Merge branch 'master' into cc/pinecone

cookbook/README.md

@@ -50,7 +50,7 @@ Notebook | Description
 [press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
 [program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
 [qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
-[rag_upstage_layout_analysis_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb) | End-to-end RAG example using Upstage Layout Analysis and Groundedness Check.
+[rag_upstage_document_parse_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_document_parse_groundedness_check.ipynb) | End-to-end RAG example using Upstage Document Parse and Groundedness Check.
 [retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
 [sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
 [self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.

cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb → cookbook/rag_upstage_document_parse_groundedness_check.ipynb

@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# RAG using Upstage Layout Analysis and Groundedness Check\n",
-    "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Layout Analysis and Groundedness Check."
+    "# RAG using Upstage Document Parse and Groundedness Check\n",
+    "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Document Parse and Groundedness Check."
    ]
   },
   {
@@ -23,16 +23,16 @@
     "from langchain_core.runnables.base import RunnableSerializable\n",
     "from langchain_upstage import (\n",
     "    ChatUpstage,\n",
+    "    UpstageDocumentParseLoader,\n",
     "    UpstageEmbeddings,\n",
     "    UpstageGroundednessCheck,\n",
-    "    UpstageLayoutAnalysisLoader,\n",
     ")\n",
     "\n",
     "model = ChatUpstage()\n",
     "\n",
     "files = [\"/PATH/TO/YOUR/FILE.pdf\", \"/PATH/TO/YOUR/FILE2.pdf\"]\n",
     "\n",
-    "loader = UpstageLayoutAnalysisLoader(file_path=files, split=\"element\")\n",
+    "loader = UpstageDocumentParseLoader(file_path=files, split=\"element\")\n",
     "\n",
     "docs = loader.load()\n",
     "\n",

docs/cassettes/chat_token_usage_tracking_05f22a1d-b021-490f-8840-f628a07459f2.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/chat_token_usage_tracking_07f0c872-6b6c-4fed-a129-9b5a858505be.msgpack.zlib

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docs/cassettes/chat_token_usage_tracking_0b1523d8-127e-4314-82fa-bd97aca37f9a.msgpack.zlib

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