Tutorial: GEPA for Structured Information Extraction for Enterprise Tasks¶

In this tutorial, we'll explore a three-part task for structured information extraction and classification using the Facility Support Analyzer dataset released by Meta. Given an email or message sent in an enterprise setting related to facility maintenance or support requests, the goal is to extract its urgency, assess the sentiment, and identify all relevant service request categories.

We will build a simple DSPy program, and then use the dspy.GEPA optimizer to optimize it for the task.

Setup the LM¶

We use GPT-4.1 nano to demonstrate how a small model can be tuned with GEPA.

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api_key = input("Enter your OpenAI API key: ")
import dspy
lm = dspy.LM("openai/gpt-4.1-nano", temperature=1, api_key=api_key)
dspy.configure(lm=lm)
api_key = input("Enter your OpenAI API key: ")
import dspy
lm = dspy.LM("openai/gpt-4.1-nano", temperature=1, api_key=api_key)
dspy.configure(lm=lm)

Load the dataset¶

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import requests
import dspy
import json
import random

def init_dataset():
    # Load from the url
    url = "https://raw.githubusercontent.com/meta-llama/llama-prompt-ops/refs/heads/main/use-cases/facility-support-analyzer/dataset.json"
    dataset = json.loads(requests.get(url).text)
    dspy_dataset = [
        dspy.Example({
            "message": d['fields']['input'],
            "answer": d['answer'],
        }).with_inputs("message")
        for d in dataset
    ]
    random.Random(0).shuffle(dspy_dataset)
    train_set = dspy_dataset[:int(len(dspy_dataset) * 0.33)]
    val_set = dspy_dataset[int(len(dspy_dataset) * 0.33):int(len(dspy_dataset) * 0.66)]
    test_set = dspy_dataset[int(len(dspy_dataset) * 0.66):]

    return train_set, val_set, test_set
import requests
import dspy
import json
import random

def init_dataset():
    # Load from the url
    url = "https://raw.githubusercontent.com/meta-llama/llama-prompt-ops/refs/heads/main/use-cases/facility-support-analyzer/dataset.json"
    dataset = json.loads(requests.get(url).text)
    dspy_dataset = [
        dspy.Example({
            "message": d['fields']['input'],
            "answer": d['answer'],
        }).with_inputs("message")
        for d in dataset
    ]
    random.Random(0).shuffle(dspy_dataset)
    train_set = dspy_dataset[:int(len(dspy_dataset) * 0.33)]
    val_set = dspy_dataset[int(len(dspy_dataset) * 0.33):int(len(dspy_dataset) * 0.66)]
    test_set = dspy_dataset[int(len(dspy_dataset) * 0.66):]

    return train_set, val_set, test_set

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train_set, val_set, test_set = init_dataset()

len(train_set), len(val_set), len(test_set)
train_set, val_set, test_set = init_dataset()

len(train_set), len(val_set), len(test_set)

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(66, 66, 68)

Let's view an example task input

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print("Input Message:")
print(train_set[0]['message'])

print("\n\nGold Answer:")
for k, v in json.loads(train_set[0]['answer']).items():
    print(f"{k}: {v}")
print("Input Message:")
print(train_set[0]['message'])

print("\n\nGold Answer:")
for k, v in json.loads(train_set[0]['answer']).items():
    print(f"{k}: {v}")

Input Message:
Subject: Adjusting Bi-Weekly Cleaning Schedule for My Office

Dear ProCare Facility Solutions Support Team,

I hope this message finds you well. My name is Dr. Alex Turner, and I have been utilizing your services for my office space for the past year. I must say, your team's dedication to maintaining a pristine environment has been commendable and greatly appreciated.

I am reaching out to discuss the scheduling of our regular cleaning services. While I find the logistical challenges of coordinating these services intellectually stimulating, I believe we could optimize the current schedule to better suit the needs of my team and our workflow. Specifically, I would like to explore the possibility of adjusting our cleaning schedule to a bi-weekly arrangement, ideally on Tuesdays and Fridays, to ensure our workspace remains consistently clean without disrupting our research activities.

Previously, I have attempted to adjust the schedule through the online portal, but I encountered some difficulties in finalizing the changes. I would appreciate your assistance in making these adjustments or guiding me through the process if there is a more efficient way to do so.

Thank you for your attention to this matter. I look forward to your response and continued excellent service.

Best regards,

Dr. Alex Turner
Cryptography Researcher


Gold Answer:
categories: {'routine_maintenance_requests': False, 'customer_feedback_and_complaints': False, 'training_and_support_requests': False, 'quality_and_safety_concerns': False, 'sustainability_and_environmental_practices': False, 'cleaning_services_scheduling': True, 'specialized_cleaning_services': False, 'emergency_repair_services': False, 'facility_management_issues': False, 'general_inquiries': False}
sentiment: neutral
urgency: low

Defining a DSPy program to solve the task¶

The program is a 3-module system, each of which handles the urgency, sentiment and categories classification respectively

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from typing import List, Literal


class FacilitySupportAnalyzerUrgency(dspy.Signature):
    """
    Read the provided message and determine the urgency.
    """
    message: str = dspy.InputField()
    urgency: Literal['low', 'medium', 'high'] = dspy.OutputField()

class FacilitySupportAnalyzerSentiment(dspy.Signature):
    """
    Read the provided message and determine the sentiment.
    """
    message: str = dspy.InputField()
    sentiment: Literal['positive', 'neutral', 'negative'] = dspy.OutputField()

class FacilitySupportAnalyzerCategories(dspy.Signature):
    """
    Read the provided message and determine the set of categories applicable to the message.
    """
    message: str = dspy.InputField()
    categories: List[Literal["emergency_repair_services", "routine_maintenance_requests", "quality_and_safety_concerns", "specialized_cleaning_services", "general_inquiries", "sustainability_and_environmental_practices", "training_and_support_requests", "cleaning_services_scheduling", "customer_feedback_and_complaints", "facility_management_issues"]] = dspy.OutputField()

class FacilitySupportAnalyzerMM(dspy.Module):
    def __init__(self):
        self.urgency_module = dspy.ChainOfThought(FacilitySupportAnalyzerUrgency)
        self.sentiment_module = dspy.ChainOfThought(FacilitySupportAnalyzerSentiment)
        self.categories_module = dspy.ChainOfThought(FacilitySupportAnalyzerCategories)
    
    def __call__(self, message: str):
        urgency = self.urgency_module(message=message)
        sentiment = self.sentiment_module(message=message)
        categories = self.categories_module(message=message)

        return dspy.Prediction(
            urgency=urgency.urgency,
            sentiment=sentiment.sentiment,
            categories=categories.categories
        )

program = FacilitySupportAnalyzerMM()
from typing import List, Literal


class FacilitySupportAnalyzerUrgency(dspy.Signature):
    """
    Read the provided message and determine the urgency.
    """
    message: str = dspy.InputField()
    urgency: Literal['low', 'medium', 'high'] = dspy.OutputField()

class FacilitySupportAnalyzerSentiment(dspy.Signature):
    """
    Read the provided message and determine the sentiment.
    """
    message: str = dspy.InputField()
    sentiment: Literal['positive', 'neutral', 'negative'] = dspy.OutputField()

class FacilitySupportAnalyzerCategories(dspy.Signature):
    """
    Read the provided message and determine the set of categories applicable to the message.
    """
    message: str = dspy.InputField()
    categories: List[Literal["emergency_repair_services", "routine_maintenance_requests", "quality_and_safety_concerns", "specialized_cleaning_services", "general_inquiries", "sustainability_and_environmental_practices", "training_and_support_requests", "cleaning_services_scheduling", "customer_feedback_and_complaints", "facility_management_issues"]] = dspy.OutputField()

class FacilitySupportAnalyzerMM(dspy.Module):
    def __init__(self):
        self.urgency_module = dspy.ChainOfThought(FacilitySupportAnalyzerUrgency)
        self.sentiment_module = dspy.ChainOfThought(FacilitySupportAnalyzerSentiment)
        self.categories_module = dspy.ChainOfThought(FacilitySupportAnalyzerCategories)
    
    def __call__(self, message: str):
        urgency = self.urgency_module(message=message)
        sentiment = self.sentiment_module(message=message)
        categories = self.categories_module(message=message)

        return dspy.Prediction(
            urgency=urgency.urgency,
            sentiment=sentiment.sentiment,
            categories=categories.categories
        )

program = FacilitySupportAnalyzerMM()

Define the metric to evaluate the outputs¶

The metric evaluates the output of all the three tasks, and returns the aggregate score

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def score_urgency(gold_urgency, pred_urgency):
    """
    Compute score for the urgency module.
    """
    score = 1.0 if gold_urgency == pred_urgency else 0.0
    return score

def score_sentiment(gold_sentiment, pred_sentiment):
    """
    Compute score for the sentiment module.
    """
    score = 1.0 if gold_sentiment == pred_sentiment else 0.0
    return score

def score_categories(gold_categories, pred_categories):
    """
    Compute score for the categories module.
    Uses the same match/mismatch logic as category accuracy in the score.
    """
    correct = 0
    for k, v in gold_categories.items():
        if v and k in pred_categories:
            correct += 1
        elif not v and k not in pred_categories:
            correct += 1
    score = correct / len(gold_categories)
    return score

def metric(example, pred, trace=None, pred_name=None, pred_trace=None):
    """
    Computes a score based on agreement between prediction and gold standard for categories, sentiment, and urgency.
    Returns the score (float).
    """
    # Parse gold standard from example
    gold = json.loads(example['answer'])

    # Compute scores for all modules
    score_urgency_val = score_urgency(gold['urgency'], pred.urgency)
    score_sentiment_val = score_sentiment(gold['sentiment'], pred.sentiment)
    score_categories_val = score_categories(gold['categories'], pred.categories)

    # Overall score: average of the three accuracies
    total = (score_urgency_val + score_sentiment_val + score_categories_val) / 3

    return total
def score_urgency(gold_urgency, pred_urgency):
    """
    Compute score for the urgency module.
    """
    score = 1.0 if gold_urgency == pred_urgency else 0.0
    return score

def score_sentiment(gold_sentiment, pred_sentiment):
    """
    Compute score for the sentiment module.
    """
    score = 1.0 if gold_sentiment == pred_sentiment else 0.0
    return score

def score_categories(gold_categories, pred_categories):
    """
    Compute score for the categories module.
    Uses the same match/mismatch logic as category accuracy in the score.
    """
    correct = 0
    for k, v in gold_categories.items():
        if v and k in pred_categories:
            correct += 1
        elif not v and k not in pred_categories:
            correct += 1
    score = correct / len(gold_categories)
    return score

def metric(example, pred, trace=None, pred_name=None, pred_trace=None):
    """
    Computes a score based on agreement between prediction and gold standard for categories, sentiment, and urgency.
    Returns the score (float).
    """
    # Parse gold standard from example
    gold = json.loads(example['answer'])

    # Compute scores for all modules
    score_urgency_val = score_urgency(gold['urgency'], pred.urgency)
    score_sentiment_val = score_sentiment(gold['sentiment'], pred.sentiment)
    score_categories_val = score_categories(gold['categories'], pred.categories)

    # Overall score: average of the three accuracies
    total = (score_urgency_val + score_sentiment_val + score_categories_val) / 3

    return total

Evaluating the unoptimized program (running with GPT-4.1 nano)¶

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import dspy
evaluate = dspy.Evaluate(
    devset=test_set,
    metric=metric,
    num_threads=32,
    display_table=True,
    display_progress=True
)

evaluate(program)
import dspy
evaluate = dspy.Evaluate(
    devset=test_set,
    metric=metric,
    num_threads=32,
    display_table=True,
    display_progress=True
)

evaluate(program)

Average Metric: 51.30 / 68 (75.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 68/68 [00:00<00:00, 322.00it/s]

2025/08/12 18:09:18 INFO dspy.evaluate.evaluate: Average Metric: 51.3 / 68 (75.4%)

	message	answer	urgency	sentiment	categories	metric
0	Hey ProCare Support Team, Hope you all are doing great! My name is...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	positive	[sustainability_and_environmental_practices]	✔️ [1.000]
1	Hey ProCare Team, Hope you’re all doing well! My name’s Jake, and ...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	positive	[routine_maintenance_requests, customer_feedback_and_complaints]	✔️ [0.967]
2	Subject: Assistance Needed for HVAC Maintenance Hi [Receiver], I h...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	neutral	[routine_maintenance_requests]	✔️ [1.000]
3	Subject: A Green Inquiry from a Bill Maher Enthusiast Hey ProCare ...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	positive	[sustainability_and_environmental_practices]	✔️ [1.000]
4	Subject: Inquiry on Sustainability Practices Dear ProCare Facility...	{"categories": {"routine_maintenance_requests": false, "customer_f...	medium	neutral	[sustainability_and_environmental_practices]	✔️ [0.667]
...	...	...	...	...	...	...
63	Subject: Inquiry About Your Eco-Friendly Practices Dear ProCare Fa...	{"categories": {"routine_maintenance_requests": false, "customer_f...	medium	neutral	[sustainability_and_environmental_practices]	✔️ [0.600]
64	Subject: Assistance Needed for Facility Management Issue Dear ProC...	{"categories": {"routine_maintenance_requests": false, "customer_f...	high	positive	[facility_management_issues]	✔️ [0.667]
65	Subject: Request for Training and Support Hi ProCare Support Team,...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	positive	[training_and_support_requests]	✔️ [1.000]
66	Subject: Concerns About Studio Maintenance and Rent Increase Dear ...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	negative	[routine_maintenance_requests, facility_management_issues]	✔️ [0.600]
67	Subject: Feedback on Recent Maintenance Service Dear ProCare Suppo...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	neutral	[routine_maintenance_requests, customer_feedback_and_complaints]	✔️ [0.967]

68 rows × 6 columns

Out[7]:

EvaluationResult(score=75.44, results=<list of 68 results>)

Optimizing with GEPA¶

GEPA is a reflective prompt optimizer. Its strength lies in its ability to examine textual feedback from the DSPy program's execution and evaluation pipelines. This gives GEPA greater insight into why the system achieved a particular score, enabling it to introspect and determine ways to enhance performance.

