user_level, course_yaml, logger) + level_relevant_chapter_ids = user_infos["chapters"] + prompt_path = user_infos["prompt_path"] # CONTEXT RETRIEVAL # Load the vector database - vector_db = load_database(CHROMA_PATH)[0] + vector_db = load_database(database_path, embedding_model, provider_embeddings_name) # Search for relevant documents in the database - relevant_chunks = search_similarity_in_database(vector_db, user_query) + relevant_chunks = search_similarity_in_database( + vector_db, user_query, logger=logger + ) # ANSWER GENERATION # Check if there are relevant documents if relevant_chunks == []: - # random response betweet responses in MSGS_QUERY_NOT_RELATED - response = random.choice(MSGS_QUERY_NOT_RELATED) - print(response) - sys.exit(0) + logger.warning( + "This question does not seem to be related to the course content." + ) + # Avoids calling the model for queries that are not relevant to the course + answer = secrets.choice(MSGS_QUERY_NOT_RELATED) + logger.debug( + f"Answer generated automatically without calling the model: {answer}" + ) + nb_tokens_prompt = 0 + nb_tokens_answer = 0 else: - # Format the relevant documents for the model - relevant_chunks_formatted = format_relevant_chunks(relevant_chunks) - # Get the metadata of the top matching documents - metadatas = get_metadata(relevant_chunks) # Generate the answer - answer = generate_answer(query=user_query, chat_context=None, relevant_chunks=relevant_chunks_formatted, model_name=model_name) - # Calculate the number of tokens in the answer - logger.info("Calculating the number of tokens in the answer.") - nb_tokens_answer = calculate_nb_tokens(answer) - logger.success(f"Number of tokens in the answer: {nb_tokens_answer}\n") + answer, nb_tokens_prompt, nb_tokens_answer = generate_answer( + query=user_query, + user_level=user_level, + chat_history=None, + relevant_chunks=relevant_chunks, + level_relevant_chapter_ids=level_relevant_chapter_ids, + model_name=model_name, + provider_llm_name=provider_llm_name, + prompt_path=prompt_path, + logger=logger, + ) # ANSWER FORMATTING - # Add metadata to the answer + # Display the answer with or without metadata based on the include_metadata flag if include_metadata: - answer_with_metadata = add_metadata_to_answer(answer, metadatas) - display_answer(user_query, answer_with_metadata) - else: - display_answer(user_query, answer) + # Add metadata of the top matching documents to the answer + answer = add_metadata_to_answer(answer, relevant_chunks) + + display_answer( + user_query, answer, 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