Just checking out the docs now, how do Loaders fit into the vision alongside AI functions? I can't quite piece it together in my head. Would a function grab extra context from a loader prior to execution? Is this supported now?
Good question and actually a great illustration of how unexpectedly the "flagship" feature of a library can change!
At its core Marvin isn't just for AI functions, but a high-level library that makes it easy to interact with LLMs in a programmatic way. In fact the first version was written to make it easier for us to upload public and private knowledge into our customer service Slackbot. This happens through the `Bot` class, which is designed to help users / programs explore more complex problems. In particular, bots can use plugins to access proprietary knowledge.
Our loader classes are designed to get the knowledge into the bots. Why build "yet another LLM loader library?" We've had enough real-world use cases to know that just taking a document, chunking it, and throwing it in a vector store gives pretty bad results over large enough datasets (especially if the documents are relatively homogeneous). You have to preprocess documents in a particular way, and we wanted to take our learnings and codify them for future use.
So AI functions are definitely the on-ramp to the library, but the real power is in utilizing it to extract insight from data. Since AI functions are "just" bots under the hood, they can use plugins (pass `plugins=[...]` to the `@ai_fn` decorator) and will benefit from this as well. This is supported right now, but we are rapidly improving loader integration more broadly.
At its core Marvin isn't just for AI functions, but a high-level library that makes it easy to interact with LLMs in a programmatic way. In fact the first version was written to make it easier for us to upload public and private knowledge into our customer service Slackbot. This happens through the `Bot` class, which is designed to help users / programs explore more complex problems. In particular, bots can use plugins to access proprietary knowledge.
Our loader classes are designed to get the knowledge into the bots. Why build "yet another LLM loader library?" We've had enough real-world use cases to know that just taking a document, chunking it, and throwing it in a vector store gives pretty bad results over large enough datasets (especially if the documents are relatively homogeneous). You have to preprocess documents in a particular way, and we wanted to take our learnings and codify them for future use.
So AI functions are definitely the on-ramp to the library, but the real power is in utilizing it to extract insight from data. Since AI functions are "just" bots under the hood, they can use plugins (pass `plugins=[...]` to the `@ai_fn` decorator) and will benefit from this as well. This is supported right now, but we are rapidly improving loader integration more broadly.