youlu-openclaw-workspace/scripts/email_processor/classifier.py

#!/usr/bin/env python3
"""
Classifier - LLM-based email classification with learning.

This module builds a rich prompt for the local Ollama model (Qwen3) that
includes few-shot examples from past user decisions, per-sender statistics,
and a list of known labels. The model returns a structured response with
an action, confidence score, summary, and reason.

The prompt structure:
  1. System instructions (action definitions)
  2. Known labels (so the model reuses them)
  3. Sender statistics ("linkedin.com: deleted 8 times, kept 2 times")
  4. Few-shot examples (top 5 most relevant past decisions)
  5. The email to classify (subject, sender, recipient, body preview)
  6. Output format specification
"""

import time
from datetime import datetime
from pathlib import Path

import decision_store

LOGS_DIR = Path(__file__).parent / "logs"


def _build_prompt(email_data, config):
    """Assemble the full classification prompt with learning context.

    The prompt is built in sections, each providing different context to
    help the model make better decisions. Sections are omitted when there
    is no relevant data (e.g., no history yet for a new sender).
    """
    max_body = config.get("rules", {}).get("max_body_length", 1000)

    # Gather learning context from decision history
    examples = decision_store.get_relevant_examples(email_data, n=10)
    sender_domain = decision_store._extract_domain(email_data.get("sender", ""))
    sender_stats = decision_store.get_sender_stats(sender_domain) if sender_domain else {}
    known_labels = decision_store.get_known_labels()

    # /no_think disables Qwen3's chain-of-thought, giving faster + shorter output
    parts = ["/no_think\n"]

    # Section 1: Action definitions
    parts.append(
        "You are an email classifier. Classify the email into one of these actions:\n"
        "- delete: Spam, ads, promotions, unwanted notifications\n"
        "- archive: Informational emails worth keeping but not needing attention "
        "(receipts, shipping updates, automated confirmations)\n"
        "- keep: Important emails that need attention or action (left unread in inbox)\n"
        "- mark_read: Low-priority, leave in inbox but mark as read\n"
        "- label:<name>: Categorize with a specific label\n"
    )

    # Section 2: Known labels (helps model reuse instead of inventing)
    if known_labels:
        parts.append(f"\nLabels used before: {', '.join(sorted(known_labels))}\n")

    # Section 3: Sender statistics (strong signal for repeat senders)
    if sender_stats:
        stats_str = ", ".join(
            f"{action} {count} times" for action, count in sender_stats.items()
        )
        parts.append(f"\nHistory for {sender_domain}: {stats_str}\n")

    # Section 4: Few-shot examples (top 5 most relevant past decisions)
    if examples:
        parts.append("\n--- Past decisions (learn from these) ---")
        for ex in examples[:5]:
            parts.append(
                f"From: {ex['sender'][:60]} | To: {ex['recipient'][:40]} | "
                f"Subject: {ex['subject'][:60]} -> {ex['action']}"
            )
        parts.append("--- End examples ---\n")

    # Section 5: The email being classified
    body_preview = email_data.get("body", "")[:max_body]
    parts.append(
        f"Now classify this email:\n"
        f"Subject: {email_data.get('subject', '(No Subject)')}\n"
        f"From: {email_data.get('sender', '(Unknown)')}\n"
        f"To: {email_data.get('recipient', '(Unknown)')}\n"
        f"Body: {body_preview}\n"
    )

    # Section 6: Required output format
    parts.append(
        "Respond in this exact format (nothing else):\n"
        "Action: [delete|archive|keep|mark_read|label:<name>]\n"
        "Confidence: [0-100]\n"
        "Summary: [one sentence summary of the email]\n"
        "Reason: [brief explanation for your classification]"
    )

    return "\n".join(parts)


def _log_llm(prompt, output, email_data, action, confidence, duration):
    """Log the full LLM prompt and response to logs/llm_YYYY-MM-DD.log."""
    LOGS_DIR.mkdir(exist_ok=True)
    log_file = LOGS_DIR / f"llm_{datetime.now().strftime('%Y-%m-%d')}.log"
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    subject = email_data.get("subject", "(No Subject)")[:60]
    sender = email_data.get("sender", "(Unknown)")[:60]

    with open(log_file, "a", encoding="utf-8") as f:
        f.write(f"{'=' * 70}\n")
        f.write(f"[{timestamp}] {subject}\n")
        f.write(f"From: {sender} | Result: {action} @ {confidence}% | {duration:.1f}s\n")
        f.write(f"{'-' * 70}\n")
        f.write(f"PROMPT:\n{prompt}\n")
        f.write(f"{'-' * 70}\n")
        f.write(f"RESPONSE:\n{output}\n")
        f.write(f"{'=' * 70}\n\n")


def _parse_response(output):
    """Parse the model's text response into structured fields.

    Expected format (one per line):
        Action: delete
        Confidence: 92
        Summary: Promotional offer from retailer
        Reason: Clearly a marketing email with discount offer

    Falls back to safe defaults (keep, 50% confidence) on parse failure.
    """
    action = "keep"
    confidence = 50
    summary = "No summary"
    reason = "Unknown"

    for line in output.strip().split("\n"):
        line = line.strip()
        if line.startswith("Action:"):
            raw_action = line.replace("Action:", "").strip().lower()
            valid_actions = {"delete", "archive", "keep", "mark_read"}
            if raw_action in valid_actions or raw_action.startswith("label:"):
                action = raw_action
        elif line.startswith("Confidence:"):
            try:
                confidence = int(line.replace("Confidence:", "").strip().rstrip("%"))
                confidence = max(0, min(100, confidence))  # clamp to 0-100
            except ValueError:
                confidence = 50
        elif line.startswith("Summary:"):
            summary = line.replace("Summary:", "").strip()[:200]
        elif line.startswith("Reason:"):
            reason = line.replace("Reason:", "").strip()

    return action, confidence, summary, reason


def classify_email(email_data, config):
    """Classify an email using the local LLM with few-shot learning context.

    Connects to Ollama, sends the assembled prompt, and parses the response.
    On any error, falls back to "keep" with 0% confidence so the email
    gets queued for manual review rather than auto-acted upon.

    Args:
        email_data: dict with subject, sender, recipient, body keys.
        config:     full config dict (needs ollama.model and rules.max_body_length).

    Returns:
        Tuple of (action, confidence, summary, reason, duration_seconds).
    """
    import ollama

    prompt = _build_prompt(email_data, config)
    model = config.get("ollama", {}).get("model", "kamekichi128/qwen3-4b-instruct-2507:latest")

    start_time = time.time()
    try:
        # Low temperature for consistent classification
        response = ollama.generate(model=model, prompt=prompt, options={"temperature": 0.1})
        output = response["response"]
        action, confidence, summary, reason = _parse_response(output)
    except Exception as e:
        # On failure, default to "keep" with 0 confidence -> always queued
        output = f"ERROR: {e}"
        action = "keep"
        confidence = 0
        summary = "Classification failed"
        reason = f"error - {str(e)[:100]}"

    duration = time.time() - start_time
    _log_llm(prompt, output, email_data, action, confidence, duration)
    return action, confidence, summary, reason, duration