1. What's Pinecone and what does it solve.
2. Same with LangChain
3. What does it mean to "build something with a pre-existing model?".
4. How do people actually run these models? (e.g. if I want access to Segment-Anything, how do I get that?).
>Why do people all use pinecone to store like 10 things in memory
It's a python library for stitching together existing APIs for AI models.
Regarding 3)
It means that rather than training a new model that does your thing you use existing models and combine them in interesting ways. For example, AutoGPT works roughly like this: Give it a task, it then uses the ChatGPT API to create a plan to achieve the task. It tries to do the first item in the plan by picking a tool from a preconfigured toolbox (google search, generate an image using stablediffusion, use some predefined prompts, ...) afterwards it assesses how far it got and updates the task list and loops until the task is done.
Regarding 4)
Some can run on your machine, some run in the cloud and you'll need to pay and get an API key.
When you want to query against your manuscript, you call the OpenAI API for calculating a vector embedding for your query, locally find the chunks "near" your query, concatenate these chunks, then pass this context text with your query to GPT-3.5turbo or GPT-4.0.
I have written up small examples for doing this in Swift [1] and Common Lisp [2].
No public code yet, but I will release it with Apache 2 license when/if it works well enough for my own daily use.
I think in all the chaos of the other cool stuff you can do with these models that people are just glossing over that these Llms close the loop on search based on word or sentence embedding techniques like word2vec, GloVe, ELMo, and BERT. The fact that you can actually generate quality embeddings for arbitrary text that represents their meaning semantically as a whole is cool as shit.