It's possible to build RAG pipelines that support answering complex questions over multiple documents, and I don't think I would say the whole architecture needs to change.
Doing it fast is another story, LLMs are pretty high latency at the moment.
Doing it fast is another story, LLMs are pretty high latency at the moment.
Ex: Louie.AI will use a combination of tools to answer a question (database queries, RAG vector index lookups, Python, interactive charts, ...), and will often do multiple attempts on the same tool until it decides it has exhausted the immediate use of that tool.
Ex: Louie is even learning from usage, so as an analyst, if Louie stopped digging early and you decided to manually look further, Louie learns from this: It'll know in future sessions that it may be worth looking further in that kind of scenario.
None of that, in isolation, is unique to Louie.AI: It's just part of what it means to do a 'full' agent implementation vs a langchain/llamaindex/openai wrapper.
There's an interesting question around knowledge graph style questions here -- should an agent do iterative top-n vector similarity searches, or a single wider search over a knowledge graph, or maybe the documents should be combined into a knowledge graph and that's what's embedded. We're exploring a lot here in our bigger customer projects, and I can't say there's a clear universal answer...