partner: Upstage quick documentation update (#20869)

* Updating the provider docs page. 
The RAG example was meant to be moved to cookbook, but was merged by
mistake.

* Fix bug in Groundedness Check

---------

Co-authored-by: JuHyung-Son <sonju0427@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/providers/upstage.ipynb

@@ -53,7 +53,7 @@
     "| Chat | Build assistants using Solar Mini Chat | `from langchain_upstage import ChatUpstage` | [Go](../../chat/upstage) |\n",
     "| Text Embedding | Embed strings to vectors | `from langchain_upstage import UpstageEmbeddings` | [Go](../../text_embedding/upstage) |\n",
     "| Groundedness Check | Verify groundedness of assistant's response | `from langchain_upstage import GroundednessCheck` | [Go](../../tools/upstage_groundedness_check) |\n",
-    "| Layout Analysis | Serialize documents with tables and figures | `from langchain_upstage import UpstageLayoutAnalysis` | [Go](../../document_loaders/upstage) |\n",
+    "| Layout Analysis | Serialize documents with tables and figures | `from langchain_upstage import UpstageLayoutAnalysisLoader` | [Go](../../document_loaders/upstage) |\n",
     "\n",
     "See [documentations](https://developers.upstage.ai/) for more details about the features."
    ]
@@ -172,10 +172,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_upstage import UpstageLayoutAnalysis\n",
+    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
     "\n",
     "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysis(file_path, split=\"page\")\n",
+    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
     "\n",
     "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
     "docs = layzer.load()  # or layzer.lazy_load()\n",
@@ -183,81 +183,6 @@
     "for doc in docs[:3]:\n",
     "    print(doc)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieval-Augmented Generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List\n",
-    "\n",
-    "from langchain_community.vectorstores import DocArrayInMemorySearch\n",
-    "from langchain_core.documents.base import Document\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_core.runnables.base import RunnableSerializable\n",
-    "from langchain_upstage import (\n",
-    "    ChatUpstage,\n",
-    "    GroundednessCheck,\n",
-    "    UpstageEmbeddings,\n",
-    "    UpstageLayoutAnalysis,\n",
-    ")\n",
-    "\n",
-    "model = ChatUpstage()\n",
-    "\n",
-    "file_path = (\n",
-    "    \"/PATH/TO/YOUR/FILE.pdf\"  # e.g. \"libs/partners/upstage/tests/examples/solar.pdf\"\n",
-    ")\n",
-    "loader = UpstageLayoutAnalysis(file_path=file_path, split=\"element\")\n",
-    "docs = loader.load()\n",
-    "\n",
-    "vectorstore = DocArrayInMemorySearch.from_documents(docs, embedding=UpstageEmbeddings())\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "output_parser = StrOutputParser()\n",
-    "\n",
-    "question = \"ASK ANY QUESTION HERE.\"  # e.g. \"How many parameters in SOLAR model?\"\n",
-    "\n",
-    "retrieved_docs = retriever.get_relevant_documents(question)\n",
-    "\n",
-    "groundedness_check = GroundednessCheck()\n",
-    "groundedness = \"\"\n",
-    "while groundedness != \"grounded\":\n",
-    "    chain: RunnableSerializable = RunnablePassthrough() | prompt | model | output_parser\n",
-    "\n",
-    "    result = chain.invoke(\n",
-    "        {\n",
-    "            \"context\": retrieved_docs,\n",
-    "            \"question\": question,\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "    # convert all Documents to string\n",
-    "    def formatDocumentsAsString(docs: List[Document]) -> str:\n",
-    "        return \"\\n\".join([doc.page_content for doc in docs])\n",
-    "\n",
-    "    groundedness = groundedness_check.run(\n",
-    "        {\n",
-    "            \"context\": formatDocumentsAsString(retrieved_docs),\n",
-    "            \"assistant_message\": result,\n",
-    "        }\n",
-    "    ).content"
-   ]
   }
  ],
  "metadata": {