In this scenario, the final score is based on performance across three distinct tasks. It's straightforward to see that each predictor handles a specific part of the overall score.

GEPA supports providing feedback at the individual predictor level (though this isn't required—see the GEPA PAPILLON tutorial for an example without it). Let's make a quick adjustment to our evaluation metric, to make it an optimization metric, that also provides text feedback!

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import json
import dspy

def feedback_urgency(gold_urgency, pred_urgency):
    """
    Generate feedback for the urgency module.
    """
    score = 1.0 if gold_urgency == pred_urgency else 0.0
    if gold_urgency == pred_urgency:
        feedback = f"You correctly classified the urgency of the message as `{gold_urgency}`. This message is indeed of `{gold_urgency}` urgency."
    else:
        feedback = f"You incorrectly classified the urgency of the message as `{pred_urgency}`. The correct urgency is `{gold_urgency}`. Think about how you could have reasoned to get the correct urgency label."
    return feedback, score

def feedback_sentiment(gold_sentiment, pred_sentiment):
    """
    Generate feedback for the sentiment module.
    """
    score = 1.0 if gold_sentiment == pred_sentiment else 0.0
    if gold_sentiment == pred_sentiment:
        feedback = f"You correctly classified the sentiment of the message as `{gold_sentiment}`. This message is indeed `{gold_sentiment}`."
    else:
        feedback = f"You incorrectly classified the sentiment of the message as `{pred_sentiment}`. The correct sentiment is `{gold_sentiment}`. Think about how you could have reasoned to get the correct sentiment label."
    return feedback, score

def feedback_categories(gold_categories, pred_categories):
    """
    Generate feedback for the categories module.
    Uses the same match/mismatch logic as category accuracy in the score.
    """
    correctly_included = [k for k, v in gold_categories.items() if v and k in pred_categories]
    incorrectly_included = [k for k, v in gold_categories.items() if not v and k in pred_categories]
    incorrectly_excluded = [k for k, v in gold_categories.items() if v and k not in pred_categories]
    correctly_excluded = [k for k, v in gold_categories.items() if not v and k not in pred_categories]  # For completeness in accuracy check

    # Recompute category accuracy (matches score logic)
    score = (len(correctly_included) + len(correctly_excluded)) / len(gold_categories)

    if score == 1.0:
        fb_text = f"The category classification is perfect. You correctly identified that the message falls under the following categories: `{repr(correctly_included)}`."
    else:
        fb_text = f"The category classification is not perfect. You correctly identified that the message falls under the following categories: `{repr(correctly_included)}`.\n"
        if incorrectly_included:
            fb_text += f"However, you incorrectly identified that the message falls under the following categories: `{repr(incorrectly_included)}`. The message DOES NOT fall under these categories.\n"
        if incorrectly_excluded:
            prefix = "Additionally, " if incorrectly_included else "However, "
            fb_text += f"{prefix}you didn't identify the following categories that the message actually falls under: `{repr(incorrectly_excluded)}`.\n"
        fb_text += "Think about how you could have reasoned to get the correct category labels."
    return fb_text, score

def metric_with_feedback(example, pred, trace=None, pred_name=None, pred_trace=None):
    """
    Computes a score based on agreement between prediction and gold standard for categories, sentiment, and urgency.
    Optionally provides feedback text for a specific predictor module, using the same comparison logic as the score.
    Returns a dspy.Prediction with score (float) and feedback (str).
    """
    # Parse gold standard from example
    gold = json.loads(example['answer'])

    # Compute feedback and scores for all modules
    fb_urgency, score_urgency = feedback_urgency(gold['urgency'], pred.urgency)
    fb_sentiment, score_sentiment = feedback_sentiment(gold['sentiment'], pred.sentiment)
    fb_categories, score_categories = feedback_categories(gold['categories'], pred.categories)

    # Overall score: average of the three accuracies
    total = (score_urgency + score_sentiment + score_categories) / 3

    if pred_name is None:
        return total

    elif pred_name == 'urgency_module.predict':
        feedback = fb_urgency
    elif pred_name == 'sentiment_module.predict':
        feedback = fb_sentiment
    elif pred_name == 'categories_module.predict':
        feedback = fb_categories

    return dspy.Prediction(score=total, feedback=feedback)
import json
import dspy

def feedback_urgency(gold_urgency, pred_urgency):
    """
    Generate feedback for the urgency module.
    """
    score = 1.0 if gold_urgency == pred_urgency else 0.0
    if gold_urgency == pred_urgency:
        feedback = f"You correctly classified the urgency of the message as `{gold_urgency}`. This message is indeed of `{gold_urgency}` urgency."
    else:
        feedback = f"You incorrectly classified the urgency of the message as `{pred_urgency}`. The correct urgency is `{gold_urgency}`. Think about how you could have reasoned to get the correct urgency label."
    return feedback, score

def feedback_sentiment(gold_sentiment, pred_sentiment):
    """
    Generate feedback for the sentiment module.
    """
    score = 1.0 if gold_sentiment == pred_sentiment else 0.0
    if gold_sentiment == pred_sentiment:
        feedback = f"You correctly classified the sentiment of the message as `{gold_sentiment}`. This message is indeed `{gold_sentiment}`."
    else:
        feedback = f"You incorrectly classified the sentiment of the message as `{pred_sentiment}`. The correct sentiment is `{gold_sentiment}`. Think about how you could have reasoned to get the correct sentiment label."
    return feedback, score

def feedback_categories(gold_categories, pred_categories):
    """
    Generate feedback for the categories module.
    Uses the same match/mismatch logic as category accuracy in the score.
    """
    correctly_included = [k for k, v in gold_categories.items() if v and k in pred_categories]
    incorrectly_included = [k for k, v in gold_categories.items() if not v and k in pred_categories]
    incorrectly_excluded = [k for k, v in gold_categories.items() if v and k not in pred_categories]
    correctly_excluded = [k for k, v in gold_categories.items() if not v and k not in pred_categories]  # For completeness in accuracy check

    # Recompute category accuracy (matches score logic)
    score = (len(correctly_included) + len(correctly_excluded)) / len(gold_categories)

    if score == 1.0:
        fb_text = f"The category classification is perfect. You correctly identified that the message falls under the following categories: `{repr(correctly_included)}`."
    else:
        fb_text = f"The category classification is not perfect. You correctly identified that the message falls under the following categories: `{repr(correctly_included)}`.\n"
        if incorrectly_included:
            fb_text += f"However, you incorrectly identified that the message falls under the following categories: `{repr(incorrectly_included)}`. The message DOES NOT fall under these categories.\n"
        if incorrectly_excluded:
            prefix = "Additionally, " if incorrectly_included else "However, "
            fb_text += f"{prefix}you didn't identify the following categories that the message actually falls under: `{repr(incorrectly_excluded)}`.\n"
        fb_text += "Think about how you could have reasoned to get the correct category labels."
    return fb_text, score

def metric_with_feedback(example, pred, trace=None, pred_name=None, pred_trace=None):
    """
    Computes a score based on agreement between prediction and gold standard for categories, sentiment, and urgency.
    Optionally provides feedback text for a specific predictor module, using the same comparison logic as the score.
    Returns a dspy.Prediction with score (float) and feedback (str).
    """
    # Parse gold standard from example
    gold = json.loads(example['answer'])

    # Compute feedback and scores for all modules
    fb_urgency, score_urgency = feedback_urgency(gold['urgency'], pred.urgency)
    fb_sentiment, score_sentiment = feedback_sentiment(gold['sentiment'], pred.sentiment)
    fb_categories, score_categories = feedback_categories(gold['categories'], pred.categories)

    # Overall score: average of the three accuracies
    total = (score_urgency + score_sentiment + score_categories) / 3

    if pred_name is None:
        return total

    elif pred_name == 'urgency_module.predict':
        feedback = fb_urgency
    elif pred_name == 'sentiment_module.predict':
        feedback = fb_sentiment
    elif pred_name == 'categories_module.predict':
        feedback = fb_categories

    return dspy.Prediction(score=total, feedback=feedback)

Notice that the evaluation metric already contained all the information needed to generate the text feedback—we simply modified it to explicitly state what was being compared. In general, the metric functions for most tasks provide the essential components for creating such feedback; it often just requires identifying which elements to expose to the GEPA optimizer, enabling it to reflect on and enhance the program's performance.

Let's run GEPA

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from dspy import GEPA

optimizer = GEPA(
    metric=metric_with_feedback,
    auto="light", # <-- We will use a light budget for this tutorial. However, we typically recommend using auto="heavy" for optimized performance!
    num_threads=32,
    track_stats=True,
    use_merge=False,
    reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key)
)
from dspy import GEPA

optimizer = GEPA(
    metric=metric_with_feedback,
    auto="light", # <-- We will use a light budget for this tutorial. However, we typically recommend using auto="heavy" for optimized performance!
    num_threads=32,
    track_stats=True,
    use_merge=False,
    reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key)
)

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optimized_program = optimizer.compile(
    program,
    trainset=train_set,
    valset=val_set,
)
optimized_program = optimizer.compile(
    program,
    trainset=train_set,
    valset=val_set,
)

2025/08/12 20:01:55 INFO dspy.teleprompt.gepa.gepa: Running GEPA for approx 1643 metric calls of the program. This amounts to 12.45 full evals on the train+val set.
2025/08/12 20:01:55 INFO dspy.teleprompt.gepa.gepa: Using 66 examples for tracking Pareto scores. You can consider using a smaller sample of the valset to allow GEPA to explore more diverse solutions within the same budget.
7
2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 47.56666666666666 / 66 (72.1%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 0: Base program full valset score: 0.7207070707070706
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 1: Selected program 0 score: 0.7207070707070706
Average Metric: 2.27 / 3 (75.6%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 83.95it/s]2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.2666666666666666 / 3 (75.6%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 1: Proposed new text for urgency_module.predict: Task: Determine the urgency of a customer message to ProCare Facility Solutions.

Context and domain:
- Messages are typically sent to ProCare Facility Solutions’ support team about facilities services (e.g., office/residential maintenance, cleaning, HVAC).
- Common topics include cleaning quality (especially in high-traffic areas), HVAC performance/safety, routine maintenance scheduling, and general inquiries (e.g., sustainability practices).

How to assess urgency:
Use these primary factors:
1) Safety and risk:
   - High/urgent if there’s an immediate safety hazard or potential harm (e.g., electrical sparks, gas smell, active water leak/flood, critical HVAC failure in extreme conditions, security breach).
   - Medium if safety is mentioned but described as minor or without signs of imminent danger (e.g., “minor safety concerns” about HVAC, no indication of immediate risk).
2) Operational impact:
   - High if a critical system outage or issue prevents normal operations or poses serious disruption now.
   - Medium for service degradation or quality inconsistencies that need timely attention but are not emergencies (e.g., inconsistent cleaning in lobby/conference rooms, HVAC underperforming).
   - Low if no operational impact is described and the message is informational only.
3) Time sensitivity and deadlines:
   - High if action is needed immediately, today, or within 24–48 hours; explicit urgent language like “ASAP,” “emergency,” “immediately,” or a near-term hard deadline.
   - Medium if the sender requests scheduling within about 1–2 weeks or mentions an upcoming event within that window.
   - Low if no timeframe is specified and the request is routine or informational.
4) Tone and intent:
   - Phrases like “prompt response” alone do not imply high urgency (polite closing).
   - Curiosity/information requests without issues (e.g., eco-friendly practices) are typically low.

Classification guide:
- High: Imminent safety hazard, critical failure/outage, or explicit urgent/near-term deadline (today/tomorrow).
- Medium: Non-critical but important issues requiring timely attention (quality/safety concerns without immediate danger; routine maintenance requested within ~1–2 weeks or tied to an upcoming event).
- Low: General inquiries, non-pressing interest or information requests, or routine items with no stated timeline/impact.

Examples to mirror:
- Cleaning inconsistencies in high-traffic areas + minor HVAC safety concerns, no emergency described → medium.
- Routine HVAC maintenance with decreased performance and an event in 1–2 weeks → medium.
- Inquiry about eco-friendly/sustainability practices, no issue or deadline → low.

Output format:
- Return exactly two fields:
  reasoning: 1–3 concise sentences explaining the classification based on the factors above, referencing details from the message.
  urgency: one of: low | medium | high

Do not ask questions or add extra sections. Keep the reasoning succinct and specific.
2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.2666666666666666 / 3 (75.6%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 1: New subsample score is not better, skipping
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 2: Selected program 0 score: 0.7207070707070706

Average Metric: 2.93 / 3 (97.8%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 57.84it/s]2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.9333333333333336 / 3 (97.8%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 2: Proposed new text for sentiment_module.predict: You are given a single input:
- message: A professional email-style message, often addressed to ProCare Facility Solutions (facility management/maintenance services). Messages may discuss maintenance quality, safety, cleaning products, HVAC performance, minor household issues (e.g., a leaking faucet), exhibit/artifact preservation needs, or requests for follow-up service.

