docs: document Haystack & Vectara support

Signed-off-by: Panos Vagenas <[email protected]>

docs/examples/rag_haystack.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "source": [
     "This example leverages the\n",
-    "[Haystack Docling extension](https://github.com/DS4SD/docling-haystack), along with\n",
+    "[Haystack Docling extension](../../integrations/haystack/), along with\n",
     "Milvus-based document store and retriever instances, as well as sentence-transformers\n",
     "embeddings.\n",
     "\n",

docs/integrations/haystack.md

@@ -0,0 +1,11 @@
+Docling is available as a converter in [Haystack](https://haystack.deepset.ai/):
+
+- 📖 [Docling Haystack integration docs](https://haystack.deepset.ai/integrations/docling)
+- 💻 [Docling Haystack integration GitHub][github]
+- 🧑🏽‍🍳 [Docling Haystack integration example][example]
+- 📦 [Docling Haystack integration PyPI][pypi]
+
+[github]: https://github.com/DS4SD/docling-haystack
+[docs]: https://haystack.deepset.ai/integrations/docling
+[pypi]: https://pypi.org/project/docling-haystack
+[example]: https://ds4sd.github.io/docling/examples/rag_haystack/

docs/integrations/rhel_ai.md

@@ -1,10 +1,8 @@
-Docling is powering document processing in [Red Hat Enterprise Linux AI][home] (RHEL AI),
+Docling is powering document processing in [Red Hat Enterprise Linux AI (RHEL AI)](https://rhel.ai),
 enabling users to unlock the knowledge hidden in documents and present it to
 InstructLab's fine-tuning for aligning AI models to the user's specific data.
 
-More details can be found in this [blog post][blog].
-
-- 🏠 [RHEL AI home][home]
-
-[home]: https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux/ai
-[blog]: https://www.redhat.com/en/blog/docling-missing-document-processing-companion-generative-ai
+- 📣 [RHEL AI 1.3 announcement](https://www.redhat.com/en/about/press-releases/red-hat-delivers-next-wave-gen-ai-innovation-new-red-hat-enterprise-linux-ai-capabilities)
+- ✍️ RHEL blog posts:
+    - [RHEL AI 1.3 Docling context aware chunking: What you need to know](https://www.redhat.com/en/blog/rhel-13-docling-context-aware-chunking-what-you-need-know)
+    - [Docling: The missing document processing companion for generative AI](https://www.redhat.com/en/blog/docling-missing-document-processing-companion-generative-ai)

docs/integrations/vectara.md

@@ -0,0 +1,5 @@
+Docling is available as a document parser in [Vectara](https://www.vectara.com/).
+
+- 💻 [Vectara GitHub org](https://github.com/vectara)
+    - [vectara-ingest GitHub repo](https://github.com/vectara/vectara-ingest)
+- 📖 [Vectara docs](https://docs.vectara.com/)

mkdocs.yml

@@ -89,13 +89,15 @@ nav:
     - "Cloudera": integrations/cloudera.md
     - "Data Prep Kit": integrations/data_prep_kit.md
     - "DocETL": integrations/docetl.md
+    - "Haystack": integrations/haystack.md
     - "🐶 InstructLab": integrations/instructlab.md
     - "Kotaemon": integrations/kotaemon.md
     - "🦙 LlamaIndex": integrations/llamaindex.md
     - "Prodigy": integrations/prodigy.md
-    - "Red Hat Enterprise Linux AI": integrations/rhel_ai.md
+    - "RHEL AI": integrations/rhel_ai.md
     - "spaCy": integrations/spacy.md
     - "txtai": integrations/txtai.md
+    - "Vectara": integrations/vectara.md
     # - "LangChain 🦜🔗": integrations/langchain.md
   - Reference:
     - Python API: