deep-searcher/docs/examples/docling.md

# Docling Integration Example

This example shows how to use Docling for loading local files and crawling web content.

## Overview

The script demonstrates:

1. Configuring DeepSearcher to use Docling for both file loading and web crawling
2. Loading data from local files using Docling's document parser
3. Crawling web content from multiple sources including Markdown and PDF files
4. Querying the loaded data

## Code Example

```python
import logging
import os
from deepsearcher.offline_loading import load_from_local_files, load_from_website
from deepsearcher.online_query import query
from deepsearcher.configuration import Configuration, init_config

# Suppress unnecessary logging from third-party libraries
logging.getLogger("httpx").setLevel(logging.WARNING)

def main():
    # Step 1: Initialize configuration
    config = Configuration()

    # Configure Vector Database and Docling providers
    config.set_provider_config("vector_db", "Milvus", {})
    config.set_provider_config("file_loader", "DoclingLoader", {})
    config.set_provider_config("web_crawler", "DoclingCrawler", {})

    # Apply the configuration
    init_config(config)

    # Step 2a: Load data from a local file using DoclingLoader
    local_file = "your_local_file_or_directory"
    local_collection_name = "DoclingLocalFiles"
    local_collection_description = "Milvus Documents loaded using DoclingLoader"

    print("\n=== Loading local files using DoclingLoader ===")
    
    try:
        load_from_local_files(
            paths_or_directory=local_file, 
            collection_name=local_collection_name, 
            collection_description=local_collection_description,
            force_new_collection=True
        )
        print(f"Successfully loaded: {local_file}")
    except ValueError as e:
        print(f"Validation error: {str(e)}")
    except Exception as e:
        print(f"Error: {str(e)}")

    print("Successfully loaded all local files")

    # Step 2b: Crawl URLs using DoclingCrawler
    urls = [
        # Markdown documentation files
        "https://milvus.io/docs/quickstart.md",
        "https://milvus.io/docs/overview.md",
        # PDF example - can handle various URL formats
        "https://arxiv.org/pdf/2408.09869",
    ]
    web_collection_name = "DoclingWebCrawl"
    web_collection_description = "Milvus Documentation crawled using DoclingCrawler"

    print("\n=== Crawling web pages using DoclingCrawler ===")
    
    load_from_website(
        urls=urls,
        collection_name=web_collection_name,
        collection_description=web_collection_description,
        force_new_collection=True
    )
    print("Successfully crawled all URLs")

    # Step 3: Query the loaded data
    question = "What is Milvus?"
    result = query(question)


if __name__ == "__main__":
    main()
```

## Running the Example

1. Install DeepSearcher and Docling: `pip install deepsearcher docling`
2. Replace `your_local_file_or_directory` with your actual file/directory path
3. Run the script: `python load_and_crawl_using_docling.py`

## Key Concepts

- **Multiple Providers**: Configuring both file loader and web crawler to use Docling
- **Local Files**: Loading documents from your local filesystem
- **Web Crawling**: Retrieving content from multiple web URLs with different formats
- **Error Handling**: Graceful error handling for loading operations