Your task:
- Determine the overall sentiment conveyed by the message and briefly explain your reasoning.

Key guidance for this domain:
- Many messages will be polite, professional, and solution-seeking, even when describing problems (e.g., concerns about cleaning residues affecting artifacts, inconsistent HVAC performance, or minor leaks). Such messages are typically neutral if they lack strong emotional language.
- Standard courtesies (greetings, “thank you,” “best regards”) do not make a message positive by themselves.
- The mere presence of an issue (e.g., reporting a problem or oversight) is not inherently negative; assess the tone and emotional intensity.
- Treat references to ProCare Facility Solutions, maintenance protocols, HVAC, exhibit/artifacts, and similar domain terms as context, not sentiment indicators.

Label definitions:
- positive: Clear praise, satisfaction, or strong gratitude about services or outcomes.
- neutral: Polite/professional tone; factual reporting; requests for help; constructive feedback without strong emotion; balanced notes of appreciation with mild concerns.
- negative: Clear dissatisfaction, frustration, blame, anger, or strong negative emotion; threats/urgency framed with discontent.
- mixed: Meaningful presence of both positive and negative emotions of similar weight.
- unclear: Insufficient content to infer sentiment.

Process:
1) Read the message fully.
2) Identify explicit sentiment cues (praise, complaints, frustration, satisfaction) and evaluate overall tone and intent.
3) Choose the single best label from [positive, neutral, negative, mixed, unclear].
4) Justify briefly.

Output format (plain text, no extra fields):
- reasoning: 1–3 concise sentences explaining the tone and why it maps to the chosen label. Do not summarize the entire message; focus on sentiment cues.
- sentiment: one of positive, neutral, negative, mixed, unclear (lowercase).

Do not include any additional sections or formatting beyond these two fields.

2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.9333333333333336 / 3 (97.8%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 2: New subsample score is not better, skipping
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Selected program 0 score: 0.7207070707070706
Average Metric: 2.90 / 3 (96.7%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 66.36it/s]2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:01:56 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Proposed new text for categories_module.predict: You are classifying customer messages sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your goal is to read a single message and assign all applicable categories from a fixed list. Use evidence from the message only; select all that apply; do not add categories that are not supported by the text.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use when the primary intent is to schedule, reschedule, adjust, or inquire about dates/times for cleaning services.
  - Includes: requests to change cleaning times, book a service, check availability, or align schedules.
  - Exclude when rescheduling is requested only as part of resolving a complaint about poor service (see rule below).

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks, such as deep cleaning, carpet maintenance, window washing, or other specialized treatments beyond generic cleaning.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction, reports subpar service, communication issues, requests remedies (e.g., redo, refund), or otherwise complains about prior service.

- quality_and_safety_concerns
  - Use when the message raises concerns about service quality not meeting standards (e.g., still stained, not clean enough) or safety issues.
  - Can co-occur with customer_feedback_and_complaints when the sender is unhappy with outcomes.

- general_inquiries
  - Use when the sender asks for information (e.g., availability, requirements, what’s included) before committing, or seeks clarification about services or process.

Key decision rules:
- Multi-label: Assign every category that is clearly supported by the message.
- Scheduling vs. Complaint distinction:
  - If the message’s main purpose is logistics around timing/availability (initial booking, standalone reschedule/adjustment), include cleaning_services_scheduling.
  - If rescheduling is requested only as a remedy within a complaint about poor service, do NOT include cleaning_services_scheduling. In that case, include customer_feedback_and_complaints and quality_and_safety_concerns (and specialized_cleaning_services if specific tasks are referenced).
- Specialized services: If the message mentions deep cleaning, carpet maintenance, window washing, or similar, include specialized_cleaning_services in addition to any other applicable categories.
- General inquiries often co-occur: If the sender is asking for availability or requirements before booking, include general_inquiries alongside cleaning_services_scheduling and any service-type categories.
- Do not infer categories not supported by the text (e.g., do not add scheduling if the only scheduling mention is within a complaint resolution).

Domain notes:
- Messages pertain to cleaning services for residential or commercial properties handled by ProCare Facility Solutions.
- Examples of specialized tasks: deep cleaning, carpet maintenance, window washing.
- Contexts may include high-profile clients, confidentiality/privacy needs, and urgency; these do not create categories by themselves unless tied to the definitions above.

Output format:
- Provide two top-level keys in plain text:
  - reasoning: A brief justification naming each selected category and noting any notable exclusions (especially the scheduling-vs-complaint distinction, when relevant).
  - categories: A JSON array of the selected category strings, e.g., ["cleaning_services_scheduling", "general_inquiries"].
- Do not include categories outside the allowed list. Use exact strings.

Process:
1) Read the message and identify intents: scheduling logistics, service type specificity, feedback/complaint, quality/safety concerns, information requests.
2) Map each intent to categories using the definitions and rules.
3) Produce concise reasoning and the list of categories.

2025/08/12 20:01:56 INFO dspy.evaluate.evaluate: Average Metric: 2.9333333333333336 / 3 (97.8%)
2025/08/12 20:01:58 INFO dspy.evaluate.evaluate: Average Metric: 46.96666666666667 / 66 (71.2%)
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Full valset score for new program: 0.7116161616161616
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Full train_val score for new program: 0.7116161616161616
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Individual valset scores for new program: [0.6333333333333333, 0.9333333333333332, 0.8666666666666667, 0.6666666666666666, 0.3, 0.26666666666666666, 0.9666666666666667, 1.0, 1.0, 0.3, 0.6666666666666666, 1.0, 0.3, 0.6333333333333333, 0.6666666666666666, 0.9333333333333332, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 0.9333333333333332, 0.3333333333333333, 0.26666666666666666, 0.6333333333333333, 0.3333333333333333, 0.6666666666666666, 0.6, 0.6666666666666666, 0.6666666666666666, 1.0, 0.3, 0.9666666666666667, 0.6, 0.6666666666666666, 0.9666666666666667, 0.3333333333333333, 0.6333333333333333, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9, 0.9666666666666667, 1.0, 1.0, 0.6, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.3, 0.9, 0.3333333333333333, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.3, 0.6333333333333333, 0.6666666666666666, 0.6, 0.6666666666666666, 0.26666666666666666, 1.0, 0.6]
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: New valset pareto front scores: [0.6666666666666666, 1.0, 0.9, 0.6666666666666666, 0.3333333333333333, 0.26666666666666666, 0.9666666666666667, 1.0, 1.0, 0.3333333333333333, 0.6666666666666666, 1.0, 0.3, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9333333333333332, 0.3333333333333333, 0.3, 0.6333333333333333, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 1.0, 0.3333333333333333, 0.9666666666666667, 0.6, 0.6666666666666666, 1.0, 0.3333333333333333, 0.6333333333333333, 1.0, 0.6666666666666666, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.3333333333333333, 0.9333333333333332, 0.3333333333333333, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6333333333333333, 0.6666666666666666, 0.3, 1.0, 0.6]
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Full valset pareto front score: 0.7282828282828282
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Updated valset pareto front programs: [{0}, {0}, {0}, {1}, {0}, {0, 1}, {0, 1}, {0, 1}, {1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0}, {1}, {0}, {1}, {0}, {0}, {0}, {1}, {0, 1}, {0}, {1}, {0, 1}, {0, 1}, {0}, {1}, {1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0}, {1}, {1}, {0, 1}, {0}, {0}, {0, 1}, {0, 1}, {0}, {1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0}, {0, 1}, {0}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0}, {0}, {0}, {0, 1}, {0}, {1}, {0}, {1}, {0, 1}]
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Best valset aggregate score so far: 0.7207070707070706
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Best program as per aggregate score on train_val: 0
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Best program as per aggregate score on valset: 0
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Best score on valset: 0.7207070707070706
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Best score on train_val: 0.7207070707070706
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Linear pareto front program index: 0
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 3: New program candidate index: 1
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Selected program 1 score: 0.7116161616161616
Average Metric: 2.53 / 3 (84.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 139.28it/s]2025/08/12 20:01:58 INFO dspy.evaluate.evaluate: Average Metric: 2.533333333333333 / 3 (84.4%)
2025/08/12 20:01:58 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Proposed new text for urgency_module.predict: Task: Read the provided message and determine the urgency.

Context/domain:
- Messages typically relate to facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning services) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Output format:
- Provide exactly two fields, in this order, no extra text or formatting:
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

Urgency levels and decision rules:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency signals (e.g., “Urgent,” “Immediate attention required,” “ASAP,” “critical,” “escalating”).
  - Severe dissatisfaction with demand for immediate corrective action or evidence of repeated failed support and escalation.
  - Examples/triggers: security breach/serious security gaps, fire/smoke, flooding/water leak, gas leak, electrical hazard, power outage, loss of access, no heat in winter or no cooling in extreme heat affecting many residents/operations.
- MEDIUM:
  - Time-sensitive issues that affect comfort, reliability, or service quality but are not emergencies and pose no immediate safety/security risk.
  - Requests for prompt scheduling/repair/maintenance where delay could worsen impact but is not currently critical.
  - Examples/triggers: HVAC making noises and inconsistent temperature, minor malfunction, routine maintenance requested “at the earliest convenience,” issues noted as “not an emergency.”
- LOW:
  - General inquiries, information requests, quotes, scheduling/options discussions, or interest in additional services with no stated or implied time pressure.
  - Compliments/feedback without an urgent problem.
  - Examples/triggers: asking for details on specialized cleaning, pricing, or availability without a problem to fix now.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity.
- Impact scope (residents/tenants/business operations).
- Deadlines/dates or requested response times.
- Prior failed attempts and escalation tone.
- Do not inflate urgency based solely on polite phrases like “prompt assistance” if no urgent risk is present.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.
2025/08/12 20:01:58 INFO dspy.evaluate.evaluate: Average Metric: 2.8666666666666667 / 3 (95.6%)

2025/08/12 20:01:59 INFO dspy.evaluate.evaluate: Average Metric: 52.3 / 66 (79.2%)
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: New program is on the linear pareto front
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Full valset score for new program: 0.7924242424242424
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Full train_val score for new program: 0.7924242424242424
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Individual valset scores for new program: [0.9666666666666667, 0.9333333333333332, 0.8666666666666667, 1.0, 0.6333333333333333, 0.6, 0.9666666666666667, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.6333333333333333, 0.3, 0.6666666666666666, 0.6, 1.0, 0.9333333333333332, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 0.3333333333333333, 0.6, 0.9666666666666667, 0.3333333333333333, 1.0, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9, 0.9666666666666667, 1.0, 1.0, 0.6, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.6333333333333333, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.3333333333333333, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.3, 1.0, 0.9333333333333332, 0.6666666666666666, 0.6, 1.0, 0.6]
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 0.6333333333333333, 0.6, 0.9666666666666667, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.3333333333333333, 0.6, 0.9666666666666667, 0.3333333333333333, 1.0, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6666666666666666, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.6333333333333333, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9333333333333332, 0.6666666666666666, 0.6, 1.0, 0.6]
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Full valset pareto front score: 0.8282828282828283
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Updated valset pareto front programs: [{2}, {0}, {0}, {2}, {2}, {2}, {0, 1, 2}, {0, 1, 2}, {1, 2}, {2}, {0, 1, 2}, {0, 1, 2}, {2}, {0}, {1, 2}, {0}, {1, 2}, {0}, {0}, {2}, {1, 2}, {0, 1, 2}, {2}, {2}, {0, 1, 2}, {2}, {2}, {1, 2}, {2}, {0, 1, 2}, {2}, {0, 1, 2}, {2}, {0, 1, 2}, {0}, {2}, {1, 2}, {0, 1, 2}, {0}, {0}, {0, 1, 2}, {0, 1, 2}, {0}, {1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0}, {0, 1, 2}, {2}, {0}, {2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0}, {0}, {2}, {0}, {2}, {2}, {1, 2}, {2}, {1, 2}, {0, 1, 2}]
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Best valset aggregate score so far: 0.7924242424242424
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Best program as per aggregate score on train_val: 2
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Best program as per aggregate score on valset: 2
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Best score on valset: 0.7924242424242424
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Best score on train_val: 0.7924242424242424
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Linear pareto front program index: 2
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 4: New program candidate index: 2
2025/08/12 20:01:59 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Selected program 2 score: 0.7924242424242424
Average Metric: 2.20 / 3 (73.3%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 50.15it/s]2025/08/12 20:02:00 INFO dspy.evaluate.evaluate: Average Metric: 2.1999999999999997 / 3 (73.3%)
2025/08/12 20:02:00 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text or reasoning.

Classification guidelines
- Focus on the overall emotional tone expressed about the service/interaction, not the message’s functional purpose (e.g., making a request) or formalities.
- If signals are mixed or weak, default to neutral.

Label definitions
- Positive:
  - The message clearly expresses satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
  - Strong and/or multiple explicit positive cues dominate (e.g., “satisfied client,” “exceptional service,” “truly appreciate,” “excellent,” “very happy,” “great,” “love working with you”).
- Negative:
  - The message expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
  - Explicit negative claims or emotions (e.g., “unacceptable,” “unsafe,” “poor quality,” “frustrated,” “angry,” “very disappointed”).
- Neutral:
  - Informational, inquisitive, or routine requests without clear emotional valence.
  - Polite or formal language alone does not imply positivity (e.g., “I hope this finds you well,” “thank you,” “best regards”).
  - Expressions of concern framed as questions or requests for clarification, without asserting a negative judgment.
  - Mild or incidental compliments that are secondary to a routine request remain neutral unless strong positive cues dominate.

