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Fixed typo in RAG with Gemma+ES+HF cookbook #128

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Jun 27, 2024
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Fixed typo in RAG with Gemma+ES+HF cookbook #128

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2 changes: 1 addition & 1 deletion notebooks/en/rag_with_hugging_face_gemma_elasticsearch.ipynb
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"\n",
"Authored By: [lloydmeta](https://huggingface.co/lloydmeta)\n",
"\n",
"This notebook walks you through building a Retrieve-Augmented Generation (RAG) powered by Elasticsearch (ES) and Hugging Face models, letting you toggle between ES-vectorising (your ES cluster vectorises for you when ingesting and querying) vs self-vectorising (you vectorise all your data before sending it to ES).\n",
"This notebook walks you through building a Retrieval-Augmented Generation (RAG) powered by Elasticsearch (ES) and Hugging Face models, letting you toggle between ES-vectorising (your ES cluster vectorises for you when ingesting and querying) vs self-vectorising (you vectorise all your data before sending it to ES).\n",
"\n",
"What should you use for your use case? *It depends* 🤷‍♂️. ES-vectorising means your clients don't have to implement it, so that's the default here; however, if you don't have any ML nodes, or your own embedding setup is better/faster, feel free to set `USE_ELASTICSEARCH_VECTORISATION` to `False` in the `Choose data and query vectorisation options` section below!\n",
"\n",
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