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upload-time = "2025-09-14T22:17:51.533Z" }, +] From 8d730ef1862592dd9bd0f8f5d06f6d9d5a43f4d5 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 26 Jan 2026 17:06:58 +0100 Subject: [PATCH 02/61] refactor: use click and pathlib.Path for CLI --- src/create_database.py | 194 ++++++++++++++---------------------- src/parse_clean_markdown.py | 107 +++++++------------- src/query_chatbot.py | 164 +++++++++++++----------------- 3 files changed, 181 insertions(+), 284 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 4da7cea..91a9899 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -1,11 +1,11 @@ """Creates the vectorial Chroma database from Markdown files in the specified directory. -This script loads Markdown files from the specified directory, concatenates their content, +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] + python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] Arguments: ========== @@ -17,7 +17,7 @@ The size of the text chunks to be created. Default is 1000. --chunk-overlap : int (optional) The overlap between text chunks. Default is 200. - + Example: ======== @@ -28,34 +28,24 @@ 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 pathlib import Path -from dotenv import load_dotenv +import click 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 DirectoryLoader, TextLoader +from langchain_core.documents import Document +from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import ( MarkdownHeaderTextSplitter, RecursiveCharacterTextSplitter, ) - +from loguru import logger # CONSTANTS CHUNK_SIZE = 1000 @@ -63,84 +53,6 @@ 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})." - ) - 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. @@ -401,7 +313,7 @@ def add_file_names_to_metadata( f"{chapter_number.group(0).strip().zfill(2)}_" ): # zfill(2) to pad with zeros chunk.metadata["file_name"] = file_name - break + break elif appendix_letter.group(0) == file_name.split("_")[1]: chunk.metadata["file_name"] = file_name break @@ -504,40 +416,82 @@ def add_url_to_metadata(chunks: list[Document]) -> list[Document]: return chunks -def save_to_chroma(chunks: list[Document], chroma_output_path: str) -> None: +def save_to_chroma(chunks: list[Document], chroma_output_path: Path) -> 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. + chroma_output_path : Path + Path to the directory where the ChromaDB database will be saved. """ - logger.info("Saving to Chroma...") + logger.info("Saving to ChromaDB...") - # Clear out the database first. - if os.path.exists(chroma_output_path): + # 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) + # Create a new ChromaDB from the documents + embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL) Chroma.from_documents( - chunks, - model_embedding, - persist_directory=chroma_output_path, - collection_metadata={"hnsw:space": "cosine"}, - ) # distance metric + documents=chunks, + embedding=embeddings, + persist_directory=str(chroma_output_path), + collection_metadata={"hnsw:space": "cosine"}, # distance metric + ) - logger.success(f"Saved {len(chunks)} chunks to {chroma_output_path}.") + logger.info(f"Saved {len(chunks)} chunks to {chroma_output_path}.") -def generate_data_store() -> None: +@click.command() +@click.option( + "-d", + "--data-path", + required=True, + type=click.Path(exists=True, file_okay=False, dir_okay=True, path_type=Path), + help="Directory containing the processed Markdown files of the Python course.", +) +@click.option( + "-c", + "--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( + "-s", + "--chunk-size", + default=CHUNK_SIZE, + type=int, + help="Size of the text chunks to be created.", +) +@click.option( + "-o", + "--chunk-overlap", + default=CHUNK_OVERLAP, + type=int, + help="Overlap between text chunks.", +) +def generate_data_store( + data_path: Path, chroma_path: Path, chunk_size: int, chunk_overlap: int +) -> 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. + # Validate the CLI arguments + if chunk_size <= 0: + logger.error("The chunk size must be a positive integer.") + sys.exit(1) + if chunk_overlap < 0: + logger.error("The chunk overlap must be a non-negative integer.") + sys.exit(1) + if chunk_overlap >= chunk_size: + logger.error( + f"The chunk overlap ({chunk_overlap}) must be less than the chunk size ({chunk_size})." + ) + sys.exit(1) + + # Load the environment variables with LLM api keys load_dotenv() # load documents from the specified directory @@ -565,7 +519,7 @@ def generate_data_store() -> None: chunks_with_file_names = add_file_names_to_metadata(chunks_with_tokens, file_names) # add URL to the chunks - chunks_with_url = add_url_to_metadata(chunks_with_file_names) + chunks_with_url = add_url_to_metadata(chunks_with_file_names) # save the chunks to ChromaDB save_to_chroma(chunks_with_url, chroma_path) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 46e2f27..2eef2be 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -1,6 +1,6 @@ """Module for processing Markdown files. -This module provides functions 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. Usage: @@ -8,9 +8,9 @@ python src/parse_clean_markdown.py --in source_dir --out dest_dir Where: - source_dir : str + source_dir : Path The source directory containing Markdown files to be processed. - dest_dir : str + dest_dir : Path The destination directory to save the processed Markdown files. Example: @@ -21,49 +21,14 @@ and save the processed files to the 'data/markdown_processed' 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 argparse +from pathlib import Path from typing import Union +import click from loguru import logger -# FUNCTIONS -def get_args() -> tuple[str, str]: - """Get source and destination directories from command line arguments. - - Returns - ------- - tuple[str, str] - A tuple containing the source and destination directories. - """ - 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 - - def clean_python_comments(content: str) -> str: """Remove spaces between '#' and comments in Python code blocks in Markdown content. @@ -115,7 +80,7 @@ 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("Cleaning comments in Python code blocks complete.") return "\n".join(cleaned_content) @@ -175,53 +140,55 @@ 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: +@click.command() +@click.option( + "--in", + "source_dir", + required=True, + type=click.Path(exists=True, file_okay=False, path_type=Path), + help="Source directory containing Markdown files.", +) +@click.option( + "--out", + "dest_dir", + required=True, + type=click.Path(file_okay=False, path_type=Path), + help="Destination directory to save processed files.", +) +def process_md_files(source_dir: Path, dest_dir: Path) -> None: """Process Markdown files in the source directory and save them to the destination directory. Parameters ---------- - source_dir : str + source_dir : Path The source directory containing Markdown files. - dest_dir : str + dest_dir : Path The destination directory to save processed files. """ - 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")]) + logger.info("Processing Markdown files...") - for filename in markdown_files: - logger.info(f"Processing file: {filename}") + dest_dir.mkdir(parents=True, exist_ok=True) - # 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) + for source_path in sorted(source_dir.glob("*.md")): + logger.info(f"Processing file: {source_path.name}") - # Read the content of the source file - with open(source_path, "r", encoding="utf-8") as file: - content = file.read() + content = source_path.read_text(encoding="utf-8") - # Clean Python comments content = clean_python_comments(content) - # Renumber headers - if filename.startswith("annexe"): - annex_character = str(filename.split("_")[1]) + if source_path.name.startswith("annexe"): + annex_character = source_path.stem.split("_")[1] content = renumber_headers(content, annex_character) - if re.match(r"\d{2}_", filename): - chapter_number = int(filename.split("_")[0]) + elif re.match(r"\d{2}_", source_path.name): + chapter_number = int(source_path.stem.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) + dest_path = dest_dir / source_path.name + dest_path.write_text(content, encoding="utf-8") - logger.success("Markdown files processed successfully.\n") + logger.success("Markdown files processed 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..f5c1499 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -1,14 +1,14 @@ """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 +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] - + [--include-metadata] + Arguments: ========== "Your question here" : The query text for which you want to search for answers. @@ -17,7 +17,7 @@ ======== --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) @@ -30,36 +30,25 @@ And it will include metadata in the response. """ -# 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 import re import sys -import random -import argparse -from typing import Tuple, Union, List +from typing import List, Tuple, Union +import click import tiktoken from dotenv import load_dotenv -from loguru import logger -from langchain_core.documents import Document +from langchain.prompts import ChatPromptTemplate +from langchain.schema import AIMessage, HumanMessage 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 +from langchain_mistralai.chat_models import ChatMistralAI +from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from loguru import logger - -# CONSTANTS CHROMA_PATH = "chroma_db" EMBEDDING_MODEL = "text-embedding-3-large" OPENAI_MODELS = ["gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"] @@ -94,69 +83,29 @@ QUERY_EXAMPLES = [ "Quelle est la différence entre une liste et un set ?", "Comment écrire une boucle ?", - "Comment afficher un float avec 2 chiffres avec la virgule ?" + "Comment afficher un float avec 2 chiffres avec la virgule ?", ] MSGS_QUERY_NOT_RELATED = [ - ("Je suis désolé, je ne peux pas répondre à cette question. " - "Mon domaine d'expertise est la programmation Python. " - "N'hésite pas à me poser des questions liées à ce sujet, je serai ravi de t'aider."), - ("Désolé, je suis un assistant pour l'apprentissage de la programmation Python. " - "Je ne suis pas en mesure de répondre à cette question."), - ("Je ne suis pas sûr de comprendre ta question. " - "Peux-tu la reformuler en utilisant des termes plus simples ?"), + ( + "Je suis désolé, je ne peux pas répondre à cette question. " + "Mon domaine d'expertise est la programmation Python. " + "N'hésite pas à me poser des questions liées à ce sujet, je serai ravi de t'aider." + ), + ( + "Désolé, je suis un assistant pour l'apprentissage de la programmation Python. " + "Je ne suis pas en mesure de répondre à cette question." + ), + ( + "Je ne suis pas sûr de comprendre ta question. " + "Peux-tu la reformuler en utilisant des termes plus simples ?" + ), ] -# FUNCTIONS -def get_args() -> Tuple[str, str, bool]: - """Parse the command line arguments. - - Returns - ------- - Tuple[str, str, bool] - A tuple containing the query, the model name and a flag to include metadata. - """ - 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") - - return args.query, args.model, args.include_metadata - def get_available_llm_models() -> List[str]: """List LLM models based on API keys found in the environment variables. - + Returns ------- List[str] @@ -226,7 +175,7 @@ def search_similarity_in_database( """ if logger_flag: logger.info("Searching for relevant documents in the database...") - + # Define the retriever retriever = vector_db.as_retriever( search_type="similarity_score_threshold", @@ -268,11 +217,11 @@ def format_relevant_chunks(relevant_chunks: list) -> str: 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 @@ -323,7 +272,8 @@ def format_chat_history( return chat_history -def contextualize_question(chat_history_formatted: list[Union[HumanMessage, AIMessage]] +def contextualize_question( + chat_history_formatted: list[Union[HumanMessage, AIMessage]], ) -> str: """Add context to the user query using the chat history. @@ -331,7 +281,7 @@ def contextualize_question(chat_history_formatted: list[Union[HumanMessage, AIMe ---------- chat_history_formatted : list[Union[HumanMessage, AIMessage]] The formatted chat history. - + Returns ------- chat_context : str @@ -394,7 +344,11 @@ 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 + query: str, + chat_context: str, + relevant_chunks: list, + model_name: str, + logger_flag: bool = True, ) -> str: """Generate an answer to the user query. @@ -522,9 +476,7 @@ 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 à consulter les rubriques suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n- {sources_text}" - ) + sources_string = f"Pour plus d'informations, je t'invite à consulter les rubriques suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n- {sources_text}" # Add the sources to the response response_with_metadata = f"{answer_from_model}\n\n{sources_string}" @@ -557,12 +509,32 @@ def display_answer(user_query: str, final_response: Union[str, dict]) -> None: logger.success("Results displayed successfully.") -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( + "--model", + "model_name", + type=str, + default=DEFAULT_LLM_MODEL, + show_default=True, + help="The name or identifier of the model to be used for generating responses.", +) +@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, model_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 @@ -570,7 +542,6 @@ def interrogate_model() -> None: logger.error(f"Model {model_name} is not available.") sys.exit(1) - # CONTEXT RETRIEVAL # Load the vector database vector_db = load_database(CHROMA_PATH)[0] @@ -590,7 +561,12 @@ def interrogate_model() -> None: # 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) + 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) From 81194b0779f32a299987117a84588f0e6b16630f Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 26 Jan 2026 17:17:38 +0100 Subject: [PATCH 03/61] fix: update LangChain imports to restore compatibility --- src/query_chatbot.py | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index f5c1499..d129390 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -39,13 +39,11 @@ import click import tiktoken from dotenv import load_dotenv -from langchain.prompts import ChatPromptTemplate -from langchain.schema import AIMessage, HumanMessage +from langchain.messages import AIMessage, HumanMessage from langchain_chroma import Chroma from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser -from langchain_groq import ChatGroq -from langchain_mistralai.chat_models import ChatMistralAI +from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI, OpenAIEmbeddings from loguru import logger @@ -374,11 +372,7 @@ def generate_answer( logger.info("Generating an answer to the user query...") # 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: + if model_name in OPENAI_MODELS: chat_model = ChatOpenAI(model=model_name) # Define the prompt template answer_prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) @@ -537,10 +531,10 @@ def interrogate_model(user_query: str, model_name: str, include_metadata: bool) load_dotenv() # 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) + # 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) # CONTEXT RETRIEVAL # Load the vector database From c4b528b654ca8d30a83c853915567821d94ce815 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 26 Jan 2026 18:19:57 +0100 Subject: [PATCH 04/61] refactor: move prompt to txt file in markdown format for clarity --- prompt/zero_shot.txt | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 prompt/zero_shot.txt diff --git a/prompt/zero_shot.txt b/prompt/zero_shot.txt new file mode 100644 index 0000000..176c8c9 --- /dev/null +++ b/prompt/zero_shot.txt @@ -0,0 +1,31 @@ +# Python Programming for Biology - Contextual Q&A Task + +## Rôle + +Tu es un tuteur pédagogique spécialisé en Bio-informatique. Ton objectif est d'aider des étudiants en biologie à maîtriser Python en utilisant exclusivement les ressources fournies. + +## Instructions + +1. **Source unique** : Réponds à la question en te basant **uniquement** sur le texte fourni dans la section "## DOCUMENTS DE RÉFÉRENCE (CONTEXTE)". +2. **Honnêteté intellectuelle** : Si la réponse n'est pas dans le contexte, réponds exactement : "Désolé, je suis un assistant pour l'apprentissage de la programmation Python. Je ne suis pas en mesure de répondre à cette question." +3. **Style** : Utilise un ton bienveillant et pédagogique. Vulgarise les concepts informatiques difficiles sans perdre la précision biologique. +4. **Format** : Utilise du Markdown pour la clarté (blocs de code pour Python, gras pour les termes clés). + +## DOCUMENTS DE RÉFÉRENCE (CONTEXTE) + +Voici les seules ressources que tu es autorisé à utiliser pour répondre : +<<< +{contexte} +>>> + +## TÂCHE + +En tenant compte de l'historique suivant : +{chat_history} + +Réponds de manière concise et claire à la question suivante de l'étudiant : +"{question}" + +--- + +**Réponse attendue :** \ No newline at end of file From 492c57bae0a757b0433e86ffcc50fc344f69e24a Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 26 Jan 2026 18:20:27 +0100 Subject: [PATCH 05/61] feat: add prompt_path option to CLI to query the chatbot --- src/query_chatbot.py | 52 ++++++++++++++++++++------------------------ 1 file changed, 24 insertions(+), 28 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index d129390..bb8baa7 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -7,6 +7,7 @@ Usage: ====== python src/query_chatbot.py --query "Your question here" [--model "model_name"] + [--prompt_path "prompt_path"] [--include-metadata] Arguments: @@ -15,18 +16,21 @@ Options: ======== - --model "model_name" : The name or identifier of the model to be used for generating responses. + --model (str) : The name or identifier of the model to be used for generating responses. (Default model: DEFAULT_LLM_MODEL) + --prompt_path (Path) : File path to the text file containing the prompt template. + (Default: "prompts/few_shot.txt") + --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) Example: ======== - python src/query_chatbot.py --query "D'où vient le nom Python ?" --model "gpt-4o" --include-metadata + python src/query_chatbot.py --query "D'où vient le nom Python ?" --model "gpt-4o" --prompt_path "prompts/zero_shot.txt" --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. +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 the zero_shot prompt. And it will include metadata in the response. """ @@ -34,6 +38,7 @@ import random import re import sys +from pathlib import Path from typing import List, Tuple, Union import click @@ -55,29 +60,6 @@ DEFAULT_LLM_MODEL = OPENAI_MODELS[0] OPENAI_MODEL_NAME = OPENAI_MODELS[0] -PROMPT_TEMPLATE = """ -Tu es un assistant conversationnel pour aider des étudiants en biologie à apprendre la programmation Python. -Tu dois fournir une réponse à la question posée en te basant strictement sur les extraits de cours donnés dans le contexte. -Utilise uniquement le contexte suivant pour répondre à la question. -La discussion précédente peut t'aider à comprendre le contexte de la question, -mais tu ne dois pas l'utiliser pour répondre à la question. - -Discussion précédente : -{chat_history} - -Question : -"{question}" - -Contexte : -"{contexte}" - -Répond en français à la question posée de façon claire et concise. -La réponse doit être compréhensible pour des étudiants débutants en programmation Python. -Si tu ne connais pas la réponse, 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. -""" - QUERY_EXAMPLES = [ "Quelle est la différence entre une liste et un set ?", "Comment écrire une boucle ?", @@ -346,6 +328,7 @@ def generate_answer( chat_context: str, relevant_chunks: list, model_name: str, + prompt_path: Path, logger_flag: bool = True, ) -> str: """Generate an answer to the user query. @@ -374,8 +357,11 @@ def generate_answer( # Define the model if model_name in OPENAI_MODELS: chat_model = ChatOpenAI(model=model_name) + # Retrieve the prompt template + with open(prompt_path, encoding="utf-8") as f: + prompt_template_content = f.read() # 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 @@ -519,13 +505,22 @@ def display_answer(user_query: str, final_response: Union[str, dict]) -> None: show_default=True, help="The name or identifier of the model to be used for generating responses.", ) +@click.option( + "--prompt_path", + type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True), + default="prompt/zero_shot.txt", + show_default=True, + help="File path to the text file containing the prompt template.", +) @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, model_name: str, include_metadata: bool) -> None: +def interrogate_model( + user_query: str, model_name: str, prompt_path: Path, include_metadata: bool +) -> None: """Interrogate the AI model to search for answers in a vector database.""" # Load the environment variables load_dotenv() @@ -560,6 +555,7 @@ def interrogate_model(user_query: str, model_name: str, include_metadata: bool) chat_context=None, relevant_chunks=relevant_chunks_formatted, model_name=model_name, + prompt_path=prompt_path, ) # Calculate the number of tokens in the answer logger.info("Calculating the number of tokens in the answer.") From a74c62dc50a6aa547262435df7eff8513080b432 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 26 Jan 2026 18:43:10 +0100 Subject: [PATCH 06/61] refactor: streamline document processing when creating the database. --- src/create_database.py | 42 +++++++++++++++++++----------------------- 1 file changed, 19 insertions(+), 23 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 91a9899..c8b2304 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -493,36 +493,32 @@ def generate_data_store( # Load the environment variables with LLM api keys load_dotenv() - + 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) + for doc in load_documents(data_path): + # split text of the current document into chunks + chunks = split_text(doc.page_content, chunk_size, chunk_overlap) - # 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) - # remove small chunks - chunks_cleaned = remove_small_chunks(chunks, min_nb_char=100) + # add index to the metadata + chunks_with_index = add_index_to_metadata(chunks_cleaned) - # 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_with_index) - # add number of tokens to the metadata - chunks_with_tokens = add_token_number_to_metadata(chunks_with_index) - - # add file names to the chunks - chunks_with_file_names = add_file_names_to_metadata(chunks_with_tokens, file_names) + # add file name to the chunk metadata + chunks_with_file_name = add_file_names_to_metadata( + chunks_with_tokens, [doc.metadata.get("source", "")] + ) + # add URL to the chunk metadata if available + chunks_with_url = add_url_to_metadata(chunks_with_file_name) - # add URL to the chunks - chunks_with_url = add_url_to_metadata(chunks_with_file_names) + all_chunks.extend(chunks_with_url) - # save the chunks to ChromaDB - save_to_chroma(chunks_with_url, chroma_path) + # save chunks for this document to ChromaDB + save_to_chroma(all_chunks, chroma_path) # MAIN PROGRAM From ec5235216b4d3a489c72c44009fd46ff3c20b1fd Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 12:24:33 +0100 Subject: [PATCH 07/61] chore: add pre-commit configuration and dev dependency for prek --- .pre-commit-config.yaml | 37 +++++++++++++++++++++++++++++++++++++ pyproject.toml | 5 +++++ uv.lock | 32 ++++++++++++++++++++++++++++++++ 3 files changed, 74 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..03f472c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,37 @@ +# Install pre-commit hooks with: +# prek install +exclude: "scripts/*|tmp/*|.*.mdp|" +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: v0.14.13 + 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/pyproject.toml b/pyproject.toml index 71fcd1a..10da8b0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,3 +27,8 @@ dependencies = [ "gradio>=4.43", "click>=8.3.0", ] + +[dependency-groups] 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files changed, 921 deletions(-) delete mode 100644 src/create_quiz.py delete mode 100644 src/gradio_app_battle.py delete mode 100644 src/gradio_app_quiz.py 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 (Débutant or Avancé). - -Example: -======== - python src/create_quiz.py --chapter Listes --quiz-type QCM --python-level Débutant - -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 création de quiz sur Python destiné à des étudiants. -Créer un quiz de type {quiz_type} avec 5 questions sur le chapitre suivant : {chapter} pour un étudiant de niveau {level}. -La question doit être au format markdown et doit porter sur le contenu du chapitre fourni et doit être accompagnée de réponses possibles et d'une explication détaillée pour chaque réponse. -N'hésites pas à varier les questions (ex: questions basés sur le cours, trouver l'erreur dans le code, trouver la sortie du code, compléter le code, etc.). - -Le format de sortie doit seulement etre un JSON comportant les 5 questions de quiz avec les clés 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'étudiant -- question (str): la question posée -- answers (dict): les réponses possibles numérotées par ordre alphabétique (A, B, C, D) -- correct_answer (str): la réponse correcte avec la lettre correspondante -- explanation (str): l'explication détaillée pour chaque réponse possible - -Je veux dans explanation, une explication détaillée pour chaque réponse possible de manière à ce que l'étudiant comprenne pourquoi chaque réponse est correcte ou incorrecte. -Le ton doit être pédagogique et bienveillant. - -Voici un exemple de format de sortie attendue : -{{ - "questions": [ - {{ - "chapter": "Variables", - "python_level": "Débutant", - "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 réponse correcte est B car x vaut 5 et y vaut 10, donc x + y vaut 15. Les autres réponses sont incorrectes car elles ne correspondent pas à la somme de x et y." - }}, - {{ - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Quelle structure conditionnelle est utilisée pour exécuter un bloc de code si une condition est vraie ?", - "answers": {{ - "A": "for", - "B": "while", - "C": "if", - "D": "def" - }}, - "correct_answer": "C", - "explanation": "La réponse correcte est C car la structure conditionnelle if est utilisée pour exécuter un bloc de code si une condition est vraie. Les réponses A ("for") et B ("while") sont utilisées pour les boucles afin de répéter un bloc de code plusieurs fois et la réponse D ("def") est utilisée pour définir une fonction." - }} - ] -}} - -Voici le contenu du chapitre sur lequel tu dois créer 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 ["Débutant", "Avancé"]: - logger.error(f"The Python level {args.python_level} is not valid. Please choose between 'Débutant' and 'Avancé'.") - 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_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 == "⬅️ La réponse A est meilleure": - logger.info(f"Model {model_a} vs {model_b}: {model_a}") - elif button_label == "🟰 Les deux réponses se valent": - logger.info(f"Model {model_a} vs {model_b}: Tie") - elif button_label == "➡️ La réponse B est meilleure": - logger.info(f"Model {model_a} vs {model_b}: {model_b}") - elif button_label == "👎 Les deux réponses 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 donné ton avis " - "et contribué à l'amélioration 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 réponses fournies par deux modèles de langages tirés au hasard - et vote pour la meilleure réponse. - """) - # Define Chatbots - with gr.Row(): - for i in range(NUM_MODELS): - label = "Modèle A" if i == 0 else "Modèle 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="⬅️ La réponse A est meilleure", size="sm", elem_classes="btn-upcite") - tie_btn = gr.Button(value="🟰 Les deux réponses se valent", size="sm") - bothbad_btn = gr.Button(value="👎 Les deux réponses sont mauvaises", size="sm") - rightvote_btn = gr.Button(value="➡️ La réponse 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_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("Désolé, aucun quiz n'a été trouvé pour les paramètres donnés."), - 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="Sélectionnez 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="Sélectionnez 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"## Réponse\n\nBravo ! " - f"La réponse correct est {correct_answer} \n\n" - f"{explanation_text}") - css_id = "correct-answer" - else: - message = (f"## Réponse\n\n" - f"Désolé, ce n'est pas la réponse {quiz_radio}. " - f"La bonne réponse 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("*Sélectionnez les paramètres 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 = ["Débutant", "Confirmé"] - level_choice = gr.Dropdown( - choices=level_names, - value=level_names[0], - label="Niveau de difficulté :" - ) - - quiz_type_choice = gr.Dropdown( - choices=QUIZ_TYPES, - value=QUIZ_TYPES[0], - label="Type :" - ) - create_quiz_button = gr.Button("Générer 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="Réponse 1 :", visible=False) - answer_2_md = gr.Markdown(label="Réponse 2 :", visible=False) - else: - with gr.Row(equal_height=True): - answer_1_md = gr.Markdown(label="Réponse 1 :", visible=False) - answer_2_md = gr.Markdown(label="Réponse 2 :", visible=False) - with gr.Row(equal_height=True): - answer_3_md = gr.Markdown(label="Réponse 3 :", visible=False) - answer_4_md = gr.Markdown(label="Réponse 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 From 1e9590bf5d3c2753fa89c3eb63d0536ce31cf8b1 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 13:39:21 +0100 Subject: [PATCH 09/61] feat: extend header renumbering to support level 5 headers in Markdown processing --- src/parse_clean_markdown.