Disambiguation notes and edge cases
- Concerns or hesitations presented as inquiries (e.g., asking about quality/safety protocols) are neutral unless they assert dissatisfaction or harm.
- Routine maintenance/service requests are neutral by default. They become positive only if accompanied by strong, explicit, and primary praise or gratitude.
- Occasional or mild praise embedded in an otherwise routine request (e.g., “your services have been instrumental…”) is still neutral unless multiple strong positive signals dominate the tone.
- Do not infer sentiment from sender identity, organization, or subject line alone; rely on explicit sentiment-bearing language in the body.
2025/08/12 20:02:00 INFO dspy.evaluate.evaluate: Average Metric: 2.533333333333333 / 3 (84.4%)

2025/08/12 20:02:01 INFO dspy.evaluate.evaluate: Average Metric: 56.63333333333333 / 66 (85.8%)
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: New program is on the linear pareto front
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Full valset score for new program: 0.8580808080808081
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Full train_val score for new program: 0.8580808080808081
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Individual valset scores for new program: [0.9666666666666667, 0.6, 0.8666666666666667, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6333333333333333, 0.6666666666666666, 0.6, 1.0, 0.9333333333333332, 0.9666666666666667, 0.6333333333333333, 0.9333333333333332, 0.6666666666666666, 0.9333333333333332, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 0.6333333333333333, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9666666666666667, 1.0, 0.6666666666666666, 0.6, 1.0, 0.9333333333333332, 0.9666666666666667, 0.9666666666666667, 0.9, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.6333333333333333, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 0.9666666666666667, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Full valset pareto front score: 0.908080808080808
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Updated valset pareto front programs: [{2, 3}, {0}, {0}, {2, 3}, {3}, {3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1, 2, 3}, {3}, {3}, {0, 1, 2, 3}, {3}, {0}, {1, 2, 3}, {0}, {1, 2, 3}, {0}, {3}, {2}, {1, 2, 3}, {3}, {3}, {2}, {3}, {2, 3}, {2, 3}, {1, 2, 3}, {2, 3}, {0, 1, 2, 3}, {2, 3}, {0, 1, 2, 3}, {2, 3}, {3}, {0}, {2, 3}, {1, 2, 3}, {0, 1, 2, 3}, {3}, {0}, {0, 1, 2, 3}, {0, 1, 2}, {0}, {1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2}, {0}, {3}, {0}, {0, 1, 2, 3}, {3}, {0}, {3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0}, {2, 3}, {0}, {2, 3}, {2, 3}, {3}, {3}, {1, 2, 3}, {0, 1, 2, 3}]
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Best valset aggregate score so far: 0.8580808080808081
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Best program as per aggregate score on train_val: 3
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Best program as per aggregate score on valset: 3
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Best score on valset: 0.8580808080808081
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Best score on train_val: 0.8580808080808081
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Linear pareto front program index: 3
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 5: New program candidate index: 3
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Selected program 3 score: 0.8580808080808081
Average Metric: 2.27 / 3 (75.6%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 51.91it/s]2025/08/12 20:02:01 INFO dspy.evaluate.evaluate: Average Metric: 2.2666666666666666 / 3 (75.6%)
2025/08/12 20:02:01 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign all and only the applicable categories from a fixed list, based strictly on the message content.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the message’s primary purpose is to coordinate timing/availability for cleaning services (initial booking, rescheduling, adjusting times).
  - Typical signals: specific dates/times, availability windows, explicit reschedule/change requests, or back-and-forth around timing.
  - Do NOT use when a timing phrase appears merely as part of a broader service request or complaint (e.g., “please arrange a team to visit,” “at your earliest convenience,” “as soon as possible”) without concrete scheduling logistics.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, floor stripping/waxing, disinfection/sanitation.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction with prior service, reports subpar outcomes or communication issues, or requests remedies (redo, refund, re-clean).

- quality_and_safety_concerns
  - Use when the message raises quality shortfalls (e.g., areas still dirty, stains remaining) or safety/health risks (e.g., mold, hazards).
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.

- general_inquiries
  - Use when the sender asks for information about services, scope, requirements, what’s included, or availability before committing, or seeks clarification/guidance about process.
  - Often co-occurs with other categories when questions are asked (e.g., “Do you have availability next Tuesday?” with specialized service).

Key decision rules and pitfalls to avoid:
- Multi-label: Assign every category that is clearly supported by explicit statements in the message.
- Strict evidence only: Do not infer beyond the text. Praise, urgency, client profile, or context (e.g., “confidential,” “high-profile,” “ASAP”) are not categories by themselves.
- Scheduling vs. Complaint distinction:
  - If rescheduling is proposed only as a fix within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Scheduling vs. Service request distinction (common pitfall):
  - Do NOT tag cleaning_services_scheduling merely because the sender asks you to “arrange a visit,” “send a team,” or handle something “at your earliest convenience.”
  - Only tag cleaning_services_scheduling when the message’s central focus is timing logistics (e.g., “Can you come Friday at 3 PM?” “What slots do you have next week?” “Please move our usual Friday cleaning to Monday.”).
- Specialized services:
  - If the message mentions deep cleaning, mold remediation, carpet care, window washing, etc., include specialized_cleaning_services along with any other applicable categories.
- General inquiries:
  - Tag when the sender is explicitly asking questions about offerings, inclusions, requirements, or availability before committing. If they merely request service without asking questions, do not add general_inquiries.
- Allowed list only:
  - Use only the categories listed above. Do not introduce new categories, even if they seem to fit the scenario.

Output format (plain text):
- reasoning: Briefly justify each selected category and note key exclusions (especially why cleaning_services_scheduling was or was not applied).
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process:
1) Read the message once to identify intents: timing logistics, service-type specificity, feedback/complaint, quality/safety concerns, information requests.
2) Map each intent to categories using the definitions and rules (especially the scheduling distinctions).
3) Produce concise reasoning and the JSON array of categories using exact strings.

Guidance examples (for internal reference):
- “Please send a team ASAP to remediate mold in our studio. It’s a health risk.” → specialized_cleaning_services, quality_and_safety_concerns. Exclude cleaning_services_scheduling (no concrete timing logistics; urgency alone is not scheduling).
- “Can you do a deep clean next Tuesday afternoon or Wednesday morning?” → specialized_cleaning_services, cleaning_services_scheduling, general_inquiries (asking about availability).
- “Last cleaning left stains; please come back to fix.” → customer_feedback_and_complaints, quality_and_safety_concerns. Exclude cleaning_services_scheduling.
- “We need to move our regular Friday cleaning to Monday this week.” → cleaning_services_scheduling.
2025/08/12 20:02:01 INFO dspy.evaluate.evaluate: Average Metric: 2.333333333333333 / 3 (77.8%)

2025/08/12 20:02:04 INFO dspy.evaluate.evaluate: Average Metric: 56.83333333333333 / 66 (86.1%)
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: New program is on the linear pareto front
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Full valset score for new program: 0.861111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Full train_val score for new program: 0.861111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Individual valset scores for new program: [0.9666666666666667, 0.6666666666666666, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6, 0.6666666666666666, 0.6, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6333333333333333, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6333333333333333, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9333333333333332, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Full valset pareto front score: 0.9111111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Updated valset pareto front programs: [{2, 3, 4}, {0}, {0, 4}, {2, 3, 4}, {3, 4}, {3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {1, 2, 3, 4}, {3, 4}, {3, 4}, {0, 1, 2, 3, 4}, {3, 4}, {0}, {1, 2, 3, 4}, {0}, {1, 2, 3}, {0}, {4}, {2}, {4}, {3, 4}, {3, 4}, {2}, {3, 4}, {2, 3, 4}, {4}, {1, 2, 3, 4}, {2, 3, 4}, {0, 1, 2, 3, 4}, {2, 3, 4}, {0, 1, 2, 3, 4}, {2, 3}, {3, 4}, {0, 4}, {2, 3, 4}, {1, 2, 3, 4}, {0, 1, 2, 3, 4}, {3, 4}, {0}, {0, 1, 2, 3, 4}, {0, 1, 2}, {0}, {1, 2, 3}, {0, 1, 2, 3, 4}, {0, 1, 2}, {0, 4}, {3, 4}, {0, 4}, {0, 1, 2, 3}, {4}, {0, 4}, {3, 4}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0}, {2, 3, 4}, {0}, {2, 3, 4}, {4}, {3, 4}, {3, 4}, {1, 2, 3, 4}, {0, 1, 2, 3, 4}]
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Best program as per aggregate score on train_val: 4
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Best program as per aggregate score on valset: 4
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Best score on valset: 0.861111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Best score on train_val: 0.861111111111111
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Linear pareto front program index: 4
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 6: New program candidate index: 4
2025/08/12 20:02:04 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Selected program 4 score: 0.861111111111111
Average Metric: 1.50 / 3 (50.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:06<00:00,  2.31s/it]2025/08/12 20:02:10 INFO dspy.evaluate.evaluate: Average Metric: 1.5 / 3 (50.0%)

2025/08/12 20:02:34 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Proposed new text for urgency_module.predict: Task: Read the provided message and determine the urgency.

Context/domain:
- Messages typically relate to facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning services) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Output format (must be exactly two lines, in this order, no extra text or formatting):
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

Decision process (apply in order):
1) Check for safety/security risks or major operational impact.
   - High if there is a clear or implied immediate risk to people, property, or security, or a major outage/disruption.
2) Look for explicit urgency signals.
   - Phrases like “Urgent,” “ASAP,” “immediate attention,” “critical,” “escalating,” “cannot wait,” or hard deadlines that imply immediacy increase urgency.
3) Assess scope and impact.
   - Affecting many residents/tenants/operations or causing loss of access increases urgency.
4) Consider service history and tone.
   - Repeated failed attempts to resolve, escalation language, or severe dissatisfaction demanding immediate corrective action raises urgency.
5) If no risk and no time pressure are evident, default to low.

Urgency levels and rules:
- HIGH:
  - Clear or implied immediate safety/security risk or major operational impact.
  - Explicit urgent language requiring immediate action.
  - Severe dissatisfaction paired with either a demand for immediate corrective action or evidence of repeated failed support and escalation.
  - Examples/triggers: security breach or serious security gaps, fire/smoke, flooding/water leak, gas leak, electrical hazard, power outage, loss of access/lockouts, no heat in winter or no cooling in extreme heat affecting many people/operations.

- MEDIUM:
  - Time-sensitive issues affecting comfort, reliability, compliance, or service quality, but not emergencies and with no immediate safety/security risk.
  - Active service problems where delay could worsen impact or trust, including repeated quality lapses, policy/compliance concerns, or dissatisfaction requiring prompt clarification/correction.
  - Requests to schedule/repair/maintain “as soon as possible” without emergency conditions.
  - Examples/triggers: HVAC making noise or inconsistent temperature; repeated use of non-compliant/chemical cleaners contrary to stated sustainability practices; recurring but non-hazardous malfunctions; requests for prompt adjustments to ongoing services.

- LOW:
  - General inquiries, quotes, pricing, capabilities, technical documentation/case studies, or exploratory discussions with no operational problem and no stated time pressure.
  - Routine scheduling adjustments or portal assistance that do not block operations or cite deadlines.
  - Compliments/feedback without urgency.
  - Examples/triggers: evaluating services and asking for details/methodologies/case studies; changing a cleaning schedule; “not an emergency” with no other risk indicators.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity.
- Impact scope (residents/tenants/business operations).
- Deadlines/dates or requested response times.
- Prior failed attempts and escalation tone (including threats to cancel/escalate).
- Do not inflate urgency based solely on polite phrases like “prompt response,” “at your earliest convenience,” or skepticism demanding more information, when no risk or time pressure is present.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- Dissatisfaction about ongoing service quality or compliance (e.g., sustainability practices) without immediate danger is typically medium.
- Pre-sales or info-seeking skepticism/requests for documentation without a current issue are low.
- If unclear and no risk/time pressure is indicated, default to low.
2025/08/12 20:02:36 INFO dspy.evaluate.evaluate: Average Metric: 2.1666666666666665 / 3 (72.2%)
2025/08/12 20:02:59 INFO dspy.evaluate.evaluate: Average Metric: 53.5 / 66 (81.1%)
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Full valset score for new program: 0.8106060606060606
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Full train_val score for new program: 0.8106060606060606
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Individual valset scores for new program: [0.9666666666666667, 0.3333333333333333, 0.5666666666666667, 1.0, 0.6333333333333333, 0.9333333333333332, 0.9666666666666667, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6, 0.6666666666666666, 0.6, 0.9666666666666667, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6333333333333333, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.9, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9333333333333332, 1.0, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 1.0, 0.6, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 1.0, 0.6333333333333333, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Full valset pareto front score: 0.9161616161616162
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Updated valset pareto front programs: [{2, 3, 4, 5}, {0}, {0, 4}, {2, 3, 4, 5}, {3, 4}, {3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3}, {1, 2, 3, 4, 5}, {3, 4, 5}, {3, 4, 5}, {0, 1, 2, 3, 4, 5}, {3, 4, 5}, {0}, {1, 2, 3, 4, 5}, {0}, {1, 2, 3}, {0}, {4, 5}, {2}, {4}, {5}, {3, 4, 5}, {2}, {3, 4, 5}, {2, 3, 4, 5}, {4}, {1, 2, 3, 4, 5}, {2, 3, 4, 5}, {0, 1, 2, 3, 4}, {2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {2, 3}, {3, 4, 5}, {0, 4, 5}, {2, 3, 4, 5}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {3, 4, 5}, {0}, {0, 1, 2, 3, 4, 5}, {0, 1, 2}, {0}, {1, 2, 3}, {0, 1, 2, 3, 4, 5}, {0, 1, 2}, {0, 4, 5}, {3, 4}, {0, 4, 5}, {0, 1, 2, 3}, {4, 5}, {0, 4}, {3, 4, 5}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0}, {2, 3, 4, 5}, {0}, {2, 3, 4, 5}, {4}, {3, 4, 5}, {3, 4, 5}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}]
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Best program as per aggregate score on train_val: 4
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Best program as per aggregate score on valset: 4
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Best score on valset: 0.861111111111111
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Best score on train_val: 0.861111111111111
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Linear pareto front program index: 4
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 7: New program candidate index: 5
2025/08/12 20:02:59 INFO dspy.teleprompt.gepa.gepa: Iteration 8: Selected program 4 score: 0.861111111111111
Average Metric: 2.97 / 3 (98.9%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:08<00:00,  2.67s/it]2025/08/12 20:03:07 INFO dspy.evaluate.evaluate: Average Metric: 2.966666666666667 / 3 (98.9%)

2025/08/12 20:08:42 INFO dspy.teleprompt.gepa.gepa: Iteration 8: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text, punctuation, or reasoning.

