[Feature]: Integrate Docling for Document Ingestion and Processing

[Harmeet10000]

Prerequisites

• I have searched the existing issues to avoid duplicates
• I understand that this is just a suggestion and might not be implemented

Problem Statement

Our current document ingestion and processing pipeline may lack robust capabilities for handling diverse document types, extracting structured information, or preparing documents effectively for LangChain applications. This can lead to:

• Difficulty in processing complex document formats (e.g., PDFs with tables, images, or varying layouts).
• Suboptimal text extraction, leading to poorer quality embeddings and RAG results.
• Manual effort required to pre-process documents before they can be used by LangChain.
• Challenges in scaling document ingestion for a wider variety of data sources.

Proposed Solution

Integrate Docling into our FastAPI-based LangChain project to enhance document ingestion, parsing, and preparation. Docling provides advanced capabilities for extracting and structuring information from various document types, which would significantly improve the quality of data fed into our RAG and LLM applications.

Key aspects of the integration would include:

• Document Loading: Utilize Docling to load and parse a wide range of document formats (PDFs, Word docs, HTML, etc.).
• Content Extraction: Leverage Docling's intelligent content extraction to get clean, semantically meaningful text.
• Structure Recognition: Potentially use Docling's capabilities to identify and extract structured elements like tables, headings, and lists, which can then be represented effectively for LangChain (e.g., as structured documents or in metadata).
• Chunking/Splitting: Post-Docling processing, we can apply LangChain's chunking strategies to prepare the text for vectorization.

This integration would streamline the pre-processing stage, reduce manual effort, and lay a better foundation for more accurate and comprehensive LLM responses.

Alternatives Considered

• Using other open-source parsers directly: We could explore individual libraries like pdfminer.six, python-docx, or custom HTML parsers. However, Docling often provides a more unified and intelligent approach, especially for complex documents or diverse formats, potentially reducing development effort for advanced parsing needs.
• Manual pre-processing: Continuing with current methods or basic loaders, which may involve more manual steps or custom code for each document type, leading to higher maintenance.

Additional Context

Integrating Docling is a strategic move to ensure the robustness and scalability of our RAG pipeline, especially as we aim to process more varied and complex information. It helps to ensure that the "G" in RAG (Generation) is built on the highest quality "R" (Retrieval) possible.
See Docling Documentation for more information.

Priority

High