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 2eef2be..58e83f5 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -110,6 +110,7 @@ def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: 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 = [] @@ -118,9 +119,9 @@ 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 @@ -173,12 +174,12 @@ def process_md_files(source_dir: Path, dest_dir: Path) -> None: logger.info(f"Processing file: {source_path.name}") content = source_path.read_text(encoding="utf-8") - content = clean_python_comments(content) if source_path.name.startswith("annexe"): annex_character = source_path.stem.split("_")[1] content = renumber_headers(content, annex_character) + elif re.match(r"\d{2}_", source_path.name): chapter_number = int(source_path.stem.split("_")[0]) content = renumber_headers(content, chapter_number) From 0af9c619fde119a1657a27a482b70b2abc233d69 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 14:01:48 +0100 Subject: [PATCH 10/61] doc(parse_clean_markdown): add comments and improve docstrings --- src/parse_clean_markdown.py | 59 +++++++++++++++++++++++++++++++------ 1 file changed, 50 insertions(+), 9 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 58e83f5..6f331a9 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -1,7 +1,38 @@ """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. +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: ====== @@ -17,8 +48,9 @@ ======== python src/parse_clean_markdown.py --in data/markdown_raw --out data/markdown_processed -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 located in the 'data/markdown_raw' +directory and saves the cleaned and renumbered files to the +'data/markdown_processed' directory. """ import re @@ -58,6 +90,7 @@ 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) @@ -85,15 +118,15 @@ def clean_python_comments(content: str) -> str: return "\n".join(cleaned_content) -def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: +def renumber_headers(content: str, header_prefix: Union[int, str]) -> 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. Returns ------- @@ -104,9 +137,9 @@ 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. @@ -126,7 +159,7 @@ def renumber_headers(content: str, chapter_number: Union[int, str]) -> str: 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 @@ -168,22 +201,30 @@ def process_md_files(source_dir: Path, dest_dir: Path) -> None: """ logger.info("Processing Markdown files...") + # Create the output directory if it does not exist dest_dir.mkdir(parents=True, exist_ok=True) + # Iterate over all Markdown files in sorted order for source_path in sorted(source_dir.glob("*.md")): logger.info(f"Processing file: {source_path.name}") + # Read file content content = source_path.read_text(encoding="utf-8") + + # Remove or normalize Python-style comments content = clean_python_comments(content) + # Handle appendix files (e.g., annexe_A_*.md) if source_path.name.startswith("annexe"): annex_character = source_path.stem.split("_")[1] content = renumber_headers(content, annex_character) + # Handle numbered chapter files (e.g., 01_intro.md) elif re.match(r"\d{2}_", source_path.name): chapter_number = int(source_path.stem.split("_")[0]) content = renumber_headers(content, chapter_number) + # Write processed content to destination directory dest_path = dest_dir / source_path.name dest_path.write_text(content, encoding="utf-8") From e1e5a122239cdbcbc030f9f7365535cf3054d1b0 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 16:05:44 +0100 Subject: [PATCH 11/61] feat: enhance `create_database` script to support model and provider options for embeddings + add logs --- src/create_database.py | 254 +++++++++++++++++++---------------------- 1 file changed, 115 insertions(+), 139 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index c8b2304..9721d8a 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -5,7 +5,7 @@ Usage: ====== - python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] + python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] --model-name [model-name] --provider-name [provider-name] Arguments: ========== @@ -17,21 +17,30 @@ The size of the text chunks to be created. Default is 1000. --chunk-overlap : int (optional) The overlap between text chunks. Default is 200. + --model-name : 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". + --provider-name : 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 + python src/create_database.py --data-path data/markdown_processed --chroma-path chroma_db --model-name text-embedding-3-large --provider-name openai 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. +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. """ +import os import re import shutil import sys import unicodedata +from datetime import datetime from pathlib import Path import click @@ -47,10 +56,7 @@ ) from loguru import logger -# CONSTANTS -CHUNK_SIZE = 1000 -CHUNK_OVERLAP = 200 -EMBEDDING_MODEL = "text-embedding-3-large" +from logger import create_logger def load_documents(data_dir: str) -> list[Document]: @@ -66,80 +72,23 @@ def load_documents(data_dir: str) -> list[Document]: documents : list of Document List of Markdown documents. """ - # Load Markdown documents from the specified directory. - logger.info("Loading Markdown documents...") + logger.info(f"Loading Markdown documents from {data_dir}...") + # Load Markdown documents from the specified directory loader = DirectoryLoader( - data_dir, glob="*.md", show_progress=True, loader_cls=TextLoader + data_dir, glob="*.md", show_progress=False, loader_cls=TextLoader ) documents = loader.load() - # 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"Loaded {len(documents)} 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. - - 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]: +def split_text_into_chunks( + content: str, chunk_size: int, chunk_overlap: int +) -> list[Document]: """Split concatenated Markdown content into chunks based on headers and word limits. Parameters: @@ -157,8 +106,6 @@ def split_text(content: str, chunk_size: int, chunk_overlap: int) -> list[Docume 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"), @@ -183,13 +130,7 @@ 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 @@ -210,18 +151,13 @@ def remove_small_chunks( 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 (less than {min_nb_char} characters)." + ) return chunks_cleaned @@ -238,14 +174,10 @@ def add_index_to_metadata(chunks: list[Document]) -> list[Document]: 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 @@ -262,8 +194,6 @@ def add_token_number_to_metadata(chunks: list[Document]) -> list[Document]: 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") @@ -276,13 +206,16 @@ 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_name: str ) -> list[Document]: """Add file names to the metadata of the text chunks. @@ -290,16 +223,14 @@ def add_file_names_to_metadata( ---------- 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. + file_name : str + File name of the Markdown documents. Returns ------- chunks : list of Document List of text chunks with file names added to their metadata. """ - logger.info("Adding file names to metadata...") - # Add file names to metadata of each chunk for chunk in chunks: # Extract chapter_name from the metadata @@ -308,17 +239,14 @@ def add_file_names_to_metadata( 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") + 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 return chunks @@ -387,8 +315,6 @@ def add_url_to_metadata(chunks: list[Document]) -> list[Document]: 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: # Extract file name from the metadata @@ -410,38 +336,58 @@ 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"Added URL: {chunk.metadata['url']}") return chunks -def save_to_chroma(chunks: list[Document], chroma_output_path: Path) -> None: +def save_to_chroma( + chunks: list[Document], + model_name: str, + provider_name: str, + chroma_output_path: Path, +) -> None: """Save text chunks to ChromaDB. Parameters ---------- chunks : list[Document] List of text chunks to save to ChromaDB. + 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.info("Saving to ChromaDB...") - # Clear the existing database if it exists if chroma_output_path.exists(): shutil.rmtree(chroma_output_path) - # Create a new ChromaDB from the documents - embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL) + # Get the API key and base URL based on the provider + if provider_name == "openrouter": + api_key = os.getenv("OPENROUTER_API_KEY") + base_url = "https://openrouter.ai/api/v1" + elif provider_name == "openai": + api_key = os.getenv("OPENAI_API_KEY") + base_url = None + + # Create the embeddings for each chunks + embeddings = OpenAIEmbeddings( + model=model_name, + base_url=base_url, + api_key=api_key, + ) + + # Create a ChromaDB with the embeddings Chroma.from_documents( documents=chunks, embedding=embeddings, persist_directory=str(chroma_output_path), collection_metadata={"hnsw:space": "cosine"}, # distance metric ) - - logger.info(f"Saved {len(chunks)} chunks to {chroma_output_path}.") + logger.success(f"Saved {len(chunks)} chunks to {chroma_output_path} successfully!") @click.command() @@ -462,29 +408,46 @@ def save_to_chroma(chunks: list[Document], chroma_output_path: Path) -> None: @click.option( "-s", "--chunk-size", - default=CHUNK_SIZE, - type=int, + default=1000, + type=click.IntRange(min=1), help="Size of the text chunks to be created.", ) @click.option( "-o", "--chunk-overlap", - default=CHUNK_OVERLAP, - type=int, + default=200, + type=click.IntRange(min=0), help="Overlap between text chunks.", ) +@click.option( + "-m", + "--model-name", + default="text-embedding-3-large", + type=str, + help="Name of the embedding model, chosen from OpenRouter’s available models.", +) +@click.option( + "-p", + "--provider-name", + default="openrouter", + type=str, + help="Name of the embedding provider to use.", +) def generate_data_store( - data_path: Path, chroma_path: Path, chunk_size: int, chunk_overlap: int + data_path: Path, + chroma_path: Path, + chunk_size: int, + chunk_overlap: int, + model_name: str, + provider_name: str, ) -> None: """Generates data store by loading, splitting text into chunks, adding metadata and saving the chunks to ChromaDB.""" + # Set-up the logger + log_path = f"logs/create_database_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" + logger = create_logger(log_path) + logger.info("Creating Chroma database...") # Validate the CLI arguments - if chunk_size <= 0: - logger.error("The chunk size must be a positive integer.") - sys.exit(1) - if chunk_overlap < 0: - logger.error("The chunk overlap must be a non-negative integer.") - sys.exit(1) if chunk_overlap >= chunk_size: logger.error( f"The chunk overlap ({chunk_overlap}) must be less than the chunk size ({chunk_size})." @@ -493,11 +456,16 @@ def generate_data_store( # Load the environment variables with LLM api keys load_dotenv() + all_chunks = [] # load documents from the specified directory - for doc in load_documents(data_path): + documents = load_documents(data_path) + logger.info("Processing files...") + for doc in documents: + file_name = doc.metadata.get("source", "") + logger.info(f"File: {file_name}") # split text of the current document into chunks - chunks = split_text(doc.page_content, chunk_size, chunk_overlap) + chunks = split_text_into_chunks(doc.page_content, chunk_size, chunk_overlap) # remove small chunks chunks_cleaned = remove_small_chunks(chunks, min_nb_char=100) @@ -510,15 +478,23 @@ def generate_data_store( # add file name to the chunk metadata chunks_with_file_name = add_file_names_to_metadata( - chunks_with_tokens, [doc.metadata.get("source", "")] + chunks_with_tokens, file_name ) # add URL to the chunk metadata if available chunks_with_url = add_url_to_metadata(chunks_with_file_name) all_chunks.extend(chunks_with_url) - # save chunks for this document to ChromaDB - save_to_chroma(all_chunks, chroma_path) + logger.info("Summary:") + logger.info(f"Total number of files processed: {len(documents)}") + logger.info(f"Total number of chunks: {len(all_chunks)}") + logger.info( + f"Total number of characters: {sum(len(chunk.page_content) for chunk in all_chunks):,}" + ) + count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in all_chunks) + logger.info(f"Total number of tokens: {count_tokens:,}") + # save the embeddings to ChromaDB + save_to_chroma(all_chunks, model_name, provider_name, chroma_path) # MAIN PROGRAM From 2628212525d398bfee5870aaf1c712669ddbec76 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 16:06:03 +0100 Subject: [PATCH 12/61] feat: add logger module with configurable logging to file and stdout --- .gitignore | 1 + src/logger.py | 43 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 44 insertions(+) create mode 100644 src/logger.py diff --git a/.gitignore b/.gitignore index 03d5cda..25725f7 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ # vector database chroma_* +logs/ tmp/ .DS_Store.env 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 From a43861b4329cfb681eeb44854f82f0670460c855 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 16:10:47 +0100 Subject: [PATCH 13/61] feat: improve logging with an example of chunk metadata for each markdown file. --- src/create_database.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/create_database.py b/src/create_database.py index 9721d8a..996a255 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -463,7 +463,7 @@ def generate_data_store( logger.info("Processing files...") for doc in documents: file_name = doc.metadata.get("source", "") - logger.info(f"File: {file_name}") + 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) @@ -484,6 +484,7 @@ def generate_data_store( chunks_with_url = add_url_to_metadata(chunks_with_file_name) all_chunks.extend(chunks_with_url) + logger.info(f"Example chunk metadata: {all_chunks[-1].metadata}") logger.info("Summary:") logger.info(f"Total number of files processed: {len(documents)}") From 538a039b63de14a3c25a7fd208d8e7e35f2f7b43 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:16:21 +0100 Subject: [PATCH 14/61] refactor: remove useless constants --- src/query_chatbot.py | 86 ++++++++++++++++++++------------------------ 1 file changed, 39 insertions(+), 47 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index bb8baa7..c9058bf 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -34,7 +34,7 @@ And it will include metadata in the response. """ -import os +import datetime import random import re import sys @@ -52,19 +52,7 @@ from langchain_openai import ChatOpenAI, OpenAIEmbeddings from loguru import logger -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] - -QUERY_EXAMPLES = [ - "Quelle est la différence entre une liste et un set ?", - "Comment écrire une boucle ?", - "Comment afficher un float avec 2 chiffres avec la virgule ?", -] +from logger import create_logger MSGS_QUERY_NOT_RELATED = [ ( @@ -76,32 +64,10 @@ "Désolé, je suis un assistant pour l'apprentissage de la programmation Python. " "Je ne suis pas en mesure de répondre à cette question." ), - ( - "Je ne suis pas sûr de comprendre ta question. " - "Peux-tu la reformuler en utilisant des termes plus simples ?" - ), ] -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. - """ - 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 - - -def load_database(vector_db_path: str) -> Tuple[Chroma, int]: +def load_database(vector_db_path: str, embedding_model: str) -> Tuple[Chroma, int]: """Prepare the vector database. Returns @@ -111,7 +77,7 @@ def load_database(vector_db_path: str) -> Tuple[Chroma, int]: """ logger.info("Loading the vector database.") embedding_function = OpenAIEmbeddings( - model=EMBEDDING_MODEL + model=embedding_model ) # define the embedding model # Load the database from the specified directory vector_db = Chroma( @@ -501,10 +467,32 @@ def display_answer(user_query: str, final_response: Union[str, dict]) -> None: "--model", "model_name", type=str, - default=DEFAULT_LLM_MODEL, + default="gpt-4o", show_default=True, help="The name or identifier of the model to be used for generating responses.", ) +@click.option( + "--provider", + "provider_name", + default="openrouter", + type=str, + 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="data/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.", +) @click.option( "--prompt_path", type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True), @@ -519,21 +507,25 @@ def display_answer(user_query: str, final_response: Union[str, dict]) -> None: help="Include metadata in the response if this flag is provided.", ) def interrogate_model( - user_query: str, model_name: str, prompt_path: Path, include_metadata: bool + user_query: str, + model_name: str, + provider_name: str, + database_path: str, + embedding_model: str, + prompt_path: Path, + include_metadata: bool, ) -> None: """Interrogate the AI model to search for answers in a vector database.""" + # Set-up the logger + log_path = f"logs/query_chatbot_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" + logger = create_logger(log_path) + # Load the environment variables load_dotenv() - # 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) - # CONTEXT RETRIEVAL # Load the vector database - vector_db = load_database(CHROMA_PATH)[0] + vector_db = load_database(database_path, embedding_model)[0] # Search for relevant documents in the database relevant_chunks = search_similarity_in_database(vector_db, user_query) From c43ff0064943a89087f1729d9ce0617d53dac947 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:19:14 +0100 Subject: [PATCH 15/61] docs: fix logs --- src/query_chatbot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index c9058bf..019bb8f 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -244,7 +244,7 @@ def contextualize_question( chat_context += f"Réponse {i // 2 + 1}: {message.content}\n" logger.info(f"Chat context: {chat_context}") - logger.success("Contextualized query constructed successfully.\n") + logger.success("Contextualized query constructed successfully.") return chat_context From d57545263c0e0bd6c23088b90d218ad08250bb6e Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:20:34 +0100 Subject: [PATCH 16/61] chore: update pre-commit --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 03f472c..52451ee 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ # Install pre-commit hooks with: # prek install -exclude: "scripts/*|tmp/*|.*.mdp|" +exclude: "|tmp/*|" repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 From bb76583ae5f2f46930daa0a969a52fc6118456e0 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:21:28 +0100 Subject: [PATCH 17/61] docs: update log --- src/query_chatbot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 019bb8f..5065d81 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -214,7 +214,7 @@ def format_chat_history( else: # if chat history is empty logger.info("Chat history is empty.") - logger.success("Chat history formatted successfully with 0 entries.\n") + logger.success("Chat history formatted successfully with 0 entries.") return chat_history From 79ac42a6ffefbf7e4d2afee4621d831a0e1978cf Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:26:54 +0100 Subject: [PATCH 18/61] chore: uodate dependencies --- pyproject.toml | 7 +- ruff.toml | 58 +++++++++++++++ uv.lock | 194 +------------------------------------------------ 3 files changed, 63 insertions(+), 196 deletions(-) create mode 100644 ruff.toml diff --git a/pyproject.toml b/pyproject.toml index 10da8b0..40eff7f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,12 +7,6 @@ requires-python = "==3.13.*" dependencies = [ "python-dotenv", - "pycodestyle", - "pydocstyle", - "pylint", - "black", - "ruff", - "mypy", "chromadb", "openai", "tiktoken", @@ -31,4 +25,5 @@ dependencies = [ [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/uv.lock b/uv.lock index deafe4c..05002e2 100644 --- a/uv.lock +++ b/uv.lock @@ -112,15 +112,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" }, ] -[[package]] -name = "astroid" -version = "4.0.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/c17d0f83016532a1ad87d1de96837164c99d47a3b6bbba28bd597c25b37a/astroid-4.0.3.tar.gz", hash = "sha256:08d1de40d251cc3dc4a7a12726721d475ac189e4e583d596ece7422bc176bda3", size = 406224, upload-time = "2026-01-03T22:14:26.096Z" } -wheels = [ - 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{ url = "https://files.pythonhosted.org/packages/c8/78/3565d011c61f5a43488987ee32b6f3f656e7f107ac2782dd57bdd7d91d9a/snowballstemmer-3.0.1-py3-none-any.whl", hash = "sha256:6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064", size = 103274, upload-time = "2025-05-09T16:34:50.371Z" }, -] - [[package]] name = "sqlalchemy" version = "2.0.46" From 809c54d9249885515dd3a4ad3a909abcf2f5be9b Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 17:27:46 +0100 Subject: [PATCH 19/61] docs: update logs --- src/create_database.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 996a255..38fc218 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -91,8 +91,8 @@ def split_text_into_chunks( ) -> list[Document]: """Split concatenated Markdown content into chunks based on headers and word limits. - Parameters: - ----------- + Parameters + ---------- content : str Concatenated Markdown content to be split into chunks. chunk_size : int @@ -100,8 +100,8 @@ def split_text_into_chunks( chunk_overlap : int The overlap between text chunks. - Returns: - -------- + Returns + ------- chunks : list of Document List of text chunks after splitting with content and metadata. format : [{"page_content": str, "metadata": dict}, ...] @@ -239,12 +239,12 @@ def add_file_names_to_metadata( 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 - 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]: + if ( + chapter_number + and file_name.startswith(f"{chapter_number.group(0).strip().zfill(2)}_") + ) or appendix_letter.group(0) == file_name.split("_")[ + 1 + ]: # zfill(2) to pad with zeros chunk.metadata["file_name"] = file_name break From f1505c6ed284ab617b59b885679e561d2628895e Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 27 Jan 2026 19:18:08 +0100 Subject: [PATCH 20/61] docs: improve documentation formatting with ruff --- src/create_database.py | 53 ++++++++++++++++---------- src/parse_clean_markdown.py | 15 ++++---- src/query_chatbot.py | 75 +++++++++++++++++++++---------------- 3 files changed, 85 insertions(+), 58 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 38fc218..3298ae3 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -1,11 +1,14 @@ """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] --model-name [model-name] --provider-name [provider-name] + python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] + --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] + --model-name [model-name] --provider-name [provider-name] Arguments: ========== @@ -22,17 +25,23 @@ Possible choices : https://openrouter.ai/models?fmt=cards&supported_parameters=structured_outputs&output_modalities=embeddings Default is "text-embedding-3-large". --provider-name : str (optional) - Name of the embedding provider to use. Possible choices are "openrouter" and "openai". + 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 --model-name text-embedding-3-large --provider-name openai - -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 and will be embedded with the model `text-embedding-3-large`. -And finally the vector database will be saved to the `chroma_db` directory. + python src/create_database.py --data-path data/markdown_processed \ + --chroma-path chroma_db \ + --model-name text-embedding-3-large \ + --provider-name openai + +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 and will be embedded with the model +`text-embedding-3-large`. And finally the vector database will be saved to the +`chroma_db` directory. """ import os @@ -60,7 +69,7 @@ def load_documents(data_dir: str) -> list[Document]: - """Load Markdown documents, concatenate their content, and extract the name of the Markdown files. + """Load Markdown files, concatenate their content, and extract filenames. Parameters ---------- @@ -156,7 +165,8 @@ def remove_small_chunks( chunk for chunk in chunks if len(chunk.page_content) >= min_nb_char ] logger.info( - f"Removed {len(chunks) - len(chunks_cleaned)} small chunks (less than {min_nb_char} characters)." + f"Removed {len(chunks) - len(chunks_cleaned)} small chunks \ + (less than {min_nb_char} characters)." ) return chunks_cleaned @@ -207,7 +217,8 @@ def add_token_number_to_metadata(chunks: list[Document]) -> list[Document]: chunk.metadata["nb_tokens"] = nb_tokens logger.info( - f"Total number of characters: {sum(len(chunk.page_content) for chunk in chunks):,}" + 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:,}") @@ -251,7 +262,7 @@ def add_file_names_to_metadata( return chunks -def preprocess_for_url(text: str, is_subsubsection: bool = False) -> str: +def preprocess_for_url(text: str, *, is_subsubsection: bool = False) -> str: """Preprocess text for creating URL. Parameters @@ -323,10 +334,12 @@ def add_url_to_metadata(chunks: list[Document]) -> list[Document]: # 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_subsection=True ) elif chunk.metadata.get("subsection_name", ""): # subsection - section_id = preprocess_for_url(chunk.metadata.get("subsection_name", "")) + section_id = preprocess_for_url( + chunk.metadata.get("subsection_name", ""), is_subsection=True + ) elif chunk.metadata.get("section_name", ""): # section section_id = preprocess_for_url(chunk.metadata.get("section_name", "")) else: # chapter @@ -424,7 +437,7 @@ def save_to_chroma( "--model-name", default="text-embedding-3-large", type=str, - help="Name of the embedding model, chosen from OpenRouter’s available models.", + help="Name of the embedding model, chosen from OpenRouter`s available models.", ) @click.option( "-p", @@ -441,7 +454,7 @@ def generate_data_store( model_name: str, provider_name: str, ) -> None: - """Generates data store by loading, splitting text into chunks, adding metadata and saving the chunks to ChromaDB.""" + """Build a ChromaDB store from chunked text with metadata.""" # Set-up the logger log_path = f"logs/create_database_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" logger = create_logger(log_path) @@ -450,7 +463,8 @@ def generate_data_store( # Validate the CLI arguments if chunk_overlap >= chunk_size: logger.error( - f"The chunk overlap ({chunk_overlap}) must be less than the chunk size ({chunk_size})." + f"The chunk overlap ({chunk_overlap}) must be less than \ + the chunk size ({chunk_size})." ) sys.exit(1) @@ -490,7 +504,8 @@ def generate_data_store( logger.info(f"Total number of files processed: {len(documents)}") logger.info(f"Total number of chunks: {len(all_chunks)}") logger.info( - f"Total number of characters: {sum(len(chunk.page_content) for chunk in all_chunks):,}" + f"Total number of characters: { + sum(len(chunk.page_content) for chunk in all_chunks):,}" ) count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in all_chunks) logger.info(f"Total number of tokens: {count_tokens:,}") diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 6f331a9..383003f 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -28,10 +28,10 @@ Output: ```python - #Votre premier commentaire en Python. + # Votre premier commentaire en Python. print("Hello world!") - #D'autres commandes plus utiles pourraient suivre. + # D'autres commandes plus utiles pourraient suivre. ``` Usage: @@ -46,7 +46,8 @@ Example: ======== - python src/parse_clean_markdown.py --in data/markdown_raw --out data/markdown_processed + python src/parse_clean_markdown.py --in data/markdown_raw \ + --out data/markdown_processed This command processes Markdown files located in the 'data/markdown_raw' directory and saves the cleaned and renumbered files to the @@ -55,7 +56,6 @@ import re from pathlib import Path -from typing import Union import click from loguru import logger @@ -70,7 +70,7 @@ def clean_python_comments(content: str) -> str: ``` will be converted to: ```python - #This is a comment. + # This is a comment. ``` Parameters @@ -81,7 +81,8 @@ def clean_python_comments(content: str) -> str: 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 = [] @@ -118,7 +119,7 @@ def clean_python_comments(content: str) -> str: return "\n".join(cleaned_content) -def renumber_headers(content: str, header_prefix: Union[int, str]) -> str: +def renumber_headers(content: str, header_prefix: int | str) -> str: """Renumber headers in Markdown content. Parameters diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 5065d81..654f8b6 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -1,14 +1,15 @@ """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"] - [--prompt_path "prompt_path"] - [--include-metadata] + [--prompt_path "prompt_path"] + [--include-metadata] Arguments: ========== @@ -16,30 +17,34 @@ Options: ======== - --model (str) : The name or identifier of the model to be used for generating responses. - (Default model: DEFAULT_LLM_MODEL) + --model (str): + The name of the model to be used for generating responses. + Default: "gpt-4o" - --prompt_path (Path) : File path to the text file containing the prompt template. - (Default: "prompts/few_shot.txt") + --prompt_path (Path): + File path to the text file containing the prompt template. + Default: "prompts/zero_shot.txt" - --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) + --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'où vient le nom Python ?" --model "gpt-4o" --prompt_path "prompts/zero_shot.txt" --include-metadata + python src/query_chatbot.py --query "D'où vient le nom Python ?" --model "gpt-4o" \ + --prompt_path "prompts/zero_shot.txt" --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 the zero_shot prompt. +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 the zero_shot prompt. And it will include metadata in the response. """ import datetime -import random import re +import secrets import sys from pathlib import Path -from typing import List, Tuple, Union import click import tiktoken @@ -58,7 +63,8 @@ ( "Je suis désolé, je ne peux pas répondre à cette question. " "Mon domaine d'expertise est la programmation Python. " - "N'hésite pas à me poser des questions liées à ce sujet, je serai ravi de t'aider." + "N'hésite pas à me poser des questions liées à ce sujet," + "je serai ravi de t'aider." ), ( "Désolé, je suis un assistant pour l'apprentissage de la programmation Python. " @@ -67,7 +73,7 @@ ] -def load_database(vector_db_path: str, embedding_model: str) -> Tuple[Chroma, int]: +def load_database(vector_db_path: str, embedding_model: str) -> tuple[Chroma, int]: """Prepare the vector database. Returns @@ -97,8 +103,9 @@ def search_similarity_in_database( user_query: str, nb_chunks: int = 3, score_threshold: float = 0.35, + *, logger_flag: bool = True, -) -> List[Document]: +) -> list[Document]: """Search for relevant documents in the database based on the query text. Parameters @@ -170,8 +177,8 @@ def format_relevant_chunks(relevant_chunks: list) -> str: def format_chat_history( - chat_history: list[Tuple[str, str]] = [], len_history: int = 10 -) -> List[Union[HumanMessage, AIMessage]]: + chat_history: list[tuple[str, str]], len_history: int = 10 +) -> list[HumanMessage | AIMessage]: """Format the chat history for the promt template. Parameters @@ -196,7 +203,7 @@ def format_chat_history( 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: + if human is None: formatted_history.append(HumanMessage(content=human)) else: formatted_history.append(HumanMessage(content="")) @@ -208,7 +215,8 @@ def format_chat_history( logger.info(f"AI message (cleaned): {cleaned_ai}") logger.success( - f"Chat history formatted successfully with {len(formatted_history)} entries.\n" + f"Chat history formatted successfully with {len(formatted_history)} \ + entries.\n" ) return formatted_history @@ -219,7 +227,7 @@ def format_chat_history( def contextualize_question( - chat_history_formatted: list[Union[HumanMessage, AIMessage]], + chat_history_formatted: list[HumanMessage | AIMessage], ) -> str: """Add context to the user query using the chat history. @@ -295,6 +303,7 @@ def generate_answer( relevant_chunks: list, model_name: str, prompt_path: Path, + *, logger_flag: bool = True, ) -> str: """Generate an answer to the user query. @@ -321,11 +330,10 @@ def generate_answer( logger.info("Generating an answer to the user query...") # Define the model - if model_name in OPENAI_MODELS: + if model_name.startwith("gpt"): chat_model = ChatOpenAI(model=model_name) # Retrieve the prompt template - with open(prompt_path, encoding="utf-8") as f: - prompt_template_content = f.read() + prompt_template_content = Path(prompt_path).read_text(encoding="utf-8") # Define the prompt template answer_prompt = ChatPromptTemplate.from_template(prompt_template_content) # Define the chained prompt @@ -351,13 +359,13 @@ def generate_answer( def add_metadata_to_answer( - answer_from_model, metadatas: list[dict], iu: bool = False + answer_from_model, metadatas: list[dict], *, 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. @@ -422,7 +430,9 @@ 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 à consulter les rubriques suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n- {sources_text}" + sources_string = f"Pour plus d'informations, je t'invite à consulter les rubriques \ + suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n \ + - {sources_text}" # Add the sources to the response response_with_metadata = f"{answer_from_model}\n\n{sources_string}" @@ -433,7 +443,7 @@ def add_metadata_to_answer( return response_with_metadata -def display_answer(user_query: str, final_response: Union[str, dict]) -> None: +def display_answer(user_query: str, final_response: str | dict) -> None: """Display the results. Parameters @@ -513,6 +523,7 @@ def interrogate_model( database_path: str, embedding_model: str, prompt_path: Path, + *, include_metadata: bool, ) -> None: """Interrogate the AI model to search for answers in a vector database.""" @@ -533,7 +544,7 @@ def interrogate_model( # 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) + response = secrets.choice(MSGS_QUERY_NOT_RELATED) print(response) sys.exit(0) else: From 7203587ee9309cea9be5a889a00cfbe346ad96c8 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 2 Feb 2026 03:22:38 +0100 Subject: [PATCH 21/61] chore: adding dependency --- pyproject.toml | 3 +++ uv.lock | 21 +++++++++++++++++++++ 2 files changed, 24 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 40eff7f..f2ffcfc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,9 @@ dependencies = [ "streamlit", "gradio>=4.43", "click>=8.3.0", + "htbuilder>=0.9.0", + "pydantic-settings>=2.12.0", + "pyperclip>=1.11.0", ] [dependency-groups] diff --git a/uv.lock b/uv.lock index 05002e2..122c0ba 100644 --- a/uv.lock +++ b/uv.lock @@ -231,6 +231,7 @@ dependencies = [ { name = "chromadb" }, { name = "click" }, { name = "gradio" }, + { name = "htbuilder" }, { name = "langchain" }, { name = "langchain-chroma" }, { name = "langchain-community" }, @@ -239,6 +240,8 @@ dependencies = [ { name = "langchain-text-splitters" }, { name = "loguru" }, { name = "openai" }, + { name = "pydantic-settings" }, + { name = "pyperclip" }, { name = "python-dotenv" }, { name = "streamlit" }, { name = "tiktoken" }, @@ -255,6 +258,7 @@ requires-dist = [ { name = "chromadb" }, { name = "click", specifier = ">=8.3.0" }, { name = "gradio", specifier = ">=4.43" }, + { name = "htbuilder", specifier = ">=0.9.0" }, { name = "langchain" }, { name = "langchain-chroma" }, { name = "langchain-community" }, @@ -263,6 +267,8 @@ requires-dist = [ { name = "langchain-text-splitters" }, { name = "loguru" }, { name = "openai" }, + { name = "pydantic-settings", specifier = ">=2.12.0" }, + { name = "pyperclip", specifier = ">=1.11.0" }, { name = "python-dotenv" }, { name = "streamlit" }, { name = "tiktoken" }, @@ -724,6 +730,12 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/cb/44/870d44b30e1dcfb6a65932e3e1506c103a8a5aea9103c337e7a53180322c/hf_xet-1.