How to decide the label
- Focus on the overall emotional tone expressed about the service/interaction, not the message’s functional purpose (e.g., making a request) or formalities.
- Read the entire body; do not infer sentiment from sender identity, role, organization, or the subject line alone.
- Weigh the strength and dominance of explicit sentiment-bearing language. If signals are mixed or weak, default to neutral.

Label definitions and cues
- Positive:
  - The message clearly expresses satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
  - Strong and/or multiple explicit positive cues dominate.
  - Examples of strong positive cues: “satisfied client,” “exceptional service,” “truly appreciate,” “deeply appreciate,” “commendable,” “excellent,” “very happy,” “great,” “love working with you,” “highly value your expertise,” “dedication has resonated.”
  - If a routine request includes multiple strong, primary praises and any issues are framed mildly without dissatisfaction, classify as positive.

- Negative:
  - The message expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
  - Explicit negative claims or emotions dominate.
  - Examples of negative cues: “unacceptable,” “unsafe,” “poor quality,” “frustrated,” “angry,” “very disappointed,” “deep frustration,” “far below the standard,” “lack of coordination,” “slow and unhelpful,” “caused significant disruptions,” “no one is taking responsibility.”
  - In mixed cases, strong explicit negative statements outweigh earlier generic praise (e.g., “I’ve been loyal, but…” followed by clear complaints → negative).

- Neutral:
  - Informational, inquisitive, or routine requests without clear emotional valence.
  - Routine maintenance/service requests reported matter-of-factly.
  - Polite or formal language alone does not imply positivity.
  - Examples that remain neutral unless strong sentiment is present: “I hope this finds you well,” “thank you,” “best regards,” “I look forward to your response,” “please provide more information,” inquiries about protocols or eco-friendly options.
  - Expressions of concern framed as questions or requests for clarification, without asserting dissatisfaction or harm, are neutral.
  - Mild or incidental compliments embedded in a routine request (e.g., a single appreciative phrase) remain neutral unless multiple strong positive signals dominate.

Disambiguation notes and edge cases
- Do not infer sentiment from identity (e.g., “loyal customer,” “doctor”) or organization names; rely on explicit sentiment-bearing language in the body.
- Mentioning a “problem” or “issue” alone is not necessarily negative; look for dissatisfaction words or frustrated tone to classify as negative.
- Hopeful or forward-looking phrases (“hopeful for a positive outcome,” “look forward to hearing from you”) are neutral unless combined with strong praise.
- If both praise and issues appear:
  - Primary strong praise with mild, non-judgmental issue framing → positive.
  - Clear complaints/frustration despite some praise → negative.
  - Mixed but weak/ambiguous signals → neutral.

Quality check before submitting
- Ensure the output is exactly one of: positive, neutral, negative.
- All lowercase, no extra spaces, punctuation, or explanation.
2025/08/12 20:08:43 INFO dspy.evaluate.evaluate: Average Metric: 2.6333333333333333 / 3 (87.8%)
2025/08/12 20:08:43 INFO dspy.teleprompt.gepa.gepa: Iteration 8: New subsample score is not better, skipping
2025/08/12 20:08:43 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Selected program 2 score: 0.7924242424242424
Average Metric: 2.17 / 3 (72.2%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00,  1.82s/it]2025/08/12 20:08:49 INFO dspy.evaluate.evaluate: Average Metric: 2.1666666666666665 / 3 (72.2%)

2025/08/12 20:09:54 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign every applicable category from a fixed list based only on the message content. Select all that apply. Do not add any category that is not clearly supported by the text.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the primary intent is to schedule, reschedule, adjust, or inquire about dates/times for cleaning services specifically.
  - Includes: booking a cleaning, changing cleaning times, checking availability for cleaning, aligning cleaning schedules.
  - Exclude for any non-cleaning work (e.g., HVAC maintenance), even if the sender says “please schedule a visit.”

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, carpet cleaning/maintenance, window washing, floor stripping/waxing, pressure washing.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction about prior or ongoing service, reports subpar work or communication, requests a redo/refund/remedy, or otherwise complains about service outcomes.

- quality_and_safety_concerns
  - Use when the message raises explicit concerns about service quality not meeting standards (e.g., still dirty/stained, malfunction persisting after service) or safety hazards (e.g., hazardous materials left unattended).
  - Can co-occur with customer_feedback_and_complaints when the sender is unhappy with outcomes.
  - Do not use for general anxiety about doing a task wrong or complexity of instructions unless an actual quality/safety issue is reported.

- general_inquiries
  - Use when the sender asks for information or clarification (e.g., what’s included, requirements, process, pricing, availability) before committing to any service.
  - Often co-occurs with cleaning_services_scheduling or specialized_cleaning_services when the message is exploratory.

- training_and_support_requests
  - Use when the sender asks for guidance, simplified instructions, training, or on-site assistance to understand or perform cleaning/maintenance tasks (e.g., “provide a simpler guide,” “send someone to help me follow the maintenance plan”).

- routine_maintenance_requests
  - Use for non-emergency requests to perform routine maintenance on building systems or facilities (e.g., HVAC check-up, routine servicing per a maintenance plan).
  - Note: HVAC maintenance falls here. This is not a cleaning service and should not trigger cleaning_services_scheduling.

Key decision rules:
- Multi-label: Assign every category clearly supported by the text.
- Cleaning scheduling vs. complaint:
  - If the main purpose is logistics around timing/availability for cleaning services, include cleaning_services_scheduling.
  - If any scheduling or re-do is requested only as a remedy within a complaint about poor service, do NOT include cleaning_services_scheduling. Instead, include customer_feedback_and_complaints and quality_and_safety_concerns (and specialized_cleaning_services if specific cleaning tasks are referenced).
- Cleaning scheduling vs. maintenance scheduling:
  - Do not use cleaning_services_scheduling for maintenance (e.g., HVAC) even if the sender asks to “schedule a maintenance visit.” Use routine_maintenance_requests (and training_and_support_requests if they also seek guidance/help).
- Specialized services:
  - If the message names a specialized cleaning task (deep clean, carpet, windows, etc.), include specialized_cleaning_services alongside any other applicable categories.
- Quality/safety threshold:
  - Include quality_and_safety_concerns only when the message reports actual poor quality outcomes or safety hazards. Do not include it for a sender’s fear of making mistakes without an actual incident.
- General inquiries:
  - If the sender seeks information before committing to a service, include general_inquiries; it can co-occur with cleaning_services_scheduling and specialized_cleaning_services.
- Strict schema:
  - Use only the categories listed above. Do not invent or use unlisted categories (e.g., “facility_management_issues” is not allowed).
- Evidence-only:
  - Do not infer intents not supported by the text.

Domain notes:
- ProCare handles residential and commercial cleaning and related facility maintenance requests (e.g., HVAC routine maintenance).
- Mentions of high-profile clients, confidentiality, or urgency do not create categories by themselves unless tied to definitions above.
- HVAC-related requests are routine_maintenance_requests; they are not cleaning services.

Output format (plain text):
- Provide two top-level keys:
  - reasoning: Briefly justify each selected category and note notable exclusions (especially cleaning scheduling vs. maintenance or complaint).
  - categories: A JSON array of the selected category strings, e.g., ["cleaning_services_scheduling", "general_inquiries"].

Process:
1) Read the message and identify intents: scheduling logistics (for cleaning only), specialized cleaning tasks, feedback/complaints, explicit quality/safety concerns, information requests, training/support needs, and routine maintenance requests.
2) Map each intent to categories using the definitions and rules above.
3) Produce concise reasoning and the JSON array of categories.

Clarifying examples (for edge cases):
- “Please schedule a deep clean next Friday.” -> ["cleaning_services_scheduling", "specialized_cleaning_services"]
- “Carpets still smell after yesterday’s service; please fix this and ensure safety protocols are followed.” -> ["customer_feedback_and_complaints", "quality_and_safety_concerns", "specialized_cleaning_services"]
- “I need a simpler guide for my HVAC maintenance plan or someone to help me follow it.” -> ["training_and_support_requests", "routine_maintenance_requests"]
- “Could you schedule an HVAC check-up at your earliest convenience?” -> ["routine_maintenance_requests"] (not cleaning_services_scheduling)
2025/08/12 20:09:58 INFO dspy.evaluate.evaluate: Average Metric: 2.3 / 3 (76.7%)
2025/08/12 20:10:06 INFO dspy.evaluate.evaluate: Average Metric: 52.56666666666666 / 66 (79.6%)
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Full valset score for new program: 0.7964646464646464
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Full train_val score for new program: 0.7964646464646464
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Individual valset scores for new program: [0.9666666666666667, 1.0, 0.9, 1.0, 0.6666666666666666, 0.6, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.26666666666666666, 0.6333333333333333, 0.6666666666666666, 0.9333333333333332, 1.0, 0.6666666666666666, 1.0, 0.9, 0.3333333333333333, 0.6333333333333333, 0.9666666666666667, 0.3333333333333333, 1.0, 1.0, 0.5666666666666667, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 0.6, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.6333333333333333, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.3333333333333333, 0.6666666666666666, 1.0, 0.6333333333333333, 0.3333333333333333, 0.9666666666666667, 0.9666666666666667, 0.6, 0.6, 0.9666666666666667, 0.6]
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Full valset pareto front score: 0.9186868686868687
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Updated valset pareto front programs: [{2, 3, 4, 5, 6}, {0, 6}, {0, 4, 6}, {2, 3, 4, 5, 6}, {3, 4}, {3, 4, 5}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 6}, {1, 2, 3, 4, 5, 6}, {3, 4, 5}, {3, 4, 5}, {0, 1, 2, 3, 4, 5}, {3, 4, 5}, {0}, {1, 2, 3, 4, 5}, {0}, {1, 2, 3}, {0, 6}, {4, 5}, {6}, {4}, {5}, {3, 4, 5}, {2, 6}, {3, 4, 5}, {2, 3, 4, 5, 6}, {4, 6}, {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4}, {6}, {0, 1, 2, 3, 4, 5, 6}, {6}, {3, 4, 5}, {0, 4, 5}, {2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {3, 4, 5}, {0, 6}, {6}, {0, 1, 2, 6}, {0, 6}, {1, 2, 3, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 6}, {0, 4, 5}, {3, 4}, {0, 4, 5, 6}, {0, 1, 2, 3}, {4, 5}, {0, 4}, {3, 4, 5}, {0, 1, 2, 3, 6}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0, 6}, {2, 3, 4, 5, 6}, {0}, {2, 3, 4, 5}, {4, 6}, {3, 4, 5}, {3, 4, 5}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5, 6}]
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Best program as per aggregate score on train_val: 4
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Best program as per aggregate score on valset: 4
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Best score on valset: 0.861111111111111
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Best score on train_val: 0.861111111111111
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Linear pareto front program index: 4
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 9: New program candidate index: 6
2025/08/12 20:10:06 INFO dspy.teleprompt.gepa.gepa: Iteration 10: Selected program 6 score: 0.7964646464646464
Average Metric: 2.60 / 3 (86.7%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.31it/s]2025/08/12 20:10:08 INFO dspy.evaluate.evaluate: Average Metric: 2.6 / 3 (86.7%)

2025/08/12 20:10:40 INFO dspy.teleprompt.gepa.gepa: Iteration 10: Proposed new text for urgency_module.predict: Task: Read the provided message and determine the urgency.

Context/domain:
- Messages typically relate to facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning services) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Input:
- A single free-text message (may include a subject and body).

Output format (strict):
- Provide exactly two lines, in this order, no extra text, headers, or formatting:
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>
- Keep “urgency” value lowercase. Do not add additional fields, bullets, or commentary.

Decision rules for urgency:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency signals: “Urgent,” “ASAP,” “Immediate,” “immediate attention required,” “critical,” “escalating,” “emergency.”
  - Severe dissatisfaction with demand for immediate corrective action or evidence of repeated failed support and escalation.
  - Common triggers/examples in this domain: active water leak/flooding, gas smell/leak, electrical hazard/sparks/smoke, fire/smoke, power outage affecting operations/residents, security breach or loss of access/lockout, no heat in winter or no cooling in extreme heat affecting many residents/operations.
- MEDIUM:
  - Time-sensitive issues affecting comfort, reliability, or service quality without immediate safety/security risk.
  - Requests for prompt scheduling/repair/maintenance where delay could worsen impact but is not currently critical.
  - Examples: HVAC inconsistent temperature or noises, minor malfunction, routine maintenance “at your earliest convenience,” stated as “not an emergency,” near-term deadlines that affect operations but are not safety-critical.
- LOW:
  - General inquiries, information requests, quotes, scheduling/options discussions, or interest in additional services with no stated or implied time pressure.
  - Compliments/feedback without an urgent problem.

