Fixed docstrings.

readme_ready/index/create_vector_store.py

@@ -95,23 +95,23 @@ def create_vector_store(
     """
     Creates a vector store from Markdown documents.
     
-        Loads documents from the specified root directory, splits the text into chunks,
-        creates a vector store using the selected LLM model, and saves the vector store
-        to the output path. Ignores files matching the patterns provided in the ignore list.
-    
-        Args:
-            root: The root directory containing the documents to be processed.
-            output: The directory where the vector store will be saved.
-            ignore: A list of file patterns to ignore during document loading.
-            llms: A list of LLMModels to use for generating embeddings.
-            device: The device to use for embedding generation (e.g., 'cpu' or 'cuda').
-    
-        Returns:
-            None.
-    
-        Raises:
-            IOError: If an error occurs accessing the filesystem.
-            Exception: If an error occurs during document loading, splitting, or vector store creation.
+    Loads documents from the specified root directory, splits the text into chunks,
+    creates a vector store using the selected LLM model, and saves the vector store
+    to the output path. Ignores files matching the patterns provided in the ignore list.
+
+    Args:
+        root: The root directory containing the documents to be processed.
+        output: The directory where the vector store will be saved.
+        ignore: A list of file patterns to ignore during document loading.
+        llms: A list of LLMModels to use for generating embeddings.
+        device: The device to use for embedding generation (e.g., 'cpu' or 'cuda').
+
+    Returns:
+        None.
+
+    Raises:
+        IOError: If an error occurs accessing the filesystem.
+        Exception: If an error occurs during document loading, splitting, or vector store creation.
     """
 
     llm = llms[1] if len(llms) > 1 else llms[0]

readme_ready/index/index.py

@@ -11,7 +11,7 @@
 from .process_repository import process_repository
 
 
-def index(config: AutodocRepoConfig):
+def index(config: AutodocRepoConfig) -> None:
     """
     Indexes a repository to generate documentation and vector store files.
 
@@ -26,9 +26,6 @@ def index(config: AutodocRepoConfig):
     Returns:
         None.
 
-    Raises:
-        OSError: If an error occurs while creating directories.
-        Exception: If an error occurs during repository processing, Markdown conversion, or vector store creation.
     """
     json_path = Path(config.output) / "docs" / "json"
     markdown_path = Path(config.output) / "docs" / "markdown"

readme_ready/index/process_repository.py

@@ -34,27 +34,27 @@
 from .select_model import select_model
 
 
-def process_repository(config: AutodocRepoConfig, dry_run=False):
-        """
-        Creates a vector store from Markdown documents.
-
-        Loads documents from the specified root directory, splits the text into chunks,
-        creates a vector store using the selected LLM model, and saves the vector store
-        to the output path. Ignores files matching the patterns provided in the ignore list.
-
-        Args:
-            root: The root directory containing the documents to be processed.
-            output: The directory where the vector store will be saved.
-            ignore: A list of file patterns to ignore during document loading.
-            llms: A list of LLMModels to use for generating embeddings.
-            device: The device to use for embedding generation (e.g., 'cpu' or 'cuda').
-
-        Returns:
-            None.
-
-        Raises:
-            IOError: If an error occurs accessing the filesystem.
-            Exception: If an error occurs during document loading, splitting, or vector store creation.
+def process_repository(config: AutodocRepoConfig, dry_run=False) -> None:
+    """
+    Creates a vector store from Markdown documents.
+
+    Loads documents from the specified root directory, splits the text into chunks,
+    creates a vector store using the selected LLM model, and saves the vector store
+    to the output path. Ignores files matching the patterns provided in the ignore list.
+
+    Args:
+        root: The root directory containing the documents to be processed.
+        output: The directory where the vector store will be saved.
+        ignore: A list of file patterns to ignore during document loading.
+        llms: A list of LLMModels to use for generating embeddings.
+        device: The device to use for embedding generation (e.g., 'cpu' or 'cuda').
+
+    Returns:
+        None.
+
+    Raises:
+        IOError: If an error occurs accessing the filesystem.
+        Exception: If an error occurs during document loading, splitting, or vector store creation.
     """
 
 

readme_ready/query/create_chat_chain.py

@@ -152,28 +152,28 @@ def make_qa_chain(
     """
     Creates a question-answering (QA) chain for the specified project.
 
