cjm-text-plugin-system 0.0.4

Defines standardized interfaces and data structures for text processing plugins, enabling modular NLP operations like sentence splitting, tokenization, and chunking within the cjm-plugin-system ecosystem.

cjm-text-plugin-system

Install

pip install cjm_text_plugin_system

Project Structure

nbs/
├── core.ipynb             # DTOs for text processing with character-level span tracking
├── plugin_interface.ipynb # Domain-specific plugin interface for text processing operations
└── storage.ipynb          # Standardized SQLite storage for text processing results with content hashing

Total: 3 notebooks

Module Dependencies

graph LR
    core[core<br/>Core Data Structures]
    plugin_interface[plugin_interface<br/>Text Processing Plugin Interface]
    storage[storage<br/>Text Processing Storage]

    plugin_interface --> core

1 cross-module dependencies detected

CLI Reference

No CLI commands found in this project.

Module Overview

Detailed documentation for each module in the project:

Core Data Structures (core.ipynb)

DTOs for text processing with character-level span tracking

Import

from cjm_text_plugin_system.core import (
    TextSpan,
    TextProcessResult
)

Classes

@dataclass
class TextSpan:
    "Represents a segment of text with its original character coordinates."
    
    text: str  # The text content of this span
    start_char: int  # 0-indexed start position in original string
    end_char: int  # 0-indexed end position (exclusive)
    label: str = 'sentence'  # Span type: 'sentence', 'token', 'paragraph', etc.
    metadata: Dict[str, Any] = field(...)  # Additional span metadata
    
    def to_dict(self) -> Dict[str, Any]:  # Dictionary representation
        "Convert span to dictionary for serialization."

@dataclass
class TextProcessResult:
    "Container for text processing results."
    
    spans: List[TextSpan]  # List of text spans from processing
    metadata: Dict[str, Any] = field(...)  # Processing metadata

Text Processing Plugin Interface (plugin_interface.ipynb)

Domain-specific plugin interface for text processing operations

Import

from cjm_text_plugin_system.plugin_interface import (
    TextProcessingPlugin
)

Classes

class TextProcessingPlugin(PluginInterface):
    """
    Abstract base class for plugins that perform NLP operations.
    
    Extends PluginInterface with text processing requirements:
    - `execute`: Dispatch method for different text operations
    - `split_sentences`: Split text into sentence spans with character positions
    """
    
    def execute(
            self,
            action: str = "split_sentences",  # Operation to perform: 'split_sentences', 'tokenize', etc.
            **kwargs
        ) -> Dict[str, Any]:  # JSON-serializable result
        "Execute a text processing operation."
    
    def split_sentences(
            self,
            text: str,  # Input text to split
            **kwargs
        ) -> TextProcessResult:  # Result with TextSpan objects containing character indices
        "Split text into sentence spans with accurate character positions."

Text Processing Storage (storage.ipynb)

Standardized SQLite storage for text processing results with content hashing

Import

from cjm_text_plugin_system.storage import (
    TextProcessRow,
    TextProcessStorage
)

Classes

@dataclass
class TextProcessRow:
    "A single row from the text_jobs table."
    
    job_id: str  # Unique job identifier
    input_text: str  # Original input text
    input_hash: str  # Hash of input text in "algo:hexdigest" format
    spans: Optional[List[Dict[str, Any]]]  # Processed text spans
    metadata: Optional[Dict[str, Any]]  # Processing metadata
    created_at: Optional[float]  # Unix timestamp

class TextProcessStorage:
    def __init__(
        self,
        db_path: str  # Absolute path to the SQLite database file
    )
    "Standardized SQLite storage for text processing results."
    
    def __init__(
            self,
            db_path: str  # Absolute path to the SQLite database file
        )
        "Initialize storage and create table if needed."
    
    def save(
            self,
            job_id: str,       # Unique job identifier
            input_text: str,   # Original input text
            input_hash: str,   # Hash of input text in "algo:hexdigest" format
            spans: Optional[List[Dict[str, Any]]] = None,  # Processed text spans
            metadata: Optional[Dict[str, Any]] = None       # Processing metadata
        ) -> None
        "Save a text processing result to the database."
    
    def get_by_job_id(
            self,
            job_id: str  # Job identifier to look up
        ) -> Optional[TextProcessRow]:  # Row or None if not found
        "Retrieve a text processing result by job ID."
    
    def list_jobs(
            self,
            limit: int = 100  # Maximum number of rows to return
        ) -> List[TextProcessRow]:  # List of text processing rows
        "List text processing jobs ordered by creation time (newest first)."
    
    def verify_input(
            self,
            job_id: str  # Job identifier to verify
        ) -> Optional[bool]:  # True if input matches, False if changed, None if not found
        "Verify the stored input text still matches its hash."