# Delver Implementation Plan

Delver is a high-performance, declarative tool designed to parse and split unstructured documents, with an initial focus on scanned PDF files (without OCR). This implementation plan outlines the various modules required to build Delver, ensuring a modular, scalable, and maintainable architecture.

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## Table of Contents

1. [Overview](#overview)
2. [Modules](#modules)
   - [1. Core PDF Processing](#1-core-pdf-processing)
   - [2. Template/DSL Parser](#2-templatedsl-parser)
   - [3. Document Representation](#3-document-representation)
   - [4. Matching Engine](#4-matching-engine)
   - [5. Chunking and Overlapping](#5-chunking-and-overlapping)
   - [6. Metadata Management](#6-metadata-management)
   - [7. Machine Learning Integration](#7-machine-learning-integration)
   - [8. Tokenization Module](#8-tokenization-module)
   - [9. Python Bindings](#9-python-bindings)
   - [10. Utilities](#10-utilities)
3. [Implementation Steps](#implementation-steps)
4. [Testing Strategy](#testing-strategy)
5. [Documentation](#documentation)
6. [Future Considerations](#future-considerations)

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## Overview

The implementation of Delver will be divided into several interdependent modules, each responsible for a specific aspect of the tool’s functionality. This modular approach facilitates parallel development, easier maintenance, and scalability.

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## Modules

### 1. Core PDF Processing

**Functionality:**

- **Load and Read PDFs**: Handle various PDF versions and encodings.
- **Extract Elements**: Extract raw bytes, text blocks, images, tables, and other structural elements from PDFs.
- **Error Handling**: Manage corrupted or unsupported PDF features gracefully.

**Implementation:**

- **Library**: Utilize the [`lopdf`](https://crates.io/crates/lopdf) crate for low-level PDF manipulation.
- **Tasks**:
  - Implement PDF loading and parsing functionality.
  - Extract and categorize different elements (text, images, tables) from the PDF.
  - Handle multi-page documents and maintain the order of elements.

**Deliverables:**

- PDF loader and parser.
- Data structures representing extracted PDF content.

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### 2. Template/DSL Parser

**Functionality:**

- **Parse User Templates**: Interpret the declarative templates defined by users to guide document parsing and splitting.
- **Support Hierarchical Structures**: Handle nested sections and hierarchical parsing rules inspired by JSX/HTML.

**Implementation:**

- **Library**: Use parser combinator crates like [`Nom`](https://crates.io/crates/nom) or [`winnow`](https://crates.io/crates/winnow).
- **DSL Design**: Create a JSX-like or HTML-like syntax tailored for document parsing needs.
- **Features**:
  - Define matching patterns (e.g., sections, tables).
  - Specify actions (e.g., chunking parameters, metadata additions).
  - Support nested structures to manage document hierarchies.

**Deliverables:**

- Grammar specification for the DSL.
- Parser implementation that converts DSL scripts into an Abstract Syntax Tree (AST) or intermediate representation.

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### 3. Document Representation

**Functionality:**

- **Internal Data Structures**: Represent the parsed PDF content and template rules in a structured format.
- **Hierarchical Modeling**: Model the document's nested sections and elements.

**Implementation:**

- **Data Structures**:
  - Tree-based representation where nodes correspond to document elements (sections, paragraphs, tables).
  - Nodes contain attributes and metadata as defined by the templates.

**Deliverables:**

- Internal model representing the document's structure.
- Mechanisms to traverse and manipulate the document tree.

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### 4. Matching Engine

**Functionality:**

- **Apply Template Rules**: Use the parsed templates to identify and match specific elements within the document.
- **Support Semantic and Fuzzy Matching**: Implement matching based on semantics and handle variations using techniques like Levenshtein distance.

**Implementation:**

- **Matching Algorithms**:
  - Exact matching based on user-defined patterns.
  - Fuzzy matching using algorithms like Levenshtein distance.
- **Pattern Recognition**:
  - Predefined patterns (e.g., "title element", "table element") for semantic matching.
  - Custom regex or string patterns as defined by the user.

**Deliverables:**

- Matching functions that apply template rules to document elements.
- Support for semantic and fuzzy matching criteria.