-        Initializes and configures the QA chain using the provided repository and user configurations.
-        Selects the appropriate language model (LLM), sets up the retriever with a history-aware mechanism,
-        and combines document chains for processing queries. The chain facilitates interaction with the
-        vector store to retrieve and process relevant information based on user queries.
-
-        Args:
-            project_name: The name of the project for which the QA chain is being created.
-            repository_url: The URL of the repository containing the project.
-            content_type: The type of content to be processed (e.g., 'code', 'documentation').
-            chat_prompt: The prompt template used for generating chat responses.
-            target_audience: The intended audience for the QA responses.
-            vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
-            llms: A list of LLMModels to select from for generating embeddings and responses.
-            device: The device to use for model inference (default is 'cpu').
-            on_token_stream: Optional callback for handling token streams during model inference.
-
-        Returns:
-            A retrieval chain configured for question-answering, combining the retriever and document processing chain.
-
-        Raises:
-            ValueError: If no suitable model is found in the provided LLMs.
-            RuntimeError: If there is an issue initializing the chat models or creating the chains.
+    Initializes and configures the QA chain using the provided repository and user configurations.
+    Selects the appropriate language model (LLM), sets up the retriever with a history-aware mechanism,
+    and combines document chains for processing queries. The chain facilitates interaction with the
+    vector store to retrieve and process relevant information based on user queries.
+
+    Args:
+        project_name: The name of the project for which the QA chain is being created.
+        repository_url: The URL of the repository containing the project.
+        content_type: The type of content to be processed (e.g., 'code', 'documentation').
+        chat_prompt: The prompt template used for generating chat responses.
+        target_audience: The intended audience for the QA responses.
+        vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
+        llms: A list of LLMModels to select from for generating embeddings and responses.
+        device: The device to use for model inference (default is 'cpu').
+        on_token_stream: Optional callback for handling token streams during model inference.
+
+    Returns:
+        A retrieval chain configured for question-answering, combining the retriever and document processing chain.
+
+    Raises:
+        ValueError: If no suitable model is found in the provided LLMs.
+        RuntimeError: If there is an issue initializing the chat models or creating the chains.
     """
     llm = llms[1] if len(llms) > 1 else llms[0]
     llm_name = llm.value
@@ -268,29 +268,29 @@ def make_readme_chain(
     """
     Creates a README generation chain for the specified project.
 
-        Initializes and configures the README generation chain using the provided repository, user, and README configurations.
-        Selects the appropriate language model (LLM), sets up the document processing chain with the specified prompts,
-        and integrates with the vector store to generate comprehensive README sections based on project data.
-        The chain facilitates automated generation of README files tailored to the project's specifications.
-
-        Args:
-            project_name: The name of the project for which the README is being generated.
-            repository_url: The URL of the repository containing the project.
-            content_type: The type of content to be included in the README (e.g., 'overview', 'installation').
-            chat_prompt: The prompt template used for generating README content.
-            target_audience: The intended audience for the README.
-            vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
-            llms: A list of LLMModels to select from for generating README content.
-            peft_model: An optional parameter specifying a PEFT (Parameter-Efficient Fine-Tuning) model for enhanced performance.
-            device: The device to use for model inference (default is 'cpu').
-            on_token_stream: Optional callback for handling token streams during model inference.
-
-        Returns:
-            A retrieval chain configured for README generation, combining the retriever and document processing chain.
-
-        Raises:
-            ValueError: If no suitable model is found in the provided LLMs.
-            RuntimeError: If there is an issue initializing the chat models or creating the chains.
+    Initializes and configures the README generation chain using the provided repository, user, and README configurations.
+    Selects the appropriate language model (LLM), sets up the document processing chain with the specified prompts,
+    and integrates with the vector store to generate comprehensive README sections based on project data.
+    The chain facilitates automated generation of README files tailored to the project's specifications.
+
+    Args:
+        project_name: The name of the project for which the README is being generated.
+        repository_url: The URL of the repository containing the project.
+        content_type: The type of content to be included in the README (e.g., 'overview', 'installation').
+        chat_prompt: The prompt template used for generating README content.
+        target_audience: The intended audience for the README.
+        vectorstore: An instance of HNSWLib representing the vector store containing document embeddings.
+        llms: A list of LLMModels to select from for generating README content.
+        peft_model: An optional parameter specifying a PEFT (Parameter-Efficient Fine-Tuning) model for enhanced performance.
+        device: The device to use for model inference (default is 'cpu').
+        on_token_stream: Optional callback for handling token streams during model inference.
+
+    Returns:
+        A retrieval chain configured for README generation, combining the retriever and document processing chain.
+
+    Raises:
+        ValueError: If no suitable model is found in the provided LLMs.
+        RuntimeError: If there is an issue initializing the chat models or creating the chains.
     """
     llm = llms[1] if len(llms) > 1 else llms[0]
     llm_name = llm.value

readme_ready/query/query.py

@@ -80,21 +80,21 @@ def query(repo_config: AutodocRepoConfig, user_confg: AutodocUserConfig):
     """
     Queries the repository for information based on user input.
     
-        Initializes a question-answering chain, displays a welcome message, and enters a loop to
-        prompt the user for questions about the repository. Processes each question by invoking
-        the QA chain, updates the chat history, and displays the response in Markdown format. The
-        loop continues until the user types 'exit'.
-    
-        Args:
-            repo_config: An AutodocRepoConfig instance containing configuration settings for the repository.
-            user_confg: An AutodocUserConfig instance containing user-specific configuration settings.
-    
-        Returns:
-            None.
-    
-        Raises:
-            Exception: If an error occurs during the initialization of the QA chain, displaying the welcome message,
-                or during the invocation of the QA chain.
+    Initializes a question-answering chain, displays a welcome message, and enters a loop to
+    prompt the user for questions about the repository. Processes each question by invoking
+    the QA chain, updates the chat history, and displays the response in Markdown format. The
+    loop continues until the user types 'exit'.
+
+    Args:
+        repo_config: An AutodocRepoConfig instance containing configuration settings for the repository.
+        user_confg: An AutodocUserConfig instance containing user-specific configuration settings.
+
+    Returns:
+        None.
+
+    Raises:
+        Exception: If an error occurs during the initialization of the QA chain, displaying the welcome message,
+            or during the invocation of the QA chain.
     """
     chain = init_qa_chain(repo_config, user_confg)
 
@@ -127,22 +127,22 @@ def generate_readme(
     """
     Generates a README file based on repository and user configurations.
     
-        Initializes a README generation chain, clears the terminal, and prepares the output file.
-        Iterates over the specified headings in the README configuration, generates content for each
-        section by invoking the chain, and writes the content in Markdown format to the README file.
-        Handles any RuntimeError that occurs during the process.
-    
-        Args:
-            repo_config: An AutodocRepoConfig instance containing configuration settings for the repository.
-            user_config: An AutodocUserConfig instance containing user-specific configuration settings.
-            readme_config: An AutodocReadmeConfig instance containing configuration settings for README generation.
-    
-        Returns:
-            None.
-    
-        Raises:
-            IOError: If an error occurs while accessing or writing to the filesystem.
-            Exception: If an error occurs during the initialization of the README chain or content generation.
+    Initializes a README generation chain, clears the terminal, and prepares the output file.
+    Iterates over the specified headings in the README configuration, generates content for each
+    section by invoking the chain, and writes the content in Markdown format to the README file.
+    Handles any RuntimeError that occurs during the process.
+
+    Args:
+        repo_config: An AutodocRepoConfig instance containing configuration settings for the repository.
+        user_config: An AutodocUserConfig instance containing user-specific configuration settings.
+        readme_config: An AutodocReadmeConfig instance containing configuration settings for README generation.
+
+    Returns:
+        None.
+
+    Raises:
+        IOError: If an error occurs while accessing or writing to the filesystem.
+        Exception: If an error occurs during the initialization of the README chain or content generation.
     """
     chain = init_readme_chain(repo_config, user_config)