FetchPoint

A Python library for SharePoint Online integration with federated authentication support.

Overview

FetchPoint is a clean, enterprise-ready library for SharePoint Online integration with federated authentication support. Provides secure, read-only access to files stored in SharePoint document libraries with comprehensive error handling, metadata extraction, and Excel file focus. Designed for enterprise environments with Azure AD and federated authentication.

Key Features

• Federated Authentication: Azure AD and enterprise identity provider support
• Read-Only Operations: Secure file listing and downloading
• Excel Focus: Optimized for .xlsx, .xls, .xlsm, .xlsb files
• Path Validation: Hierarchical folder navigation with detailed error reporting
• Context Manager: Clean resource management
• Comprehensive Error Handling: Detailed diagnostics for troubleshooting
• No Environment Dependencies: Explicit configuration required (environment variables optional)

Installation

uv add fetchpoint

Quick Start

from fetchpoint import SharePointClient, create_sharepoint_config

# Create configuration
config = create_sharepoint_config(
    username="user@company.com",
    password="your_password",
    sharepoint_url="https://company.sharepoint.com/sites/project"
)

# Use context manager (recommended)
with SharePointClient(config) as client:
    # List Excel files
    files = client.list_excel_files(
        library_name="Documents",
        folder_path="General/Reports"
    )

    # Download files
    results = client.download_files(
        library_name="Documents",
        folder_path="General/Reports",
        filenames=files,
        download_dir="./downloads"
    )

Configuration

Method 1: Explicit Configuration

from fetchpoint import create_sharepoint_config

config = create_sharepoint_config(
    username="user@company.com",           # Required: SharePoint username (email)
    password="your_password",              # Required: User password
    sharepoint_url="https://company.sharepoint.com/sites/yoursite",  # Required: SharePoint site URL
    timeout_seconds=30,                    # Optional: Connection timeout (default: 30)
    max_file_size_mb=100                   # Optional: File size limit (default: 100)
)

Method 2: Dictionary Configuration

from fetchpoint import SharePointClient

client = SharePointClient.from_dict({
    "username": "user@company.com",
    "password": "your_password",
    "sharepoint_url": "https://company.sharepoint.com/sites/yoursite"
})

Method 3: Environment Variables (Legacy)

# Required
SHAREPOINT_URL=https://company.sharepoint.com/sites/yoursite
SHAREPOINT_USERNAME=user@company.com
SHAREPOINT_PASSWORD=your_password

# Optional
SHAREPOINT_TIMEOUT_SECONDS=30
SHAREPOINT_MAX_FILE_SIZE_MB=100
SHAREPOINT_AUTH_TYPE=federated
SHAREPOINT_SESSION_TIMEOUT=3600
SHAREPOINT_LOG_LEVEL=INFO

Important: SHAREPOINT_URL is optional when using explicit configuration methods. When using environment variables, it's required and specifies the complete SharePoint site URL.

API Reference

SharePointClient

Main client class for SharePoint operations.

Methods

connect() -> bool

• Establish connection to SharePoint
• Returns: True if successful

test_connection() -> bool

• Validate current connection
• Returns: True if connection is valid

disconnect() -> None

• Clean up connection and resources

list_excel_files(library_name: str = "Documents", folder_path: Optional[str] = None) -> list[str]

• List Excel file names in specified location
• Args: library_name (default: "Documents"), folder_path (optional, e.g., "General/Reports")
• Returns: List of Excel filenames

list_files(library: str, path: list[str]) -> list[FileInfo]

• List files with complete metadata
• Args: library name, path segments
• Returns: List of FileInfo objects with metadata

list_folders(library_name: str = "Documents", folder_path: Optional[str] = None) -> list[str]

• List folder names in specified location
• Args: library_name (default: "Documents"), folder_path (optional)
• Returns: List of folder names

download_file(library: str, path: list[str], local_path: str) -> None

• Download single file
• Args: library name, path segments including filename, local_path

download_files(library_name: str, folder_path: str, filenames: list[str], download_dir: str) -> dict

• Download multiple files with per-file error handling
• Returns: Dictionary with success/failure status for each file

get_file_details(library_name: str, folder_path: Optional[str], filename: str) -> Optional[FileInfo]

• Get comprehensive file metadata
• Args: library_name, folder_path (optional), filename
• Returns: FileInfo object with complete metadata, or None if file not found

validate_paths(library_name: str = "Documents") -> dict

• Validate configured SharePoint paths
• Args: library_name (default: "Documents")
• Returns: Validation results with error details and available folders

discover_structure(library_name: str = "Documents", max_depth: int = 3) -> dict

• Explore SharePoint library structure
• Args: library_name (default: "Documents"), max_depth (default: 3)
• Returns: Hierarchical representation of folders and files

validate_decoupled_paths() -> dict

• Validate paths that span different SharePoint libraries
• Each path uses its own library name (first segment)
• Returns: Validation results with library-specific error details

get_file_content(library: str, path: list[str]) -> bytes

• Get file content as bytes without downloading to disk
• Args: library name, path segments including filename
• Returns: File content as bytes for in-memory processing

read_excel_content(library: str, path: list[str], sheet_name: Optional[str] = None, column_mapping: Optional[dict[str, str]] = None, skip_empty_rows: bool = True) -> list[dict[str, Any]]