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### 5. Chunking and Overlapping

**Functionality:**

- **Split Text into Chunks**: Divide matched text into chunks based on size and overlap parameters.
- **Ensure Metadata Consistency**: Attach relevant metadata to each chunk as specified by the template.

**Implementation:**

- **Chunking Logic**:
  - Implement chunking based on token counts using the [`tokenizers`](https://crates.io/crates/tokenizers) crate.
  - Handle overlapping tokens between chunks.
- **Metadata Attachment**:
  - Attach relevant metadata to each chunk as defined by the template.

**Deliverables:**

- Functions to split text into appropriately sized chunks with overlaps.
- Metadata management for chunked content.

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### 6. Metadata Management

**Functionality:**

- **Manage and Attach Metadata**: Handle the association of metadata with document elements and chunks.
- **Propagate Metadata**: Ensure metadata from nested sections is correctly propagated to child elements.

**Implementation:**

- **Data Structures**:
  - Use key-value pairs to store metadata attributes.
- **Mechanisms**:
  - Collect metadata from parent nodes when processing child nodes.
  - Ensure metadata consistency and avoid duplication.

**Deliverables:**

- Metadata handling functions and data structures.
- Integration with other modules to ensure proper metadata attachment.

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### 7. Machine Learning Integration

**Functionality:**

- **Process Specific Elements**: Use machine learning models to process elements like tables.
- **Optional Integration**: Allow users to opt-in for using local models and GPU resources.

**Implementation:**

- **Model Interfaces**:
  - Define interfaces for integrating vision-language models or other ML models.
- **Optional Dependencies**:
  - Make ML model integration optional to keep the core package lightweight.
- **Execution**:
  - Manage model loading, inference, and result handling.

**Deliverables:**

- Interfaces and modules for ML model integration.
- Documentation on how to add and configure models.

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### 8. Tokenization Module

**Functionality:**

- **Tokenize Text Content**: Break down text into tokens for chunking and other processing tasks.
- **Provide Utilities**: Offer customizable tokenization strategies to suit different languages or domains.

**Implementation:**

- **Library**: Utilize the [`tokenizers`](https://crates.io/crates/tokenizers) crate.
- **Features**:
  - Support different tokenization strategies.
  - Allow customization based on language or domain.

**Deliverables:**

- Tokenization functions and utilities.
- Integration with the chunking module.

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### 9. Python Bindings

**Functionality:**

- **Expose Core Functionalities to Python**: Allow users to interact with Delver using Python.
- **Facilitate Integration**: Enable seamless integration into Python-based workflows and pipelines.

**Implementation:**

- **Library**: Use [`PyO3`](https://crates.io/crates/pyo3) to create Python bindings.
- **Features**:
  - Bind core processing functions (e.g., loading PDFs, applying templates).
  - Ensure seamless data conversion between Rust and Python.

**Deliverables:**

- Python bindings for core modules.
- Example Python scripts demonstrating usage.

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### 10. Utilities

**Functionality:**

- **Provide Helper Functions**: Offer additional utilities to support various modules.
- **Handle Logging and Error Management**: Implement robust logging and error handling mechanisms.

**Implementation:**

- **Logging**: Use Rust’s logging crates like [`log`](https://crates.io/crates/log) and [`env_logger`](https://crates.io/crates/env_logger).
- **Error Handling**: Define custom error types and implement robust error handling strategies.
- **Configuration Management**: Allow configuration via files or environment variables.

**Deliverables:**

- Logging and error handling infrastructure.
- Configuration management utilities.

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## Implementation Steps

1. **Set Up the Project Structure**
   - Initialize a new Rust project using `cargo`.
   - Set up version control with Git, creating a repository on a platform like GitHub.
   - Define the project directory structure, separating modules into distinct directories/files.

2. **Implement Core PDF Processing**
   - Integrate the `lopdf` crate.
   - Develop functions to load and parse PDF files.
   - Extract and categorize different elements (text, images, tables).
   - Handle multi-page documents and maintain the order of elements.

3. **Design and Implement the Template/DSL Parser**
   - Define the DSL syntax inspired by JSX/HTML.
   - Implement the parser using `Nom` or `winnow`.
   - Create tests with sample templates to ensure accurate parsing.

4. **Develop Document Representation**
   - Design tree-based data structures to represent the document's hierarchical structure.
   - Implement functions to build the document tree from extracted PDF content.
   - Ensure efficient traversal and manipulation of the tree.

5. **Implement the Matching Engine**
   - Develop functions to apply template rules to document elements.
   - Incorporate fuzzy matching capabilities using algorithms like Levenshtein distance.
   - Test matching with various document samples and templates.

6. **Create Chunking and Overlapping Logic**
   - Integrate the `tokenizers` crate for tokenizing text.
   - Implement chunking functions that respect size and overlap parameters.
   - Ensure metadata is correctly attached to each chunk.

7. **Integrate Metadata Management**
   - Implement metadata collection and propagation across the document tree.
   - Ensure consistency and avoid duplication during processing.

8. **Add Machine Learning Integration**
   - Define interfaces for integrating ML models.
   - Implement optional modules for model loading and inference.
   - Provide example integrations with sample models.

9. **Build Python Bindings**
   - Use `PyO3` to expose core functionalities to Python.
   - Ensure correct data conversion between Rust and Python.
   - Create example Python scripts demonstrating how to use the bindings.

10. **Develop Utilities**
    - Implement logging using `log` and `env_logger`.
    - Set up error handling with custom error types.
    - Create configuration management utilities.

11. **Testing and Validation**
    - Develop unit tests for each module.
    - Create integration tests to ensure modules work together seamlessly.
    - Use sample PDFs and templates to validate functionality.

12. **Documentation and Examples**
    - Write comprehensive documentation for each module.
    - Provide example templates and processing scripts.
    - Create tutorials to guide users through common use cases.

13. **Optimization and Performance Tuning**
    - Profile the application to identify and address performance bottlenecks.
    - Optimize critical paths for speed and memory usage.
    - Ensure efficient handling of large documents.

14. **Release and Deployment**
    - Prepare the tool for release, including binaries and package management.
    - Ensure dependencies are correctly managed and documented.
    - Publish documentation and examples for users.

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## Testing Strategy

- **Unit Testing**: Write unit tests for individual functions and modules to ensure correctness.
- **Integration Testing**: Test how modules interact to ensure end-to-end functionality.
- **Performance Testing**: Benchmark processing times and resource usage, optimizing as needed.
- **User Testing**: Engage early users to test templates and processing on real-world documents.
- **Automated Testing**: Set up continuous integration (CI) pipelines to run tests automatically on commits.

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## Documentation

- **README.md**: Provide an overview, installation instructions, basic usage, and examples.
- **User Guides**: Detailed instructions on writing templates, processing documents, and using Python bindings.
- **API Documentation**: Comprehensive documentation of all functions, modules, and interfaces.
- **Tutorials**: Step-by-step guides to help users get started and perform complex tasks.
- **Examples**: Include sample templates and documents to illustrate functionality.

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## Future Considerations

- **OCR Integration**: Add support for OCR to process scanned images within PDFs.
- **GUI Development**: Develop a graphical interface for defining templates and processing documents visually.
- **Additional Document Formats**: Extend support to formats like DOCX, HTML, and others.
- **Cloud Integration**: Offer cloud-based processing options for scalability and accessibility.
- **Advanced NLP Features**: Incorporate natural language processing techniques for enhanced semantic understanding.
- **Enhanced DSL Features**: Continue to evolve the DSL based on user feedback, adding more expressive capabilities.

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## Conclusion

This implementation plan provides a structured approach to developing Delver, outlining the necessary modules and steps to achieve a robust, flexible, and high-performance tool for parsing and splitting unstructured documents. By adhering to this plan, you can systematically address each aspect of the tool's functionality, ensuring a coherent and maintainable codebase.

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**Next Steps:**

1. **Finalize Module Specifications**: Detail the requirements and interfaces for each module.
2. **Begin Core Development**: Start with core PDF processing and template parsing.
3. **Iterate and Refine**: Continuously test and improve each module based on feedback and testing results.
4. **Engage with Users**: Regularly gather user feedback to guide development and prioritize features.
