Semantic searching, which involves understanding the intent and contextual meaning behind search queries, is yet another popular use-case of RAG. It has several popular use cases across various domains:

  • Information Retrieval: Enhances search accuracy in databases and websites
  • E-commerce: Improves product discovery in online shopping
  • Customer Support: Powers smarter chatbots for effective responses
  • Content Discovery: Aids in finding relevant media content
  • Knowledge Management: Streamlines document and data retrieval in enterprises
  • Healthcare: Facilitates medical research and literature search
  • Legal Research: Assists in legal document and case law search
  • Academic Research: Aids in academic paper discovery
  • Language Processing: Enables multilingual search capabilities

Embedchain offers a simple yet customizable search() API that you can use for semantic search. See the example in the next section to know more.

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Example: Semantic Search over Next.JS Website + Forum

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Step 1: Set Up Your RAG Pipeline

First, let’s create your RAG pipeline. Open your Python environment and enter:

Create pipeline
from embedchain import App
app = App()

This initializes your application.

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Step 2: Populate Your Pipeline with Data

Now, let’s add data to your pipeline. We’ll include the Next.JS website and its documentation:

Ingest data sources
# Add Next.JS Website and docs
app.add("https://nextjs.org/sitemap.xml", data_type="sitemap")

# Add Next.JS Forum data
app.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.

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Step 3: Local Testing of Your Pipeline

Test the pipeline on your local machine:

Search App
app.search("Summarize the features of Next.js 14?")
[
  {
    'context': 'Next.js 14 | Next.jsBack to BlogThursday, October 26th 2023Next.js 14Posted byLee Robinson@leeerobTim Neutkens@timneutkensAs we announced at Next.js Conf, Next.js 14 is our most focused release with: Turbopack: 5,000 tests passing for App & Pages Router 53% faster local server startup 94% faster code updates with Fast Refresh Server Actions (Stable): Progressively enhanced mutations Integrated with caching & revalidating Simple function calls, or works natively with forms Partial Prerendering',
    'metadata': {
      'source': 'https://nextjs.org/blog/next-14',
      'document_id': '6c8d1a7b-ea34-4927-8823-daa29dcfc5af--b83edb69b8fc7e442ff8ca311b48510e6c80bf00caa806b3a6acb34e1bcdd5d5'
    }
  },
  {
    'context': 'Next.js 13.3 | Next.jsBack to BlogThursday, April 6th 2023Next.js 13.3Posted byDelba de Oliveira@delba_oliveiraTim Neutkens@timneutkensNext.js 13.3 adds popular community-requested features, including: File-Based Metadata API: Dynamically generate sitemaps, robots, favicons, and more. Dynamic Open Graph Images: Generate OG images using JSX, HTML, and CSS. Static Export for App Router: Static / Single-Page Application (SPA) support for Server Components. Parallel Routes and Interception: Advanced',
    'metadata': {
      'source': 'https://nextjs.org/blog/next-13-3',
      'document_id': '6c8d1a7b-ea34-4927-8823-daa29dcfc5af--b83edb69b8fc7e442ff8ca311b48510e6c80bf00caa806b3a6acb34e1bcdd5d5'
    }
  },
  {
    'context': 'Upgrading: Version 14 | Next.js MenuUsing App RouterFeatures available in /appApp Router.UpgradingVersion 14Version 14 Upgrading from 13 to 14 To update to Next.js version 14, run the following command using your preferred package manager: Terminalnpm i next@latest react@latest react-dom@latest eslint-config-next@latest Terminalyarn add next@latest react@latest react-dom@latest eslint-config-next@latest Terminalpnpm up next react react-dom eslint-config-next -latest Terminalbun add next@latest',
    'metadata': {
      'source': 'https://nextjs.org/docs/app/building-your-application/upgrading/version-14',
      'document_id': '6c8d1a7b-ea34-4927-8823-daa29dcfc5af--b83edb69b8fc7e442ff8ca311b48510e6c80bf00caa806b3a6acb34e1bcdd5d5'
    }
  }
]

The source key contains the url of the document that yielded that document chunk.

If you are interested in configuring the search further, refer to our API documentation.

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(Optional) Step 4: Deploying Your RAG Pipeline

Want to go live? Deploy your pipeline with these options:

  • Deploy on the Embedchain Platform
  • Self-host on your preferred cloud provider

For detailed deployment instructions, follow these guides:

This guide will help you swiftly set up a semantic search pipeline with Embedchain, making it easier to access and analyze specific information from large data sources.

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Need help?

In case you run into issues, feel free to contact us via any of the following methods:

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