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:e6584a52253f72c9f52f9e549d5895ca7a471608495c4ecaa6cc73dba2b24d69", size = 2905735, upload-time = "2025-10-24T19:04:35.928Z" }, ] +[[package]] +name = "htbuilder" +version = "0.9.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/be/88/0f48d1125168e9eea33879ea820000702533d103f1123d27ca69839a0db4/htbuilder-0.9.0.tar.gz", hash = "sha256:58c0bc5502c1a46b42ae9e074c43ec0f6fdc24ed334936cb17e1ed5a8938aee2", size = 10591, upload-time = "2025-01-09T07:00:26.189Z" } + [[package]] name = "httpcore" version = "1.0.9" @@ -1842,6 +1854,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, ] +[[package]] +name = "pyperclip" +version = "1.11.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e8/52/d87eba7cb129b81563019d1679026e7a112ef76855d6159d24754dbd2a51/pyperclip-1.11.0.tar.gz", hash = "sha256:244035963e4428530d9e3a6101a1ef97209c6825edab1567beac148ccc1db1b6", size = 12185, upload-time = "2025-09-26T14:40:37.245Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/df/80/fc9d01d5ed37ba4c42ca2b55b4339ae6e200b456be3a1aaddf4a9fa99b8c/pyperclip-1.11.0-py3-none-any.whl", hash = "sha256:299403e9ff44581cb9ba2ffeed69c7aa96a008622ad0c46cb575ca75b5b84273", size = 11063, upload-time = "2025-09-26T14:40:36.069Z" }, +] + [[package]] name = "pypika" version = "0.50.0" From 93b2087541bf1950826ae9a8a29cd4d452f38705 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 2 Feb 2026 03:24:04 +0100 Subject: [PATCH 22/61] feat: adding pydantic model settings to configure CLI of BioPyAssistant App --- config_app.toml | 11 ++++ src/model_config_app.py | 140 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 151 insertions(+) create mode 100644 config_app.toml create mode 100644 src/model_config_app.py diff --git a/config_app.toml b/config_app.toml new file mode 100644 index 0000000..5508f44 --- /dev/null +++ b/config_app.toml @@ -0,0 +1,11 @@ +# config.default.toml +app_name = "BioPyAssistant" +app_description = "BioPyAssistant is a teaching assistant for Patrick Fuchs and Pierre Poulain's Python programming course for biologists." +app_version = "2.0" +css_path = "assets/style.css" +vector_database_path = "chroma_db" + +[llm] +provider_name = "openrouter" +llm_model_name= "gpt-4o" +embedding_model_name = "text-embedding-3-large" diff --git a/src/model_config_app.py b/src/model_config_app.py new file mode 100644 index 0000000..d38310d --- /dev/null +++ b/src/model_config_app.py @@ -0,0 +1,140 @@ +"""Pydantic configuration models used to validate the CLI argument of BioPyAssistant App.""" + +import os +from datetime import datetime +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, DirectoryPath, Field, FilePath, SecretStr +from pydantic_settings import ( + BaseSettings, + PydanticBaseSettingsSource, + SettingsConfigDict, + TomlConfigSettingsSource, +) + + +class LLMConfig(BaseModel): + """Configuration model for Large Language Model (LLM) parameters.""" + + provider_name: Literal["openai", "openrouter"] = Field( + ..., description="Name of the LLM provider, must be 'openai' or 'openrouter'." + ) + llm_model_name: str = Field( + ..., description="Default LLM model name to use if none is provided." + ) + embedding_model_name: str = Field( + ..., + description="Name of the embedding model to use for vector representations.", + ) + api_key_openai: SecretStr | None = Field( + None, + description="Secret API key for authenticating on openai provider.", + env="OPENAI_API_KEY", + ) + api_key_openrouter: SecretStr | None = Field( + None, + description="Secret API key for authenticating on openrouter provider.", + env="OPENROUTER_API_KEY", + ) + + @property + def api_key(self) -> SecretStr: + """ + Return the correct API key based on the selected LLM provider. + + The returned key depends on the value of `self.provider`: + - If `provider` is "openai", returns the value of `api_key_openai`. + - If `provider` is "openrouter", returns the value of `api_key_openrouter`. + + Returns + ------- + SecretStr + The API key corresponding to the currently selected provider. + """ + if self.provider_name == "openai": + # if self.api_key_openai is None: + # msg = "OPENAI_API_KEY not set" + # raise ValueError(msg) + return self.api_key_openai or SecretStr(os.getenv("OPENAI_API_KEY")) + elif self.provider_name == "openrouter": + # print(self.api_key_openrouter) + # print(os.getenv("OPENROUTER_API_KEY")) + # if self.api_key_openrouter is None: + # msg = "OPENROUTER_API_KEY not set" + # raise ValueError(msg) + return self.api_key_openrouter or SecretStr(os.getenv("OPENROUTER_API_KEY")) + + +class Settings(BaseSettings, cli_parse_args=True): + """Global BioPyAssistant streamlit application settings.""" + + # Basic application info + app_name: str = Field( + "BioPyAssistant", description="Application name displayed in the UI or logs." + ) + app_description: str | None = Field( + None, description="Short app description displayed in the logs." + ) + app_version: str = Field("2.0", description="Current version of the application.") + # Paths + css_path: FilePath = Field( + ..., description="Path to the CSS file for Streamlit styling." + ) + vector_database_path: DirectoryPath = Field( + ..., description="Path to the directory where the vector database is stored." + ) + # Nested configuration for LLM-related parameters + llm: LLMConfig = Field( + ..., description="Configuration for Large Language Model parameters." + ) + + # Compute the log path + @property + def log_path(self) -> Path: + """Return the full path for today's log file.""" + log_file = Path("logs") / f"biopyassistant_app_{datetime.now():%Y-%m-%d}.log" + log_file.parent.mkdir(parents=True, exist_ok=True) + return log_file + + # Load settings from this TOML file + model_config = SettingsConfigDict(toml_file=["config_app.toml"]) + + # Reorder the priority of different settings sources + @classmethod + def settings_customise_sources( + cls, + settings_cls: type[BaseSettings], + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ) -> tuple[PydanticBaseSettingsSource, ...]: + """ + Customize the order and type of settings sources. + + Parameters + ---------- + settings_cls : type[BaseSettings] + The settings class being instantiated. Used by sources that + need access to the settings schema. + init_settings : PydanticBaseSettingsSource + Source providing values passed directly to the Settings + constructor. + env_settings : PydanticBaseSettingsSource + Source providing values from environment variables. + + Returns + ------- + tuple[PydanticBaseSettingsSource, ...] + Ordered tuple of settings sources. Earlier sources have higher + priority during value resolution. + """ + return ( + # Load settings from TOML files first + TomlConfigSettingsSource(settings_cls), + # Override with explicit constructor arguments + init_settings, + # Finally allow environment variables to override everything + env_settings, + ) From 27702ce3770e55200238a662e62b2974fbbbbe95 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 2 Feb 2026 03:25:53 +0100 Subject: [PATCH 23/61] refactor: fix the add of file_name in chunk metadata --- src/create_database.py | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 3298ae3..e4aced4 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -244,25 +244,11 @@ def add_file_names_to_metadata( """ # Add file names to metadata of 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 - if ( - chapter_number - and file_name.startswith(f"{chapter_number.group(0).strip().zfill(2)}_") - ) or appendix_letter.group(0) == file_name.split("_")[ - 1 - ]: # zfill(2) to pad with zeros - chunk.metadata["file_name"] = file_name - break - + chunk.metadata["file_name"] = file_name return chunks -def preprocess_for_url(text: str, *, is_subsubsection: bool = False) -> str: +def preprocess_for_url(text: str, *, is_subsection: bool = False) -> str: """Preprocess text for creating URL. Parameters @@ -304,7 +290,7 @@ 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_subsection: processed_text = re.sub(r"^[a-zA-Z]?\d+-?", "", processed_text) # Add a '#' at the beginning From 0a7fa9b5a8b6e07c91026415950f05daab5d41ce Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 2 Feb 2026 03:27:18 +0100 Subject: [PATCH 24/61] chore: fix some docstrings and misspelling --- src/query_chatbot.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 654f8b6..0c0862c 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -81,7 +81,7 @@ def load_database(vector_db_path: str, embedding_model: str) -> tuple[Chroma, in Chroma: The prepared vector database. int: The number of chunks in the database. """ - logger.info("Loading the vector database.") + logger.info("Loading the vector database...") embedding_function = OpenAIEmbeddings( model=embedding_model ) # define the embedding model @@ -330,7 +330,7 @@ def generate_answer( logger.info("Generating an answer to the user query...") # Define the model - if model_name.startwith("gpt"): + if model_name.startswith("gpt"): chat_model = ChatOpenAI(model=model_name) # Retrieve the prompt template prompt_template_content = Path(prompt_path).read_text(encoding="utf-8") From eee5c7bc0a55cc31860e6e3cb0744628c5a6f9e5 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 2 Feb 2026 03:28:49 +0100 Subject: [PATCH 25/61] [WIP] - feat: adding the new version of streamlit IU --- .streamlit/config.toml | 10 + assets/style.css | 91 ++++++ src/streamlit_app_v2.py | 645 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 746 insertions(+) create mode 100644 .streamlit/config.toml create mode 100644 assets/style.css create mode 100644 src/streamlit_app_v2.py diff --git a/.streamlit/config.toml b/.streamlit/config.toml new file mode 100644 index 0000000..37a26ab --- /dev/null +++ b/.streamlit/config.toml @@ -0,0 +1,10 @@ +[theme] +base = "light" +primaryColor = "#8a1538" +linkColor = "#8a1538" + +[theme.sidebar] +baseRadius="full" +buttonRadius ="full" +primaryColor ="#8a1538" +linkColor = "#8a1538" diff --git a/assets/style.css b/assets/style.css new file mode 100644 index 0000000..645a792 --- /dev/null +++ b/assets/style.css @@ -0,0 +1,91 @@ +@import url("https://fonts.googleapis.com/css2?family=Rubik+Iso:wght@400;700&display=swap"); + +.stApp { + bottom: 77px; + zoom: 1.3; +} + +a { + font-weight: 500; + opacity: 0.85; +} +a:hover { + opacity: 1; + font-weight: 700; +} + +.app-title { + text-align: center; + color: #8a1538; + font-family: "Rubik Iso", sans-serif; + font-size: 70px; + margin-bottom: 0; +} + +.app-subtitle { + text-align: center; + color: #555555; + font-size: 16px; + margin-top: 5px; +} + +.sidebar-title { + font-weight: 700; + font-size: 1.5rem; + margin-top: 1.2rem; + margin-bottom: 0.4rem; +} + +.sidebar-about { + font-size: 1rem; + color: #444; + margin-top: 2rem; + text-align: center; +} + +.sidebar-icons { + display: flex; + justify-content: center; + gap: 1.2rem; + margin-top: 0.8rem; +} + +.sidebar-icons img { + width: 38px; + height: 38px; + opacity: 0.85; + transition: opacity 0.2s ease-in-out; +} + +.sidebar-icons img:hover { + opacity: 1; + transform: scale(1.1); +} + +#MainMenu { + visibility: hidden; +} + +footer { + visibility: hidden; +} + +.app-footer { + position: fixed; + left: 0; + bottom: 0; + width: 100%; + padding: 4px 0; + color: #555555; + font-size: 13px; + font-style: italic; + text-align: center; + opacity: 1; + background-color: transparent; +} + +.app-footer hr { + display: block; + border-width: 1px; + margin: 10px; +} \ No newline at end of file diff --git a/src/streamlit_app_v2.py b/src/streamlit_app_v2.py new file mode 100644 index 0000000..d7d89c2 --- /dev/null +++ b/src/streamlit_app_v2.py @@ -0,0 +1,645 @@ +"""Streamlit app for chatbot testing.""" + +import random +import sys +import time +from datetime import datetime, timedelta +from pathlib import Path + +import loguru +import pyperclip +import streamlit as st +from dotenv import load_dotenv +from htbuilder import a, br, div, hr, img +from langchain_community.vectorstores import Chroma +from loguru import logger + +from model_config_app import Settings +from query_chatbot import ( + MSGS_QUERY_NOT_RELATED, + add_metadata_to_answer, + contextualize_question, + format_chat_history, + generate_answer, + get_metadata, + load_database, + search_similarity_in_database, +) + +BASE_DIR = Path(__file__).resolve().parent +PROJECT_ROOT = BASE_DIR.parent +CSS_PATH = PROJECT_ROOT / "assets" / "style.css" +MIN_TIME_BETWEEN_REQUESTS = timedelta(seconds=2) + +SUGGESTIONS = { + ":green[:material/loop:] Comment écrire une boucle ?": "Comment écrire une boucle ?", + ":blue[:material/list_alt:] Quelle est la différence entre une liste et un set ?": ( + "Quelle est la différence entre une liste et un set ?" + ), + ":orange[:material/decimal_increase:] Comment afficher un float avec 2 chiffres avec la virgule ?": ( + "Comment afficher un float avec 2 chiffres avec la virgule ?" + ), +} + + +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.opt(colors=True).add( + logpath, format=logger_format, level="DEBUG", mode="w" + ) + # Add logger to stdout. + logger.add(sys.stdout, format=logger_format, level=level) + return logger.opt(colors=True) + + +def apply_custom_css(css_file: Path) -> None: + """Load and injects custom CSS into the Streamlit app.""" + if css_file.exists(): + css = css_file.read_text() + st.markdown(f"", unsafe_allow_html=True) + else: + logger.warning(f"CSS file not found at {css_file}") + + +@st.cache_resource +def get_vector_db(vector_db: Path, embedding_model_name: str) -> Chroma: + """Cache the vector database to prevent reloading on every rerun. + + Returns + ------- + Chroma: The vector database containing the embedded course. + """ + vector_db, _nb_chunks = load_database(vector_db, embedding_model_name) + return vector_db + + +def create_header(app_name: str) -> None: + """Render the application header and subtitle.""" + st.markdown( + f'
{app_name}
', + unsafe_allow_html=True, + ) + + st.markdown( + """ +
+ BioPyAssistant est un assistant pédagogique pour le cours de + + programmation Python + + pour les biologistes de Patrick Fuchs et Pierre Poulain. +
+ """, + unsafe_allow_html=True, + ) + + +def create_sidebar() -> dict[str, str]: + """Render the Streamlit sidebar and collect user profile settings. + + Returns + ------- + dict[str, str] + Dictionary containing the user-selected profile information with + the following keys: + + - ``"cursus"`` : Academic track selected by the user + (e.g., "Licence", "Master"). + - ``"level"`` : User's python level + (e.g., "Débutant", "Intermédiaire", "Avancé"). + """ + with st.sidebar: + # Institutional logos + st.logo( + "https://u-paris.fr/wp-content/uploads/2022/03/UniversiteParisCite_logo_horizontal_couleur_RVB.png", + size="large", + icon_image="https://lvts.fr/wp-content/uploads/2022/03/UniversiteParis_monogramme_couleur_RVB-e1712425218876.png", + ) + st.space("large") + # Student profile + st.markdown( + '
🎓 Profil étudiant
', + unsafe_allow_html=True, + ) + st.info( + "Indiquez votre cursus et votre niveau pour des réponses adaptées.", + icon="🎯", + ) + cursus = st.pills("Cursus :material/school:", options=["Licence", "Master"]) + level = st.pills( + "Niveau :material/sort:", options=["Débutant", "Intermédiaire", "Avancé"] + ) + st.space(150) + # About section + st.markdown( + """ +
+ BioPyAssistant
+ Développé par + + + Essmay Touami + + + et + + + Pierre Poulain + + +
+ dans le cadre du projet pédagogique
+ + + LLM@UPCité + + .

+ Code source disponible sur GitHub
+ sous licence BSD 3-clause. +
+ """, + unsafe_allow_html=True, + ) + + # + st.markdown( + """ +
+ + GitHub + + + Python + +
+ """, + unsafe_allow_html=True, + ) + + return {"cursus": cursus, "level": level} + + +@st.dialog("💡 Guide d'utilisation responsable") +def show_disclaimer_dialog() -> None: + """Display a dialog outlining responsible usage guidelines for the application.""" + st.caption(""" + ### 🧠 Gardez la main sur votre réflexion + L'IA est un assistant, pas un expert infaillible. + Le **copier-coller direct est déconseillé** : + utilisez les réponses comme une base de travail que vous devez valider et enrichir + par votre esprit critique. + + ### 🛡️ Protégez votre vie privée + Ce service utilise des modèles externes. + **Ne partagez jamais de données personnelles**, + confidentielles ou sensibles dans vos échanges. + + ### 📜 Aller plus loin + Pour adopter les bonnes pratiques, consultez la [Charte d'utilisation](#). + """) + + +def image(src: str): + return img(src=src) + + +def link(url: str, text: str): + return a(text, href=url, target="_blank") + + +def layout(*args) -> None: + body = div() + + for arg in args: + body(arg) + + footer = div(cls="app-footer")( + hr(), + body, + ) + + st.markdown(str(footer), unsafe_allow_html=True) + + +def create_footer() -> None: + """Render the application footer with legal information.""" + layout( + "BioPyAssistant a été développé par ", + link("https://www.linkedin.com/in/essmay-touami/", "Essmay Touami "), + "et ", + link("https://www.linkedin.com/in/pierrepo/", "Pierre Poulain "), + "dans le cadre du projet pédagogique ", + link( + "https://u-paris.fr/aap-innovation-pedagogique-2023-decouvrez-les-projets-laureats/", + "LLM@UPCité ", + ), + ". Le code source est disponible sur ", + link("https://github.com/pierrepo/biopyassistant", "GitHub "), + "sous licence BSD 3-clause.", + br(), + "Cette application web n'utilise pas de cookie. Les résultats des votes sont " + "collectés anonymement à des fins de recherche.", + br(), + link( + "https://u-paris.fr/politique-de-confidentialite/", + "Mentions légales.", + ), + ) + + +def send_telemetry(**kwargs) -> None: + """ + Record telemetry data related to user interactions with the chatbot. + + This function collects and logs usage metrics to help monitor + application performance and user engagement. The telemetry may include, + but is not limited to: + + - Questions submitted by users. + - Number of requests per session. + - Response time of the LLM. + - Error rates or failed requests. + - Clicks on suggested prompts or examples. + - User feedback on responses (e.g., thumbs up / thumbs down). + + Parameters + ---------- + **kwargs : dict + Arbitrary keyword arguments containing additional context or metadata + for telemetry, such as the question text, response, timestamps, or + user identifiers. + """ + pass + + +def show_feedback_controls(message_index: int, assistant_msg: str) -> None: + """Display compact feedback controls for an assistant message. + + Parameters + ---------- + message_index : int + Index of the message in `st.session_state.messages` for which + the feedback is being collected. + """ + with st.container(): + option_map = { + "is_copied": ":material/content_copy:", + "has_voted_up": ":material/thumb_up:", + "has_voted_down": ":material/thumb_down:", + "has_report_msg": ":material/report:", + } + selection = st.pills( + "assistant message", + label_visibility="hidden", + options=option_map.keys(), + format_func=lambda option: option_map[option], + selection_mode="single", + ) + relevant_history = st.session_state.messages[: message_index + 1] + if selection: + if selection == "is_copied": + pyperclip.copy(assistant_msg) + st.toast("Réponse copiée !", icon="📋", duration="short") + + elif selection == "has_voted_up" or selection == "has_voted_down": + vote_type = "👍" if selection == "has_voted_up" else "👎" + logger.warning(f"Feedback {vote_type}") + logger.warning(f"Message: {assistant_msg}") + logger.debug(f"Context: {relevant_history}") + st.toast("Merci pour votre vote !", icon="✅", duration="short") + + elif selection == "has_report_msg": + with st.form(key=f"report-form-{message_index}", clear_on_submit=True): + details = st.text_area( + "Signaler / Ajouter un commentaire (optionnel)", + height=60, + ) + if st.form_submit_button("Envoyer"): + logger.critical("Report 🚨") + logger.critical(f"Commentaire: {details or 'Aucun'}") + logger.critical(f"Message: {assistant_msg}") + logger.debug(f"Context: {relevant_history}") + st.toast( + "Merci pour votre signalement !", icon="⚠️", duration="short" + ) + + +def transform_messages(messages: list[dict[str, str]]) -> list[tuple[str, str]]: + """ + Convert Streamlit message history into a list of (User, Assistant) tuples. + + Args: + messages: List of message dictionaries from st.session_state. + + Returns + ------- + A list of tuples compatible with LangChain history formats. + """ + history_tuples = [] + temp_user_msg = None + + for msg in messages: + if msg["role"] == "user": + temp_user_msg = msg["content"] + elif msg["role"] == "assistant" and temp_user_msg is not None: + history_tuples.append((temp_user_msg, msg["content"])) + temp_user_msg = None + + return history_tuples + + +def generate_response(user_query: str, vector_db: Chroma) -> str: + """Generate a response to the user question. + + Parameters + ---------- + user_query : str + The user question. + vector_db: + + Returns + ------- + response : str + The response predicted by the model. + """ + # Transform the chat history into a list of tuples + chat_history = transform_messages(st.session_state.messages) + # 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=user_query) + + # 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: + # Get the metadata of the relevant documents + metadata = get_metadata(context) + # Generate the answer + answer = generate_answer( + query=user_query, + chat_context=chat_context, + relevant_chunks=context, + model_name="gpt-4o", + prompt_path="prompt/zero_shot.txt", + ) + # Add metadata to the answer + final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) + + logger.info(f"Response generated: {final_answer}") + logger.success("Response generated successfully.\n") + + return final_answer + + +def stream_text(text: str): + """Simulate a typing effect in the UI.""" + for token in text: + yield token + time.sleep(0.01) + + +def clear_conversation(): + st.session_state.messages = [] + st.session_state.initial_question = None + st.session_state.selected_suggestion = None + + +def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: + """Handle the main chat interface with the user. + + Parameters + ---------- + vector_db : Chroma + Vector database used to retrieve context for the LLM responses. + student_infos : dict[str, str] + Dictionary containing the student's profile information: + - "cursus" : Academic track selected by the user (e.g., "Licence", "Master"). + - "level" : User's Python or academic proficiency level + (e.g., "Débutant", "Intermédiaire", "Avancé"). + """ + # Check if the student has provided both cursus and level + has_student_infos = (student_infos["cursus"] and student_infos["level"]) is not None + user_just_asked_initial_question = ( + "initial_question" in st.session_state and st.session_state.initial_question + ) + print(user_just_asked_initial_question) + # Determine if this is the first user interaction + user_just_clicked_suggestion = ( + "selected_suggestion" in st.session_state + and st.session_state.selected_suggestion + ) + user_first_interaction = ( + user_just_asked_initial_question or user_just_clicked_suggestion + ) + # If first interaction but no student info, show a toast and block sending + if user_first_interaction and not has_student_infos: + st.toast( + "Veuillez renseigner votre cursus et votre niveau pour continuer.", + icon="⚠️", + duration="short", + ) + user_first_interaction = False + + # Check if there is already message histor + has_message_history = ( + "messages" in st.session_state and len(st.session_state.messages) > 0 + ) + # --- Initial State (No conversation yet) --- + if not user_first_interaction and not has_message_history: + st.session_state.messages = [] + + # Display welcome container with chat input and suggestions + with st.container(): + st.chat_input( + "Pose moi une question sur le cours...", key="initial_question" + ) + st.pills( + label="Examples", + label_visibility="collapsed", + options=SUGGESTIONS, + key="selected_suggestion", + ) + # Show disclaimer for ethical use + st.button( + ( + " :small[:gray[:material/chat_error: Les réponses générées " + " peuvent être incorrectes ou incomplètes, gardez toujours un esprit" + " critique !]]" + ), + type="tertiary", + on_click=show_disclaimer_dialog, + ) + + # Stop execution so the chat doesn't process until a question is asked + st.stop() + + # --- First step of the conversation --- + # Show chat input at the bottom when a question has been asked. + user_message = st.chat_input("Posez une question complémentaire...") + if not user_message: + if user_just_asked_initial_question: + user_message = st.session_state.initial_question + if user_just_clicked_suggestion: + user_message = SUGGESTIONS[st.session_state.selected_suggestion] + + st.button( + "Restart", + icon=":material/refresh:", + on_click=clear_conversation, + ) + if "prev_question_timestamp" not in st.session_state: + st.session_state.prev_question_timestamp = datetime.fromtimestamp(0) + + # --- Active Chat History --- + # Display chat messages from history as speech bubbles. + for i, message in enumerate(st.session_state.messages): + with st.chat_message(message["role"]): + if message["role"] == "assistant": + st.container() # Fix ghost message bug. + + st.markdown(message["content"]) + if message["role"] == "assistant": + show_feedback_controls(i, message["content"]) + + # When the user posts a message... + if user_message: + # Streamlit's Markdown engine interprets "$" as LaTeX code (used to + # display math). The line below fixes it. + user_message = user_message.replace("$", r"\$") + + # Display message as a speech bubble. + with st.chat_message("user"): + st.text(user_message) + + # Display assistant response as a speech bubble. + with st.chat_message("assistant"): + with st.spinner("Waiting..."): + # Rate-limit the input if needed. + question_timestamp = datetime.now() + time_diff = ( + question_timestamp - st.session_state.prev_question_timestamp + ) + st.session_state.prev_question_timestamp = question_timestamp + + if time_diff < MIN_TIME_BETWEEN_REQUESTS: + time.sleep(time_diff.seconds + time_diff.microseconds * 0.001) + + user_message = user_message.replace("'", "") + + # Build a detailed prompt. + # if DEBUG_MODE: + # with st.status("Computing prompt...") as status: + # full_prompt = build_question_prompt(user_message) + # st.code(full_prompt) + # status.update(label="Prompt computed") + # else: + # with st.spinner("Researching..."): + # full_prompt = build_question_prompt(user_message) + + # Send prompt to LLM. + with st.spinner("En réflexion..."): + response = generate_response(user_message, vector_db) + rep_stream = stream_text(response) + + # Put everything after the spinners in a container to fix the + # ghost message bug. + with st.container(): + # Stream the LLM response. + response = st.write_stream(rep_stream) + + # Add messages to chat history. + st.session_state.messages.append( + {"role": "user", "content": user_message} + ) + st.session_state.messages.append( + {"role": "assistant", "content": response} + ) + + # Other stuff. + show_feedback_controls(len(st.session_state.messages) - 1, response) + send_telemetry(question=user_message, response=response) + + +def main(): + """Run the BioPyAssistant Streamlit app.""" + # Load environment variables + load_dotenv() + # Initialize settings from `config_app.toml` + settings = Settings() + # Configure logger once + if "welcome_logged" not in st.session_state and not settings.log_path.exists(): + logger = create_logger(settings.log_path) + logger.info( + f"👋 Welcome to BioPyAssistant v{settings.app_version}!" + ) + if settings.app_description: + logger.info(f"{settings.app_description}\n") + st.session_state["welcome_logged"] = True + + # Set the page configuration + st.set_page_config( + page_title=settings.app_name, page_icon="data/img/logo_round.webp" + ) + # Apply the css style + apply_custom_css(settings.css_path) + + # Create the header + create_header(settings.app_name) + + # Create a sidebar and get the user inputs + student_infos = create_sidebar() + + # Create footer + # create_footer() + + # Load the vector database one + vector_db = get_vector_db( + settings.vector_database_path, settings.llm.embedding_model_name + ) + + # Chat with the bot + chat_with_bot(vector_db, student_infos) + + +if __name__ == "__main__": + main() From 5e9c73e3983588a7dea20ad19a40e3f44fa2cc90 Mon Sep 17 00:00:00 2001 From: essmaw Date: Fri, 6 Feb 2026 17:19:47 +0100 Subject: [PATCH 26/61] feat: adding prompt path to the setting model. --- config_app.toml | 1 + src/model_config_app.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/config_app.toml b/config_app.toml index 5508f44..c382e8c 100644 --- a/config_app.toml +++ b/config_app.toml @@ -9,3 +9,4 @@ vector_database_path = "chroma_db" provider_name = "openrouter" llm_model_name= "gpt-4o" embedding_model_name = "text-embedding-3-large" +prompt_path = "prompt/zero_shot.txt" diff --git a/src/model_config_app.py b/src/model_config_app.py index d38310d..c88cca8 100644 --- a/src/model_config_app.py +++ b/src/model_config_app.py @@ -27,6 +27,10 @@ class LLMConfig(BaseModel): ..., description="Name of the embedding model to use for vector representations.", ) + prompt_path: FilePath = Field( + ..., + description="Path to the prompt template file used for generating responses.", + ) api_key_openai: SecretStr | None = Field( None, description="Secret API key for authenticating on openai provider.", From b1faabd595abda958e3836a9bca4d0c8873f8800 Mon Sep 17 00:00:00 2001 From: essmaw Date: Fri, 6 Feb 2026 17:23:06 +0100 Subject: [PATCH 27/61] feat: simplify footer, adding the llm and prompt settings and improve a little bit logging in chatbot application. --- assets/style.css | 24 +++-- src/query_chatbot.py | 31 +++--- src/streamlit_app_v2.py | 219 ++++++++++++++++++---------------------- 3 files changed, 132 insertions(+), 142 deletions(-) diff --git a/assets/style.css b/assets/style.css index 645a792..49bc021 100644 --- a/assets/style.css +++ b/assets/style.css @@ -1,8 +1,7 @@ @import url("https://fonts.googleapis.com/css2?family=Rubik+Iso:wght@400;700&display=swap"); .stApp { - bottom: 77px; - zoom: 1.3; + zoom: 1.1; } a { @@ -53,7 +52,7 @@ a:hover { .sidebar-icons img { width: 38px; height: 38px; - opacity: 0.85; + opacity: 0.9; transition: opacity 0.2s ease-in-out; } @@ -70,14 +69,16 @@ footer { visibility: hidden; } -.app-footer { +.app-footer, +.app-footer * { position: fixed; left: 0; bottom: 0; width: 100%; - padding: 4px 0; + padding: 4px 10px; color: #555555; - font-size: 13px; + font-size: 15px; + line-height: 1.; font-style: italic; text-align: center; opacity: 1; @@ -88,4 +89,15 @@ footer { display: block; border-width: 1px; margin: 10px; +} + +.app-footer a { + color: #8a1538; + font-weight: 500; + text-decoration: none; +} + +.app-footer a:hover { + opacity: 1; + font-weight: 700; } \ No newline at end of file diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 0c0862c..0ad8fd4 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -47,6 +47,7 @@ from pathlib import Path import click +import loguru import tiktoken from dotenv import load_dotenv from langchain.messages import AIMessage, HumanMessage @@ -73,7 +74,9 @@ ] -def load_database(vector_db_path: str, embedding_model: str) -> tuple[Chroma, int]: +def load_database( + vector_db_path: str, embedding_model: str, logger: "loguru.Logger" = loguru.logger +) -> tuple[Chroma, int]: """Prepare the vector database. Returns @@ -92,9 +95,7 @@ def load_database(vector_db_path: str, embedding_model: str) -> tuple[Chroma, in # 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 @@ -214,15 +215,15 @@ def format_chat_history( 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" - ) + # 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.") + # logger.info("Chat history is empty.") + # logger.success("Chat history formatted successfully with 0 entries.") return chat_history @@ -251,8 +252,8 @@ def contextualize_question( elif isinstance(message, AIMessage): chat_context += f"Réponse {i // 2 + 1}: {message.content}\n" - logger.info(f"Chat context: {chat_context}") - logger.success("Contextualized query constructed successfully.") + # logger.info(f"Chat context: {chat_context}") + # logger.success("Contextualized query constructed successfully.") return chat_context @@ -270,9 +271,9 @@ def get_metadata(relevant_chunks: list) -> list[dict]: metadatas : list List of metadata dictionaries for the top matching documents. """ - logger.info("Extracting metadata of 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") + # logger.success("Metadata extracted successfully.\n") return metadatas @@ -437,8 +438,8 @@ def add_metadata_to_answer( # 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") + # logger.info(f"Answer with metadata: {response_with_metadata}") + # logger.success("Metadata added to the response successfully.\n") return response_with_metadata diff --git a/src/streamlit_app_v2.py b/src/streamlit_app_v2.py index d7d89c2..9819149 100644 --- a/src/streamlit_app_v2.py +++ b/src/streamlit_app_v2.py @@ -1,16 +1,15 @@ """Streamlit app for chatbot testing.""" -import random +import secrets import sys import time -from datetime import datetime, timedelta +from datetime import timedelta from pathlib import Path import loguru import pyperclip import streamlit as st from dotenv import load_dotenv -from htbuilder import a, br, div, hr, img from langchain_community.vectorstores import Chroma from loguru import logger @@ -90,14 +89,16 @@ def apply_custom_css(css_file: Path) -> None: @st.cache_resource -def get_vector_db(vector_db: Path, embedding_model_name: str) -> Chroma: +def get_vector_db( + vector_db: Path, embedding_model_name: str, logger: "loguru.Logger" = loguru.logger +) -> Chroma: """Cache the vector database to prevent reloading on every rerun. Returns ------- Chroma: The vector database containing the embedded course. """ - vector_db, _nb_chunks = load_database(vector_db, embedding_model_name) + vector_db, _nb_chunks = load_database(vector_db, embedding_model_name, logger) return vector_db @@ -115,11 +116,13 @@ def create_header(app_name: str) -> None: programmation Python +
