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docs: add information extraction example (#2199)

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* docs: add information exctraction example

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>

* update README

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>

* minor typo

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>

* update README

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>

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Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>
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Panos Vagenas
2025-09-05 11:27:09 +02:00
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@@ -4,6 +4,7 @@ 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
- 💬 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) and [enrichments](./enrich_doclingdocument.py)