procurement-ingest/lambdas/wo/email_processor/handler.py

"""
Email processor Lambda.

Triggered by S3 events when SES delivers an email.
Parses the raw email, sends it to Claude for structured extraction,
then writes the result to DynamoDB.
"""

import email
import hashlib
import json
import logging
import os
import re
from datetime import datetime, timezone
from email import policy

import boto3

from template_parser import try_deterministic_parse

logger = logging.getLogger()
logger.setLevel(logging.INFO)

s3 = boto3.client("s3")
dynamodb = boto3.resource("dynamodb")
bedrock = boto3.client("bedrock-runtime")

WORK_ORDERS_TABLE = os.environ.get("WORK_ORDERS_TABLE", "WorkOrders")
COMMENTS_TABLE = os.environ.get("COMMENTS_TABLE", "WorkOrderComments")
BEDROCK_MODEL_ID = os.environ.get(
    "BEDROCK_MODEL_ID", "us.anthropic.claude-haiku-4-5-20251001-v1:0"
)

# CloudWatch EMF namespace + metric for parse-outcome observability.
METRIC_NAMESPACE = "Seahaven/WorkorderIngest"
METRIC_NAME = "ParseOutcome"

EXTRACTION_PROMPT = """\
You are an email parser for a facilities maintenance work order system.
The emails come from Amazon's APM system (via Hexagon EAM / HxGN SmartCloud).

Analyze the following email and extract structured data. Return ONLY valid JSON with these fields:

{
  "email_type": "new_work_order" | "update" | "comment" | "cancellation",
  "work_order_id": "string or null",
  "description": "work order description or null",
  "status": "new" | "assigned" | "in_progress" | "on_hold" | "completed" | "cancelled" | "unknown",
  "site_code": "building/site code like WIL1, ZDL8, etc. or null",
  "building": "full building identifier or null",
  "address": "physical address or null",
  "severity": "severity level or null",
  "priority": "priority level or null",
  "date_reported": "ISO 8601 date or null",
  "scheduled_start": "ISO 8601 date or null",
  "due_date": "ISO 8601 date or null",
  "assigned_to": "person/team assigned or null",
  "commenter": "person who left a comment or null",
  "comment_text": "the comment text or null",
  "comment_time": "ISO 8601 datetime of the comment or null"
}

Rules:
- "email_type" detection:
  - "new_work_order": email announces a new WO assignment
  - "comment": email contains a new comment on an existing WO
  - "cancellation": email announces a WO has been cancelled
  - "update": any other update to an existing WO (status change, reassignment, etc.)
- Extract the site_code from the building field (e.g., "WIL1" from "building WIL1")
- Dates should be converted to ISO 8601 format
- If a field is not present in the email, set it to null
- Do NOT invent or infer data that is not explicitly in the email
"""


def parse_raw_email(raw_bytes: bytes) -> dict:
    """Parse a raw email into subject, sender, body text."""
    msg = email.message_from_bytes(raw_bytes, policy=policy.default)

    subject = msg.get("Subject", "")
    sender = msg.get("From", "")
    to = msg.get("To", "")
    cc = msg.get("Cc", "")
    date = msg.get("Date", "")

    body = ""
    if msg.is_multipart():
        for part in msg.walk():
            content_type = part.get_content_type()
            if content_type == "text/plain":
                body = part.get_content()
                break
            elif content_type == "text/html" and not body:
                body = part.get_content()
    else:
        body = msg.get_content()

    return {
        "subject": subject,
        "sender": sender,
        "to": to,
        "cc": cc,
        "date": date,
        "body": body,
    }


def extract_with_bedrock(email_data: dict) -> dict:
    """Send parsed email to Claude on Bedrock for structured extraction."""
    email_text = (
        f"Subject: {email_data['subject']}\n"
        f"From: {email_data['sender']}\n"
        f"To: {email_data['to']}\n"
        f"CC: {email_data['cc']}\n"
        f"Date: {email_data['date']}\n"
        f"\n---\n\n"
        f"{email_data['body']}"
    )