pour les biologistes de Patrick Fuchs et Pierre Poulain. """, unsafe_allow_html=True, ) + st.space(30) def create_sidebar() -> dict[str, str]: @@ -149,21 +152,21 @@ def create_sidebar() -> dict[str, str]: '
🎓 Profil étudiant
', unsafe_allow_html=True, ) - st.info( - "Indiquez votre cursus et votre niveau pour des réponses adaptées.", - icon="🎯", - ) cursus = st.pills("Cursus :material/school:", options=["Licence", "Master"]) level = st.pills( "Niveau :material/sort:", options=["Débutant", "Intermédiaire", "Avancé"] ) - st.space(150) + st.space(20) + st.info( + "Indiquez votre cursus et votre niveau pour des réponses adaptées.", + icon="🎯", + ) + st.space(250) # About section st.markdown( """
- BioPyAssistant
- Développé par + BioPyAssistant a été développé par @@ -232,55 +235,26 @@ def show_disclaimer_dialog() -> None: """) -def image(src: str): - return img(src=src) - - -def link(url: str, text: str): - return a(text, href=url, target="_blank") - - -def layout(*args) -> None: - body = div() - - for arg in args: - body(arg) - - footer = div(cls="app-footer")( - hr(), - body, - ) - - st.markdown(str(footer), unsafe_allow_html=True) - - def create_footer() -> None: """Render the application footer with legal information.""" - layout( - "BioPyAssistant a été développé par ", - link("https://www.linkedin.com/in/essmay-touami/", "Essmay Touami "), - "et ", - link("https://www.linkedin.com/in/pierrepo/", "Pierre Poulain "), - "dans le cadre du projet pédagogique ", - link( - "https://u-paris.fr/aap-innovation-pedagogique-2023-decouvrez-les-projets-laureats/", - "LLM@UPCité ", - ), - ". Le code source est disponible sur ", - link("https://github.com/pierrepo/biopyassistant", "GitHub "), - "sous licence BSD 3-clause.", - br(), - "Cette application web n'utilise pas de cookie. Les résultats des votes sont " - "collectés anonymement à des fins de recherche.", - br(), - link( - "https://u-paris.fr/politique-de-confidentialite/", - "Mentions légales.", - ), + st.html( + """ +
+

+ Cette application web n'utilise pas de cookie. Les résultats des votes sont collectés anonymement à des fins de recherche. +
+ + Mentions légales. + +

+
+ """ ) -def send_telemetry(**kwargs) -> None: +def send_telemetry( + **kwargs, +) -> None: # TODO: ask pierre what telemetry to log and how to log it """ Record telemetry data related to user interactions with the chatbot. @@ -313,6 +287,9 @@ def show_feedback_controls(message_index: int, assistant_msg: str) -> None: message_index : int Index of the message in `st.session_state.messages` for which the feedback is being collected. + assistant_msg : str + The content of the assistant message to which the feedback controls + are attached. """ with st.container(): option_map = { @@ -357,6 +334,34 @@ def show_feedback_controls(message_index: int, assistant_msg: str) -> None: ) +def display_welcome_chat() -> None: + """Display the initial welcome chat message and suggestions.""" + st.session_state.messages = [] + + # Display welcome container with chat input and suggestions + with st.container(): + st.chat_input("Pose moi une question sur le cours...", key="initial_question") + st.pills( + label="Examples", + label_visibility="collapsed", + options=SUGGESTIONS, + key="selected_suggestion", + ) + # Show disclaimer for ethical use + st.button( + ( + " :small[:gray[:material/chat_error: Les réponses générées " + " peuvent être incorrectes ou incomplètes, gardez toujours un esprit" + " critique !]]" + ), + type="tertiary", + on_click=show_disclaimer_dialog, + ) + + # Stop execution so the chat doesn't process until a question is asked + st.stop() + + def transform_messages(messages: list[dict[str, str]]) -> list[tuple[str, str]]: """ Convert Streamlit message history into a list of (User, Assistant) tuples. @@ -381,14 +386,21 @@ def transform_messages(messages: list[dict[str, str]]) -> list[tuple[str, str]]: return history_tuples -def generate_response(user_query: str, vector_db: Chroma) -> str: +def generate_response( + user_query: str, context: str, model_name: str, prompt_path: str +) -> str: """Generate a response to the user question. Parameters ---------- user_query : str The user question. - vector_db: + context : str + The relevant context retrieved from the vector database. + model_name : str + The name of the LLM to use for generating the response. + prompt_path : str + Path to the prompt template to use for generating the response. Returns ------- @@ -401,15 +413,13 @@ def generate_response(user_query: str, vector_db: Chroma) -> str: 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=user_query) # 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) + response = secrets.choice(MSGS_QUERY_NOT_RELATED) return response else: # Get the metadata of the relevant documents @@ -419,14 +429,15 @@ def generate_response(user_query: str, vector_db: Chroma) -> str: query=user_query, chat_context=chat_context, relevant_chunks=context, - model_name="gpt-4o", - prompt_path="prompt/zero_shot.txt", + model_name=model_name, + prompt_path=prompt_path, + logger_flag=False, ) # Add metadata to the answer final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) - logger.info(f"Response generated: {final_answer}") - logger.success("Response generated successfully.\n") + # logger.info(f"Response generated: {final_answer}") + # logger.success("Response generated successfully.\n") return final_answer @@ -444,7 +455,9 @@ def clear_conversation(): st.session_state.selected_suggestion = None -def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: +def chat_with_bot( + vector_db: Chroma, student_infos: dict, model_name: str, prompt_path: str +) -> None: """Handle the main chat interface with the user. Parameters @@ -462,7 +475,6 @@ def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: user_just_asked_initial_question = ( "initial_question" in st.session_state and st.session_state.initial_question ) - print(user_just_asked_initial_question) # Determine if this is the first user interaction user_just_clicked_suggestion = ( "selected_suggestion" in st.session_state @@ -486,32 +498,7 @@ def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: ) # --- Initial State (No conversation yet) --- if not user_first_interaction and not has_message_history: - st.session_state.messages = [] - - # Display welcome container with chat input and suggestions - with st.container(): - st.chat_input( - "Pose moi une question sur le cours...", key="initial_question" - ) - st.pills( - label="Examples", - label_visibility="collapsed", - options=SUGGESTIONS, - key="selected_suggestion", - ) - # Show disclaimer for ethical use - st.button( - ( - " :small[:gray[:material/chat_error: Les réponses générées " - " peuvent être incorrectes ou incomplètes, gardez toujours un esprit" - " critique !]]" - ), - type="tertiary", - on_click=show_disclaimer_dialog, - ) - - # Stop execution so the chat doesn't process until a question is asked - st.stop() + display_welcome_chat() # --- First step of the conversation --- # Show chat input at the bottom when a question has been asked. @@ -527,8 +514,6 @@ def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: icon=":material/refresh:", on_click=clear_conversation, ) - if "prev_question_timestamp" not in st.session_state: - st.session_state.prev_question_timestamp = datetime.fromtimestamp(0) # --- Active Chat History --- # Display chat messages from history as speech bubbles. @@ -553,32 +538,20 @@ def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: # Display assistant response as a speech bubble. with st.chat_message("assistant"): - with st.spinner("Waiting..."): - # Rate-limit the input if needed. - question_timestamp = datetime.now() - time_diff = ( - question_timestamp - st.session_state.prev_question_timestamp + # Search for relevant context in the vector database. + with st.spinner("En recherche de contexte..."): + context = search_similarity_in_database( + vector_db=vector_db, + user_query=user_message, + # user_level=student_infos["level"], + # user_cursus=student_infos["cursus"], + logger_flag=False, ) - st.session_state.prev_question_timestamp = question_timestamp - if time_diff < MIN_TIME_BETWEEN_REQUESTS: - time.sleep(time_diff.seconds + time_diff.microseconds * 0.001) - - user_message = user_message.replace("'", "") - - # Build a detailed prompt. - # if DEBUG_MODE: - # with st.status("Computing prompt...") as status: - # full_prompt = build_question_prompt(user_message) - # st.code(full_prompt) - # status.update(label="Prompt computed") - # else: - # with st.spinner("Researching..."): - # full_prompt = build_question_prompt(user_message) - - # Send prompt to LLM. with st.spinner("En réflexion..."): - response = generate_response(user_message, vector_db) + response = generate_response( + user_message, context, model_name, prompt_path + ) rep_stream = stream_text(response) # Put everything after the spinners in a container to fix the @@ -595,8 +568,10 @@ def chat_with_bot(vector_db: Chroma, student_infos: dict) -> None: {"role": "assistant", "content": response} ) - # Other stuff. + # Show feedback controls (copy, thumbs up/down, report) show_feedback_controls(len(st.session_state.messages) - 1, response) + # Send telemetry data for logging and analysis + # (to be implemented in the future) send_telemetry(question=user_message, response=response) @@ -630,7 +605,7 @@ def main(): student_infos = create_sidebar() # Create footer - # create_footer() + create_footer() # Load the vector database one vector_db = get_vector_db( @@ -638,7 +613,9 @@ def main(): ) # Chat with the bot - chat_with_bot(vector_db, student_infos) + chat_with_bot( + vector_db, student_infos, settings.llm.llm_model_name, settings.llm.prompt_path + ) if __name__ == "__main__": From 83e5eaa3d774182aca952a6f774b8354ffefb46a Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 01:33:52 +0100 Subject: [PATCH 28/61] feat: create YAML file listing all Python chapters and levels --- data/chapters_and_levels.yaml | 235 ++++++++++++++++++++++++++++++++++ 1 file changed, 235 insertions(+) create mode 100644 data/chapters_and_levels.yaml diff --git a/data/chapters_and_levels.yaml b/data/chapters_and_levels.yaml new file mode 100644 index 0000000..793b349 --- /dev/null +++ b/data/chapters_and_levels.yaml @@ -0,0 +1,235 @@ +# chapters_and_levels.yaml +# Python course structure for the application. + +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 + source_file_path: data/markdown_raw/01_introduction.md + processed_file_path: data/markdown_processed/01_introduction.md + + - id: 02 + title: Variables + source_file_path: data/markdown_raw/02_variables.md + processed_file_path: data/markdown_processed/02_variables.md + + - id: 03 + title: Affichage + source_file_path: data/markdown_raw/03_affichage.md + processed_file_path: data/markdown_processed/03_affichage.md + + - id: 04 + title: Listes + source_file_path: data/markdown_raw/04_listes.md + processed_file_path: data/markdown_processed/04_listes.md + + - id: 05 + title: Boucles et comparaisons + source_file_path: data/markdown_raw/05_boucles_comparaisons.md + processed_file_path: data/markdown_processed/05_boucles_comparaisons.md + + - id: 06 + title: Tests + source_file_path: data/markdown_raw/06_tests.md + processed_file_path: data/markdown_processed/06_tests.md + + - id: 07 + title: Fichiers + source_file_path: data/markdown_raw/07_fichiers.md + processed_file_path: data/markdown_processed/07_fichiers.md + + - id: 08 + title: Dictionnaires et tuples + source_file_path: data/markdown_raw/08_dictionnaires_tuples.md + processed_file_path: data/markdown_processed/08_dictionnaires_tuples.md + + - id: 9 + title: Modules + source_file_path: data/markdown_raw/09_modules.md + processed_file_path: data/markdown_processed/09_modules.md + + - id: 10 + title: Fonctions + source_file_path: data/markdown_raw/10_fonctions.md + processed_file_path: data/markdown_processed/10_fonctions.md + + - id: 11 + title: Plus sur les chaînes de caractères + source_file_path: data/markdown_raw/11_plus_sur_les_chaines_de_caracteres.md + processed_file_path: data/markdown_processed/11_plus_sur_les_chaines_de_caracteres.md + + - id: 12 + title: Plus sur les listes + source_file_path: data/markdown_raw/12_plus_sur_les_listes.md + processed_file_path: data/markdown_processed/12_plus_sur_les_listes.md + + - id: 13 + title: Plus sur les fonctions + source_file_path: data/markdown_raw/13_plus_sur_les_fonctions.md + processed_file_path: data/markdown_processed/13_plus_sur_les_fonctions.md + + - id: 14 + title: Conteneurs + source_file_path: data/markdown_raw/14_conteneurs.md + processed_file_path: data/markdown_processed/14_conteneurs.md + + - id: 15 + title: Création de modules + source_file_path: data/markdown_raw/15_creation_modules.md + processed_file_path: data/markdown_processed/15_creation_de_modules.md + + - id: 16 + title: Bonnes pratiques en programmation Python + source_file_path: data/markdown_raw/16_bonnes_pratiques.md + processed_file_path: data/markdown_processed/16_bonnes_pratiques.md + + - id: 17 + title: Expressions régulières et parsing + source_file_path: data/markdown_raw/17_expressions_regulieres.md + processed_file_path: data/markdown_processed/17_expressions_regulieres.md + + - id: 18 + title: Jupyter et ses notebooks + source_file_path: data/markdown_raw/18_jupyter.md + processed_file_path: data/markdown_processed/18_jupyter.md + + - id: 19 + title: Module Biopython + source_file_path: data/markdown_raw/19_module_biopython.md + processed_file_path: data/markdown_processed/19_module_biopython.md + + - id: 20 + title: Module Numpy + source_file_path: data/markdown_raw/20_module_numpy.md + processed_file_path: data/markdown_processed/20_module_numpy.md + + - id: 21 + title: Module Matplotlib + source_file_path: data/markdown_raw/21_module_matplotlib.md + processed_file_path: data/markdown_processed/21_module_matplotlib.md + + - id: 22 + title: Module Pandas + source_file_path: data/markdown_raw/22_module_pandas.md + processed_file_path: data/markdown_processed/22_module_pandas.md + + - id: 23 + title: Avoir la classe avec les objets + source_file_path: data/markdown_raw/23_avoir_la_classe_avec_les_objets.md + processed_file_path: data/markdown_processed/23_avoir_la_classe_avec_les_objets.md + + - id: 24 + title: Avoir plus la classe avec les objets + source_file_path: data/markdown_raw/24_avoir_plus_la_classe_avec_les_objets.md + processed_file_path: data/markdown_processed/24_avoir_plus_la_classe_avec_les_objets.md + + - id: 25 + title: Fenêtres graphiques et Tkinter + source_file_path: data/markdown_raw/25_tkinter.md + processed_file_path: data/markdown_processed/25_tkinter.md + + - id: 26 + title: Remarques complémentaires + source_file_path: data/markdown_raw/26_remarques_complementaires.md + processed_file_path: data/markdown_processed/26_remarques_complementaires.md + + - id: 27 + title: Mini-projets + source_file_path: data/markdown_raw/27_mini_projets.md + processed_file_path: data/markdown_processed/27_mini_projets.md + + - id: A + title: Quelques formats de données en biologies + source_file_path: data/markdown_raw/annexe_A_formats_fichiers.md + processed_file_path: data/markdown_processed/annexe_A_formats_fichiers.md + + - id: B + title: Installation de Python + source_file_path: data/markdown_raw/annexe_B_install_python.md + processed_file_path: data/markdown_processed/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: debutant + display_name: "Débutant (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. + + chapters: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 11 + - 12 + + - name: intermediaire + display_name: "Intermédiaire (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. + + chapters: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - B + + - name: avance + display_name: "Avancé (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. + + chapters: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - 17 + - 18 + - 19 + - 20 + - 21 + - 22 + - 23 + - 24 + - 27 + - B From cb0b263265b1125528ec49669db108d19cb11e13 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 01:34:57 +0100 Subject: [PATCH 29/61] refactor(parse_clean_markdown): load chapters and output paths from YAML instead of CLI directories --- src/parse_clean_markdown.py | 180 ++++++++++++++++++++++++------------ 1 file changed, 122 insertions(+), 58 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 383003f..0588169 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -36,32 +36,66 @@ Usage: ====== - python src/parse_clean_markdown.py --in source_dir --out dest_dir + python src/parse_clean_markdown.py --config path/to/chapters_and_levels.yaml Where: - source_dir : Path - The source directory containing Markdown files to be processed. - dest_dir : Path - 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 + python src/parse_clean_markdown.py --config data/chapters_and_levels.yaml -This command processes Markdown files located in the 'data/markdown_raw' -directory and saves the cleaned and renumbered 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. """ import re +from datetime import datetime from pathlib import Path import click -from loguru import logger +import loguru +import yaml +from logger import create_logger -def clean_python_comments(content: str) -> str: + +def load_chapters_from_yaml( + yaml_path: Path, logger: "loguru.Logger" = loguru.logger +) -> list[dict]: + """Load chapters and levels from a YAML file. + + 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, each containing chapter information. + """ + logger.info(f"Loading chapters from YAML file: {yaml_path}...") + try: + with yaml_path.open("r", encoding="utf-8") as file: + data = yaml.safe_load(file) + chapters = data.get("chapters", []) + logger.info(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: @@ -77,6 +111,8 @@ def clean_python_comments(content: str) -> str: ---------- content : str Content of Markdown file. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- @@ -114,12 +150,14 @@ 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("Cleaning comments in Python code blocks complete.") + logger.success("Python code block comments cleaned.") return "\n".join(cleaned_content) -def renumber_headers(content: str, header_prefix: int | str) -> str: +def renumber_headers( + content: str, header_prefix: int | str, logger: "loguru.Logger" = loguru.logger +) -> str: """Renumber headers in Markdown content. Parameters @@ -128,6 +166,8 @@ def renumber_headers(content: str, header_prefix: int | str) -> str: The Markdown content to renumber. header_prefix : Union[int, str] The chapter number or appendix letter to use for renumbering headers. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- @@ -177,59 +217,83 @@ def renumber_headers(content: str, header_prefix: int | str) -> str: @click.command() @click.option( - "--in", - "source_dir", - required=True, - type=click.Path(exists=True, file_okay=False, path_type=Path), - help="Source directory containing Markdown files.", -) -@click.option( - "--out", - "dest_dir", + "--config", + "yaml_path", required=True, - type=click.Path(file_okay=False, path_type=Path), - help="Destination directory to save processed files.", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help="Path to the YAML file defining chapters and levels.", ) -def process_md_files(source_dir: Path, dest_dir: Path) -> None: - """Process Markdown files in the source directory and save them to the destination directory. +def process_md_files(yaml_path: Path) -> None: + """Process Markdown files listed in a YAML file and save them. Parameters ---------- - source_dir : Path - The source directory containing Markdown files. - dest_dir : Path - 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...") - - # Create the output directory if it does not exist - dest_dir.mkdir(parents=True, exist_ok=True) - - # Iterate over all Markdown files in sorted order - for source_path in sorted(source_dir.glob("*.md")): + # 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) + + # 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_file = chapter.get("source_file_path") + if not source_file: + logger.warning(f"No source file defined for chapter {chapter_name}") + continue + # Check if source file exists + source_path = Path(source_file) + if not source_path.exists(): + logger.warning(f"Markdown file not found: {source_path}") + continue logger.info(f"Processing file: {source_path.name}") - - # Read file content + # Read content content = source_path.read_text(encoding="utf-8") - - # Remove or normalize Python-style comments - content = clean_python_comments(content) - - # Handle appendix files (e.g., annexe_A_*.md) - if source_path.name.startswith("annexe"): - annex_character = source_path.stem.split("_")[1] - content = renumber_headers(content, annex_character) - - # Handle numbered chapter files (e.g., 01_intro.md) + # Clean Python-style comments + content = clean_python_comments(content, logger) + # Renumber headers + # 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(source_path.stem.split("_")[0]) - content = renumber_headers(content, chapter_number) - - # Write processed content to destination directory - dest_path = dest_dir / source_path.name - dest_path.write_text(content, encoding="utf-8") - - logger.success("Markdown files processed successfully") + chapter_number = int(stem.split("_")[0]) + content = renumber_headers(content, chapter_number, logger) + + # Get destination file path from YAML + dest_path = Path(chapter.get("processed_file_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 From 6a04d817e0b58ed7b0259eda3f47ac641c8987dc Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:16:54 +0100 Subject: [PATCH 30/61] docs: change log level to success for chapter loading message --- src/parse_clean_markdown.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 0588169..7e15192 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -85,7 +85,7 @@ def load_chapters_from_yaml( with yaml_path.open("r", encoding="utf-8") as file: data = yaml.safe_load(file) chapters = data.get("chapters", []) - logger.info(f"Loaded {len(chapters)} chapters successfully.") + logger.success(f"Loaded {len(chapters)} chapters successfully.") return chapters except FileNotFoundError: logger.error(f"YAML file not found: {yaml_path}") @@ -221,7 +221,9 @@ def renumber_headers( "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.", + 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. From ff1bdf0516cb0f0a20063bb99b948085104f994d Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:20:04 +0100 Subject: [PATCH 31/61] refactor(create_database): update argument structure to use YAML for course data + improve logging --- src/create_database.py | 154 ++++++++++++++++++++++++++++------------- 1 file changed, 107 insertions(+), 47 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index e4aced4..48e2e76 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -6,14 +6,16 @@ Usage: ====== - python src/create_database.py --data-path [data-path] --chroma-path [chroma-path] + python src/create_database.py --course-yaml [course-yaml] + --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] --model-name [model-name] --provider-name [provider-name] 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) @@ -32,16 +34,16 @@ Example: ======== - python src/create_database.py --data-path data/markdown_processed \ + python src/create_database.py --course-yaml data/chapters_and_levels.yaml \ --chroma-path chroma_db \ --model-name text-embedding-3-large \ --provider-name openai 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 and will be embedded with the model -`text-embedding-3-large`. And finally the vector database will be saved to the -`chroma_db` directory. +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. """ import os @@ -53,50 +55,85 @@ from pathlib import Path import click +import loguru import tiktoken from dotenv import load_dotenv from langchain_chroma import Chroma -from langchain_community.document_loaders import DirectoryLoader, TextLoader +from langchain_community.document_loaders import TextLoader from langchain_core.documents import Document from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import ( MarkdownHeaderTextSplitter, RecursiveCharacterTextSplitter, ) -from loguru import logger from logger import create_logger +from parse_clean_markdown import load_chapters_from_yaml -def load_documents(data_dir: str) -> list[Document]: +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. """ - logger.info(f"Loading Markdown documents from {data_dir}...") - # Load Markdown documents from the specified directory - loader = DirectoryLoader( - data_dir, glob="*.md", show_progress=False, 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_file_path") + # Check if the processed file path is defined for the chapter + if not processed_path: + logger.warning( + f"No processed_file_path defined for chapter {chapter.get('name')}" + ) + 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 the file name documents = sorted(documents, key=lambda x: x.metadata.get("source", "")) - logger.success(f"Loaded {len(documents)} Markdown documents successfully.") + logger.success( + f"Converted {len(documents)}/{len(chapters)} Markdown documents successfully." + ) return documents def split_text_into_chunks( - content: str, chunk_size: int, chunk_overlap: int + 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. @@ -108,6 +145,8 @@ def split_text_into_chunks( 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 ------- @@ -144,7 +183,9 @@ def split_text_into_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. @@ -154,6 +195,8 @@ 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 ------- @@ -165,8 +208,8 @@ def remove_small_chunks( chunk for chunk in chunks if len(chunk.page_content) >= min_nb_char ] logger.info( - f"Removed {len(chunks) - len(chunks_cleaned)} small chunks \ - (less than {min_nb_char} characters)." + f"Removed {len(chunks) - len(chunks_cleaned)} small chunks" + f" (less than {min_nb_char} characters)." ) return chunks_cleaned @@ -191,13 +234,18 @@ def add_index_to_metadata(chunks: list[Document]) -> list[Document]: 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 ------- @@ -226,7 +274,7 @@ def add_token_number_to_metadata(chunks: list[Document]) -> list[Document]: def add_file_names_to_metadata( - chunks: list[Document], file_name: str + chunks: list[Document], file_name: str, logger: "loguru.Logger" = loguru.logger ) -> list[Document]: """Add file names to the metadata of the text chunks. @@ -236,6 +284,8 @@ def add_file_names_to_metadata( List of text chunks to which file names are to be added. file_name : str File name of the Markdown documents. + logger: "loguru.Logger" + Logger for logging messages. Returns ------- @@ -299,13 +349,17 @@ def preprocess_for_url(text: str, *, is_subsection: 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 ------- @@ -313,7 +367,7 @@ def add_url_to_metadata(chunks: list[Document]) -> list[Document]: List of text chunks with URL added to their 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", "") @@ -335,7 +389,7 @@ 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.debug(f"Added URL: {chunk.metadata['url']}") + logger.debug(f"Chunk {i} URL: {chunk.metadata['url']}") return chunks @@ -345,6 +399,7 @@ def save_to_chroma( model_name: str, provider_name: str, chroma_output_path: Path, + logger: "loguru.Logger" = loguru.logger, ) -> None: """Save text chunks to ChromaDB. @@ -358,6 +413,8 @@ def save_to_chroma( 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 ChromaDB...") # Clear the existing database if it exists @@ -391,11 +448,14 @@ def save_to_chroma( @click.command() @click.option( - "-d", - "--data-path", + "--course-yaml", required=True, - type=click.Path(exists=True, file_okay=False, dir_okay=True, path_type=Path), - help="Directory containing the processed Markdown files of the Python course.", + 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( "-c", @@ -433,7 +493,7 @@ def save_to_chroma( help="Name of the embedding provider to use.", ) def generate_data_store( - data_path: Path, + course_yaml: Path, chroma_path: Path, chunk_size: int, chunk_overlap: int, @@ -442,7 +502,7 @@ def generate_data_store( ) -> None: """Build a ChromaDB store from chunked text with metadata.""" # Set-up the logger - log_path = f"logs/create_database_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" + log_path = f"logs/{datetime.now().strftime('%Y%m%d')}/create_database.log" logger = create_logger(log_path) logger.info("Creating Chroma database...") @@ -459,29 +519,30 @@ def generate_data_store( all_chunks = [] # load documents from the specified directory - documents = load_documents(data_path) - logger.info("Processing files...") + 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) - + chunks = split_text_into_chunks( + doc.page_content, chunk_size, chunk_overlap, logger + ) # remove small chunks - chunks_cleaned = remove_small_chunks(chunks, min_nb_char=100) + 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_with_index) + chunks_with_tokens = add_token_number_to_metadata(chunks_with_index, logger) # add file name to the chunk metadata chunks_with_file_name = add_file_names_to_metadata( - chunks_with_tokens, file_name + 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) + chunks_with_url = add_url_to_metadata(chunks_with_file_name, logger) all_chunks.extend(chunks_with_url) logger.info(f"Example chunk metadata: {all_chunks[-1].metadata}") @@ -490,15 +551,14 @@ def generate_data_store( logger.info(f"Total number of files processed: {len(documents)}") logger.info(f"Total number of chunks: {len(all_chunks)}") logger.info( - f"Total number of