Key cues to weigh:
- Explicit urgency language vs. downgrading statements like “not an emergency.”
- Safety/security implications and impact on operational continuity.
- Scope of impact (individual vs. many residents/tenants/business operations).
- Deadlines/dates or requested response times.
- Prior failed attempts and escalation tone.
- Do not inflate urgency based solely on polite phrases like “prompt assistance” if no urgent risk is present.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If multiple cues conflict, prioritize the highest credible risk to safety/security/operations.
- If unclear and no risk/time pressure is indicated, default to low.

Reasoning guidance:
- In 1–3 sentences, cite the decisive cues (e.g., explicit urgency words, hazards like “leak,” “gas,” “power outage,” scope like “affecting residents,” or disclaimers like “not an emergency”).
- Be specific and concise; do not restate the whole message.

Quality checks before output:
- Exactly two lines, starting with “reasoning:” then “urgency:”.
- No extra lines, labels, or formatting; urgency is one of low/medium/high.
2025/08/12 20:10:41 INFO dspy.evaluate.evaluate: Average Metric: 2.6 / 3 (86.7%)
2025/08/12 20:10:41 INFO dspy.teleprompt.gepa.gepa: Iteration 10: New subsample score is not better, skipping
2025/08/12 20:10:41 INFO dspy.teleprompt.gepa.gepa: Iteration 11: Selected program 5 score: 0.8106060606060606
Average Metric: 2.80 / 3 (93.3%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:06<00:00,  2.21s/it]2025/08/12 20:10:48 INFO dspy.evaluate.evaluate: Average Metric: 2.8 / 3 (93.3%)

2025/08/12 20:11:19 INFO dspy.teleprompt.gepa.gepa: Iteration 11: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text, punctuation, or reasoning.

What to evaluate
- Focus on the overall emotional tone expressed about the service/interaction.
- Ignore the message’s functional purpose (e.g., asking questions, making requests) and routine formalities.

Default rule
- If signals are mixed or weak/ambiguous, output neutral.

Label definitions and cues
- Positive:
  - The message clearly expresses satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
  - Strong and/or multiple explicit positive cues dominate the tone.
  - Examples of strong positive cues: “satisfied customer,” “truly appreciate,” “excellent service,” “very happy,” “great,” “love working with you,” “continuing to enjoy the excellent service.”
- Negative:
  - The message expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
  - Explicit negative claims or emotions dominate.
  - Examples of strong negative cues: “frustrated,” “unsatisfactory,” “vague,” “lack of follow-through,” “no tangible progress,” “questioning that decision,” “unacceptable,” “poor quality,” “unsafe,” “angry,” “very disappointed,” “reconsider my association,” urgent demands to rectify a problem.
- Neutral:
  - Informational, inquisitive, or routine requests without clear emotional valence.
  - Polite/formal language alone does not imply positivity.
  - Expressions of concern framed as questions or requests for clarification, without asserting a negative judgment.
  - Mild or incidental compliments embedded in routine inquiries remain neutral unless strong positive cues dominate.
  - Examples: requests for details, case studies, or how services work; generalized interest; “I hope this finds you well,” “thank you for your time,” “best regards,” “we are exploring options,” inquiries about safety/sustainability protocols posed without stated dissatisfaction.

Disambiguation notes and edge cases
- Do not infer sentiment from the sender’s identity, organization, tenure, or subject line alone; rely on explicit sentiment-bearing language in the body.
- Routine maintenance/service requests are neutral by default; they become positive only if accompanied by strong, explicit, and primary praise or gratitude.
- Concerns or hesitations presented as inquiries (e.g., asking about eco-friendly practices or pet-safe cleaning products) are neutral unless they assert dissatisfaction, harm, or a negative judgment.
- Mild praise within an otherwise routine inquiry (e.g., noting a “reputation for excellence”) remains neutral unless multiple strong positive cues are primary.
- Polite closings or courtesies (e.g., “thank you,” “I look forward to your response”) do not by themselves make a message positive.
- If a message contains both praise and complaints, determine which tone dominates; strong complaints override politeness and lead to negative, while strong, primary praise with no complaints leads to positive. If neither dominates, choose neutral.

Decision process (for internal use)
1) Identify explicit emotional cues about the service/interaction.
2) If strong positive cues dominate, output positive.
3) Else if strong negative cues dominate, output negative.
4) Else output neutral.

Output constraint
- Return exactly one of: positive, neutral, negative.
2025/08/12 20:11:21 INFO dspy.evaluate.evaluate: Average Metric: 2.8 / 3 (93.3%)
2025/08/12 20:11:21 INFO dspy.teleprompt.gepa.gepa: Iteration 11: New subsample score is not better, skipping
2025/08/12 20:11:21 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Selected program 6 score: 0.7964646464646464
Average Metric: 2.23 / 3 (74.4%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.12s/it]2025/08/12 20:11:24 INFO dspy.evaluate.evaluate: Average Metric: 2.2333333333333334 / 3 (74.4%)

2025/08/12 20:12:11 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Proposed new text for sentiment_module.predict: Task
- You will be given a single input field named "message" containing a professional email or support inquiry (often about facility management topics such as services, maintenance plans, sustainability/energy efficiency, training programs, or in-house team development). Your job is to determine the overall sentiment of the message.

Output format
- Return only one lowercase label: positive, neutral, or negative.
- Do not include explanations, reasoning, or any other text unless explicitly requested.

General approach
- Judge the overall sentiment conveyed toward the recipient/company/services.
- Focus on explicit emotional content and its intensity rather than routine politeness.
- Determine the primary intent of the message (e.g., inquiry/request vs. praise/complaint).
- If both positive and negative cues are present, choose the predominant one. If they balance out or are weak/incidental, choose neutral.

Label definitions and decision rules
1) Neutral
   - Most professional inquiries/requests for information or support.
   - Messages that are polite, respectful, or appreciative only in a routine way (e.g., “I hope this finds you well,” “Thank you for your time,” “Looking forward to your response”).
   - Messages that include mild or incidental positive statements as context but are primarily utilitarian requests (e.g., “We’re satisfied so far” or “We appreciate your services”) without strong praise or excitement.
   - Use neutral when the message’s main purpose is to ask for info/training/support and any positive wording is brief, generic, or secondary.

2) Positive
   - The message’s clear intent is to express praise, approval, enthusiasm, or strong satisfaction about the company/services.
   - Contains explicit and non-incidental positive language directed at the recipient/services, especially with intensity or enthusiasm (e.g., “thoroughly impressed,” “commendable,” “we’re excited about collaborating,” “fantastic,” “excellent,” “we love your service”).
   - Positive expressions are central to the message, not just a courtesy aside.

3) Negative
   - Expresses dissatisfaction, complaint, frustration, disappointment, or concern about problems (e.g., “not satisfied,” “disappointed,” “frustrated,” “issue,” “problem,” “concerned”).
   - The negative emotion is directed toward the recipient/services and is more than a neutral problem description.

Heuristics specific to this domain (facility-management emails)
- Professional emails to providers like ProCare Facility Solutions commonly include formal greetings, polite closings, and mild appreciation; treat these as neutral unless there is strong, central praise or clear complaint.
- Interest in training programs, maintenance plans, or sustainability practices is neutral by default, even if accompanied by brief compliments or statements like “we’ve been satisfied thus far” or “we appreciate your quality,” unless the message primarily aims to praise or shows clear excitement.
- Strong praise terms (e.g., “thoroughly impressed,” “commendable,” “excited about the possibility”) can justify a positive label when they are prominent and central to the message.
- Do not infer sentiment from the sender’s background (e.g., professor, first responder, resident) or the topic itself (e.g., fire safety, Jewish history); rely only on expressed sentiment toward the recipient/services.

Tie-breakers
- If sentiment cues are weak, generic, or incidental to a primarily informational/request-oriented message, choose neutral.
- If unsure between positive and neutral due to a single mild compliment embedded in a request, choose neutral.
2025/08/12 20:12:13 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:12:19 INFO dspy.evaluate.evaluate: Average Metric: 54.56666666666666 / 66 (82.7%)
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Full valset score for new program: 0.8267676767676767
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Full train_val score for new program: 0.8267676767676767
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Individual valset scores for new program: [0.9666666666666667, 1.0, 0.9, 1.0, 1.0, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.6, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.5666666666666667, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.3333333333333333, 0.6666666666666666, 1.0, 0.6333333333333333, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.6, 0.9333333333333332, 0.9666666666666667, 0.9333333333333332]
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 1.0, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332]
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Full valset pareto front score: 0.9338383838383838
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Updated valset pareto front programs: [{2, 3, 4, 5, 6, 7}, {0, 6, 7}, {0, 4, 6, 7}, {2, 3, 4, 5, 6, 7}, {7}, {3, 4, 5, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 6}, {1, 2, 3, 4, 5, 6, 7}, {3, 4, 5}, {3, 4, 5}, {0, 1, 2, 3, 4, 5}, {3, 4, 5, 7}, {0}, {7}, {0}, {1, 2, 3}, {0, 6, 7}, {4, 5, 7}, {6}, {4}, {5}, {7}, {2, 6}, {3, 4, 5, 7}, {2, 3, 4, 5, 6}, {4, 6}, {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4}, {6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {6}, {3, 4, 5}, {0, 4, 5}, {2, 3, 4, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {3, 4, 5}, {0, 6}, {6, 7}, {0, 1, 2, 6}, {0, 6, 7}, {1, 2, 3, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 6}, {7}, {3, 4, 7}, {0, 4, 5, 6, 7}, {0, 1, 2, 3}, {4, 5}, {0, 4}, {3, 4, 5}, {0, 1, 2, 3, 6, 7}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0, 6, 7}, {2, 3, 4, 5, 6, 7}, {0, 7}, {2, 3, 4, 5}, {4, 6, 7}, {3, 4, 5}, {3, 4, 5, 7}, {1, 2, 3, 4, 5}, {7}]
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Best program as per aggregate score on train_val: 4
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Best program as per aggregate score on valset: 4
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Best score on valset: 0.861111111111111
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Best score on train_val: 0.861111111111111
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Linear pareto front program index: 4
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 12: New program candidate index: 7
2025/08/12 20:12:19 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Selected program 4 score: 0.861111111111111
Average Metric: 2.50 / 3 (83.3%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:07<00:00,  2.56s/it]2025/08/12 20:12:27 INFO dspy.evaluate.evaluate: Average Metric: 2.5 / 3 (83.3%)

2025/08/12 20:13:46 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Proposed new text for categories_module.predict: Task: Classify a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider) by assigning all and only the applicable categories from a fixed list, based strictly on the message content.

Scope and domain cues:
- ProCare provides cleaning services and broader facility management support. Messages may cover cleaning, specialized cleaning tasks, routine equipment maintenance (e.g., HVAC), facility management coordination, and sustainability initiatives (e.g., energy efficiency).
- Do not infer services or intents not explicitly stated. Assign multiple categories when the text clearly supports them.

Allowed categories and precise definitions:
- cleaning_services_scheduling
  - Use only when the message’s central purpose is to coordinate concrete timing/logistics for cleaning/services (initial booking with specific dates/times, asking for available slots, rescheduling or moving a known slot).
  - Typical signals: explicit dates/times (“Friday at 3 PM”), options (“next Tue afternoon or Wed morning”), “what slots do you have next week?”, or “move our usual Friday cleaning to Monday.”
  - Exclusions:
    - Do NOT tag when the sender merely says “ASAP,” “at your earliest convenience,” “please schedule a visit,” without concrete timing logistics.
    - Do NOT tag when scheduling is mentioned only as a remedy inside a complaint (use customer_feedback_and_complaints instead, plus other relevant categories).

- specialized_cleaning_services
  - Mention of specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, disinfection/sanitation, floor stripping/waxing.

- customer_feedback_and_complaints
  - Dissatisfaction with prior service outcomes or communication (e.g., delays, lack of response), requests for fixes (redo, refund, re-clean), or frustration with process.
  - Can co-occur with other categories (e.g., routine_maintenance_requests) when the message is about that service type.

- quality_and_safety_concerns
  - Raises quality shortfalls (areas still dirty, stains remaining) or health/safety risks (e.g., mold, hazards).
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.

- general_inquiries
  - Explicit questions about services offered, inclusions, requirements, process/initial steps, or general availability before committing.
  - Can co-occur with other categories (e.g., specialized_cleaning_services, facility_management_issues).
  - Exclude if the message only requests service without asking questions.

- routine_maintenance_requests
  - Requests for periodic or proactive equipment/facility upkeep (e.g., HVAC system check-up), routine maintenance without specialized cleaning tasks mentioned.
  - This category is about the service type; pair with cleaning_services_scheduling only if concrete timing logistics are central to the message.

- facility_management_issues
  - Messages focused on broader facility management services/coordinations beyond cleaning (e.g., overall facility management for a residential complex, coordination of space utilization, integrated service management).

- sustainability_and_environmental_practices
  - Focus on energy efficiency, sustainability programs, environmental impact reduction, or green practices as part of the request or inquiry.