    resp = bedrock.invoke_model(
        modelId=BEDROCK_MODEL_ID,
        body=json.dumps(
            {
                "anthropic_version": "bedrock-2023-05-31",
                "max_tokens": 1024,
                "messages": [
                    {
                        "role": "user",
                        "content": f"{EXTRACTION_PROMPT}\n\nEMAIL:\n{email_text}",
                    }
                ],
            }
        ),
    )
    response_text = json.loads(resp["body"].read())["content"][0]["text"]

    # Extract JSON from response (handle markdown code blocks)
    json_match = re.search(r"```(?:json)?\s*(.*?)```", response_text, re.DOTALL)
    if json_match:
        response_text = json_match.group(1)

    return json.loads(response_text.strip())


def emit_parse_metric(method, template_id, reason_code, work_order_id):
    """Emit one CloudWatch EMF line recording the parse outcome.

    Zero-latency (no PutMetricData API call): the extraction path is async and
    the role already has logs:PutLogEvents. ParseMethod/TemplateId are the only
    promoted (dimensioned) fields to keep cardinality low; ReasonCode and
    work_order_id ride along as Logs-Insights-queryable properties.

    Two dimension sets are published: ["ParseMethod"] (aggregated across all
    template ids -- the series the fallback-rate alarm queries) AND
    ["ParseMethod", "TemplateId"] (per-template breakdown for Logs Insights /
    dashboards). CloudWatch materializes only the exact dimension sets listed
    here and does NOT auto-aggregate, so the alarm's single-dimension query
    would receive no data unless ["ParseMethod"] is emitted explicitly."""
    emf = {
        "_aws": {
            "CloudWatchMetrics": [
                {
                    "Namespace": METRIC_NAMESPACE,
                    "Dimensions": [["ParseMethod"], ["ParseMethod", "TemplateId"]],
                    "Metrics": [{"Name": METRIC_NAME, "Unit": "Count"}],
                }
            ],
        },
        "ParseMethod": method,
        "TemplateId": template_id or "unknown",
        "ReasonCode": reason_code or "ok",
        "work_order_id": work_order_id or "",
        METRIC_NAME: 1,
    }
    print(json.dumps(emf))


def save_work_order(parsed: dict, s3_key: str):
    """Create or update a work order in DynamoDB."""
    table = dynamodb.Table(WORK_ORDERS_TABLE)
    work_order_id = parsed["work_order_id"]
    now = datetime.now(timezone.utc).isoformat()

    # Build update expression dynamically from non-null fields
    field_map = {
        "description": "description",
        "status": "wo_status",  # 'status' is a DynamoDB reserved word
        "site_code": "site_code",
        "building": "building",
        "address": "address",
        "severity": "severity",
        "priority": "priority",
        "date_reported": "date_reported",
        "scheduled_start": "scheduled_start",
        "due_date": "due_date",
        "assigned_to": "assigned_to",
    }

    update_parts = ["#updated_at = :updated_at", "#source_key = :source_key"]
    attr_names = {
        "#updated_at": "updated_at",
        "#source_key": "source_email_s3_key",
    }
    attr_values = {
        ":updated_at": now,
        ":source_key": s3_key,
    }

    for src_field, dynamo_field in field_map.items():
        value = parsed.get(src_field)
        if value is not None:
            placeholder = f":{dynamo_field}"
            name_placeholder = f"#{dynamo_field}"
            update_parts.append(f"{name_placeholder} = {placeholder}")
            attr_names[name_placeholder] = dynamo_field
            attr_values[placeholder] = value

    # For new items, set created_at
    update_parts.append("#created_at = if_not_exists(#created_at, :created_at)")
    attr_names["#created_at"] = "created_at"
    attr_values[":created_at"] = now

    # Customer is always AMAZON for now
    update_parts.append("#customer = :customer")
    attr_names["#customer"] = "customer"
    attr_values[":customer"] = "AMAZON"