characters: { - sum(len(chunk.page_content) for chunk in all_chunks):,}" + f"Total number of characters: \ + {sum(len(chunk.page_content) for chunk in all_chunks):,}" ) count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in all_chunks) logger.info(f"Total number of tokens: {count_tokens:,}") # save the embeddings to ChromaDB - save_to_chroma(all_chunks, model_name, provider_name, chroma_path) + save_to_chroma(all_chunks, model_name, provider_name, chroma_path, logger) -# MAIN PROGRAM if __name__ == "__main__": generate_data_store() From 71de1daab4b530b9410a2d5ce9f8a0e9ce2bd798 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:22:01 +0100 Subject: [PATCH 32/61] feat(create_database): add chapter IDs and separate file_path and file_name to chunks metadata --- src/create_database.py | 41 ++++++++++++++++++++++++++++++++--------- 1 file changed, 32 insertions(+), 9 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 48e2e76..7c17414 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -274,27 +274,50 @@ def add_token_number_to_metadata( def add_file_names_to_metadata( - chunks: list[Document], file_name: str, logger: "loguru.Logger" = loguru.logger + 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_name : str - File name 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. """ - # Add file names to metadata of each chunk + 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 = int(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 metadata to each chunk for chunk in chunks: - chunk.metadata["file_name"] = file_name + chunk.metadata["file_path"] = file_path + chunk.metadata["file_name"] = Path(file_path).name + chunk.metadata["chapter_id"] = chapter_id + return chunks @@ -537,7 +560,7 @@ def generate_data_store( # add number of tokens to the metadata chunks_with_tokens = add_token_number_to_metadata(chunks_with_index, logger) - # add file name to the chunk metadata + # 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 ) From be07be06edea00869953e577cb6d0c1ddb17ea03 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:27:02 +0100 Subject: [PATCH 33/61] chore: remove unuseful files --- .streamlit/config.toml | 10 - assets/style.css | 103 ------- data/quiz.json | 455 ----------------------------- src/gradio_app.py | 127 -------- src/gradio_app_chat.py | 190 ------------ src/streamlit_app.py | 190 ------------ src/streamlit_app_v2.py | 622 ---------------------------------------- 7 files changed, 1697 deletions(-) delete mode 100644 .streamlit/config.toml delete mode 100644 assets/style.css delete mode 100644 data/quiz.json delete mode 100644 src/gradio_app.py delete mode 100644 src/gradio_app_chat.py delete mode 100644 src/streamlit_app.py delete mode 100644 src/streamlit_app_v2.py diff --git a/.streamlit/config.toml b/.streamlit/config.toml deleted file mode 100644 index 37a26ab..0000000 --- a/.streamlit/config.toml +++ /dev/null @@ -1,10 +0,0 @@ -[theme] -base = "light" -primaryColor = "#8a1538" -linkColor = "#8a1538" - -[theme.sidebar] -baseRadius="full" -buttonRadius ="full" -primaryColor ="#8a1538" -linkColor = "#8a1538" diff --git a/assets/style.css b/assets/style.css deleted file mode 100644 index 49bc021..0000000 --- a/assets/style.css +++ /dev/null @@ -1,103 +0,0 @@ -@import url("https://fonts.googleapis.com/css2?family=Rubik+Iso:wght@400;700&display=swap"); - -.stApp { - zoom: 1.1; -} - -a { - font-weight: 500; - opacity: 0.85; -} -a:hover { - opacity: 1; - font-weight: 700; -} - -.app-title { - text-align: center; - color: #8a1538; - font-family: "Rubik Iso", sans-serif; - font-size: 70px; - margin-bottom: 0; -} - -.app-subtitle { - text-align: center; - color: #555555; - font-size: 16px; - margin-top: 5px; -} - -.sidebar-title { - font-weight: 700; - font-size: 1.5rem; - margin-top: 1.2rem; - margin-bottom: 0.4rem; -} - -.sidebar-about { - font-size: 1rem; - color: #444; - margin-top: 2rem; - text-align: center; -} - -.sidebar-icons { - display: flex; - justify-content: center; - gap: 1.2rem; - margin-top: 0.8rem; -} - -.sidebar-icons img { - width: 38px; - height: 38px; - opacity: 0.9; - transition: opacity 0.2s ease-in-out; -} - -.sidebar-icons img:hover { - opacity: 1; - transform: scale(1.1); -} - -#MainMenu { - visibility: hidden; -} - -footer { - visibility: hidden; -} - -.app-footer, -.app-footer * { - position: fixed; - left: 0; - bottom: 0; - width: 100%; - padding: 4px 10px; - color: #555555; - font-size: 15px; - line-height: 1.; - font-style: italic; - text-align: center; - opacity: 1; - background-color: transparent; -} - -.app-footer hr { - display: block; - border-width: 1px; - margin: 10px; -} - -.app-footer a { - color: #8a1538; - font-weight: 500; - text-decoration: none; -} - -.app-footer a:hover { - opacity: 1; - font-weight: 700; -} \ No newline at end of file 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": "Débutant", - "type": "QCM", - "question": "Quelle est l'origine du nom du langage de programmation Python ?", - "answers": { - "A": "D'un hommage à un serpent.", - "B": "D'un hommage à une série télévisée.", - "C": "Du nom de son créateur.", - "D": "D'une abréviation." - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B car le nom *Python* vient d'un hommage à la série télévisée *Monty Python's Flying Circus* dont Guido van Rossum, le créateur de Python, est fan. Les autres réponses sont incorrectes : A est une fausse information, C est incorrect car le créateur s'appelle Guido van Rossum, et D est également incorrect car *Python* n'est pas une abréviation." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "QCM", - "question": "Quel est l'avantage principal d'un langage interprété comme Python ?", - "answers": { - "A": "Il est plus rapide que les langages compilés.", - "B": "Il permet d'exécuter le code directement sans compilation préalable.", - "C": "Il nécessite moins de mémoire pour fonctionner.", - "D": "Il ne peut pas être utilisé sur plusieurs plateformes." - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B car un langage interprété comme Python permet d'exécuter le code directement sans compilation préalable, contrairement aux langages compilés comme le C ou le C++. La réponse A est incorrecte car les langages interprétés sont généralement plus lents que les langages compilés. La réponse C est incorrecte car le besoin en mémoire dépend du programme, et non du fait que le langage soit interprété ou compilé. La réponse D est incorrecte car Python est multiplateforme." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "QCM", - "question": "Quelle commande permet de quitter l'interpréteur Python sous Windows ?", - "answers": { - "A": "exit()", - "B": "Ctrl + D", - "C": "Ctrl + Z puis Entrée", - "D": "quit()" - }, - "correct_answer": "C", - "explanation": "La réponse correcte est C car sous Windows, on quitte l'interpréteur Python en pressant simultanément les touches Ctrl et Z puis Entrée. La réponse A (exit()) est correcte mais n'est pas spécifique à Windows, car elle fonctionne aussi sous Linux et Mac OS X. La réponse B (Ctrl + D) est incorrecte pour Windows mais correcte pour Linux et Mac OS X. La réponse D (quit()) est correcte mais, comme pour exit(), n'est pas spécifique à Windows." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "QCM", - "question": "Quel éditeur de texte est recommandé pour les débutants sous Windows ?", - "answers": { - "A": "Visual Studio Code", - "B": "Sublime Text", - "C": "notepad++", - "D": "gedit" - }, - "correct_answer": "C", - "explanation": "La réponse correcte est C car notepad++ est recommandé pour les débutants sous Windows. Visual Studio Code (A) et Sublime Text (B) sont également de très bons éditeurs de texte mais ne sont pas spécifiquement recommandés aux débutants dans ce cours. La réponse D (gedit) est incorrecte car gedit est recommandé pour les utilisateurs de Linux." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "QCM", - "question": "Quelle est la commande pour lancer l'interpréteur Python depuis un shell ?", - "answers": { - "A": "run python", - "B": "execute python", - "C": "start python", - "D": "python" - }, - "correct_answer": "D", - "explanation": "La réponse correcte est D car la commande pour lancer l'interpréteur Python depuis un shell est simplement 'python'. Les autres réponses (A, B, et C) sont incorrectes car elles ne sont pas les commandes reconnues pour lancer l'interpréteur Python." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "Vrai ou Faux", - "question": "Python est un langage de programmation créé en 1989 par Guido van Rossum.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. Python a bien été créé en 1989 par Guido van Rossum. Il s'est inspiré de la série télévisée Monty Python's Flying Circus pour nommer le langage. La réponse B est incorrecte car elle contredit cette information." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "Vrai ou Faux", - "question": "La version 2 de Python est toujours maintenue et est recommandée pour de nouveaux projets.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. La version 2 de Python est obsolète et n'est plus maintenue. La version 3 est le standard actuel et recommandée pour tous les nouveaux projets. La réponse A est incorrecte car elle contredit cette information." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "Vrai ou Faux", - "question": "Python est un langage compilé, ce qui signifie qu'il doit être transformé en code machine avant d'être exécuté.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Python est un langage interprété, ce qui signifie que le code est lu et exécuté directement par l'interpréteur Python sans nécessiter de compilation préalable. La réponse A est incorrecte car elle décrit le fonctionnement des langages compilés comme le C ou le C++." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "Vrai ou Faux", - "question": "Miniconda est un logiciel recommandé pour installer Python et gérer les modules supplémentaires.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. Miniconda est recommandé pour installer Python et gérer les modules supplémentaires nécessaires, comme NumPy, scipy, matplotlib, pandas, et Biopython. La réponse B est incorrecte car elle contredit cette recommandation." - }, - { - "chapter": "Introduction", - "python_level": "Débutant", - "type": "Vrai ou Faux", - "question": "Les éditeurs de texte comme Microsoft Word ou LibreOffice Writer sont adaptés pour écrire du code Python.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Microsoft Word et LibreOffice Writer sont des traitements de texte et ne sont pas adaptés pour écrire du code Python. Il est recommandé d'utiliser des éditeurs de texte comme notepad++, BBEdit, CotEditor, gedit, Visual Studio Code, Sublime Text, emacs, vim, ou geany. La réponse A est incorrecte car elle suggère l'utilisation de traitements de texte, ce qui n'est pas approprié pour l'écriture de code." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "Vrai/Faux", - "question": "En Python, la déclaration et l'initialisation d'une variable se font en même temps.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A car en Python, lorsque vous déclarez une variable, vous l'initialisez également avec une valeur. Par exemple, l'instruction `x = 2` déclare et initialise la variable `x` avec la valeur 2. Répondre 'Faux' serait incorrect car Python ne permet pas de déclarer une variable sans l'initialiser." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "Vrai/Faux", - "question": "La ligne de code suivante : \n```python\nx = 5\ny = x\n``` signifie que la variable y est égale à 5.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A car `y = x` assigne la valeur de `x` à `y`. Puisque `x` vaut 5, `y` prendra également la valeur 5. Répondre 'Faux' serait incorrect car cela signifierait ne pas comprendre l'opération d'affectation." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "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 réponse correcte est A car `y = 3.14` assigne un nombre décimal à `y`, ce qui en fait une variable de type float. Répondre 'Faux' serait incorrect car cela signifie ne pas reconnaître le type float." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "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 réponse 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. Répondre 'Vrai' serait incorrect car violerait les règles de nommage des variables en Python." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "Vrai/Faux", - "question": "La fonction `type()` peut être utilisée pour déterminer le type d'une variable.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A car la fonction `type()` retourne le type de la variable passée en argument. Par exemple, `type(3)` retournera ``. Répondre 'Faux' serait incorrect car cela signifie ne pas connaître l'utilisation de la fonction `type()`." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Que se passe-t-il lorsque vous exécutez 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 réponse correcte est A. Lorsque `y` est assigné à `x`, `y` prend la valeur actuelle de `x`, qui est 5. Même si `x` est ensuite mis à jour à 10, `y` conserve la valeur 5 car les variables sont indépendantes après l'affectation initiale. B est incorrect car `y` ne change pas lorsque `x` est mis à jour. C est incorrect car le code ne produit pas d'erreur. D est incorrect car `y` n'est jamais assigné à 0." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Quel type de variable est créé avec l'instruction suivante ?\n```python\nx = 3.14\n```", - "answers": { - "A": "int", - "B": "float", - "C": "str", - "D": "bool" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Le nombre 3.14 est un nombre décimal, et en Python, les nombres décimaux sont de type float. A est incorrect car int représente des nombres entiers sans partie décimale. C est incorrect car str représente des chaînes de caractères. D est incorrect car bool représente des valeurs booléennes (True ou False)." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Quel est le résultat 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 réponse correcte est A. La variable `x` est 2 et `y` est calculée 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 erronée. C est incorrect car il ne prend pas en compte la valeur correcte de `y`. D est incorrect car il ne représente aucune des opérations du code." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Quel est le type de la variable `z` après l'exécution du code suivant ?\n```python\nz = 'True'\n```", - "answers": { - "A": "bool", - "B": "str", - "C": "int", - "D": "float" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Même si 'True' est une valeur booléenne en Python, les guillemets simples autour de True signifient que c'est une chaîne de caractères (str). A est incorrect car `True` sans guillemets serait un booléen, mais ici c'est une chaîne. C est incorrect car `z` n'est pas un entier. D est incorrect car `z` n'est pas un nombre décimal." - }, - { - "chapter": "Variables", - "python_level": "Débutant", - "type": "QCM", - "question": "Quelle opération est correcte pour convertir une chaîne de caractères '123' en un entier ?", - "answers": { - "A": "int('123')", - "B": "str(123)", - "C": "float('123')", - "D": "bool('123')" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. La fonction `int()` convertit une chaîne de caractères contenant un nombre entier en un entier. B est incorrect car `str(123)` convertit un entier en une chaîne de caractères. C est incorrect car `float('123')` convertit la chaîne en un nombre décimal, pas en un entier. D est incorrect car `bool('123')` convertit la chaîne en une valeur booléenne, ce qui serait True." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "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 réponse correcte est B car la variable `y` est initialement une chaîne de caractères ('10'). En utilisant `int(y)`, nous convertissons cette chaîne de caractères en un entier (10), ce qui permet alors l'addition avec `x` qui est 5. Les autres réponses sont incorrectes : A est incorrect car il concatène des chaînes de caractères, C est incorrect car la conversion a été faite correctement, et D est incorrect car il ne réalise pas l'opération mais affiche les valeurs sous forme de chaîne." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "type": "QCM", - "question": "Quel est le type de la variable `z` après l'exécution 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 réponse correcte est B car l'opérateur de division `/` en Python renvoie toujours un `float`, même si les deux opérandes sont des entiers. Les autres réponses sont incorrectes : A est incorrect car le type n'est pas `int`, C est incorrect car ce n'est pas une chaîne de caractères, et D est incorrect car ce n'est pas un booléen." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "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 réponse correcte est B. Une `TypeError` se produit car nous essayons de concaténer une chaîne de caractères ('La valeur de z est: ') avec un entier (z). Pour corriger cela, z devrait être converti en chaîne de caractères en utilisant `str(z)`. Les autres réponses sont incorrectes : A est incorrect car il n'y a pas d'erreur de syntaxe, C est incorrect car toutes les variables sont définies, et D est incorrect car il y a effectivement une erreur de type." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "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 réponse correcte est C. Voici le déroulement 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 réponses sont incorrectes : A est incorrect après la première opération 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": "Avancé", - "type": "QCM", - "question": "Complétez le code pour qu'il affiche 'Python est génial!'.\n```python\nmot1 = 'Python'\nmot2 = 'est'\nmot3 = 'génial!'\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 réponse correcte est A car nous devons concaténer les variables `mot1`, `mot2`, et `mot3` avec des espaces entre elles pour former la phrase 'Python est génial!'. Les autres réponses sont incorrectes : B est incorrect car cela traiterait les noms de variables comme des chaînes de caractères, C est incorrect car les variables ne sont pas séparées par des virgules, et D est incorrect car il manque les espaces entre les mots." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "type": "Vrai ou Faux", - "question": "En Python, la déclaration et l'initialisation d'une variable se font en une seule instruction.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. En Python, lorsqu'on déclare une variable, on l'initialise également en lui assignant une valeur. Par exemple, l'instruction `x = 2` déclare et initialise la variable `x` avec la valeur 2. Ce n'est pas le cas dans certains autres langages de programmation où la déclaration et l'initialisation sont des étapes distinctes." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "type": "Vrai ou Faux", - "question": "En Python, une variable de type entier `int` et de type float `float` peuvent être utilisées ensemble dans une expression arithmétique et le résultat sera de type entier.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Lorsque vous mélangez des entiers et des floats dans une expression arithmétique en Python, le résultat sera de type float. Par exemple, `3 + 2.5` donnera `5.5`, qui est un float. Python convertit automatiquement le résultat en float pour préserver la précision." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "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 réponse 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": "Avancé", - "type": "Vrai ou Faux", - "question": "En Python, le symbole `=` est utilisé pour tester l'égalité entre deux valeurs.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. En Python, le symbole `=` est utilisé pour l'affectation, c'est-à-dire pour assigner une valeur à une variable. Pour tester l'égalité entre deux valeurs, on utilise le symbole `==`. Par exemple, `x = 2` assigne la valeur 2 à la variable `x`, tandis que `x == 2` teste si `x` est égal à 2." - }, - { - "chapter": "Variables", - "python_level": "Avancé", - "type": "Vrai ou Faux", - "question": "Le type d'une variable en Python peut être changé dynamiquement.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. En Python, le type d'une variable est dynamique, ce qui signifie qu'il peut changer au cours de l'exécution du programme. Par exemple, on peut assigner un entier à une variable `x` (comme dans `x = 2`), puis une chaîne de caractères (comme dans `x = 'hello'`). Python gère automatiquement le changement de type." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "Avancé", - "question": "Python a été créé en 1989 par Guido van Rossum.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. Python a en effet été créé en 1989 par Guido van Rossum. Le nom *Python* rend hommage à la série télévisée *Monty Python's Flying Circus* dont G. van Rossum est fan. La première version publique de ce langage a été publiée en 1991." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "Avancé", - "question": "Python 2 est encore largement utilisé et maintenu à jour.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Python 2 est obsolète et n'est plus maintenu. Il est recommandé d'utiliser Python 3, plus précisément la version 3.11 publiée en octobre 2022." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "Avancé", - "question": "Python est un langage compilé, similaire à C ou C++.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Python est un langage interprété, ce qui signifie qu'un script Python n'a pas besoin d'être compilé pour être exécuté, contrairement aux langages comme le C ou le C++. Cette caractéristique permet de tester rapidement des commandes et de débugger facilement les programmes." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "Avancé", - "question": "Il est possible d'installer Python sur un téléphone portable.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "A", - "explanation": "La réponse correcte est A. Python est multiplateforme et peut être installé sur de nombreux systèmes d'exploitation, y compris sur des téléphones portables. Cela inclut des systèmes comme Android et iOS." - }, - { - "type": "Vrai ou Faux", - "chapter": "Introduction", - "python_level": "Avancé", - "question": "Pour écrire du code Python, il est recommandé d'utiliser un traitement de texte comme Microsoft Word.", - "answers": { - "A": "Vrai", - "B": "Faux" - }, - "correct_answer": "B", - "explanation": "La réponse correcte est B. Les traitements de texte comme Microsoft Word ne doivent pas être utilisés pour écrire du code Python. Il est recommandé d'utiliser des éditeurs 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/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 pédagogique - pour le [cours de programmation Python](https://python.sdv.u-paris.fr/) pour les biologistes de Patrick Fuchs et Pierre Poulain. - - N'hésite-pas à 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 légales](https://u-paris.fr/politique-de-confidentialite/). - Cette application web n'utilise pas de cookie. - Les résultats des votes sont collectés anonymement à des fins de recherche. - - BioPyAssistant a été développé par [Essmay Touami](https://www.linkedin.com/in/essmay-touami/) et [Pierre Poulain](https://www.linkedin.com/in/pierrepo/) dans le cadre du projet pédagogique [LLM@UPCité](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_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/streamlit_app.py b/src/streamlit_app.py deleted file mode 100644 index 686f88c..0000000 --- a/src/streamlit_app.py +++ /dev/null @@ -1,190 +0,0 @@ -"""Streamlit app for chatbot testing. - -Usage: -====== - streamlit run src/streamlit_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 random - -import streamlit as st -from loguru import logger -from langchain_community.vectorstores import Chroma - - -# 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, MSGS_QUERY_NOT_RELATED, OPENAI_MODEL_NAME, CHROMA_PATH - - -# FUNCTIONS -def create_header() -> None: - st.markdown( - f""" -
-

🐍 BioPyAssistant 🐍

-
- """, - unsafe_allow_html=True - ) - st.markdown("---") - - -def insert_multiple_spaces(n: int = 1) -> None: - """Insert multiple spaces in the streamlit app.""" - for _ in range(n): - st.sidebar.markdown("") - - -def create_sidebar(): - """Create the sidebar.""" - st.sidebar.title("Options avancées") - - # Insert multiple spaces - insert_multiple_spaces(3) - - # Initialize total cost if not already set - if 'total_cost' not in st.session_state: - st.session_state.total_cost = 0.0 - - # Insert multiple spaces - insert_multiple_spaces(3) - - # Clear the conversation - if st.sidebar.button("Effacer la conversation"): - st.session_state.messages = [ - {"role": "assistant", "content": "Bonjour, je suis BioPyAssistant, ton assistant pour répondre à tes questions sur Python. Comment puis-je t'aider ?"} - ] - st.session_state.total_cost = 0.0 - - -def transform_messages(messages: list[dict]) -> list[tuple]: - """Transform the messages into a list of tuples. - - Parameters - ---------- - messages : list[dict] - The chat history messages. - - Returns - ------- - list[tuple] - The chat history messages as a list of tuples. - """ - logger.info("Transforming the messages into a list of tuples.") - messages_tuples = [] - user_message = None - - # Ignorer le premier message automatique de l'assistant - start_index = 1 if messages[0]['role'] == 'assistant' else 0 - - for message in messages[start_index:]: - if message['role'] == 'user': - user_message = message['content'] - elif message['role'] == 'assistant' and user_message is not None: - messages_tuples.append((user_message, message['content'])) - user_message = None # Réinitialiser le message de l'utilisateur après avoir créé le tuple - - return messages_tuples - - -def generate_response(user_query: str, vector_db: Chroma) -> str: - """Generate a response to the user question. - - Parameters - ---------- - user_query : str - The user question. - - Returns - ------- - response : str - The response predicted by the model. - """ - # Transform the chat history into a list of tuples - chat_history = transform_messages(st.session_state.messages) - # 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=user_query) - - # 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 : - # Get the metadata of the relevant documents - metadata = get_metadata(context) - # Generate the answer - answer = generate_answer(query=user_query, chat_context=chat_context, relevant_chunks=context, model_name=OPENAI_MODEL_NAME) - # Add metadata to the answer - final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) - - logger.info(f"Response generated: {final_answer}") - logger.success("Response generated successfully.\n") - - return final_answer - - -def chat_with_bot(vector_db: Chroma) -> None: - """Chat with the bot""" - # Initialize the chat with a welcome message - if "messages" not in st.session_state.keys(): # Initialize the chat message history - st.session_state.messages = [ - {"role": "assistant", "content": "Bonjour, je suis BioPyAssistant, ton assistant pour répondre à tes questions sur Python. Comment puis-je t'aider ?"} - ] - - # Get the user question - if user_query := st.chat_input("Pose moi une question sur le cours !"): # Prompt for user input and save to chat history - st.session_state.messages.append({"role": "user", "content": user_query}) - - for message in st.session_state.messages: # Display the prior chat messages - with st.chat_message(message["role"]): - st.write(message["content"]) - - # If last message is not from assistant, generate a new response - if st.session_state.messages[-1]["role"] != "assistant": - with st.chat_message("assistant"): - with st.spinner("En réflexion..."): - response = generate_response(user_query, vector_db) - st.write(response) - - # Add the response to the chat history - message = {"role": "assistant", "content": response} - st.session_state.messages.append(message) - - -def main(): - # Set the page configuration - st.set_page_config(page_title="BioPyAssistant", page_icon="data/logo_round.webp") - - # Create the header - create_header() - - # Create a sidebar and get the user inputs - create_sidebar() - - # Load the vector database - vector_db = load_database(CHROMA_PATH)[0] - - # Chat with the bot - chat_with_bot(vector_db) - - -# MAIN PROGRAM -if __name__ == "__main__": - main() diff --git a/src/streamlit_app_v2.py b/src/streamlit_app_v2.py deleted file mode 100644 index 9819149..0000000 --- a/src/streamlit_app_v2.py +++ /dev/null @@ -1,622 +0,0 @@ -"""Streamlit app for chatbot testing.""" - -import secrets -import sys -import time -from datetime import timedelta -from pathlib import Path - -import loguru -import pyperclip -import streamlit as st -from dotenv import load_dotenv -from langchain_community.vectorstores import Chroma -from loguru import logger - -from model_config_app import Settings -from query_chatbot import ( - MSGS_QUERY_NOT_RELATED, - add_metadata_to_answer, - contextualize_question, - format_chat_history, - generate_answer, - get_metadata, - load_database, - search_similarity_in_database, -) - -BASE_DIR = Path(__file__).resolve().parent -PROJECT_ROOT = BASE_DIR.parent -CSS_PATH = PROJECT_ROOT / "assets" / "style.css" -MIN_TIME_BETWEEN_REQUESTS = timedelta(seconds=2) - -SUGGESTIONS = { - ":green[:material/loop:] Comment écrire une boucle ?": "Comment écrire une boucle ?", - ":blue[:material/list_alt:] Quelle est la différence entre une liste et un set ?": ( - "Quelle est la différence entre une liste et un set ?" - ), - ":orange[:material/decimal_increase:] Comment afficher un float avec 2 chiffres avec la virgule ?": ( - "Comment afficher un float avec 2 chiffres avec la virgule ?" - ), -} - - -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.opt(colors=True).add( - logpath, format=logger_format, level="DEBUG", mode="w" - ) - # Add logger to stdout. - logger.add(sys.stdout, format=logger_format, level=level) - return logger.opt(colors=True) - - -def apply_custom_css(css_file: Path) -> None: - """Load and injects custom CSS into the Streamlit app.""" - if css_file.exists(): - css = css_file.read_text() - st.markdown(f"", unsafe_allow_html=True) - else: - logger.warning(f"CSS file not found at {css_file}") - - -@st.cache_resource -def get_vector_db( - vector_db: Path, embedding_model_name: str, logger: "loguru.Logger" = loguru.logger -) -> Chroma: - """Cache the vector database to prevent reloading on every rerun. - - Returns - ------- - Chroma: The vector database containing the embedded course. - """ - vector_db, _nb_chunks = load_database(vector_db, embedding_model_name, logger) - return vector_db - - -def create_header(app_name: str) -> None: - """Render the application header and subtitle.""" - st.markdown( - f'
{app_name}
', - unsafe_allow_html=True, - ) - - st.markdown( - """ -
- BioPyAssistant est un assistant pédagogique pour le cours de - - programmation Python - -
- pour les biologistes de Patrick Fuchs et Pierre Poulain. -
- """, - unsafe_allow_html=True, - ) - st.space(30) - - -def create_sidebar() -> dict[str, str]: - """Render the Streamlit sidebar and collect user profile settings. - - Returns - ------- - dict[str, str] - Dictionary containing the user-selected profile information with - the following keys: - - - ``"cursus"`` : Academic track selected by the user - (e.g., "Licence", "Master"). - - ``"level"`` : User's python level - (e.g., "Débutant", "Intermédiaire", "Avancé"). - """ - with st.sidebar: - # Institutional logos - st.logo( - "https://u-paris.fr/wp-content/uploads/2022/03/UniversiteParisCite_logo_horizontal_couleur_RVB.png", - size="large", - icon_image="https://lvts.fr/wp-content/uploads/2022/03/UniversiteParis_monogramme_couleur_RVB-e1712425218876.png", - ) - st.space("large") - # Student profile - st.markdown( - '
🎓 Profil étudiant
', - unsafe_allow_html=True, - ) - cursus = st.pills("Cursus :material/school:", options=["Licence", "Master"]) - level = st.pills( - "Niveau :material/sort:", options=["Débutant", "Intermédiaire", "Avancé"] - ) - st.space(20) - st.info( - "Indiquez votre cursus et votre niveau pour des réponses adaptées.", - icon="🎯", - ) - st.space(250) - # About section - st.markdown( - """ -
- BioPyAssistant a été développé par - - - Essmay Touami - - - et - - - Pierre Poulain - - -
- dans le cadre du projet pédagogique
- - - LLM@UPCité - - .

