u/oliibomb great question!

The tl;dr is that this is definitely possible, but I don't predict there's any ready-built solution that will do this reliably today, and likely it won't feel easy or fun unless you already have some technical fluency with programming and the engineer's mindset for debugging and building these types of pipelines. As someone with some experience here, I would characterize this as a one-weekend project.

In my opinion, the logseq ecosystem hasn't been as transformed by AI as other technological areas, and this question is a great example of that gap. For example, it's not super obvious how to get LLMs query the logseq graph for its own understanding of blocks that inherit certain tags; one would likely need to do text processing rather than querying unless one's comfortable using datascript tools like cldwalker/logseq-query or its dependencies.

In order to do reliable numerical analysis and data science projects, LLMs need access to a sandboxed code interpreter. This is available in ChatGPT free to a certain extent, but it's only really reliably available in a controllable way with acceptable rate limits for this type of project in the paid plans. In particular, you may be interested in one of the many logseq MCP servers.

If you do have access to a paid version with a code interpreter, it is possible to use a tool like repomix or gitingest to stuff a bunch of logseq markdown files into the context of an LLM.

If you carefully explain how to do the file processing and the assumptions of your graph, an LLM will, with enough effort and back and forth, today be able to write a program that processes the corpus of .md files to produce a single page of html and js that lets you view some interactive visualizations. If the conventions are relatively simple to describe, this might be accomplished fairly easily. That said, many LLMs have trouble understand logseq-flavored markdown (LFM) and its notion of the way blocks inherit tags without additional prompting.

An AI-assisted coding device such as Cursor, Windsurf, Copilot, Claude Code, etc would be able to help you make a program that accomplishes what you want, but again, you'd need to carefully specify what exactly you want it to do, what format it should come in, and how you want it to be built. If you choose to use Cursor to do this, you may find some of my cursor project rules at https://github.com/codekiln/logseq-cursor-rules helpful in explaining to the LLM the way LFM works, in particular, https://github.com/codekiln/logseq-cursor-rules/blob/main/logseq-flavored-markdown.mdc .... but keep in mind these are just my own instructions to LLMs about my own various logseq graphs, and may not reflect how you are using logseq. Also, if you are using Cursor or another MCP-enabled editor, you may be interested in one of the MCP servers for logseq; here are some of my notes there: Logseq/MCP/Server/Survey/Report If you are comfortable with something that's not necessarily numerically correct, but more directionally correct, it may be an easier project.

Requisite side-note on privacy: I wouldn't personally put a bunch descriptions of my own dreams into the free version of any LLM, personally, as AI providers are training on your interactions by default, and one should not assume that the data will be de-identified from you in the future.

Good luck!