Does Discourse support export conversations as an organized bulk of data?

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I’m not sure that AI is my case. I collect data from different sources and the main idea is to find really important highlights (words, urls, proofs etc) and create a structured data which could answer these questions:

  1. What was the proper order on the way of accepting the certain solution related to the some task. In order to reconstruct the actual picture of events.
  2. The algorithm should detect important mentions in a tons of poor-quality conversations (especially email with many levels of attachments and non-trusted web publications)
  3. Define valuable links between different actions, approximately such way: news → blog → public mood and needs → chat/email decision → used strategy → real actions → approved assumptions → related persons → explaination the result

So I use some python template for this:

PRODUCT_RELATIVE_SOURCES = {
    "websites": {
        "company1": [
            "blog",
            "vacancies",
            "news",
            "tags"
        ]
    },
    "social-networks": {
        "network1": [
            "feed",
            "story",
            "public",
            "direct",
            "tags"
        ]
    },
    "messengers": {
        "messenger1": [
            "chat1",
            "room1",
            "bot1",
            "direct",
            "tags"
        ]
    }
   "mailboxes": {
        "box1": [
            "subject",
            "body",
            "sender",
            "cc",
            "meta"
        ]
    }
}

EXCLUDE_SOURCES = {
    "main",
    "libs",
    "opt"
}

Probably I wonder to have similar data structure exported from Discourse (through API maybe). Initially I asked the question concerning Discourse for Teams, because I found many similar with Slack and our team is not satisfied with Slack. Payable function of history is almost useless.

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