1. RAG: A simple model looks at the question, pulls up some associated data into the context and hopes that it helps.
2. Self-RAG: The model "intentionally"/agentically triggers a lookup for some topic. This can be via a traditional RAG or just string search, ie. grep.
3. Full Context: Just jam everything in the context window. The model uses its attention mechanism to pick out the parts it needs. Best but most expensive of the three, especially with repeated queries.
Aider uses kind of a hybrid of 2 and 3: you specify files that go in the context, but Aider also uses Tree-Sitter to get a map of the entire codebase, ie. function headers, class definitions etc., that is provided in full. On that basis, the model can then request additional files to be added to the context.
I'm still not sure I get the difference between 1 and 2. What is "pulls up some associated data into the context" vs ""intentionally"/agentically triggers a lookup for some topic"?
1. Tends to use embeddings with a similarity search. Sometimes called "retrieval". This is faster but similarity search doesn't alway work quite as well as you might want it to.
2. Instead lets the agent decide what to bring into context by using tools on the codebase. Since the tools used are fast enough, this gives you effectively "verified answers" so long as the agent didn't screw up its inputs to the tool (which will happen, most likely).
Does it make sense to use vector search for code? It's more for vague texts. In the code relevant parts can be found by exact name match. (in most cases. both methods aren't exclusive)
Vector search for code can be quite interesting - I've used it for things like "find me code that downloads stuff" and it's worked well. I think text search is usually better for code though.
