Utilizing large language models (LLMs) for question answering is a transformative application, bringing significant benefits to various real-world situations. Embedchain extensively supports tasks related to question answering, including summarization, content creation, language translation, and data analysis. The versatility of question answering with LLMs enables solutions for numerous practical applications such as:
Educational Aid: Enhancing learning experiences and aiding with homework
Customer Support: Addressing and resolving customer queries efficiently
Research Assistance: Facilitating academic and professional research endeavors
Healthcare Information: Providing fundamental medical knowledge
Now, let’s add data to your pipeline. We’ll include the Next.JS website and its documentation:
Ingest data sources
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# Add Next.JS Website and docsapp.add("https://nextjs.org/sitemap.xml", data_type="sitemap")# Add Next.JS Forum dataapp.add("https://nextjs-forum.com/sitemap.xml", data_type="sitemap")
This step incorporates over 15K pages from the Next.JS website and forum into your pipeline. For more data source options, check the Embedchain data sources overview.
If you are looking to configure the RAG pipeline further, feel free to checkout the API reference.In case you run into issues, feel free to contact us via any of the following methods: