When we say “custom”, we mean that you can customize the loader and chunker to your needs. This is done by passing a custom loader and chunker to the add method.
The custom loader and chunker must be a class that inherits from the BaseLoader and BaseChunker classes respectively.
If the data_type is not a valid data type, the add method will fallback to the custom data type and expect a custom loader and chunker to be passed by the user.
Example:
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from embedchain import Appfrom embedchain.loaders.github import GithubLoaderapp = App()loader = GithubLoader(config={"token": "ghp_xxx"})app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)app.query("What is Embedchain?")# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.