- Code source disponible sur GitHub
- sous licence BSD 3-clause. -
- """, - unsafe_allow_html=True, - ) - - # - st.markdown( - """ -
- - GitHub - - - Python - -
- """, - unsafe_allow_html=True, - ) - - return {"cursus": cursus, "level": level} - - -@st.dialog("💡 Guide d'utilisation responsable") -def show_disclaimer_dialog() -> None: - """Display a dialog outlining responsible usage guidelines for the application.""" - st.caption(""" - ### 🧠 Gardez la main sur votre réflexion - L'IA est un assistant, pas un expert infaillible. - Le **copier-coller direct est déconseillé** : - utilisez les réponses comme une base de travail que vous devez valider et enrichir - par votre esprit critique. - - ### 🛡️ Protégez votre vie privée - Ce service utilise des modèles externes. - **Ne partagez jamais de données personnelles**, - confidentielles ou sensibles dans vos échanges. - - ### 📜 Aller plus loin - Pour adopter les bonnes pratiques, consultez la [Charte d'utilisation](#). - """) - - -def create_footer() -> None: - """Render the application footer with legal information.""" - st.html( - """ -
-

- Cette application web n'utilise pas de cookie. Les résultats des votes sont collectés anonymement à des fins de recherche. -
- - Mentions légales. - -

-
- """ - ) - - -def send_telemetry( - **kwargs, -) -> None: # TODO: ask pierre what telemetry to log and how to log it - """ - Record telemetry data related to user interactions with the chatbot. - - This function collects and logs usage metrics to help monitor - application performance and user engagement. The telemetry may include, - but is not limited to: - - - Questions submitted by users. - - Number of requests per session. - - Response time of the LLM. - - Error rates or failed requests. - - Clicks on suggested prompts or examples. - - User feedback on responses (e.g., thumbs up / thumbs down). - - Parameters - ---------- - **kwargs : dict - Arbitrary keyword arguments containing additional context or metadata - for telemetry, such as the question text, response, timestamps, or - user identifiers. - """ - pass - - -def show_feedback_controls(message_index: int, assistant_msg: str) -> None: - """Display compact feedback controls for an assistant message. - - Parameters - ---------- - message_index : int - Index of the message in `st.session_state.messages` for which - the feedback is being collected. - assistant_msg : str - The content of the assistant message to which the feedback controls - are attached. - """ - with st.container(): - option_map = { - "is_copied": ":material/content_copy:", - "has_voted_up": ":material/thumb_up:", - "has_voted_down": ":material/thumb_down:", - "has_report_msg": ":material/report:", - } - selection = st.pills( - "assistant message", - label_visibility="hidden", - options=option_map.keys(), - format_func=lambda option: option_map[option], - selection_mode="single", - ) - relevant_history = st.session_state.messages[: message_index + 1] - if selection: - if selection == "is_copied": - pyperclip.copy(assistant_msg) - st.toast("Réponse copiée !", icon="📋", duration="short") - - elif selection == "has_voted_up" or selection == "has_voted_down": - vote_type = "👍" if selection == "has_voted_up" else "👎" - logger.warning(f"Feedback {vote_type}") - logger.warning(f"Message: {assistant_msg}") - logger.debug(f"Context: {relevant_history}") - st.toast("Merci pour votre vote !", icon="✅", duration="short") - - elif selection == "has_report_msg": - with st.form(key=f"report-form-{message_index}", clear_on_submit=True): - details = st.text_area( - "Signaler / Ajouter un commentaire (optionnel)", - height=60, - ) - if st.form_submit_button("Envoyer"): - logger.critical("Report 🚨") - logger.critical(f"Commentaire: {details or 'Aucun'}") - logger.critical(f"Message: {assistant_msg}") - logger.debug(f"Context: {relevant_history}") - st.toast( - "Merci pour votre signalement !", icon="⚠️", duration="short" - ) - - -def display_welcome_chat() -> None: - """Display the initial welcome chat message and suggestions.""" - st.session_state.messages = [] - - # Display welcome container with chat input and suggestions - with st.container(): - st.chat_input("Pose moi une question sur le cours...", key="initial_question") - st.pills( - label="Examples", - label_visibility="collapsed", - options=SUGGESTIONS, - key="selected_suggestion", - ) - # Show disclaimer for ethical use - st.button( - ( - " :small[:gray[:material/chat_error: Les réponses générées " - " peuvent être incorrectes ou incomplètes, gardez toujours un esprit" - " critique !]]" - ), - type="tertiary", - on_click=show_disclaimer_dialog, - ) - - # Stop execution so the chat doesn't process until a question is asked - st.stop() - - -def transform_messages(messages: list[dict[str, str]]) -> list[tuple[str, str]]: - """ - Convert Streamlit message history into a list of (User, Assistant) tuples. - - Args: - messages: List of message dictionaries from st.session_state. - - Returns - ------- - A list of tuples compatible with LangChain history formats. - """ - history_tuples = [] - temp_user_msg = None - - for msg in messages: - if msg["role"] == "user": - temp_user_msg = msg["content"] - elif msg["role"] == "assistant" and temp_user_msg is not None: - history_tuples.append((temp_user_msg, msg["content"])) - temp_user_msg = None - - return history_tuples - - -def generate_response( - user_query: str, context: str, model_name: str, prompt_path: str -) -> str: - """Generate a response to the user question. - - Parameters - ---------- - user_query : str - The user question. - context : str - The relevant context retrieved from the vector database. - model_name : str - The name of the LLM to use for generating the response. - prompt_path : str - Path to the prompt template to use for generating the response. - - Returns - ------- - response : str - The response predicted by the model. - """ - # Transform the chat history into a list of tuples - chat_history = transform_messages(st.session_state.messages) - # 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) - - # 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 = secrets.choice(MSGS_QUERY_NOT_RELATED) - return response - else: - # Get the metadata of the relevant documents - metadata = get_metadata(context) - # Generate the answer - answer = generate_answer( - query=user_query, - chat_context=chat_context, - relevant_chunks=context, - model_name=model_name, - prompt_path=prompt_path, - logger_flag=False, - ) - # Add metadata to the answer - final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) - - # logger.info(f"Response generated: {final_answer}") - # logger.success("Response generated successfully.\n") - - return final_answer - - -def stream_text(text: str): - """Simulate a typing effect in the UI.""" - for token in text: - yield token - time.sleep(0.01) - - -def clear_conversation(): - st.session_state.messages = [] - st.session_state.initial_question = None - st.session_state.selected_suggestion = None - - -def chat_with_bot( - vector_db: Chroma, student_infos: dict, model_name: str, prompt_path: str -) -> None: - """Handle the main chat interface with the user. - - Parameters - ---------- - vector_db : Chroma - Vector database used to retrieve context for the LLM responses. - student_infos : dict[str, str] - Dictionary containing the student's profile information: - - "cursus" : Academic track selected by the user (e.g., "Licence", "Master"). - - "level" : User's Python or academic proficiency level - (e.g., "Débutant", "Intermédiaire", "Avancé"). - """ - # Check if the student has provided both cursus and level - has_student_infos = (student_infos["cursus"] and student_infos["level"]) is not None - user_just_asked_initial_question = ( - "initial_question" in st.session_state and st.session_state.initial_question - ) - # Determine if this is the first user interaction - user_just_clicked_suggestion = ( - "selected_suggestion" in st.session_state - and st.session_state.selected_suggestion - ) - user_first_interaction = ( - user_just_asked_initial_question or user_just_clicked_suggestion - ) - # If first interaction but no student info, show a toast and block sending - if user_first_interaction and not has_student_infos: - st.toast( - "Veuillez renseigner votre cursus et votre niveau pour continuer.", - icon="⚠️", - duration="short", - ) - user_first_interaction = False - - # Check if there is already message histor - has_message_history = ( - "messages" in st.session_state and len(st.session_state.messages) > 0 - ) - # --- Initial State (No conversation yet) --- - if not user_first_interaction and not has_message_history: - display_welcome_chat() - - # --- First step of the conversation --- - # Show chat input at the bottom when a question has been asked. - user_message = st.chat_input("Posez une question complémentaire...") - if not user_message: - if user_just_asked_initial_question: - user_message = st.session_state.initial_question - if user_just_clicked_suggestion: - user_message = SUGGESTIONS[st.session_state.selected_suggestion] - - st.button( - "Restart", - icon=":material/refresh:", - on_click=clear_conversation, - ) - - # --- Active Chat History --- - # Display chat messages from history as speech bubbles. - for i, message in enumerate(st.session_state.messages): - with st.chat_message(message["role"]): - if message["role"] == "assistant": - st.container() # Fix ghost message bug. - - st.markdown(message["content"]) - if message["role"] == "assistant": - show_feedback_controls(i, message["content"]) - - # When the user posts a message... - if user_message: - # Streamlit's Markdown engine interprets "$" as LaTeX code (used to - # display math). The line below fixes it. - user_message = user_message.replace("$", r"\$") - - # Display message as a speech bubble. - with st.chat_message("user"): - st.text(user_message) - - # Display assistant response as a speech bubble. - with st.chat_message("assistant"): - # Search for relevant context in the vector database. - with st.spinner("En recherche de contexte..."): - context = search_similarity_in_database( - vector_db=vector_db, - user_query=user_message, - # user_level=student_infos["level"], - # user_cursus=student_infos["cursus"], - logger_flag=False, - ) - - with st.spinner("En réflexion..."): - response = generate_response( - user_message, context, model_name, prompt_path - ) - rep_stream = stream_text(response) - - # Put everything after the spinners in a container to fix the - # ghost message bug. - with st.container(): - # Stream the LLM response. - response = st.write_stream(rep_stream) - - # Add messages to chat history. - st.session_state.messages.append( - {"role": "user", "content": user_message} - ) - st.session_state.messages.append( - {"role": "assistant", "content": response} - ) - - # Show feedback controls (copy, thumbs up/down, report) - show_feedback_controls(len(st.session_state.messages) - 1, response) - # Send telemetry data for logging and analysis - # (to be implemented in the future) - send_telemetry(question=user_message, response=response) - - -def main(): - """Run the BioPyAssistant Streamlit app.""" - # Load environment variables - load_dotenv() - # Initialize settings from `config_app.toml` - settings = Settings() - # Configure logger once - if "welcome_logged" not in st.session_state and not settings.log_path.exists(): - logger = create_logger(settings.log_path) - logger.info( - f"👋 Welcome to BioPyAssistant v{settings.app_version}!" - ) - if settings.app_description: - logger.info(f"{settings.app_description}\n") - st.session_state["welcome_logged"] = True - - # Set the page configuration - st.set_page_config( - page_title=settings.app_name, page_icon="data/img/logo_round.webp" - ) - # Apply the css style - apply_custom_css(settings.css_path) - - # Create the header - create_header(settings.app_name) - - # Create a sidebar and get the user inputs - student_infos = create_sidebar() - - # Create footer - create_footer() - - # Load the vector database one - vector_db = get_vector_db( - settings.vector_database_path, settings.llm.embedding_model_name - ) - - # Chat with the bot - chat_with_bot( - vector_db, student_infos, settings.llm.llm_model_name, settings.llm.prompt_path - ) - - -if __name__ == "__main__": - main() From 2f1ca0858d922b713bfbfdc08a4f7bf469c776dd Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:28:20 +0100 Subject: [PATCH 34/61] chore: remove unused gradio dependency --- pyproject.toml | 1 - uv.lock | 226 ------------------------------------------------- 2 files changed, 227 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f2ffcfc..f5b14cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ dependencies = [ "langchain-text-splitters", "loguru", "streamlit", - 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[[package]] name = "tornado" version = "6.5.4" From d25335b01da90703797605b357b67440e9710b82 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 03:39:55 +0100 Subject: [PATCH 35/61] chore(app): remove model config settings (not part of this PR) --- src/model_config_app.py | 144 ---------------------------------------- 1 file changed, 144 deletions(-) delete mode 100644 src/model_config_app.py diff --git a/src/model_config_app.py b/src/model_config_app.py deleted file mode 100644 index c88cca8..0000000 --- a/src/model_config_app.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Pydantic configuration models used to validate the CLI argument of BioPyAssistant App.""" - -import os -from datetime import datetime -from pathlib import Path -from typing import Literal - -from pydantic import BaseModel, DirectoryPath, Field, FilePath, SecretStr -from pydantic_settings import ( - BaseSettings, - PydanticBaseSettingsSource, - SettingsConfigDict, - TomlConfigSettingsSource, -) - - -class LLMConfig(BaseModel): - """Configuration model for Large Language Model (LLM) parameters.""" - - provider_name: Literal["openai", "openrouter"] = Field( - ..., description="Name of the LLM provider, must be 'openai' or 'openrouter'." - ) - llm_model_name: str = Field( - ..., description="Default LLM model name to use if none is provided." - ) - embedding_model_name: str = Field( - ..., - description="Name of the embedding model to use for vector representations.", - ) - prompt_path: FilePath = Field( - ..., - description="Path to the prompt template file used for generating responses.", - ) - api_key_openai: SecretStr | None = Field( - None, - description="Secret API key for authenticating on openai provider.", - env="OPENAI_API_KEY", - ) - api_key_openrouter: SecretStr | None = Field( - None, - description="Secret API key for authenticating on openrouter provider.", - env="OPENROUTER_API_KEY", - ) - - @property - def api_key(self) -> SecretStr: - """ - Return the correct API key based on the selected LLM provider. - - The returned key depends on the value of `self.provider`: - - If `provider` is "openai", returns the value of `api_key_openai`. - - If `provider` is "openrouter", returns the value of `api_key_openrouter`. - - Returns - ------- - SecretStr - The API key corresponding to the currently selected provider. - """ - if self.provider_name == "openai": - # if self.api_key_openai is None: - # msg = "OPENAI_API_KEY not set" - # raise ValueError(msg) - return self.api_key_openai or SecretStr(os.getenv("OPENAI_API_KEY")) - elif self.provider_name == "openrouter": - # print(self.api_key_openrouter) - # print(os.getenv("OPENROUTER_API_KEY")) - # if self.api_key_openrouter is None: - # msg = "OPENROUTER_API_KEY not set" - # raise ValueError(msg) - return self.api_key_openrouter or SecretStr(os.getenv("OPENROUTER_API_KEY")) - - -class Settings(BaseSettings, cli_parse_args=True): - """Global BioPyAssistant streamlit application settings.""" - - # Basic application info - app_name: str = Field( - "BioPyAssistant", description="Application name displayed in the UI or logs." - ) - app_description: str | None = Field( - None, description="Short app description displayed in the logs." - ) - app_version: str = Field("2.0", description="Current version of the application.") - # Paths - css_path: FilePath = Field( - ..., description="Path to the CSS file for Streamlit styling." - ) - vector_database_path: DirectoryPath = Field( - ..., description="Path to the directory where the vector database is stored." - ) - # Nested configuration for LLM-related parameters - llm: LLMConfig = Field( - ..., description="Configuration for Large Language Model parameters." - ) - - # Compute the log path - @property - def log_path(self) -> Path: - """Return the full path for today's log file.""" - log_file = Path("logs") / f"biopyassistant_app_{datetime.now():%Y-%m-%d}.log" - log_file.parent.mkdir(parents=True, exist_ok=True) - return log_file - - # Load settings from this TOML file - model_config = SettingsConfigDict(toml_file=["config_app.toml"]) - - # Reorder the priority of different settings sources - @classmethod - def settings_customise_sources( - cls, - settings_cls: type[BaseSettings], - init_settings: PydanticBaseSettingsSource, - env_settings: PydanticBaseSettingsSource, - dotenv_settings: PydanticBaseSettingsSource, - file_secret_settings: PydanticBaseSettingsSource, - ) -> tuple[PydanticBaseSettingsSource, ...]: - """ - Customize the order and type of settings sources. - - Parameters - ---------- - settings_cls : type[BaseSettings] - The settings class being instantiated. Used by sources that - need access to the settings schema. - init_settings : PydanticBaseSettingsSource - Source providing values passed directly to the Settings - constructor. - env_settings : PydanticBaseSettingsSource - Source providing values from environment variables. - - Returns - ------- - tuple[PydanticBaseSettingsSource, ...] - Ordered tuple of settings sources. Earlier sources have higher - priority during value resolution. - """ - return ( - # Load settings from TOML files first - TomlConfigSettingsSource(settings_cls), - # Override with explicit constructor arguments - init_settings, - # Finally allow environment variables to override everything - env_settings, - ) From dd4f61ad8b7a44e63bba6df8de5911cf8ccea438 Mon Sep 17 00:00:00 2001 From: essmaw Date: Mon, 9 Feb 2026 21:30:34 +0100 Subject: [PATCH 36/61] chore(pre-commit): update ruff version and format configuration --- .pre-commit-config.yaml | 59 ++++++++++++++++++++--------------------- 1 file changed, 29 insertions(+), 30 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 52451ee..0c058a9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,36 +2,35 @@ # 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/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/asottile/pyupgrade + rev: v3.21.2 + hooks: + - id: pyupgrade -- repo: https://github.com/astral-sh/ruff-pre-commit - # Ruff version. - rev: v0.14.13 - 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 + - 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 From abdbb0b7ac0cbc7c75cb6f395176ea6e185144e4 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 00:44:31 +0100 Subject: [PATCH 37/61] feat(create_database): enable embeddings with all OpenRouter models and persist in Chroma --- src/create_database.py | 203 +++++++++++++++++++++++++++++------------ 1 file changed, 146 insertions(+), 57 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 7c17414..f795386 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -61,16 +61,93 @@ from langchain_chroma import Chroma 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, ) +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, + ) + + 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]: @@ -102,7 +179,8 @@ def load_documents( # Check if the processed file path is defined for the chapter if not processed_path: logger.warning( - f"No processed_file_path defined for chapter {chapter.get('name')}" + f"No processed_file_path defined for chapter id={chapter.get('id')} " + f"title={chapter.get('title')}" ) continue @@ -214,26 +292,6 @@ def remove_small_chunks( 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. - """ - # Add an index to metadata of each chunk - for index, chunk in enumerate(chunks): - chunk.metadata["id"] = index - - return chunks - - def add_token_number_to_metadata( chunks: list[Document], logger: "loguru.Logger" = loguru.logger, @@ -299,7 +357,7 @@ def add_file_names_to_metadata( # 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 = int(match_chapter.group(1)) + 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) @@ -321,7 +379,7 @@ def add_file_names_to_metadata( return chunks -def preprocess_for_url(text: str, *, is_subsection: bool = False) -> str: +def preprocess_for_url(text: str, *, is_subsubsection_name: bool = False) -> str: """Preprocess text for creating URL. Parameters @@ -363,7 +421,7 @@ def preprocess_for_url(text: str, *, is_subsection: bool = False) -> str: processed_text = re.sub(r"[^a-zA-Z]*$", "", processed_text) # Remove the subsubsection number - if is_subsection: + if is_subsubsection_name: processed_text = re.sub(r"^[a-zA-Z]?\d+-?", "", processed_text) # Add a '#' at the beginning @@ -392,17 +450,16 @@ def add_url_to_metadata( # Add URL to metadata of each chunk 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", ""), is_subsection=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", ""), is_subsection=True - ) + section_id = preprocess_for_url(chunk.metadata.get("subsection_name", "")) elif chunk.metadata.get("section_name", ""): # section section_id = preprocess_for_url(chunk.metadata.get("section_name", "")) else: # chapter @@ -417,6 +474,50 @@ def add_url_to_metadata( return chunks +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, @@ -439,30 +540,22 @@ def save_to_chroma( logger: "loguru.Logger" Logger for logging messages. """ - logger.info("Saving to ChromaDB...") + 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) - # Get the API key and base URL based on the provider - if provider_name == "openrouter": - api_key = os.getenv("OPENROUTER_API_KEY") - base_url = "https://openrouter.ai/api/v1" - elif provider_name == "openai": - api_key = os.getenv("OPENAI_API_KEY") - base_url = None - - # Create the embeddings for each chunks - embeddings = OpenAIEmbeddings( - model=model_name, - base_url=base_url, - api_key=api_key, - ) + # 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))] # Create a ChromaDB with the embeddings Chroma.from_documents( documents=chunks, - embedding=embeddings, + ids=uuids, + embedding=embeddings_function, persist_directory=str(chroma_output_path), collection_metadata={"hnsw:space": "cosine"}, # distance metric ) @@ -491,7 +584,7 @@ def save_to_chroma( "-s", "--chunk-size", default=1000, - type=click.IntRange(min=1), + type=click.IntRange(min=50), help="Size of the text chunks to be created.", ) @click.option( @@ -506,13 +599,14 @@ def save_to_chroma( "--model-name", default="text-embedding-3-large", type=str, - help="Name of the embedding model, chosen from OpenRouter`s available models.", + help="Name of the embedding model," + "chosen from OpenAI and OpenRouter`s embedding models. ", ) @click.option( "-p", "--provider-name", - default="openrouter", - type=str, + default="openai", + type=click.Choice(["openrouter", "openai"], case_sensitive=False), help="Name of the embedding provider to use.", ) def generate_data_store( @@ -537,9 +631,6 @@ def generate_data_store( ) sys.exit(1) - # Load the environment variables with LLM api keys - load_dotenv() - all_chunks = [] # load documents from the specified directory documents = load_documents(course_yaml, logger) @@ -554,11 +645,8 @@ def generate_data_store( # 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_with_index, logger) + chunks_with_tokens = add_token_number_to_metadata(chunks_cleaned, logger) # add file name and chapter id to the chunk metadata chunks_with_file_name = add_file_names_to_metadata( @@ -568,7 +656,7 @@ def generate_data_store( chunks_with_url = add_url_to_metadata(chunks_with_file_name, logger) all_chunks.extend(chunks_with_url) - logger.info(f"Example chunk metadata: {all_chunks[-1].metadata}") + logger.debug(f"Example chunk metadata: {all_chunks[-1].metadata}") logger.info("Summary:") logger.info(f"Total number of files processed: {len(documents)}") @@ -579,6 +667,7 @@ def generate_data_store( ) count_tokens = sum(chunk.metadata["nb_tokens"] for chunk in all_chunks) logger.info(f"Total number of tokens: {count_tokens:,}") + # save the embeddings to ChromaDB save_to_chroma(all_chunks, model_name, provider_name, chroma_path, logger) From adc182d5922d2453fa98042a2476026ba19b2aaf Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 00:50:47 +0100 Subject: [PATCH 38/61] feat(query_chatbot): add level-filtered retrieval, vector DB and LLM provider selection, refactor and improve logging --- src/query_chatbot.py | 618 +++++++++++++++++++++++++++---------------- 1 file changed, 386 insertions(+), 232 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 0ad8fd4..eaadece 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -7,20 +7,59 @@ Usage: ====== - python src/query_chatbot.py --query "Your question here" [--model "model_name"] - [--prompt_path "prompt_path"] - [--include-metadata] + python src/query_chatbot.py --query "Your question here" + --level "user_level" + [--yaml-path "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"] + [--prompt_path "prompt_path"] + [--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: + - "debutant" : for beginner users + - "intermediaire" : for intermediate users + - "avance" : for advanced users + Options: ======== + --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: "data/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" + --prompt_path (Path): File path to the text file containing the prompt template. Default: "prompts/zero_shot.txt" @@ -32,32 +71,38 @@ Example: ======== - python src/query_chatbot.py --query "D'où vient le nom Python ?" --model "gpt-4o" \ + python src/query_chatbot.py --query "D'où vient le nom Python ?" \ + --level "debutant" --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" \ --prompt_path "prompts/zero_shot.txt" --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 the zero_shot prompt. -And it will include metadata in the response. +This command will search for answers to the query "Qu'est-ce que Python ?" from a +begginer 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. """ -import datetime -import re +import os import secrets -import sys +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 langchain.messages import AIMessage, HumanMessage from langchain_chroma import Chroma from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate -from langchain_openai import ChatOpenAI, OpenAIEmbeddings -from loguru import logger +from langchain_openai import ChatOpenAI +from create_database import create_embeddings_function from logger import create_logger MSGS_QUERY_NOT_RELATED = [ @@ -72,22 +117,50 @@ "Je ne suis pas en mesure de répondre à cette question." ), ] +MSGS_QUERY_OUT_OF_SCOPE_LEVEL = [ + ( + "Cette question fait référence à des notions qui ne sont pas encore abordées " + "dans ce cours. Je te conseille de te concentrer d`abord sur les chapitres " + "actuellement au programme." + ), + ( + "Je suis désolé, mais cette question semble faire référence à des notions qui " + "ne sont pas encore couvertes dans le cours. Je te recommande de suivre les " + "chapitres dans l'ordre pour une meilleure compréhension." + ), +] def load_database( - vector_db_path: str, embedding_model: str, logger: "loguru.Logger" = loguru.logger -) -> tuple[Chroma, int]: + 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 + # 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 @@ -95,17 +168,54 @@ def load_database( # 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 get_level_relevant_chapter_ids( + user_level: str, course_yaml: Path, logger: "loguru.Logger" = loguru.logger +) -> list[str]: + """Get the list of chapter IDs relevant to the user level. + + Parameters + ---------- + user_level : str + The user level used to adapt model responses. + course_yaml : Path + The file path to the YAML file defining the course chapters and student levels. + logger: "loguru.Logger" + Logger for logging messages. + + Returns + ------- + list[str]: List of chapter IDs relevant to the user level. + """ + logger.info("Getting the list of chapter IDs relevant to the user level...") + with open(course_yaml, encoding="utf-8") as f: + course_data = yaml.safe_load(f) + # Get the list of chapter IDs relevant to the user level + levels = course_data.get("levels", []) + if not levels: + logger.warning("No levels defined in YAML file.") + return [] + + for level in levels: + if level.get("name") == user_level: + chapter_ids = [str(ch_id) for ch_id in level.get("chapters", [])] + logger.success(f"Chapter IDs for level '{user_level}': {chapter_ids}") + return chapter_ids + + logger.warning(f"User level '{user_level}' not found in YAML file.") + return [] def search_similarity_in_database( vector_db: Chroma, user_query: str, + level_relevant_chapter_ids: list[str], nb_chunks: int = 3, score_threshold: float = 0.35, - *, - logger_flag: bool = True, + logger: "loguru.Logger" = loguru.logger, ) -> list[Document]: """Search for relevant documents in the database based on the query text. @@ -115,167 +225,116 @@ def search_similarity_in_database( The textual database to search. user_query : str The query text. + level_relevant_chapter_ids : list[str] + List of chapter IDs relevant to the user level. nb_chunks : int 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...") + + # Create a filter dictionary to restrict the search to user-level relevant chapters + level_relevant_chapter_ids_filter_dict = { + "chapter_id": {"$in": level_relevant_chapter_ids} + } # 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, + "filter": level_relevant_chapter_ids_filter_dict, + }, ) - - # 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[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(): + logger.debug(f"{line}") + 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 is 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.") - return chat_history + return relevant_chunks -def contextualize_question( - chat_history_formatted: list[HumanMessage | AIMessage], +def generate_answer_without_model( + vector_db: Chroma, + user_query: str, + logger: "loguru.Logger" = loguru.logger, + nb_chunks: int = 3, + score_threshold: float = 0.35, ) -> str: - """Add context to the user query using the chat history. + """Generate an answer to the user query without calling the model. - Parameters - ---------- - chat_history_formatted : list[Union[HumanMessage, AIMessage]] - The formatted chat history. + This function is used when no relevant documents are found in the database for the + user query. It displays personalized messages to the user based on whether the + query is not related to the user level or not related to the course at all. - 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"Réponse {i // 2 + 1}: {message.content}\n" - - # logger.info(f"Chat context: {chat_context}") - # logger.success("Contextualized query constructed successfully.") - - 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. + vector_db : Chroma + The textual database to search. + user_query : str + The query text. + logger : loguru.Logger + Logger for logging messages. + nb_chunks : int + The number of top matching documents to retrieve. + score_threshold : float + The relevance score threshold for filtering the results. Returns ------- - metadatas : list - List of metadata dictionaries for the top matching documents. + response : str + The response message to be displayed to the user. """ - # 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 + logger.debug(f"Question: {user_query}") + # Define the retriever without level filter + retriever = vector_db.