Comment by lchoquel

When the business logic becomes complex, our motto which is to break it down into smaller problems, until they are manageable.

Regarding large docs, I know what you mean and we've been there: at one point we considered focusing on that feature, build a kind of agentic system to master large docs. At the time in 2023-2024, everyone was relying on vector store RAG and embeddings to solve that problem but we always thought that solution wouldn't bring enough reliability. We wanted the LLM to actually read the whole docs, in order to know what's in there and where. The idea was to read and "take notes" according to different aspects of what it's reading, and synthesize hierarchically from bottom to top. So, that part of the work could be done once and the structured "notes" could be exploited for each use case, pretty efficiently thanks to the zoom-in/out provided by the architecture. We went pretty far in building that solution, which we called "Pyramid RAG" internally, and the aim was to build AI workflows of top of that. But at some point, the building of the pyramid became pretty complex, and we realized we needed AI workflows to build the pyramid itself, and we needed to to do it right. That's when we decided to focus on what would become Pipelex. Now, in our new setting, solving the "large docs" problem is a large problem we can break down into a collection of Pipelex workflows. So it's part of our plan to provide those building blocks, and we hope to involve the community as much as possible, to cover all the possible applications.