update RAG examples and banner text

Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>

docs/examples/rag_langchain.ipynb

@@ -49,18 +49,6 @@
     "load_dotenv()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic|torch\")\n",
-    "warnings.filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -86,54 +74,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from enum import Enum\n",
     "from typing import Iterator\n",
     "\n",
     "from langchain_core.document_loaders import BaseLoader\n",
     "from langchain_core.documents import Document as LCDocument\n",
-    "from pydantic import BaseModel\n",
     "\n",
     "from docling.document_converter import DocumentConverter\n",
     "\n",
-    "\n",
-    "class DocumentMetadata(BaseModel):\n",
-    "    dl_doc_hash: str\n",
-    "    # source: str\n",
-    "\n",
-    "\n",
     "class DoclingPDFLoader(BaseLoader):\n",
-    "    class ParseType(str, Enum):\n",
-    "        MARKDOWN = \"markdown\"\n",
-    "        # JSON = \"json\"\n",
     "\n",
-    "    def __init__(self, file_path: str | list[str], parse_type: ParseType) -> None:\n",
+    "    def __init__(self, file_path: str | list[str]) -> None:\n",
     "        self._file_paths = file_path if isinstance(file_path, list) else [file_path]\n",
-    "        self._parse_type = parse_type\n",
     "        self._converter = DocumentConverter()\n",
     "\n",
     "    def lazy_load(self) -> Iterator[LCDocument]:\n",
     "        for source in self._file_paths:\n",
-    "            dl_doc = self._converter.convert_single(source).output\n",
+    "            dl_doc = self._converter.convert(source).document\n",
-    "            match self._parse_type:\n",
+    "            text = dl_doc.export_to_markdown()\n",
-    "                case self.ParseType.MARKDOWN:\n",
+    "            yield LCDocument(page_content=text)"
-    "                    text = dl_doc.export_to_markdown()\n",
+   ]
-    "                # case self.ParseType.JSON:\n",
+  },
-    "                #     text = dl_doc.model_dump_json()\n",
+  {
-    "                case _:\n",
+   "cell_type": "code",
-    "                    raise RuntimeError(\n",
+   "execution_count": 4,
-    "                        f\"Unexpected parse type encountered: {self._parse_type}\"\n",
+   "metadata": {},
-    "                    )\n",
+   "outputs": [],
-    "            lc_doc = LCDocument(\n",
+   "source": [
-    "                page_content=text,\n",
+    "FILE_PATH = \"https://raw.githubusercontent.com/DS4SD/docling/main/tests/data/2206.01062.pdf\"  # DocLayNet paper"
-    "                metadata=DocumentMetadata(\n",
-    "                    dl_doc_hash=dl_doc.file_info.document_hash,\n",
-    "                ).model_dump(),\n",
-    "            )\n",
-    "            yield lc_doc"
    ]
   },
   {
@@ -141,37 +112,10 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "FILE_PATH = \"https://arxiv.org/pdf/2206.01062\"  # DocLayNet paper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b38d07d5fed4618a44ecf261e1e5c44",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fetching 7 files:   0%|          | 0/7 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
-    "loader = DoclingPDFLoader(\n",
+    "loader = DoclingPDFLoader(file_path=FILE_PATH)\n",
-    "    file_path=FILE_PATH,\n",
-    "    parse_type=DoclingPDFLoader.ParseType.MARKDOWN,\n",
-    ")\n",
     "text_splitter = RecursiveCharacterTextSplitter(\n",
     "    chunk_size=1000,\n",
     "    chunk_overlap=200,\n",
@@ -187,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +176,7 @@
     "from langchain_milvus import Milvus\n",
     "\n",
     "MILVUS_URI = os.environ.get(\n",
-    "    \"MILVUS_URL\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n",
+    "    \"MILVUS_URI\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n",
     ")\n",
     "\n",
     "vectorstore = Milvus.from_documents(\n",
@@ -252,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,16 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'The human annotation of DocLayNet was performed on 80863 pages.\\n\\nExplanation:\\nThe information is found in the paragraph \"DocLayNet contains 80863 PDF pages\" in the context.'"
+       "'- 80,863 pages were human annotated for DocLayNet.'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,13 +280,6 @@
    "source": [
     "rag_chain.invoke(\"How many pages were human annotated for DocLayNet?\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/examples/rag_llamaindex.ipynb

@@ -14,6 +14,13 @@
     "# RAG with LlamaIndex 🦙"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ℹ️ 👉 **The LlamaIndex Docling extension update to Docling v2 is ongoing; in the meanwhile, this notebook is showing current extension output, based on Docling v1.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/integrations/llamaindex.md

@@ -4,6 +4,10 @@ Docling is available as an official LlamaIndex extension!
 
 To get started, check out the [step-by-step guide in LlamaIndex \[↗\]](https://docs.llamaindex.ai/en/stable/examples/data_connectors/DoclingReaderDemo/)<!--{target="_blank"}-->.
 
+!!! info "Docling v2"
+
+    The LlamaIndex Docling extension update to Docling v2 is ongoing.
+
 ## Components
 
 ### Docling Reader

docs/overrides/main.html

@@ -1,5 +1,5 @@
 {% extends "base.html" %}
 
 {% block announce %}
-  <p>🎉 Docling is going v2, <a href="{{ 'v2' | url }}">check out</a> what's new and how to get started!</p>
+  <p>🎉 Docling has gone v2! <a href="{{ 'v2' | url }}">Check out</a> what's new and how to get started!</p>
 {% endblock %}