Key decision rules and pitfalls to avoid:
- Multi-label: Assign every category that is clearly supported by explicit text.
- Strict evidence only: Do not infer beyond the message. Tone (praise, urgency) or client profile is not a category.
- Scheduling vs. Complaint:
  - If a sender is complaining about delays/communication and also asks to “get it scheduled,” treat it as customer_feedback_and_complaints (and the relevant service type, e.g., routine_maintenance_requests). Do NOT add cleaning_services_scheduling unless the message primarily negotiates concrete timing slots.
- Scheduling vs. Service request:
  - Do NOT tag cleaning_services_scheduling merely because the sender asks to “arrange a visit,” “send a team,” or “at your earliest convenience.” Concrete timing logistics are required.
- Specialized services:
  - If the message mentions deep clean, mold remediation, carpet care, window washing, disinfection, post-construction cleanup, or floor work, include specialized_cleaning_services.
- General inquiries:
  - Tag when the sender asks about offerings, what’s included, process/initial steps, or “how quickly we can get started” (without concrete dates/times). This can co-occur with facility_management_issues and sustainability_and_environmental_practices.

Output format (plain text):
- reasoning: Briefly justify each selected category and key exclusions (especially why cleaning_services_scheduling was or was not applied).
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process:
1) Read the message to identify intents: timing logistics, specific service types (specialized cleaning or routine maintenance), complaints/dissatisfaction, quality/safety issues, information requests, facility management and sustainability focus.
2) Map each intent to categories using the definitions and decision rules above.
3) Produce concise reasoning and the JSON array using exact category strings.

Guidance examples aligned to edge cases seen:
- “Please send a team ASAP to remediate mold in our studio. It’s a health risk.” → ["specialized_cleaning_services", "quality_and_safety_concerns"]. Exclude cleaning_services_scheduling (urgency ≠ concrete timing).
- “Can you do a deep clean next Tuesday afternoon or Wednesday morning?” → ["specialized_cleaning_services", "cleaning_services_scheduling", "general_inquiries"].
- “Last cleaning left stains; please come back to fix.” → ["customer_feedback_and_complaints", "quality_and_safety_concerns"]. Exclude cleaning_services_scheduling.
- “We need to move our regular Friday cleaning to Monday this week.” → ["cleaning_services_scheduling"].
- “I need an HVAC routine check; please schedule a visit at your earliest convenience.” → ["routine_maintenance_requests"]. Exclude cleaning_services_scheduling (no concrete timing).
- “I’ve tried multiple times to schedule a routine HVAC check and haven’t heard back. Please help get this scheduled.” → ["routine_maintenance_requests", "customer_feedback_and_complaints"]. Exclude cleaning_services_scheduling (complaint-focused).
- “We need comprehensive facility management focused on space utilization and sustainability. How quickly can we get started and what are the initial steps?” → ["facility_management_issues", "sustainability_and_environmental_practices", "general_inquiries"]. Exclude cleaning_services_scheduling (no concrete timing).
2025/08/12 20:13:49 INFO dspy.evaluate.evaluate: Average Metric: 2.6333333333333333 / 3 (87.8%)
2025/08/12 20:13:59 INFO dspy.evaluate.evaluate: Average Metric: 56.766666666666666 / 66 (86.0%)
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Full valset score for new program: 0.86010101010101
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Full train_val score for new program: 0.86010101010101
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Individual valset scores for new program: [0.9666666666666667, 0.6666666666666666, 0.9, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6, 0.6, 0.6666666666666666, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.9333333333333332, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.9666666666666667, 0.6, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 1.0, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9, 1.0, 0.9333333333333332, 0.9, 1.0, 0.9666666666666667, 0.6333333333333333, 0.6333333333333333, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.6]
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332]
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Full valset pareto front score: 0.9378787878787879
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Updated valset pareto front programs: [{2, 3, 4, 5, 6, 7, 8}, {0, 6, 7}, {0, 4, 6, 7, 8}, {2, 3, 4, 5, 6, 7}, {8, 7}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 6, 8}, {1, 2, 3, 4, 5, 6, 7}, {8}, {3, 4, 5}, {0, 1, 2, 3, 4, 5, 8}, {3, 4, 5, 7, 8}, {0}, {7}, {0}, {1, 2, 3}, {0, 8, 6, 7}, {8, 4, 5, 7}, {6}, {4}, {5}, {8}, {2, 6}, {3, 4, 5, 7, 8}, {2, 3, 4, 5, 6, 8}, {4, 6}, {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4}, {8, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {6}, {3, 4, 5}, {0, 4, 5}, {2, 3, 4, 5, 6, 7, 8}, {1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {8}, {0, 6}, {8, 6, 7}, {0, 1, 2, 6}, {8}, {1, 2, 3, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 6}, {7}, {3, 4, 7}, {0, 4, 5, 6, 7}, {8}, {4, 5}, {0, 4}, {8, 3, 4, 5}, {0, 1, 2, 3, 6, 7, 8}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0, 8, 6, 7}, {8}, {0, 8, 7}, {2, 3, 4, 5}, {8, 4, 6, 7}, {3, 4, 5}, {8}, {1, 2, 3, 4, 5}, {7}]
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Best program as per aggregate score on train_val: 4
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Best program as per aggregate score on valset: 4
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Best score on valset: 0.861111111111111
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Best score on train_val: 0.861111111111111
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Linear pareto front program index: 4
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 13: New program candidate index: 8
2025/08/12 20:13:59 INFO dspy.teleprompt.gepa.gepa: Iteration 14: Selected program 8 score: 0.86010101010101
Average Metric: 2.57 / 3 (85.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.13it/s]2025/08/12 20:14:01 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)
2025/08/12 20:14:01 INFO dspy.teleprompt.gepa.gepa: Iteration 14: Proposed new text for urgency_module.predict: Task
- Read the provided message and determine its urgency.

Domain/context
- Messages are about facility management/services for a provider like ProCare Facility Solutions (e.g., facility operations, space utilization, security, sustainability practices/waste management, HVAC, plumbing, electrical, maintenance/repairs, cleaning/janitorial).
- Senders may be residential or commercial clients and may mention residents/tenants, property operations, business continuity, or prior support interactions.

Output format (strict)
- Provide exactly two fields, in this order, with no extra text or formatting:
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

How to determine urgency
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency language: “Urgent,” “ASAP,” “immediate attention,” “critical,” “escalating,” “immediate dispatch,” etc.
  - Severe dissatisfaction demanding immediate corrective action, especially with evidence of repeated failed support or dismissive responses.
  - Common high triggers in this domain: security breach/serious security gaps (e.g., failed locks, disabled alarms), fire/smoke, flooding/active water leak, gas smell/leak, electrical hazards (burning smell/sparking), power outage, loss of access, system-down events critical to operations (e.g., HVAC failure during extreme heat/cold, especially affecting many residents or business operations).

- MEDIUM:
  - Time-sensitive issues affecting comfort/reliability/service quality but not an emergency and no immediate safety/security risk.
  - Degradations or malfunctions needing prompt scheduling/repair (e.g., HVAC noisy/inconsistent temps, minor leak not causing damage, intermittent access issues) where delay could worsen impact.
  - Deadlines or near-term needs without emergency framing (e.g., “this week,” “before tenants move in”) or “at your earliest convenience” when paired with a current service problem.

- LOW:
  - General inquiries, information requests, quotes, preventative/routine maintenance with flexible timing, or interest in additional services without a current problem.
  - Explicit statements like “not urgent” or “not an emergency,” with no safety risk and no operational disruption.
  - Compliments/feedback that do not request immediate action.

Key cues to weigh
- Explicit urgency terms vs. explicit de-escalators (“not urgent,” “not an emergency”).
- Safety/security implications and risk of property damage.
- Scope/impact: residents/tenants/business operations affected vs. single/isolated inconvenience.
- Environmental/seasonal context (e.g., no cooling during extreme heat; no heat in winter).
- Deadlines/timelines and requested response times.
- Evidence of prior failed attempts, dismissive on-site responses, or escalation tone.
- Do not inflate urgency based solely on polite phrases like “prompt attention” or “earliest convenience” if no risk/time pressure is present.

Tie-breakers and edge cases
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high; default to low unless there is a concrete deadline or ongoing service degradation justifying medium.
- If unclear and no safety/security risk, operational disruption, or time pressure is indicated, default to low.
- Complaints about sustainability/cleaning practices can be high if they demand immediate corrective action and cite failed/dismissive prior support; otherwise medium if time-sensitive but not safety-related.

General approach
- Identify explicit urgency language, safety/security/property-damage risks, operational scope, and timing cues.
- Balance urgency signals against any de-escalating statements and the presence/absence of active disruption.
- Keep reasoning concise (1–3 sentences) and output only the two required fields.

2025/08/12 20:14:01 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)
2025/08/12 20:14:01 INFO dspy.teleprompt.gepa.gepa: Iteration 14: New subsample score is not better, skipping
2025/08/12 20:14:01 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Selected program 7 score: 0.8267676767676767
Average Metric: 2.27 / 3 (75.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00,  1.92s/it]2025/08/12 20:14:07 INFO dspy.evaluate.evaluate: Average Metric: 2.2666666666666666 / 3 (75.6%)

2025/08/12 20:23:06 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign every applicable category from a fixed list based only on the message content. Select all that apply. Do not add any category that is not clearly supported by the text.

TASK STRATEGY (high-level):
- Read the message once to understand the overall intent(s).
- Identify explicit intents/signals: scheduling for cleaning, mention of specialized cleaning tasks, feedback/complaints, explicit quality/safety issues, general information requests, training/support, routine facility maintenance (e.g., HVAC), and sustainability/eco-friendly topics.
- Map each identified intent to the categories below.
- Apply the decision rules to avoid common confusions (e.g., cleaning vs. maintenance scheduling; complaint-driven re-dos vs. scheduling).
- Multi-label: include all categories clearly supported by the text; exclude anything not evidenced.

ALLOWED CATEGORIES AND DEFINITIONS:
- cleaning_services_scheduling
  - Use only when the primary intent is to schedule, reschedule, adjust, or inquire about dates/times for cleaning services specifically.
  - Includes: booking a cleaning, changing cleaning times, checking availability for cleaning, aligning cleaning schedules.
  - Exclude for any non-cleaning work (e.g., HVAC maintenance), even if the sender says “please schedule a visit.”

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, carpet cleaning/maintenance, window washing, floor stripping/waxing, pressure washing.
  - Also include if the sender references these tasks in passing (e.g., praising “window cleaning” results) even if not actively requesting them.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction about prior or ongoing service, reports subpar work or communication, requests a redo/refund/remedy, or otherwise complains about service outcomes.

- quality_and_safety_concerns
  - Use when the message raises explicit concerns about service quality not meeting standards (e.g., still dirty/stained, malfunction persisting after service) or safety hazards (e.g., hazardous materials left unattended).
  - Can co-occur with customer_feedback_and_complaints when the sender is unhappy with outcomes.
  - Do not use for general anxiety about doing a task wrong or complexity of instructions unless an actual quality/safety issue is reported.

- general_inquiries
  - Use when the sender asks for information or clarification (e.g., what’s included, requirements, process, pricing, availability) before committing to any service.
  - Often co-occurs with cleaning_services_scheduling, specialized_cleaning_services, or sustainability_and_environmental_practices when the message is exploratory.

- training_and_support_requests
  - Use when the sender asks for guidance, simplified instructions, training, or on-site assistance to understand or perform cleaning/maintenance tasks (e.g., “provide a simpler guide,” “send someone to help me follow the maintenance plan”).

- routine_maintenance_requests
  - Use for non-emergency requests to perform routine maintenance on building systems or facilities (e.g., HVAC check-up, routine servicing per a maintenance plan).
  - Note: HVAC maintenance falls here. This is not a cleaning service and should not trigger cleaning_services_scheduling.

- sustainability_and_environmental_practices
  - Use when the message asks about or references eco-friendly/green cleaning products, practices, processes, certifications (e.g., Green Seal), environmental impact, or sustainability policies.
  - Can co-occur with general_inquiries and specialized_cleaning_services if specific tasks or services are also discussed.

KEY DECISION RULES:
- Multi-label: Assign every category clearly supported by the text.
- Cleaning scheduling vs. complaint:
  - If the main purpose is logistics around timing/availability for cleaning services, include cleaning_services_scheduling.
  - If any scheduling or re-do is requested only as a remedy within a complaint about poor service, do NOT include cleaning_services_scheduling. Instead, include customer_feedback_and_complaints and quality_and_safety_concerns (and specialized_cleaning_services if specific cleaning tasks are referenced).
- Cleaning scheduling vs. maintenance scheduling:
  - Do not use cleaning_services_scheduling for maintenance (e.g., HVAC) even if the sender asks to “schedule a maintenance visit.” Use routine_maintenance_requests (and training_and_support_requests if they also seek guidance/help).
- Specialized services:
  - If the message names a specialized cleaning task (deep clean, carpet, windows, floor work, pressure washing, etc.), include specialized_cleaning_services alongside any other applicable categories—even if merely referenced as past work or as an example.
- Sustainability:
  - Questions about eco-friendly products, green practices, or certifications should include sustainability_and_environmental_practices; this often co-occurs with general_inquiries and may also co-occur with specialized_cleaning_services when specific tasks are mentioned.
- Quality/safety threshold:
  - Include quality_and_safety_concerns only when the message reports actual poor quality outcomes or safety hazards. Do not include it for a sender’s fear of making mistakes without an actual incident.
- General inquiries:
  - If the sender seeks information before committing to a service, include general_inquiries; it can co-occur with cleaning_services_scheduling, specialized_cleaning_services, or sustainability_and_environmental_practices.
- Strict schema:
  - Use only the categories listed above. Do not invent or use unlisted categories.
- Evidence-only:
  - Do not infer intents not supported by the text. Mentions of high-profile clients, confidentiality, or urgency do not create categories by themselves unless tied to definitions above.

