In this space, you can explore numerous Docling application recipes & end-to-end workflows!

Here some of our picks to get you started:

- 🔀 conversion examples ranging from [simple conversion to Markdown](./minimal.py) and export of [figures](./export_figures.py) & [tables](./export_tables.py), to [VLM](./minimal_vlm_pipeline.py) and [audio](./minimal_asr_pipeline.py) pipelines, as well as specialized formats like [XBRL](./xbrl_conversion.ipynb)
- 💬 various RAG examples, e.g. based on [LangChain](./rag_langchain.ipynb), [LlamaIndex](./rag_llamaindex.ipynb), or [Haystack](./rag_haystack.ipynb), including [visual grounding](./visual_grounding.ipynb), and using different vector stores like [Milvus](./rag_milvus.ipynb), [Weaviate](./rag_weaviate.ipynb), or [Qdrant](./retrieval_qdrant.ipynb)
- 📤 [{==\[:fontawesome-solid-flask:{ title="beta feature" } beta\]==} structured data extraction](./extraction.ipynb)
- examples for ✍️ [serialization](./serialization.ipynb) and ✂️ [chunking](./hybrid_chunking.ipynb), including [user-defined customizations](./advanced_chunking_and_serialization.ipynb)
- 🖼️ [picture annotations](./pictures_description.ipynb), [remote picture annotations](./pictures_description_api.py), [API usage capture](./picture_description_api_usage.py), and [enrichments](./enrich_doclingdocument.py)
- 🤝 [**Agent skill**](./agent_skill/docling-document-intelligence/README.md) for Cursor and other assistants (`SKILL.md`, pipeline reference, `docling-convert.py` / `docling-evaluate.py` helpers)

👈 ... and there is much more: explore all the examples using the navigation menu on the side

<div class="grid" style="text-align: center">
    <div class="card">
        Visual grounding
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        <img loading="lazy" alt="RAG with visual grounding" src="../assets/visual_grounding.png" height="150px" />
    </div>
    <div class="card">
        Picture annotations
        <hr />
        <img loading="lazy" alt="Picture annotation" src="../assets/picture_annotations.png" height="150px" />
    </div>
</div>