• Read Excel file directly from SharePoint as structured data
• Args: library, path, optional sheet_name, column_mapping, skip_empty_rows
• Returns: List of dictionaries representing Excel rows

get_excel_sheet_names(library: str, path: list[str]) -> list[str]

• Get list of sheet names from an Excel file in SharePoint
• Args: library name, path segments including filename
• Returns: List of sheet names in the workbook

Configuration Functions

create_sharepoint_config(...) -> SharePointAuthConfig

• Create configuration with explicit parameters

create_config_from_dict(config_dict: dict) -> SharePointAuthConfig

• Create configuration from dictionary

create_authenticated_context(config: SharePointAuthConfig) -> ClientContext

• Create authenticated SharePoint context

Models

SharePointAuthConfig

• Configuration model with validation
• Fields: username, password, sharepoint_url, timeout_seconds, max_file_size_mb

FileInfo

• File metadata model
• Fields: name, size_bytes, size_mb, created_date, modified_date, file_type, library, relative_path, created_by, modified_by

FileType

• Enum for supported Excel extensions
• Values: XLSX, XLS, XLSM, XLSB

Exceptions

All exceptions inherit from SharePointError:

• AuthenticationError: Authentication failures
• FederatedAuthError: Federated authentication issues (Azure AD specific)
• ConnectionError: Connection problems
• FileNotFoundError: File not found in SharePoint
• FileDownloadError: Download failures
• FileSizeLimitError: File exceeds size limit
• ConfigurationError: Invalid configuration
• PermissionError: Access denied
• LibraryNotFoundError: Document library not found
• InvalidFileTypeError: Unsupported file type

Excel Operations

FetchPoint provides powerful Excel processing capabilities for direct data extraction from SharePoint:

Reading Excel Data

with SharePointClient(config) as client:
    # Read Excel file as structured data
    data = client.read_excel_content(
        library="Documents",
        path=["General", "Reports", "monthly_data.xlsx"],
        sheet_name="Summary",  # Optional: specify sheet
        column_mapping={"Employee Name": "employee_name", "Salary": "salary"},  # Optional: rename columns
        skip_empty_rows=True  # Optional: skip empty rows
    )
    
    # data is now a list of dictionaries
    for row in data:
        print(f"Employee: {row['employee_name']}, Salary: {row['salary']}")

Working with Excel Sheets

with SharePointClient(config) as client:
    # Get all sheet names in a workbook
    sheets = client.get_excel_sheet_names(
        library="Documents",
        path=["General", "Reports", "workbook.xlsx"]
    )
    print(f"Available sheets: {sheets}")
    
    # Read specific sheet
    data = client.read_excel_content(
        library="Documents",
        path=["General", "Reports", "workbook.xlsx"],
        sheet_name=sheets[0]  # Use first sheet
    )

In-Memory Processing

with SharePointClient(config) as client:
    # Get file content without downloading
    content_bytes = client.get_file_content(
        library="Documents",
        path=["General", "Reports", "data.xlsx"]
    )
    
    # Process bytes with other libraries or save locally
    with open("local_file.xlsx", "wb") as f:
        f.write(content_bytes)

Security

• Passwords stored as SecretStr (Pydantic)
• Usernames masked in logs (first 3 characters only)
• Read-only operations only
• Configurable file size limits (default: 100MB)
• No environment dependencies by default

Error Handling

FetchPoint provides detailed error messages with context:

try:
    with SharePointClient(config) as client:
        files = client.list_excel_files("Documents", "NonExistent/Path")
except LibraryNotFoundError as e:
    print(f"Library error: {e}")
    print(f"Available libraries: {e.context.get('available_libraries', [])}")

Development

For project developers working on the fetchpoint library:

Setup

# Install dependencies
uv sync --all-groups

# Build wheel package
uv build --wheel

Development Commands

Code Quality (run after every change):

# Format code
uv run ruff format src

# Lint with auto-fix
uv run ruff check --fix src

# Type checking
uv run pyright src

# Run tests
uv run pytest src -vv

# Run tests with coverage
uv run pytest src --cov=src --cov-report=term-missing

Complete validation workflow:

uv run ruff format src && uv run ruff check --fix src && uv run pyright src && uv run pytest src -vv

Testing

• Tests located in __tests__/ directories co-located with source code
• Use pytest with extensions (pytest-asyncio, pytest-mock, pytest-cov)
• Minimum 90% coverage for critical components

Version Management

FetchPoint uses a single source of truth for version management:

• Version Source: src/fetchpoint/__init__.py contains __version__ = "x.y.z"
• Dynamic Configuration: pyproject.toml reads version automatically from __init__.py
• Publishing Workflow:
  1. Update version in src/fetchpoint/__init__.py
  2. Build: uv build --wheel
  3. Publish: uv publish --token $PYPI_TOKEN

Update uv.lock via:

uv lock --refresh

Version Access:

import fetchpoint
print(fetchpoint.__version__)  # e.g., "0.2.0"

Publishing Quick Reference

just validate
rm -rf dist/
uv build --wheel && uv build --sdist
uv publish --token $PYPI_TOKEN

Roadmap

• Enhanced Excel processing capabilities
• Batch operations for large datasets
• Advanced filtering and search features

License

Open source library for SharePoint Online integration.