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cookbook/README.md

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[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).
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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 renamed to cookbook/rag_upstage_document_parse_groundedness_check.ipynb

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

docs/docs/concepts/lcel.mdx

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chain = RunnableSequence([runnable1, runnable2])
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```
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### The `.pipe` method`
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### The `.pipe` method
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If you have moral qualms with operator overloading, you can use the `.pipe` method instead. This is equivalent to the `|` operator.
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