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={ + "k": nb_chunks, + "score_threshold": score_threshold, + }, + ) + # Perform a similarity search + relevant_chunks = retriever.invoke(user_query) + # If there are some relevant chunks but they are not related to the user level + if relevant_chunks != []: + logger.warning( + "This question seems to be related to the course " + "but not relevant to the user level." + ) + response = secrets.choice(MSGS_QUERY_OUT_OF_SCOPE_LEVEL) + logger.debug( + f"Answer generated automatically without calling the model: {response}" + ) + else: + logger.warning( + "This question does not seem to be related to the course content." + ) + response = secrets.choice(MSGS_QUERY_NOT_RELATED) + logger.debug( + f"Answer generated automatically without calling the model: {response}" + ) + return response def calculate_nb_tokens(text: str) -> int: @@ -300,39 +359,63 @@ def calculate_nb_tokens(text: str) -> int: def generate_answer( query: str, - chat_context: str, + provider_llm_name: str, + chat_history: str | None, relevant_chunks: list, model_name: str, prompt_path: Path, - *, - logger_flag: bool = True, -) -> str: + 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. + 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. 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.startswith("gpt"): - chat_model = ChatOpenAI(model=model_name) + if provider_llm_name == "openai": + api_key = os.getenv("OPENAI_API_KEY") + chat_model = ChatOpenAI(model=model_name, openai_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 @@ -341,26 +424,25 @@ def generate_answer( 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") + logger.debug(f"Question: {query}") + logger.debug(f"Answer generated by the model: {answer}") + # 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) - return answer + 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. @@ -368,8 +450,8 @@ def add_metadata_to_answer( ---------- 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. @@ -378,11 +460,12 @@ 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 @@ -431,39 +514,57 @@ 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 à consulter les rubriques \ - suivantes du [cours en ligne](https://python.sdv.u-paris.fr/) :\n \ - - {sources_text}" + sources_string = ( + "Pour plus d'informations, je t'invite à 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: 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("Réponse:") + 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!" + ) @click.command() @@ -474,6 +575,27 @@ def display_answer(user_query: str, final_response: str | dict) -> None: required=True, help="The query text for which you want to search for answers.", ) +@click.option( + "--level", + "user_level", + type=click.Choice( + ["debutant", "intermediaire", "avance"], + 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", @@ -483,10 +605,13 @@ def display_answer(user_query: str, final_response: str | dict) -> None: help="The name or identifier of the model to be used for generating responses.", ) @click.option( - "--provider", - "provider_name", - default="openrouter", - type=str, + "--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( @@ -502,7 +627,18 @@ def display_answer(user_query: str, final_response: str | dict) -> None: "embedding_model", default="text-embedding-3-large", type=str, - help="Name of the embedding model to use.", + 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( "--prompt_path", @@ -519,60 +655,78 @@ def display_answer(user_query: str, final_response: str | dict) -> None: ) def interrogate_model( user_query: str, + user_level: str, + course_yaml: Path, model_name: str, - provider_name: str, + provider_llm_name: str, database_path: str, embedding_model: str, + provider_embeddings_name: str, prompt_path: Path, *, include_metadata: bool, ) -> None: """Interrogate the AI model to search for answers in a vector database.""" + start_time = perf_counter() # Set-up the logger - log_path = f"logs/query_chatbot_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" + 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) - - # Load the environment variables - load_dotenv() + # Log the user query and parameters + logger.info("Starting the command line interface for querying the chatbot...") + logger.debug(f"User query: {user_query}") + logger.debug(f"User level: {user_level}") + logger.debug(f"YAML path: {course_yaml}") + logger.debug(f"Model name: {model_name}") + logger.debug(f"Provider LLM name: {provider_llm_name}") + logger.debug(f"Database path: {database_path}") + logger.debug(f"Embedding model: {embedding_model}") + logger.debug(f"Provider embeddings name: {provider_embeddings_name}") + logger.debug(f"Prompt path: {prompt_path}") + logger.debug(f"Include metadata: {include_metadata}") # CONTEXT RETRIEVAL # Load the vector database - vector_db = load_database(database_path, embedding_model)[0] + vector_db = load_database(database_path, embedding_model, provider_embeddings_name) + # Get the list of chapter IDs relevant to the user level + level_relevant_chapter_ids = get_level_relevant_chapter_ids( + user_level, course_yaml, logger + ) # 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, level_relevant_chapter_ids, logger=logger + ) # ANSWER GENERATION # Check if there are relevant documents if relevant_chunks == []: - # random response betweet responses in MSGS_QUERY_NOT_RELATED - response = secrets.choice(MSGS_QUERY_NOT_RELATED) - print(response) - sys.exit(0) + # Avoids calling the model for queries that are not relevant to the course + answer = generate_answer_without_model(vector_db, user_query) + 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( + answer, nb_tokens_prompt, nb_tokens_answer = generate_answer( query=user_query, - chat_context=None, - relevant_chunks=relevant_chunks_formatted, + chat_history=None, + relevant_chunks=relevant_chunks, model_name=model_name, + provider_llm_name=provider_llm_name, prompt_path=prompt_path, + logger=logger, ) - # 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 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, start_time, logger, nb_tokens_prompt, nb_tokens_answer + ) # MAIN PROGRAM From 01db3e7abb39d1ac600fc5a3015fe3e25a354634 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 00:54:15 +0100 Subject: [PATCH 39/61] chore(prompts): rename prompt directory to prompts --- {prompt => prompts}/zero_shot.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {prompt => prompts}/zero_shot.txt (100%) diff --git a/prompt/zero_shot.txt b/prompts/zero_shot.txt similarity index 100% rename from prompt/zero_shot.txt rename to prompts/zero_shot.txt From 508e123e10387df1506ce11cc231b911ad528cd3 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 00:55:34 +0100 Subject: [PATCH 40/61] fix(parse_clean_markdown): validate processed_file_path before creating Path --- src/parse_clean_markdown.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index 7e15192..ed0cd54 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -277,10 +277,11 @@ def process_md_files(yaml_path: Path) -> None: content = renumber_headers(content, chapter_number, logger) # Get destination file path from YAML - dest_path = Path(chapter.get("processed_file_path")) - if not dest_path: + dest_file = chapter.get("processed_file_path") + if not dest_file: logger.warning(f"No processed file path defined for chapter {chapter_name}") continue + dest_path = Path(dest_file) # Create output directory if it does not exist dest_path.parent.mkdir(parents=True, exist_ok=True) # Save processed content to destination path From 87397f91fd915ac2a1637858dbd44a9766e2459d Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 00:56:14 +0100 Subject: [PATCH 41/61] chore(chapters_and_levels): update chapter IDs to string format --- data/chapters_and_levels.yaml | 158 +++++++++++++++++----------------- 1 file changed, 79 insertions(+), 79 deletions(-) diff --git a/data/chapters_and_levels.yaml b/data/chapters_and_levels.yaml index 793b349..6e756fe 100644 --- a/data/chapters_and_levels.yaml +++ b/data/chapters_and_levels.yaml @@ -5,137 +5,137 @@ chapters: # List of all available Python chapters and annexes. # These entries define which Markdown files are ingested into the vector database. - - id: 01 + - id: "01" title: Introduction source_file_path: data/markdown_raw/01_introduction.md processed_file_path: data/markdown_processed/01_introduction.md - - id: 02 + - id: "02" title: Variables source_file_path: data/markdown_raw/02_variables.md processed_file_path: data/markdown_processed/02_variables.md - - id: 03 + - id: "03" title: Affichage source_file_path: data/markdown_raw/03_affichage.md processed_file_path: data/markdown_processed/03_affichage.md - - id: 04 + - id: "04" title: Listes source_file_path: data/markdown_raw/04_listes.md processed_file_path: data/markdown_processed/04_listes.md - - id: 05 + - id: "05" title: Boucles et comparaisons source_file_path: data/markdown_raw/05_boucles_comparaisons.md processed_file_path: data/markdown_processed/05_boucles_comparaisons.md - - id: 06 + - id: "06" title: Tests source_file_path: data/markdown_raw/06_tests.md processed_file_path: data/markdown_processed/06_tests.md - - id: 07 + - id: "07" title: Fichiers source_file_path: data/markdown_raw/07_fichiers.md processed_file_path: data/markdown_processed/07_fichiers.md - - id: 08 + - id: "08" title: Dictionnaires et tuples source_file_path: data/markdown_raw/08_dictionnaires_tuples.md processed_file_path: data/markdown_processed/08_dictionnaires_tuples.md - - id: 9 + - id: "09" title: Modules source_file_path: data/markdown_raw/09_modules.md processed_file_path: data/markdown_processed/09_modules.md - - id: 10 + - id: "10" title: Fonctions source_file_path: data/markdown_raw/10_fonctions.md processed_file_path: data/markdown_processed/10_fonctions.md - - id: 11 + - id: "11" title: Plus sur les chaînes de caractères source_file_path: data/markdown_raw/11_plus_sur_les_chaines_de_caracteres.md processed_file_path: data/markdown_processed/11_plus_sur_les_chaines_de_caracteres.md - - id: 12 + - id: "12" title: Plus sur les listes source_file_path: data/markdown_raw/12_plus_sur_les_listes.md processed_file_path: data/markdown_processed/12_plus_sur_les_listes.md - - id: 13 + - id: "13" title: Plus sur les fonctions source_file_path: data/markdown_raw/13_plus_sur_les_fonctions.md processed_file_path: data/markdown_processed/13_plus_sur_les_fonctions.md - - id: 14 + - id: "14" title: Conteneurs source_file_path: data/markdown_raw/14_conteneurs.md processed_file_path: data/markdown_processed/14_conteneurs.md - - id: 15 + - id: "15" title: Création de modules source_file_path: data/markdown_raw/15_creation_modules.md processed_file_path: data/markdown_processed/15_creation_de_modules.md - - id: 16 + - id: "16" title: Bonnes pratiques en programmation Python source_file_path: data/markdown_raw/16_bonnes_pratiques.md processed_file_path: data/markdown_processed/16_bonnes_pratiques.md - - id: 17 + - id: "17" title: Expressions régulières et parsing source_file_path: data/markdown_raw/17_expressions_regulieres.md processed_file_path: data/markdown_processed/17_expressions_regulieres.md - - id: 18 + - id: "18" title: Jupyter et ses notebooks source_file_path: data/markdown_raw/18_jupyter.md processed_file_path: data/markdown_processed/18_jupyter.md - - id: 19 + - id: "19" title: Module Biopython source_file_path: data/markdown_raw/19_module_biopython.md processed_file_path: data/markdown_processed/19_module_biopython.md - - id: 20 + - id: "20" title: Module Numpy source_file_path: data/markdown_raw/20_module_numpy.md processed_file_path: data/markdown_processed/20_module_numpy.md - - id: 21 + - id: "21" title: Module Matplotlib source_file_path: data/markdown_raw/21_module_matplotlib.md processed_file_path: data/markdown_processed/21_module_matplotlib.md - - id: 22 + - id: "22" title: Module Pandas source_file_path: data/markdown_raw/22_module_pandas.md processed_file_path: data/markdown_processed/22_module_pandas.md - - id: 23 + - id: "23" title: Avoir la classe avec les objets source_file_path: data/markdown_raw/23_avoir_la_classe_avec_les_objets.md processed_file_path: data/markdown_processed/23_avoir_la_classe_avec_les_objets.md - - id: 24 + - id: "24" title: Avoir plus la classe avec les objets source_file_path: data/markdown_raw/24_avoir_plus_la_classe_avec_les_objets.md processed_file_path: data/markdown_processed/24_avoir_plus_la_classe_avec_les_objets.md - - id: 25 + - id: "25" title: Fenêtres graphiques et Tkinter source_file_path: data/markdown_raw/25_tkinter.md processed_file_path: data/markdown_processed/25_tkinter.md - - id: 26 + - id: "26" title: Remarques complémentaires source_file_path: data/markdown_raw/26_remarques_complementaires.md processed_file_path: data/markdown_processed/26_remarques_complementaires.md - - id: 27 + - id: "27" title: Mini-projets source_file_path: data/markdown_raw/27_mini_projets.md processed_file_path: data/markdown_processed/27_mini_projets.md @@ -162,15 +162,15 @@ levels: and are looking for guidance on fundamental Python topics. chapters: - - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 11 - - 12 + - "01" + - "02" + - "03" + - "04" + - "05" + - "06" + - "07" + - "11" + - "12" - name: intermediaire display_name: "Intermédiaire (Python 1)" @@ -180,23 +180,23 @@ levels: certain fundamentals while deepening their understanding. chapters: - - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - - 10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 - - B + - "01" + - "02" + - "03" + - "04" + - "05" + - "06" + - "07" + - "08" + - "09" + - "10" + - "11" + - "12" + - "13" + - "14" + - "15" + - "16" + - "B" - name: avance display_name: "Avancé (Python 2)" @@ -207,29 +207,29 @@ levels: and are ready to explore more complex concepts. chapters: - - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - - 10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 - - 21 - - 22 - - 23 - - 24 - - 27 - - B + - "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" From ebc0872a47dbe4f18923cd7ca5d7a697e9d3fd35 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 01:36:48 +0100 Subject: [PATCH 42/61] chore(readme): update setup instructions to use uv for environment management and clarify API key requirements --- README.md | 60 +++++++++++++++++++++++++++---------------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 2b21fcc..8b70f7a 100644 --- a/README.md +++ b/README.md @@ -32,18 +32,15 @@ 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): @@ -59,27 +56,30 @@ rm -rf cours-python ```bash rm -f data/markdown_processed/*.md -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 ``` 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` -### 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 \ + --model-name text-embedding-3-large \ + --provider-name 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 +91,31 @@ 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" \ + --prompt_path "path_to_prompt_template" \ + --include-metadata ``` ### Options -- 🤖 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**: For instance: `debutant`, `intermediaire`, `avance`, etc. This helps the chatbot tailor its responses to the user's level of understanding. +- 🤖 **Model Selection**: For instance: `gpt-4o`, `deepseek/deepseek-v3.2`, etc. +- 📄 **Prompt Template**: The file path to the text file containing the instruction and format for the chatbot's response. +- 📝 **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 "intermediaire" \ + --model gpt-4o \ + --prompt_path "prompts/zero_shot.txt" \ + --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. The response will be generated based on the prompt template located at [`prompts/zero_shot.txt`](prompts/zero_shot.txt) and will include metadata about the sources of the answer. Output: @@ -124,8 +134,6 @@ For more information, you can refer to the following sources: ## Usage (web interface) -### Streamlit app - ```bash streamlit run src/streamlit_app.py @@ -134,12 +142,4 @@ 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. From 1b6488818fdddbd7fd167d133aa429ae94db723a Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 01:37:35 +0100 Subject: [PATCH 43/61] docs: update scripts to use 'uv' for execution --- src/create_database.py | 4 ++-- src/parse_clean_markdown.py | 8 ++++---- src/query_chatbot.py | 8 ++++---- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index f795386..4467bd0 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -6,7 +6,7 @@ Usage: ====== - python src/create_database.py --course-yaml [course-yaml] + uv run src/create_database.py --course-yaml [course-yaml] --chroma-path [chroma-path] --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] --model-name [model-name] --provider-name [provider-name] @@ -34,7 +34,7 @@ Example: ======== - python src/create_database.py --course-yaml data/chapters_and_levels.yaml \ + uv run src/create_database.py --course-yaml data/chapters_and_levels.yaml \ --chroma-path chroma_db \ --model-name text-embedding-3-large \ --provider-name openai diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index ed0cd54..aad2448 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -28,15 +28,15 @@ Output: ```python - # Votre premier commentaire en Python. + #Votre premier commentaire en Python. print("Hello world!") - # D'autres commandes plus utiles pourraient suivre. + #D'autres commandes plus utiles pourraient suivre. ``` Usage: ====== - python src/parse_clean_markdown.py --config path/to/chapters_and_levels.yaml + uv run src/parse_clean_markdown.py --config path/to/chapters_and_levels.yaml Where: config : Path @@ -46,7 +46,7 @@ Example: ======== - python src/parse_clean_markdown.py --config data/chapters_and_levels.yaml + uv run src/parse_clean_markdown.py --config data/chapters_and_levels.yaml 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. diff --git a/src/query_chatbot.py b/src/query_chatbot.py index eaadece..1a3b496 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -7,7 +7,7 @@ Usage: ====== - python src/query_chatbot.py --query "Your question here" + uv run src/query_chatbot.py --query "Your question here" --level "user_level" [--yaml-path "path_to_yaml_file"] [--model "model_name"] @@ -71,7 +71,7 @@ Example: ======== - python src/query_chatbot.py --query "D'où vient le nom Python ?" \ + uv run src/query_chatbot.py --query "D'où vient le nom Python ?" \ --level "debutant" --model "gpt-4o" \ --course-yaml "data/chapters_and_levels.yaml" \ --provider-llm "openai" --db-path "chroma_db" \ @@ -618,7 +618,7 @@ def display_answer( "--db-path", "database_path", type=click.Path(exists=True, file_okay=False, dir_okay=True), - default="data/chroma_db", + default="chroma_db", show_default=True, help="File path to the Chroma database containing the context embeddings.", ) @@ -643,7 +643,7 @@ def display_answer( @click.option( "--prompt_path", type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True), - default="prompt/zero_shot.txt", + default="prompts/zero_shot.txt", show_default=True, help="File path to the text file containing the prompt template.", ) From 6c33371fa462fca503ec9ba33577ff0c65f2320d Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 01:42:09 +0100 Subject: [PATCH 44/61] chore: revert Streamlit app to previous version for history --- src/streamlit_app.py | 190 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 src/streamlit_app.py diff --git a/src/streamlit_app.py b/src/streamlit_app.py new file mode 100644 index 0000000..686f88c --- /dev/null +++ b/src/streamlit_app.py @@ -0,0 +1,190 @@ +"""Streamlit app for chatbot testing. + +Usage: +====== + streamlit run src/streamlit_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 random + +import streamlit as st +from loguru import logger +from langchain_community.vectorstores import Chroma + + +# 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, MSGS_QUERY_NOT_RELATED, OPENAI_MODEL_NAME, CHROMA_PATH + + +# FUNCTIONS +def create_header() -> None: + st.markdown( + f""" +
+

🐍 BioPyAssistant 🐍

+
+ """, + unsafe_allow_html=True + ) + st.markdown("---") + + +def insert_multiple_spaces(n: int = 1) -> None: + """Insert multiple spaces in the streamlit app.""" + for _ in range(n): + st.sidebar.markdown("") + + +def create_sidebar(): + """Create the sidebar.""" + st.sidebar.title("Options avancées") + + # Insert multiple spaces + insert_multiple_spaces(3) + + # Initialize total cost if not already set + if 'total_cost' not in st.session_state: + st.session_state.total_cost = 0.0 + + # Insert multiple spaces + insert_multiple_spaces(3) + + # Clear the conversation + if st.sidebar.button("Effacer la conversation"): + st.session_state.messages = [ + {"role": "assistant", "content": "Bonjour, je suis BioPyAssistant, ton assistant pour répondre à tes questions sur Python. Comment puis-je t'aider ?"} + ] + st.session_state.total_cost = 0.0 + + +def transform_messages(messages: list[dict]) -> list[tuple]: + """Transform the messages into a list of tuples. + + Parameters + ---------- + messages : list[dict] + The chat history messages. + + Returns + ------- + list[tuple] + The chat history messages as a list of tuples. + """ + logger.info("Transforming the messages into a list of tuples.") + messages_tuples = [] + user_message = None + + # Ignorer le premier message automatique de l'assistant + start_index = 1 if messages[0]['role'] == 'assistant' else 0 + + for message in messages[start_index:]: + if message['role'] == 'user': + user_message = message['content'] + elif message['role'] == 'assistant' and user_message is not None: + messages_tuples.append((user_message, message['content'])) + user_message = None # Réinitialiser le message de l'utilisateur après avoir créé le tuple + + return messages_tuples + + +def generate_response(user_query: str, vector_db: Chroma) -> str: + """Generate a response to the user question. + + Parameters + ---------- + user_query : str + The user question. + + Returns + ------- + response : str + The response predicted by the model. + """ + # Transform the chat history into a list of tuples + chat_history = transform_messages(st.session_state.messages) + # 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=user_query) + + # 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 : + # Get the metadata of the relevant documents + metadata = get_metadata(context) + # Generate the answer + answer = generate_answer(query=user_query, chat_context=chat_context, relevant_chunks=context, model_name=OPENAI_MODEL_NAME) + # Add metadata to the answer + final_answer = add_metadata_to_answer(answer, metadatas=metadata, iu=True) + + logger.info(f"Response generated: {final_answer}") + logger.success("Response generated successfully.\n") + + return final_answer + + +def chat_with_bot(vector_db: Chroma) -> None: + """Chat with the bot""" + # Initialize the chat with a welcome message + if "messages" not in st.session_state.keys(): # Initialize the chat message history + st.session_state.messages = [ + {"role": "assistant", "content": "Bonjour, je suis BioPyAssistant, ton assistant pour répondre à tes questions sur Python. Comment puis-je t'aider ?"} + ] + + # Get the user question + if user_query := st.chat_input("Pose moi une question sur le cours !"): # Prompt for user input and save to chat history + st.session_state.messages.append({"role": "user", "content": user_query}) + + for message in st.session_state.messages: # Display the prior chat messages + with st.chat_message(message["role"]): + st.write(message["content"]) + + # If last message is not from assistant, generate a new response + if st.session_state.messages[-1]["role"] != "assistant": + with st.chat_message("assistant"): + with st.spinner("En réflexion..."): + response = generate_response(user_query, vector_db) + st.write(response) + + # Add the response to the chat history + message = {"role": "assistant", "content": response} + st.session_state.messages.append(message) + + +def main(): + # Set the page configuration + st.set_page_config(page_title="BioPyAssistant", page_icon="data/logo_round.webp") + + # Create the header + create_header() + + # Create a sidebar and get the user inputs + create_sidebar() + + # Load the vector database + vector_db = load_database(CHROMA_PATH)[0] + + # Chat with the bot + chat_with_bot(vector_db) + + +# MAIN PROGRAM +if __name__ == "__main__": + main() From 55ba0fb4b2ebfcb80aca198e9dbf97d82f576c2c Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 01:54:10 +0100 Subject: [PATCH 45/61] chore: remove deprecated config_app.toml file --- config_app.toml | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 config_app.toml diff --git a/config_app.toml b/config_app.toml deleted file mode 100644 index c382e8c..0000000 --- a/config_app.toml +++ /dev/null @@ -1,12 +0,0 @@ -# config.default.toml -app_name = "BioPyAssistant" -app_description = "BioPyAssistant is a teaching assistant for Patrick Fuchs and Pierre Poulain's Python programming course for biologists." -app_version = "2.0" -css_path = "assets/style.css" -vector_database_path = "chroma_db" - -[llm] -provider_name = "openrouter" -llm_model_name= "gpt-4o" -embedding_model_name = "text-embedding-3-large" -prompt_path = "prompt/zero_shot.txt" From a4c7d37302bc1bdebfc848ff41a94f916f18c0e9 Mon Sep 17 00:00:00 2001 From: essmaw Date: Tue, 10 Feb 2026 01:54:39 +0100 Subject: [PATCH 46/61] chore: update logging messages and argument names --- src/create_database.py | 10 ++++------ src/query_chatbot.py | 10 +++++----- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 4467bd0..37f1f9d 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -626,8 +626,8 @@ def generate_data_store( # Validate the CLI arguments if chunk_overlap >= chunk_size: logger.error( - f"The chunk overlap ({chunk_overlap}) must be less than \ - the chunk size ({chunk_size})." + f"The chunk overlap ({chunk_overlap}) must be less than " + f" the chunk size ({chunk_size})." ) sys.exit(1) @@ -661,10 +661,8 @@ def generate_data_store( logger.info("Summary:") logger.info(f"Total number of files processed: {len(documents)}") logger.info(f"Total number of chunks: {len(all_chunks)}") - logger.info( - f"Total number of characters: \ - {sum(len(chunk.page_content) for chunk in all_chunks):,}" - ) + total_characters = sum(len(chunk.page_content) for chunk in 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:,}") diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 1a3b496..7ea6f66 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -9,7 +9,7 @@ ====== uv run src/query_chatbot.py --query "Your question here" --level "user_level" - [--yaml-path "path_to_yaml_file"] + [--course-yaml "path_to_yaml_file"] [--model "model_name"] [--provider-llm "provider_llm_name"] [--db-path "database_path"] @@ -47,7 +47,7 @@ --db-path (str): File path to the Chroma database containing the context embeddings. - Default: "data/chroma_db" + Default: "chroma_db" --embedding-model (str): Name of the embedding model to use. @@ -79,7 +79,7 @@ --prompt_path "prompts/zero_shot.txt" --include-metadata This command will search for answers to the query "Qu'est-ce que Python ?" from a -begginer user in the Chroma database located at "data/chroma_db" +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. @@ -120,7 +120,7 @@ MSGS_QUERY_OUT_OF_SCOPE_LEVEL = [ ( "Cette question fait référence à des notions qui ne sont pas encore abordées " - "dans ce cours. Je te conseille de te concentrer d`abord sur les chapitres " + "dans ce cours. Je te conseille de te concentrer d'abord sur les chapitres " "actuellement au programme." ), ( @@ -407,7 +407,7 @@ def generate_answer( # Define the model if provider_llm_name == "openai": api_key = os.getenv("OPENAI_API_KEY") - chat_model = ChatOpenAI(model=model_name, openai_api_key=api_key) + chat_model = ChatOpenAI(model=model_name, ai_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 From 1787b52719cb881a077ee78101c099987f30426e Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 14:58:36 +0100 Subject: [PATCH 47/61] fixes(query_chatbot): rename ai_api_key to api_key for ChatOpenAI initialization --- src/query_chatbot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index 7ea6f66..e67f954 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -407,7 +407,7 @@ def generate_answer( # Define the model if provider_llm_name == "openai": api_key = os.getenv("OPENAI_API_KEY") - chat_model = ChatOpenAI(model=model_name, ai_api_key=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 From 7200618bf3b177df7c8b4067cd3ec005a5423e0a Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 16:53:05 +0100 Subject: [PATCH 48/61] refactor(prompts): rename prompt folder and files for consistency --- prompts/zero_shot.txt => prompt_templates/common.