FastAPI service that classifies email using a configurable LLM backend, returns richer structured extraction, and tracks duplicate classifications using Outlook-aware dedupe.

Environment configuration

LLM defaults:

export LLM_PROVIDER=openai
export LLM_BASE_URL=http://ollama.internal.henryhosted.com:9292/v1
export LLM_API_KEY=none
export LLM_MODEL=qwen2.5-7b-instruct.q4_k_m
export LLM_TEMPERATURE=0.1
export LLM_TIMEOUT_SECONDS=60
export LLM_MAX_RETRIES=3

MiniMax via Anthropic-compatible API:

export LLM_PROVIDER=anthropic
export LLM_BASE_URL=https://api.minimax.io/anthropic
export LLM_API_KEY=your_minimax_key
export LLM_MODEL=MiniMax-M2.7

Optional local dedupe store path:

export EMAIL_CLASSIFIER_DB_PATH=.data/email_classifier.db

Input shape

The request model accepts either:

simplified input via email_data
or native Outlook-style fields directly

Full Outlook-shaped example:

{
  "id": "AAMk...",
  "internetMessageId": "<...@...>",
  "conversationId": "AAQk...",
  "subject": "MB Printer",
  "bodyPreview": "Good morning, ...",
  "body": {
    "contentType": "html",
    "content": "<html>...(full HTML body)</html>"
  },
  "sender": {
    "emailAddress": {
      "name": "Bobbi Johnson",
      "address": "bobbi.johnson@grandportage.com"
    }
  },
  "from": {
    "emailAddress": {
      "name": "Bobbi Johnson",
      "address": "bobbi.johnson@grandportage.com"
    }
  },
  "toRecipients": [
    {
      "emailAddress": {
        "name": "IT Helpdesk Mail",
        "address": "helpdeskmail@grandportage.com"
      }
    }
  ],
  "ccRecipients": [],
  "bccRecipients": [],
  "replyTo": [],
  "receivedDateTime": "2026-02-19T15:27:35Z",
  "sentDateTime": "2026-02-19T15:27:32Z",
  "hasAttachments": false,
  "importance": "normal",
  "isRead": false,
  "flag": { "flagStatus": "notFlagged" },
  "provider": "anthropic",
  "base_url": "https://api.minimax.io/anthropic",
  "model": "MiniMax-M2.7"
}

Simplified request example:

{
  "email_data": {
    "subject": "MB Printer",
    "body": "<html>...</html>"
  },
  "id": "AAMk...",
  "conversationId": "AAQk..."
}

Response example

{
  "needs_action": true,
  "category": "question",
  "priority": "high",
  "task_description": "Investigate MB Printer issue and reply",
  "reasoning": "The email appears to describe an issue requiring action.",
  "confidence": 0.91,
  "details": {
    "summary": "Printer issue reported in the MB area.",
    "suggested_title": "Handle MB Printer issue",
    "suggested_notes": "Review the printer problem, identify urgency, and reply with next steps.",
    "deadline": null,
    "people": [],
    "organizations": [],
    "attachments_referenced": [],
    "next_steps": ["Review issue", "Respond to sender"],
    "key_points": ["Printer issue reported"],
    "source_signals": ["request"],
    "dedupe_key": "..."
  },
  "dedupe": {
    "status": "new",
    "seen_count": 1,
    "matched_on": "none",
    "message_id": "AAMk...",
    "conversation_id": "AAQk...",
    "fingerprint": "..."
  }
}

Dedupe precedence

id for exact Outlook message match
conversationId for thread grouping
normalized subject + preview/body fingerprint fallback

Statuses:

new: no prior similar email seen
duplicate: same dedupe target and same extracted result as before
updated: matched prior email, but extracted result changed

This is intentionally heuristic for the fallback path.

Notes

No Todoist integration lives in this API.
Dedupe is local and intended to help downstream workflows avoid obvious duplicates.
SQLite is used for lightweight local dedupe tracking.