    # Track the record type (new_work_order, update, comment)
    email_type = parsed.get("email_type")
    if email_type:
        update_parts.append("#record_type = :record_type")
        attr_names["#record_type"] = "record_type"
        attr_values[":record_type"] = email_type

    table.update_item(
        Key={"work_order_id": work_order_id},
        UpdateExpression="SET " + ", ".join(update_parts),
        ExpressionAttributeNames=attr_names,
        ExpressionAttributeValues=attr_values,
    )

    logger.info(f"Saved work order {work_order_id}")


def save_event(parsed: dict, s3_key: str, object_key: str):
    """Save an event to the events table. Every email creates an event entry.

    Issue #23: the range key (attr name stays ``comment_id``) must be unique per
    source email AND identical across Lambda async retries of the same S3 object.
    The uniqueness suffix is a deterministic hash of the S3 object key alone (not
    the s3:// URI, so it is stable across a bucket rename), and wall-clock now()
    is kept OUT of the key. When comment_time is absent we use the literal
    'nocomment' segment rather than now() -- otherwise each retry would produce a
    different key and duplicate the row. Two distinct emails on the same WO map
    to distinct object keys -> distinct rows."""
    table = dynamodb.Table(COMMENTS_TABLE)

    work_order_id = parsed["work_order_id"]
    email_type = parsed.get("email_type", "unknown")
    key_suffix = hashlib.sha256(object_key.encode("utf-8")).hexdigest()[:12]
    comment_time = parsed.get("comment_time")
    time_part = comment_time if comment_time else "nocomment"
    event_id = f"{work_order_id}#{time_part}#{key_suffix}"
    # created_at is display-only; may fall back to now() without affecting the key.
    display_time = comment_time or datetime.now(timezone.utc).isoformat()

    item = {
        "work_order_id": work_order_id,
        "comment_id": event_id,  # keeping key name for table compatibility
        "record_type": email_type,
        "commenter": parsed.get("commenter") or "",
        "text": parsed.get("comment_text") or "",
        "created_at": display_time,
        "source_email_s3_key": s3_key,
        "ingested_at": datetime.now(timezone.utc).isoformat(),
    }

    table.put_item(Item=item)
    logger.info(f"Saved event {event_id} (type={email_type})")


def handler(event, context):
    """Lambda entry point. Triggered by S3 ObjectCreated events."""
    for record in event.get("Records", []):
        bucket = record["s3"]["bucket"]["name"]
        key = record["s3"]["object"]["key"]

        logger.info(f"Processing email: s3://{bucket}/{key}")

        # Fetch raw email from S3
        response = s3.get_object(Bucket=bucket, Key=key)
        raw_email = response["Body"].read()

        # Parse the raw email
        email_data = parse_raw_email(raw_email)
        logger.info(f"Subject: {email_data['subject']}")

        # Deterministic template parse first; fall back to the AI extractor only
        # on a miss or an invalid (fail-closed) result.
        parsed, method, template_id, reason = try_deterministic_parse(email_data)
        if parsed is None:
            parsed = extract_with_bedrock(email_data)
            method = "ai_fallback"
        logger.info(
            f"Parsed ({method}/{template_id}/{reason}): "
            f"type={parsed.get('email_type')}, wo={parsed.get('work_order_id')}"
        )

        emit_parse_metric(method, template_id, reason, parsed.get("work_order_id"))

        if not parsed.get("work_order_id"):
            logger.warning(f"No work order ID found in email, skipping: {key}")
            continue

        s3_key = f"s3://{bucket}/{key}"

        # Always upsert the work order with any new info
        save_work_order(parsed, s3_key)

        # Save every email as an event for history tracking. The raw object key
        # (not the s3:// URI) drives the retry-idempotent comment_id suffix.
        save_event(parsed, s3_key, key)

    return {"statusCode": 200, "body": "OK"}