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename prompts/zero_shot.txt => prompt_templates/common.txt (100%) diff --git a/prompts/zero_shot.txt b/prompt_templates/common.txt similarity index 100% rename from prompts/zero_shot.txt rename to prompt_templates/common.txt From 1838eb9d9d05917879be6fe53293f0e568a8d64d Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 16:58:18 +0100 Subject: [PATCH 49/61] refactor(yml file): add quotes to values, replace source_file_path and processed_file_path with file_name, and use base_path to avoid redundancy --- data/chapters_and_levels.yaml | 167 +++++++++++++++------------------- 1 file changed, 75 insertions(+), 92 deletions(-) diff --git a/data/chapters_and_levels.yaml b/data/chapters_and_levels.yaml index 6e756fe..57db403 100644 --- a/data/chapters_and_levels.yaml +++ b/data/chapters_and_levels.yaml @@ -1,159 +1,136 @@ # 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 - source_file_path: data/markdown_raw/01_introduction.md - processed_file_path: data/markdown_processed/01_introduction.md + title: "Introduction" + file_name: "01_introduction.md" - id: "02" - title: Variables - source_file_path: data/markdown_raw/02_variables.md - processed_file_path: data/markdown_processed/02_variables.md + title: "Variables" + file_name: "02_variables.md" - id: "03" - title: Affichage - source_file_path: data/markdown_raw/03_affichage.md - processed_file_path: data/markdown_processed/03_affichage.md + title: "Affichage" + file_name: "03_affichage.md" - id: "04" - title: Listes - source_file_path: data/markdown_raw/04_listes.md - processed_file_path: data/markdown_processed/04_listes.md + title: "Listes" + file_name: "04_listes.md" - id: "05" - title: Boucles et comparaisons - source_file_path: data/markdown_raw/05_boucles_comparaisons.md - processed_file_path: data/markdown_processed/05_boucles_comparaisons.md + title: "Boucles et comparaisons" + file_name: "05_boucles_comparaisons.md" - id: "06" - title: Tests - source_file_path: data/markdown_raw/06_tests.md - processed_file_path: data/markdown_processed/06_tests.md + title: "Tests" + file_name: "06_tests.md" - id: "07" - title: Fichiers - source_file_path: data/markdown_raw/07_fichiers.md - processed_file_path: data/markdown_processed/07_fichiers.md + title: "Fichiers" + file_name: "07_fichiers.md" - id: "08" - title: Dictionnaires et tuples - source_file_path: data/markdown_raw/08_dictionnaires_tuples.md - processed_file_path: data/markdown_processed/08_dictionnaires_tuples.md + title: "Dictionnaires et tuples" + file_name: "08_dictionnaires_tuples.md" - id: "09" - title: Modules - source_file_path: data/markdown_raw/09_modules.md - processed_file_path: data/markdown_processed/09_modules.md + title: "Modules" + file_name: "09_modules.md" - id: "10" - title: Fonctions - source_file_path: data/markdown_raw/10_fonctions.md - processed_file_path: data/markdown_processed/10_fonctions.md + title: "Fonctions" + file_name: "10_fonctions.md" - id: "11" - title: Plus sur les chaînes de caractères - source_file_path: data/markdown_raw/11_plus_sur_les_chaines_de_caracteres.md - processed_file_path: data/markdown_processed/11_plus_sur_les_chaines_de_caracteres.md + title: "Plus sur les chaînes de caractères" + file_name: "11_plus_sur_les_chaines_de_caracteres.md" - id: "12" - title: Plus sur les listes - source_file_path: data/markdown_raw/12_plus_sur_les_listes.md - processed_file_path: data/markdown_processed/12_plus_sur_les_listes.md + title: "Plus sur les listes" + file_name: "12_plus_sur_les_listes.md" - id: "13" - title: Plus sur les fonctions - source_file_path: data/markdown_raw/13_plus_sur_les_fonctions.md - processed_file_path: data/markdown_processed/13_plus_sur_les_fonctions.md + title: "Plus sur les fonctions" + file_name: "13_plus_sur_les_fonctions.md" - id: "14" - title: Conteneurs - source_file_path: data/markdown_raw/14_conteneurs.md - processed_file_path: data/markdown_processed/14_conteneurs.md + title: "Conteneurs" + file_name: "14_conteneurs.md" - id: "15" - title: Création de modules - source_file_path: data/markdown_raw/15_creation_modules.md - processed_file_path: data/markdown_processed/15_creation_de_modules.md + title: "Création de modules" + file_name: "15_creation_modules.md" - id: "16" - title: Bonnes pratiques en programmation Python - source_file_path: data/markdown_raw/16_bonnes_pratiques.md - processed_file_path: data/markdown_processed/16_bonnes_pratiques.md + title: "Bonnes pratiques en programmation Python" + file_name: "16_bonnes_pratiques.md" - id: "17" - title: Expressions régulières et parsing - source_file_path: data/markdown_raw/17_expressions_regulieres.md - processed_file_path: data/markdown_processed/17_expressions_regulieres.md + title: "Expressions régulières et parsing" + file_name: "17_expressions_regulieres.md" - id: "18" - title: Jupyter et ses notebooks - source_file_path: data/markdown_raw/18_jupyter.md - processed_file_path: data/markdown_processed/18_jupyter.md + title: "Jupyter et ses notebooks" + file_name: "18_jupyter.md" - id: "19" - title: Module Biopython - source_file_path: data/markdown_raw/19_module_biopython.md - processed_file_path: data/markdown_processed/19_module_biopython.md + title: "Module Biopython" + file_name: "19_module_biopython.md" - id: "20" - title: Module Numpy - source_file_path: data/markdown_raw/20_module_numpy.md - processed_file_path: data/markdown_processed/20_module_numpy.md + title: "Module Numpy" + file_name: "20_module_numpy.md" - id: "21" - title: Module Matplotlib - source_file_path: data/markdown_raw/21_module_matplotlib.md - processed_file_path: data/markdown_processed/21_module_matplotlib.md + title: "Module Matplotlib" + file_name: "21_module_matplotlib.md" - id: "22" - title: Module Pandas - source_file_path: data/markdown_raw/22_module_pandas.md - processed_file_path: data/markdown_processed/22_module_pandas.md + title: "Module Pandas" + file_name: "22_module_pandas.md" - id: "23" - title: Avoir la classe avec les objets - source_file_path: data/markdown_raw/23_avoir_la_classe_avec_les_objets.md - processed_file_path: data/markdown_processed/23_avoir_la_classe_avec_les_objets.md + 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 - source_file_path: data/markdown_raw/24_avoir_plus_la_classe_avec_les_objets.md - processed_file_path: data/markdown_processed/24_avoir_plus_la_classe_avec_les_objets.md + title: "Avoir plus la classe avec les objets" + file_name: "24_avoir_plus_la_classe_avec_les_objets.md" - id: "25" - title: Fenêtres graphiques et Tkinter - source_file_path: data/markdown_raw/25_tkinter.md - processed_file_path: data/markdown_processed/25_tkinter.md + title: "Fenêtres graphiques et Tkinter" + file_name: "25_tkinter.md" - id: "26" - title: Remarques complémentaires - source_file_path: data/markdown_raw/26_remarques_complementaires.md - processed_file_path: data/markdown_processed/26_remarques_complementaires.md + title: "Remarques complémentaires" + file_name: "26_remarques_complementaires.md" - id: "27" - title: Mini-projets - source_file_path: data/markdown_raw/27_mini_projets.md - processed_file_path: data/markdown_processed/27_mini_projets.md + title: "Mini-projets" + file_name: "27_mini_projets.md" - - id: A - title: Quelques formats de données en biologies - source_file_path: data/markdown_raw/annexe_A_formats_fichiers.md - processed_file_path: data/markdown_processed/annexe_A_formats_fichiers.md + - id: "A" + title: "Quelques formats de données en biologies" + file_name: "annexe_A_formats_fichiers.md" - - id: B - title: Installation de Python - source_file_path: data/markdown_raw/annexe_B_install_python.md - processed_file_path: data/markdown_processed/annexe_B_install_python.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: debutant + - name: "beginner" display_name: "Débutant (L2 / L3)" comment: > This level is for students who have just started learning Python @@ -161,6 +138,8 @@ levels: They are likely in their second or third year of university (L2/L3) and are looking for guidance on fundamental Python topics. + prompt_file: "prompt_templates/common.txt" + chapters: - "01" - "02" @@ -172,13 +151,15 @@ levels: - "11" - "12" - - name: intermediaire + - name: "intermediate" display_name: "Intermédiaire (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_file: "prompt_templates/common.txt" + chapters: - "01" - "02" @@ -198,7 +179,7 @@ levels: - "16" - "B" - - name: avance + - name: "advanced" display_name: "Avancé (Python 2)" comment: > This level is for students who have a solid understanding of Python @@ -206,6 +187,8 @@ levels: They have completed a Python 1 course or have equivalent experience and are ready to explore more complex concepts. + prompt_file: "prompt_templates/common.txt" + chapters: - "01" - "02" From d8c2ef8b622c20c9be1790a097f6d1b5d79a3788 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 17:01:01 +0100 Subject: [PATCH 50/61] chore(gitignore): ignore raw and processed course data paths --- .gitignore | 4 ++-- data/markdown_processed/.gitkeep | 0 data/markdown_raw/.gitkeep | 0 3 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 data/markdown_processed/.gitkeep delete mode 100644 data/markdown_raw/.gitkeep diff --git a/.gitignore b/.gitignore index 25725f7..e29d036 100644 --- a/.gitignore +++ b/.gitignore @@ -9,8 +9,8 @@ tmp/ .DS_Store.env # course content -data/markdown_raw/* -data/markdown_processed/* +data/course_raw/* +data/course_processed/* # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/data/markdown_processed/.gitkeep b/data/markdown_processed/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/data/markdown_raw/.gitkeep b/data/markdown_raw/.gitkeep deleted file mode 100644 index e69de29..0000000 From f19eb97680db5ccdbe3bb82b3cbc0ccce5be193f Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 17:06:35 +0100 Subject: [PATCH 51/61] refactor(parse_and_clean_markdown): build chapter source and destination paths from yml file --- src/parse_clean_markdown.py | 62 +++++++++++++++++++++++++++++++------ 1 file changed, 53 insertions(+), 9 deletions(-) diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index aad2448..b99a9ff 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -63,10 +63,36 @@ from logger import create_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. + + 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[Path, Path] + A tuple containing the source path and the processed path for the chapter. + """ + source_path = raw_base_path / file_name + processed_path = processed_base_path / file_name + return source_path, processed_path + + def load_chapters_from_yaml( yaml_path: Path, logger: "loguru.Logger" = loguru.logger ) -> list[dict]: - """Load chapters and levels from a YAML file. + """Load chapters from a YAML file and construct paths. Parameters ---------- @@ -78,13 +104,33 @@ def load_chapters_from_yaml( Returns ------- list[dict] - A list of dictionaries, each containing chapter information. + A list of dictionaries with chapter information, including constructed paths + for source and processed Markdown course files. """ - logger.info(f"Loading chapters from YAML file: {yaml_path}...") + 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) + # 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: @@ -248,12 +294,11 @@ def process_md_files(yaml_path: Path) -> None: logger.info(f"Chapter: {chapter_name} ({i}/{len(chapters)})") # Get source file path from YAML - source_file = chapter.get("source_file_path") - if not source_file: + 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 - source_path = Path(source_file) if not source_path.exists(): logger.warning(f"Markdown file not found: {source_path}") continue @@ -277,11 +322,10 @@ def process_md_files(yaml_path: Path) -> None: content = renumber_headers(content, chapter_number, logger) # Get destination file path from YAML - dest_file = chapter.get("processed_file_path") - if not dest_file: + dest_path = chapter.get("processed_path") + if not dest_path: logger.warning(f"No processed file path defined for chapter {chapter_name}") continue - dest_path = Path(dest_file) # Create output directory if it does not exist dest_path.parent.mkdir(parents=True, exist_ok=True) # Save processed content to destination path From 2b33578c18cd26bfde9531b53f98d474577dcc68 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 19:28:43 +0100 Subject: [PATCH 52/61] refactor(yml): update prompt_file to prompt_path for consistency --- data/chapters_and_levels.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/data/chapters_and_levels.yaml b/data/chapters_and_levels.yaml index 57db403..6339af5 100644 --- a/data/chapters_and_levels.yaml +++ b/data/chapters_and_levels.yaml @@ -138,7 +138,7 @@ levels: They are likely in their second or third year of university (L2/L3) and are looking for guidance on fundamental Python topics. - prompt_file: "prompt_templates/common.txt" + prompt_path: "prompt_templates/common.txt" chapters: - "01" @@ -158,7 +158,7 @@ levels: knowledge. They have some experience but may need to review certain fundamentals while deepening their understanding. - prompt_file: "prompt_templates/common.txt" + prompt_path: "prompt_templates/common.txt" chapters: - "01" @@ -187,7 +187,7 @@ levels: They have completed a Python 1 course or have equivalent experience and are ready to explore more complex concepts. - prompt_file: "prompt_templates/common.txt" + prompt_path: "prompt_templates/common.txt" chapters: - "01" From b66f18cf9304fcb50815e5beb6f379b504e6c8fc Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 19:31:16 +0100 Subject: [PATCH 53/61] feat(query_chatbot): answer to the user question even though it is not relevant to the user level + improve logging --- src/query_chatbot.py | 322 ++++++++++++++++++++++--------------------- 1 file changed, 167 insertions(+), 155 deletions(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index e67f954..c912996 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -15,7 +15,6 @@ [--db-path "database_path"] [--embedding-model "embedding_model"] [--provider-emb "provider_embeddings_name"] - [--prompt_path "prompt_path"] [--include-metadata] Arguments: @@ -23,9 +22,7 @@ "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: - - "debutant" : for beginner users - - "intermediaire" : for intermediate users - - "avance" : for advanced users + "beginner", "intermediate", "advanced". Options: @@ -60,10 +57,6 @@ It can be either "openai" or "openrouter". Default: "openai" - --prompt_path (Path): - File path to the text file containing the prompt template. - Default: "prompts/zero_shot.txt" - --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. @@ -72,11 +65,10 @@ Example: ======== uv run src/query_chatbot.py --query "D'où vient le nom Python ?" \ - --level "debutant" --model "gpt-4o" \ + --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" \ - --prompt_path "prompts/zero_shot.txt" --include-metadata + --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" @@ -116,21 +108,131 @@ "Désolé, je suis un assistant pour l'apprentissage de la programmation Python. " "Je ne suis pas en mesure de répondre à cette question." ), + ( + "Je ne suis pas sûr de pouvoir répondre à cette question, car elle ne semble " + "pas être liée à la programmation Python. Si tu as des questions sur Python, " + "n'hésite pas à me les poser, je serai heureux de t'aider !" + ), ] MSGS_QUERY_OUT_OF_SCOPE_LEVEL = [ ( "Cette question fait référence à des notions qui ne sont pas encore abordées " - "dans ce cours. Je te conseille de te concentrer d'abord sur les chapitres " - "actuellement au programme." + "dans ce cours." + ), + ( + "Cette notion n'est pas encore abordée à votre niveau actuel " + "et fait partie de la suite du programme." ), ( - "Je suis désolé, mais cette question semble faire référence à des notions qui " - "ne sont pas encore couvertes dans le cours. Je te recommande de suivre les " - "chapitres dans l'ordre pour une meilleure compréhension." + "Cette question fait référence à des notions qui dépassent " + "le cadre du niveau actuel de votre formation." ), ] +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 + ------- + dict[str, dict] + Dictionary mapping level name to its info: + { + "name": str, + "display_name": str, + "comment": str, + "prompt_path": str, + "chapters": list[str] + } + """ + 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. + + 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. + + Returns + ------- + 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. + """ + # 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, embedding_model: str, @@ -157,6 +259,8 @@ def load_database( Chroma: The prepared vector database. """ 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 @@ -172,47 +276,9 @@ def load_database( return vector_db -def get_level_relevant_chapter_ids( - user_level: str, course_yaml: Path, logger: "loguru.Logger" = loguru.logger -) -> list[str]: - """Get the list of chapter IDs relevant to the user level. - - Parameters - ---------- - user_level : str - The user level used to adapt model responses. - course_yaml : Path - The file path to the YAML file defining the course chapters and student levels. - logger: "loguru.Logger" - Logger for logging messages. - - Returns - ------- - list[str]: List of chapter IDs relevant to the user level. - """ - logger.info("Getting the list of chapter IDs relevant to the user level...") - with open(course_yaml, encoding="utf-8") as f: - course_data = yaml.safe_load(f) - # Get the list of chapter IDs relevant to the user level - levels = course_data.get("levels", []) - if not levels: - logger.warning("No levels defined in YAML file.") - return [] - - for level in levels: - if level.get("name") == user_level: - chapter_ids = [str(ch_id) for ch_id in level.get("chapters", [])] - logger.success(f"Chapter IDs for level '{user_level}': {chapter_ids}") - return chapter_ids - - logger.warning(f"User level '{user_level}' not found in YAML file.") - return [] - - def search_similarity_in_database( vector_db: Chroma, user_query: str, - level_relevant_chapter_ids: list[str], nb_chunks: int = 3, score_threshold: float = 0.35, logger: "loguru.Logger" = loguru.logger, @@ -225,8 +291,6 @@ def search_similarity_in_database( The textual database to search. user_query : str The query text. - level_relevant_chapter_ids : list[str] - List of chapter IDs relevant to the user level. nb_chunks : int The number of top matching documents to retrieve. score_threshold : float @@ -240,19 +304,12 @@ def search_similarity_in_database( List of relevant documents found in the database. """ logger.info("Searching for relevant documents in the database...") - - # Create a filter dictionary to restrict the search to user-level relevant chapters - level_relevant_chapter_ids_filter_dict = { - "chapter_id": {"$in": level_relevant_chapter_ids} - } - # Define the retriever retriever = vector_db.as_retriever( search_type="similarity_score_threshold", search_kwargs={ "k": nb_chunks, "score_threshold": score_threshold, - "filter": level_relevant_chapter_ids_filter_dict, }, ) # Perform a similarity search @@ -266,77 +323,16 @@ def search_similarity_in_database( logger.debug(f"Number of tokens: {chunk.metadata['nb_tokens']}") logger.debug("Chunk content:") for line in chunk.page_content.splitlines(): - logger.debug(f"{line}") + # 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." ) return relevant_chunks -def generate_answer_without_model( - vector_db: Chroma, - user_query: str, - logger: "loguru.Logger" = loguru.logger, - nb_chunks: int = 3, - score_threshold: float = 0.35, -) -> str: - """Generate an answer to the user query without calling the model. - - This function is used when no relevant documents are found in the database for the - user query. It displays personalized messages to the user based on whether the - query is not related to the user level or not related to the course at all. - - - Parameters - ---------- - vector_db : Chroma - The textual database to search. - user_query : str - The query text. - logger : loguru.Logger - Logger for logging messages. - nb_chunks : int - The number of top matching documents to retrieve. - score_threshold : float - The relevance score threshold for filtering the results. - - Returns - ------- - response : str - The response message to be displayed to the user. - """ - logger.debug(f"Question: {user_query}") - # Define the retriever without level filter - retriever = vector_db.as_retriever( - search_type="similarity_score_threshold", - search_kwargs={ - "k": nb_chunks, - "score_threshold": score_threshold, - }, - ) - # Perform a similarity search - relevant_chunks = retriever.invoke(user_query) - # If there are some relevant chunks but they are not related to the user level - if relevant_chunks != []: - logger.warning( - "This question seems to be related to the course " - "but not relevant to the user level." - ) - response = secrets.choice(MSGS_QUERY_OUT_OF_SCOPE_LEVEL) - logger.debug( - f"Answer generated automatically without calling the model: {response}" - ) - else: - logger.warning( - "This question does not seem to be related to the course content." - ) - response = secrets.choice(MSGS_QUERY_NOT_RELATED) - logger.debug( - f"Answer generated automatically without calling the model: {response}" - ) - return response - - def calculate_nb_tokens(text: str) -> int: """Calculate the number of tokens in a text. @@ -359,9 +355,11 @@ def calculate_nb_tokens(text: str) -> int: def generate_answer( 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", @@ -372,6 +370,8 @@ def generate_answer( ---------- query : str The user query. + 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 @@ -379,6 +379,8 @@ def generate_answer( 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. prompt_path : Path @@ -431,13 +433,32 @@ def generate_answer( # Generate the answer answer = answer_chain.invoke(input_data) logger.debug(f"Question: {query}") - logger.debug(f"Answer generated by the model: {answer}") + logger.debug(f"Prompt path: {prompt_path}") + logger.debug(f"LLM model used: {model_name} from {provider_llm_name}") + logger.debug(f"Answer generated by the LLM: {answer}") # 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 @@ -579,7 +600,7 @@ def display_answer( "--level", "user_level", type=click.Choice( - ["debutant", "intermediaire", "avance"], + ["beginner", "intermediate", "advanced"], case_sensitive=False, ), required=True, @@ -640,13 +661,6 @@ def display_answer( default="openai", help="Name of the embeddings model provider to use.", ) -@click.option( - "--prompt_path", - type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True), - default="prompts/zero_shot.txt", - show_default=True, - help="File path to the text file containing the prompt template.", -) @click.option( "--include-metadata", is_flag=True, @@ -662,7 +676,6 @@ def interrogate_model( database_path: str, embedding_model: str, provider_embeddings_name: str, - prompt_path: Path, *, include_metadata: bool, ) -> None: @@ -674,44 +687,43 @@ def interrogate_model( f"query_chatbot_{datetime.now().strftime('%H:%M:%S')}.log" ) logger = create_logger(log_path) - # Log the user query and parameters logger.info("Starting the command line interface for querying the chatbot...") - logger.debug(f"User query: {user_query}") - logger.debug(f"User level: {user_level}") - logger.debug(f"YAML path: {course_yaml}") - logger.debug(f"Model name: {model_name}") - logger.debug(f"Provider LLM name: {provider_llm_name}") - logger.debug(f"Database path: {database_path}") - logger.debug(f"Embedding model: {embedding_model}") - logger.debug(f"Provider embeddings name: {provider_embeddings_name}") - logger.debug(f"Prompt path: {prompt_path}") - logger.debug(f"Include metadata: {include_metadata}") + + # 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(database_path, embedding_model, provider_embeddings_name) - # Get the list of chapter IDs relevant to the user level - level_relevant_chapter_ids = get_level_relevant_chapter_ids( - user_level, course_yaml, logger - ) # Search for relevant documents in the database relevant_chunks = search_similarity_in_database( - vector_db, user_query, level_relevant_chapter_ids, logger=logger + vector_db, user_query, logger=logger ) # ANSWER GENERATION # Check if there are relevant documents if relevant_chunks == []: + 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 = generate_answer_without_model(vector_db, user_query) + 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: # Generate the answer 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, From 8c396442842c54d2b53794e58be3847c5d581b09 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 21:11:46 +0100 Subject: [PATCH 54/61] Refactor(create_database): update retrieval to use processed_file_path --- src/create_database.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 37f1f9d..bd84256 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -175,11 +175,11 @@ def load_documents( logger.info("Converting Markdown file to Document...") for chapter in chapters: # Get the processed Markdown file path - processed_path = chapter.get("processed_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_file_path defined for chapter id={chapter.get('id')} " + f"No processed_path defined for chapter id={chapter.get('id')} " f"title={chapter.get('title')}" ) continue From f838700eeef212b88a66959df7d58108107529d1 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 21:13:02 +0100 Subject: [PATCH 55/61] refactor(query_chatbot): log answer sentence by sentence for clarity --- src/query_chatbot.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/query_chatbot.py b/src/query_chatbot.py index c912996..9f75144 100644 --- a/src/query_chatbot.py +++ b/src/query_chatbot.py @@ -435,7 +435,10 @@ def generate_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(f"Answer generated by the LLM: {answer}") + 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) From 3fd79396bac4a134364ee66b02305cc47dc5fcc6 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 21:26:44 +0100 Subject: [PATCH 56/61] chore: added .gitkeep files to ensure data directories are tracked --- .gitignore | 3 +++ data/course_processed/.gitkeep | 0 data/course_raw/.gitkeep | 0 3 files changed, 3 insertions(+) create mode 100644 data/course_processed/.gitkeep create mode 100644 data/course_raw/.gitkeep diff --git a/.gitignore b/.gitignore index e29d036..55605d3 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,9 @@ tmp/ # course content 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/data/course_processed/.gitkeep b/data/course_processed/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data/course_raw/.gitkeep b/data/course_raw/.gitkeep new file mode 100644 index 0000000..e69de29 From 418cb3e6e46a64ddad81eca15a50fd95d7a3c8d9 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 21:30:30 +0100 Subject: [PATCH 57/61] chore: update readme --- README.md | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 8b70f7a..ec06d3e 100644 --- a/README.md +++ b/README.md @@ -47,19 +47,19 @@ uv sync ```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 +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 and OpenRouter API key @@ -94,28 +94,29 @@ This command will create a Chroma vector database from the processed Markdown fi uv run python src/query_chatbot.py --query "Your question here" \ --level "user_level" \ --model "model_name" \ - --prompt_path "path_to_prompt_template" \ + --provider-llm "provider_name" \ --include-metadata ``` ### Options -- 📚 **User Level**: For instance: `debutant`, `intermediaire`, `avance`, etc. This helps the chatbot tailor its responses to the user's level of understanding. -- 🤖 **Model Selection**: For instance: `gpt-4o`, `deepseek/deepseek-v3.2`, etc. -- 📄 **Prompt Template**: The file path to the text file containing the instruction and format for the chatbot's response. +- 📚 **User Level**: Specify the user's Python knowledge level to tailor the chatbot's responses. + Choose between: `beginner`, `intermediate`, `advanced`. +- 🤖 **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 uv run python src/query_chatbot.py --query "What is the difference between list and set ?" \ - --level "intermediaire" \ - --model gpt-4o \ - --prompt_path "prompts/zero_shot.txt" \ + --level "advanced" \ + --model "gpt-4o" \ + --provider-llm "openai" \ --include-metadata ``` -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. The response will be generated based on the prompt template located at [`prompts/zero_shot.txt`](prompts/zero_shot.txt) and will include metadata about the sources of the answer. +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: @@ -136,7 +137,7 @@ For more information, you can refer to the following sources: ```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. From 340ce0036455e7cb7fb56f5164fd6cd11d69beb4 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 21:53:23 +0100 Subject: [PATCH 58/61] refactor: add checks to avoid AttributeError/UnboundLocalError --- src/create_database.py | 2 +- src/parse_clean_markdown.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index bd84256..98b37ed 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -661,7 +661,7 @@ def generate_data_store( 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 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:,}") diff --git a/src/parse_clean_markdown.py b/src/parse_clean_markdown.py index b99a9ff..dc534d0 100644 --- a/src/parse_clean_markdown.py +++ b/src/parse_clean_markdown.py @@ -111,7 +111,7 @@ def load_chapters_from_yaml( try: with yaml_path.open("r", encoding="utf-8") as file: # Load YAML data - data = yaml.safe_load(file) + data = yaml.safe_load(file) or {} # Get chapter information chapters = data.get("chapters", []) # Get base paths of raw and processed courses @@ -150,7 +150,7 @@ def clean_python_comments(content: str, logger: "loguru.Logger" = loguru.logger) ``` will be converted to: ```python - # This is a comment. + #This is a comment. ``` Parameters @@ -285,7 +285,7 @@ def process_md_files(yaml_path: Path) -> None: logger.info("Starting Markdown processing...") # Load chapters from YAML file - chapters = load_chapters_from_yaml(yaml_path) + chapters = load_chapters_from_yaml(yaml_path, logger) # Process each chapter's Markdown file saved_count = 0 From 9bd746b56eb3fbbd7335666b41055486929243ed Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 22:21:53 +0100 Subject: [PATCH 59/61] refactor(create_database): remove short-form options (-c, -s...) on CLI --- src/create_database.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/create_database.py b/src/create_database.py index 98b37ed..e0b4f87 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -574,28 +574,24 @@ def save_to_chroma( ), ) @click.option( - "-c", "--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( - "-s", "--chunk-size", default=1000, type=click.IntRange(min=50), help="Size of the text chunks to be created.", ) @click.option( - "-o", "--chunk-overlap", default=200, type=click.IntRange(min=0), help="Overlap between text chunks.", ) @click.option( - "-m", "--model-name", default="text-embedding-3-large", type=str, @@ -603,7 +599,6 @@ def save_to_chroma( "chosen from OpenAI and OpenRouter`s embedding models. ", ) @click.option( - "-p", "--provider-name", default="openai", type=click.Choice(["openrouter", "openai"], case_sensitive=False), From 911c2744e01bba2700ec2337f69ef558af3b0795 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 22:24:11 +0100 Subject: [PATCH 60/61] refactor(prompt): translate the title in french and remove useless `<<<` --- prompt_templates/common.txt | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/prompt_templates/common.txt b/prompt_templates/common.txt index 176c8c9..8ea07a8 100644 --- a/prompt_templates/common.txt +++ b/prompt_templates/common.txt @@ -1,4 +1,4 @@ -# Python Programming for Biology - Contextual Q&A Task +# Assistant conversationnel contextuel – Programmation Python en biologie ## Rôle @@ -14,9 +14,7 @@ Tu es un tuteur pédagogique spécialisé en Bio-informatique. Ton objectif est ## DOCUMENTS DE RÉFÉRENCE (CONTEXTE) Voici les seules ressources que tu es autorisé à utiliser pour répondre : -<<< {contexte} ->>> ## TÂCHE From 5f1fe1cf3302080a0e90aae5ceb7211029a00194 Mon Sep 17 00:00:00 2001 From: essmaw Date: Wed, 11 Feb 2026 22:30:43 +0100 Subject: [PATCH 61/61] refactor(create_database): rename arguments for consistency --- README.md | 4 ++-- src/create_database.py | 25 +++++++++++++------------ 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index ec06d3e..094f783 100644 --- a/README.md +++ b/README.md @@ -78,8 +78,8 @@ OPENROUTER_API_KEY= ```bash uv run src/create_database.py --course-yaml data/chapters_and_levels.yaml \ --chroma-path chroma_db \ - --model-name text-embedding-3-large \ - --provider-name openai + --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. diff --git a/src/create_database.py b/src/create_database.py index e0b4f87..04705a3 100644 --- a/src/create_database.py +++ b/src/create_database.py @@ -7,9 +7,10 @@ Usage: ====== uv run src/create_database.py --course-yaml [course-yaml] - --chroma-path [chroma-path] - --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] - --model-name [model-name] --provider-name [provider-name] + --chroma-path [chroma-path] + --chunk-size [chunk-size] --chunk-overlap [chunk-overlap] + --embedding-model [embedding-model] + --model-provider [model-provider] Arguments: ========== @@ -22,11 +23,11 @@ The size of the text chunks to be created. Default is 1000. --chunk-overlap : int (optional) The overlap between text chunks. Default is 200. - --model-name : str (optional) + --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". - --provider-name : str (optional) + --model-provider : str (optional) Name of the embedding provider to use. Possible choices are "openrouter" and "openai". Default is "openai". @@ -36,8 +37,8 @@ ======== uv run src/create_database.py --course-yaml data/chapters_and_levels.yaml \ --chroma-path chroma_db \ - --model-name text-embedding-3-large \ - --provider-name openai + --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. @@ -592,14 +593,14 @@ def save_to_chroma( help="Overlap between text chunks.", ) @click.option( - "--model-name", + "--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( - "--provider-name", + "--model-provider", default="openai", type=click.Choice(["openrouter", "openai"], case_sensitive=False), help="Name of the embedding provider to use.", @@ -609,8 +610,8 @@ def generate_data_store( chroma_path: Path, chunk_size: int, chunk_overlap: int, - model_name: str, - provider_name: str, + embedding_model: str, + model_provider: str, ) -> None: """Build a ChromaDB store from chunked text with metadata.""" # Set-up the logger @@ -662,7 +663,7 @@ def generate_data_store( logger.info(f"Total number of tokens: {count_tokens:,}") # save the embeddings to ChromaDB - save_to_chroma(all_chunks, model_name, provider_name, chroma_path, logger) + save_to_chroma(all_chunks, embedding_model, model_provider, chroma_path, logger) if __name__ == "__main__":