Weaviate와 OpenAI를 기반으로 한 RAG 문서 질문 응답

• •OpenAI API Key

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        "content": "## Part 1: Manually upload data \nIn this example, we manually upload a 100+ page article from arXiv called [\"A Survey of Large Language Models\"](https://arxiv.org/pdf/2303.18223).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_**Note: This is a simple implementation of loading data. You can replace this block with your own (more advanced) data pipeline!**_"
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        "content": "## Part 3: Perform RAG over PDF file with Weaviate\nEnter your query by running the Chat Node and get a RAG response grounded in context."
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        "content": "# RAG over a PDF file with Weaviate\nThis workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. \n\n## Prerequisites\n1.  **An existing Weaviate cluster.** You can view instructions for setting up a **local cluster** with Docker [here](https://weaviate.io/developers/weaviate/installation/docker-compose#starter-docker-compose-file) or a **Weaviate Cloud** cluster [here](https://weaviate.io/developers/wcs/quickstart).\n2.  **API keys** to generate embeddings and power chat models. We use [OpenAI](https://openai.com/), but feel free to switch out the models as you like.\n3.  **Self-hosted n8n instance.** See this [video](https://www.youtube.com/watch?v=kq5bmrjPPAY&t=108s) for how to get set up in just three minutes.\n\n\n💚  Sign up [here](https://console.weaviate.cloud/?utm_source=recipe&utm_campaign=n8n&utm_content=n8n_arxiv_template) for a 14-day free trial of Weaviate Cloud (no credit card required)."
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        "content": "## Part 2: Embed and load data into Weaviate collection\nWe generate embeddings for the full-text of the article and store them in Weaviate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_**Note: We don't add any metadata to Weaviate in this example. To add metadata, click on the Default Data Loader node → `Add Option` → `Metadata`.**_"
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