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:
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.
First, letโs create your RAG pipeline. Open your Python environment and enter:
This initializes your application.
Now, letโs add data to your pipeline. Weโll include the Next.JS website and its documentation:
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.
Test the pipeline on your local machine:
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.
Want to go live? Deploy your pipeline with these options:
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.
In case you run into issues, feel free to contact us via any of the following methods:
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:
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.
First, letโs create your RAG pipeline. Open your Python environment and enter:
This initializes your application.
Now, letโs add data to your pipeline. Weโll include the Next.JS website and its documentation:
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.
Test the pipeline on your local machine:
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.
Want to go live? Deploy your pipeline with these options:
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.
In case you run into issues, feel free to contact us via any of the following methods: