Simplifying Document Processing with Docling for AI Applications.pdf
- 1. Simplifying Document Processing with Docling for AI Applications By - Tamanna NextGen_Outlier 1
- 2. What is Docling? Open-source Python library by IBM for document processing Parses diverse formats: Documents: PDF, DOCX, PPTX, XLSX, HTML Images: PNG, JPEG, TIFF Audio: WAV, MP3 Designed for generative AI workflows (e.g., RAG, chatbots) Key benefits: Advanced parsing (layouts, tables, formulas) Local execution for secure data processing Seamless AI framework integrations NextGen_Outlier 2
- 3. Installing Docling Requirements: Python 3.11+, macOS/Linux/Windows Steps: i. Create virtual environment: python3.11 -m venv myenv ii. Activate: source myenv/bin/activate (Windows: myenvScriptsactivate ) iii. Install: pip install docling iv. Verify: docling --version Optional: GPU support with TensorFlow/PyTorch NextGen_Outlier 3
- 4. Key Features of Docling Multi-Format Parsing: PDF, DOCX, images, audio Advanced PDF Understanding: Layouts, tables, formulas Unified Format: DoclingDocument for consistency Export Options: Markdown, HTML, JSON, DocTags Security: Local execution for air-gapped environments OCR: Supports scanned PDFs and images AI Integrations: LangChain, LlamaIndex, Crew AI, Haystack NextGen_Outlier 4
- 5. Example - Converting PDF to Markdown Code: from docling.document_converter import DocumentConverter source = "sample.pdf" converter = DocumentConverter() result = converter.convert(source) print(result.document.export_to_markdown()) Converts PDF text, tables, and images to Markdown Ideal for feeding into AI pipelines NextGen_Outlier 5
- 6. Docling Workflow Diagram (describe in PowerPoint as a flowchart): Input Document → Docling Parse → DoclingDocument → Export (Markdown, JSON) → AI Frameworks → Vector Store → LLM Query → Output (Answers) Note: In PowerPoint, use shapes and arrows to create a horizontal flowchart with these steps. NextGen_Outlier 6
- 7. Integration with LangChain Preprocess documents for Retrieval-Augmented Generation (RAG) Workflow: Convert → Load → Index → Query Code: from docling.document_converter import DocumentConverter from langchain.vectorstores import FAISS converter = DocumentConverter() result = converter.convert("report.pdf") with open("output.md", "w") as f: f.write(result.document.export_to_markdown()) # Load into LangChain and query NextGen_Outlier 7
- 8. Integration with LlamaIndex Use DoclingReader for document loading Build vector index for querying Code: from llama_index.readers.docling import DoclingReader reader = DoclingReader() documents = reader.load_data("report.pdf") index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the key findings?") NextGen_Outlier 8
- 9. Practical Use Cases Enterprise RAG: Searchable database from PDFs Research Assistant: Extract tables/formulas from papers Audio Transcription: Convert meeting recordings to text Secure Processing: Handle sensitive data locally NextGen_Outlier 9
- 10. Docling vs. Other Tools Feature Docling LangChain LlamaIndex Document Parsing Advanced Basic Moderate OCR Support Extensive Limited Limited AI Integrations Multiple Extensive Focused Local Execution Yes Yes Yes NextGen_Outlier 10
- 11. Conclusion Docling simplifies document processing for AI Key strengths: Multi-format parsing, OCR, secure execution Integrates with LangChain, LlamaIndex, Crew AI, Haystack Get started: pip install docling NextGen_Outlier 11