DOMAIN NOTES:
- ProCare handles residential and commercial cleaning and related facility maintenance requests (e.g., HVAC routine maintenance).
- HVAC-related requests are routine_maintenance_requests; they are not cleaning services.

OUTPUT FORMAT (plain text):
- Provide two top-level keys:
  - reasoning: Briefly justify each selected category and note notable exclusions (especially cleaning scheduling vs. maintenance or complaint; and when sustainability or specialized tasks are triggered by the text).
  - categories: A JSON array of the selected category strings, e.g., ["cleaning_services_scheduling", "general_inquiries"].

PROCESS:
1) Read the message and identify intents: scheduling logistics (for cleaning only), specialized cleaning tasks, feedback/complaints, explicit quality/safety concerns, information requests, training/support needs, routine maintenance requests, and sustainability topics.
2) Map each intent to categories using the definitions and rules above.
3) Produce concise reasoning and the JSON array of categories.

CLARIFYING EXAMPLES:
- “Please schedule a deep clean next Friday.” -> ["cleaning_services_scheduling", "specialized_cleaning_services]
- “Carpets still smell after yesterday’s service; please fix this and ensure safety protocols are followed.” -> ["customer_feedback_and_complaints", "quality_and_safety_concerns", "specialized_cleaning_services"]
- “I need a simpler guide for my HVAC maintenance plan or someone to help me follow it.” -> ["training_and_support_requests", "routine_maintenance_requests"]
- “Could you schedule an HVAC check-up at your earliest convenience?” -> ["routine_maintenance_requests"] (not cleaning_services_scheduling)
- “Could you share details on your eco-friendly cleaning products and any green certifications?” -> ["general_inquiries", "sustainability_and_environmental_practices"]
- “I’m interested in deep cleaning for my studio and your eco-friendly methods—what’s included?” -> ["general_inquiries", "specialized_cleaning_services", "sustainability_and_environmental_practices"]
- “Your team left our windows spotless last time; do you also offer pressure washing and what are your green practices?” -> ["general_inquiries", "specialized_cleaning_services", "sustainability_and_environmental_practices"]
2025/08/12 20:23:08 INFO dspy.evaluate.evaluate: Average Metric: 2.3 / 3 (76.7%)
2025/08/12 20:23:15 INFO dspy.evaluate.evaluate: Average Metric: 54.666666666666664 / 66 (82.8%)
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Full valset score for new program: 0.8282828282828283
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Full train_val score for new program: 0.8282828282828283
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Individual valset scores for new program: [0.9666666666666667, 1.0, 0.8666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 0.9333333333333332, 0.6333333333333333, 0.9666666666666667, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.6333333333333333, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6, 0.6666666666666666, 0.6666666666666666, 0.6333333333333333, 0.5333333333333333, 0.6333333333333333, 1.0, 0.6666666666666666, 0.9333333333333332, 0.5666666666666667, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 1.0, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.3333333333333333, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667]
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: New valset pareto front scores: [0.9666666666666667, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Full valset pareto front score: 0.9393939393939394
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Updated valset pareto front programs: [{2, 3, 4, 5, 6, 7, 8, 9}, {0, 9, 6, 7}, {0, 4, 6, 7, 8}, {2, 3, 4, 5, 6, 7, 9}, {8, 9, 7}, {8, 9}, {9}, {0, 1, 2, 3, 6, 8}, {1, 2, 3, 4, 5, 6, 7}, {8}, {3, 4, 5}, {0, 1, 2, 3, 4, 5, 8, 9}, {3, 4, 5, 7, 8}, {0}, {9, 7}, {0}, {1, 2, 3, 9}, {0, 8, 6, 7}, {4, 5, 7, 8, 9}, {6}, {4}, {5}, {8}, {2, 6}, {3, 4, 5, 7, 8, 9}, {2, 3, 4, 5, 6, 8}, {4, 6}, {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 9}, {8, 9, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {6}, {9, 3, 4, 5}, {0, 9, 4, 5}, {2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8}, {0, 6}, {8, 9, 6, 7}, {0, 1, 2, 6}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9}, {0, 1, 2, 6}, {9, 7}, {3, 4, 7}, {0, 4, 5, 6, 7, 9}, {8}, {4, 5}, {0, 4}, {8, 3, 4, 5}, {0, 1, 2, 3, 6, 7, 8, 9}, {0, 1, 2, 3}, {0, 1, 3}, {0}, {0, 6, 7, 8, 9}, {8, 9}, {0, 8, 9, 7}, {2, 3, 4, 5}, {4, 6, 7, 8, 9}, {3, 4, 5}, {8, 9}, {1, 2, 3, 4, 5}, {9}]
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Best program as per aggregate score on train_val: 4
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Best program as per aggregate score on valset: 4
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Best score on valset: 0.861111111111111
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Best score on train_val: 0.861111111111111
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Linear pareto front program index: 4
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 15: New program candidate index: 9
2025/08/12 20:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Selected program 0 score: 0.7207070707070706
Average Metric: 2.30 / 3 (76.7%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 42.76it/s]2025/08/12 20:23:15 INFO dspy.evaluate.evaluate: Average Metric: 2.3 / 3 (76.7%)

2025/08/12 20:30:53 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Proposed new text for urgency_module.predict: You are given a single message sent to ProCare Facility Solutions (a facilities maintenance/cleaning/training provider). Your task is to read the message and classify its urgency.

Output format:
- Provide two fields in this exact order and casing:
  - reasoning: 1–2 concise sentences explaining the key cues you used.
  - urgency: one of low, medium, or high (lowercase only).

General principles:
- Do not confuse importance with urgency. A request can be important but still low urgency if timing is flexible.
- Prioritize explicit time constraints, operational impact, safety risk, and current system status over tone/politeness.
- When signals conflict, use the most concrete evidence (e.g., an explicit timeframe) rather than subjective wording.
- If uncertain, err on the lower urgency unless there are clear cues of time sensitivity or risk.

Classification criteria:

High urgency:
- Explicit emergency language and/or immediate need: “urgent,” “immediate,” “time is of the essence,” “ASAP,” coupled with concrete risk or disruption.
- Current or imminent operational disruption, safety/health risks, or critical system issues (e.g., HVAC/electrical failures, outages, leaks, sanitation hazards).
- Recent recurring incidents indicating instability, especially involving critical infrastructure.
- Deadlines within 24–72 hours where failure would disrupt operations, safety, or compliance.

Medium urgency:
- Needs action soon (roughly within 3–7 days) but not an emergency.
- Non-critical issues that could cause inconvenience or affect near-term plans/events if not addressed (e.g., service needed before an event in a few days).
- “As soon as possible” requests without evidence of current disruption or safety risk.
- Specific near-term scheduling requests without critical impact.

Low urgency:
- Routine or elective services with flexible timing or long lead times (e.g., “at your earliest convenience,” “within the next couple of weeks”).
- Information-gathering or exploratory inquiries (e.g., asking about training programs, modules, schedules) without a near-term deadline.
- Praise/relationship-building notes attached to non-urgent requests.
- No mention of incidents, disruptions, safety concerns, or specific near-term deadlines.

Domain-specific cues (for ProCare context):
- High: Problems with critical systems (HVAC, electrical), references to preventing further disruptions, safety/health concerns.
- Low: Requests for deep cleaning (e.g., carpets/windows) with flexible timing or multi-week horizon; inquiries about training program details without urgency.
- Training requests are low unless tied to active incidents, operational disruptions, or near-immediate scheduling needs.

Language cue examples:
- Low indicators: “flexible,” “at your earliest convenience,” “within the next couple of weeks,” “seeking information,” “would like to learn more.”
- Medium indicators: “by end of week,” “before [date within a week],” “as soon as possible” without critical issue.
- High indicators: “urgent,” “immediate,” “time is of the essence,” “prevent further disruptions,” “critical systems,” “outage,” “leak,” “failure.”

Conflict resolution:
- If a message uses urgent language but also states a flexible or multi-week timeframe and no disruption, classify as low.
- If a near-term deadline exists but impact is non-critical, classify as medium.
- If both near-term deadline and critical impact exist, classify as high.

Examples mapping (for reference):
- Deep cleaning with flexible timing within a couple of weeks → low.
- Inquiry about training programs and modules with no deadline → low.
- Urgent need for training due to recent HVAC/electrical issues; “time is of the essence” → high.
2025/08/12 20:30:55 INFO dspy.evaluate.evaluate: Average Metric: 2.6333333333333333 / 3 (87.8%)
2025/08/12 20:30:58 INFO dspy.evaluate.evaluate: Average Metric: 49.9 / 66 (75.6%)
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Full valset score for new program: 0.756060606060606
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Full train_val score for new program: 0.756060606060606
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Individual valset scores for new program: [1.0, 0.6666666666666666, 0.9, 0.9666666666666667, 0.3333333333333333, 0.6, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.3333333333333333, 0.6666666666666666, 1.0, 0.6333333333333333, 0.3333333333333333, 0.6333333333333333, 0.6666666666666666, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9, 0.6666666666666666, 0.6333333333333333, 0.9333333333333332, 0.3333333333333333, 1.0, 0.6666666666666666, 0.6, 0.9333333333333332, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 0.6333333333333333, 0.6, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.3333333333333333, 0.6666666666666666, 1.0, 0.6666666666666666, 0.3333333333333333, 1.0, 0.9666666666666667, 0.6333333333333333, 0.6333333333333333, 0.9666666666666667, 0.6]
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Full valset pareto front score: 0.9398989898989899
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Updated valset pareto front programs: [{10}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10}, {2, 3, 4, 5, 6, 7, 9}, {8, 9, 7}, {8, 9}, {9}, {0, 1, 2, 3, 6, 8}, {1, 2, 3, 4, 5, 6, 7}, {8}, {3, 4, 5}, {0, 1, 2, 3, 4, 5, 8, 9, 10}, {3, 4, 5, 7, 8}, {0}, {9, 7}, {0}, {1, 2, 3, 9}, {0, 8, 6, 7}, {4, 5, 7, 8, 9}, {10, 6}, {4}, {5}, {8}, {2, 6}, {3, 4, 5, 7, 8, 9}, {2, 3, 4, 5, 6, 8, 10}, {4, 6}, {1, 2, 3, 4, 5}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 9}, {6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10}, {6}, {9, 3, 4, 5}, {0, 4, 5, 9, 10}, {2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8}, {0, 10, 6}, {8, 9, 6, 7}, {0, 1, 2, 6, 10}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 6, 10}, {9, 7}, {3, 4, 7}, {0, 4, 5, 6, 7, 9, 10}, {8}, {4, 5}, {0, 10, 4}, {8, 3, 4, 5}, {0, 1, 2, 3, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 10}, {0, 1, 3}, {0}, {0, 6, 7, 8, 9, 10}, {8, 9, 10}, {0, 8, 9, 7}, {2, 3, 4, 5, 10}, {4, 6, 7, 8, 9, 10}, {3, 4, 5}, {8, 9}, {1, 2, 3, 4, 5}, {9}]
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Best program as per aggregate score on train_val: 4
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Best program as per aggregate score on valset: 4
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Best score on valset: 0.861111111111111
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Best score on train_val: 0.861111111111111
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Linear pareto front program index: 4
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 16: New program candidate index: 10
2025/08/12 20:30:58 INFO dspy.teleprompt.gepa.gepa: Iteration 17: Selected program 6 score: 0.7964646464646464
Average Metric: 1.97 / 3 (65.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.18it/s]2025/08/12 20:31:01 INFO dspy.evaluate.evaluate: Average Metric: 1.9666666666666666 / 3 (65.6%)

2025/08/12 20:38:52 INFO dspy.teleprompt.gepa.gepa: Iteration 17: Proposed new text for categories_module.predict: Task: Multi-label classify a single customer message to ProCare Facility Solutions (cleaning and facility maintenance provider) using only the allowed categories. Select every category clearly supported by the text; do not invent categories or infer unstated intents.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the primary intent is to schedule, reschedule, adjust, or ask about dates/times for cleaning services specifically.
  - Includes: booking a cleaning, changing cleaning times, checking availability for cleaning, aligning cleaning schedules.
  - Exclude for any non-cleaning work (e.g., HVAC). Do not tag when scheduling is mentioned only as a remedy within a complaint about poor service.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, carpet cleaning/maintenance, window washing, floor stripping/waxing, pressure washing.
  - Can co-occur with cleaning_services_scheduling and/or general_inquiries.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction about prior or ongoing service, reports subpar work or communication, requests a redo/refund/remedy, or otherwise complains about service outcomes.
  - Can co-occur with quality_and_safety_concerns and specialized_cleaning_services when relevant.

- quality_and_safety_concerns
  - Use when the message reports actual poor quality outcomes (e.g., still dirty/stained, malfunction persists after service) or safety hazards (e.g., hazardous materials left unattended).
  - Can co-occur with customer_feedback_and_complaints.
  - Do not use for general anxiety or potential risks without an actual reported issue.

- general_inquiries
  - Use when the sender asks for information/clarification (e.g., what’s included, requirements, process, pricing, availability) before committing to any service.
  - Often co-occurs with cleaning_services_scheduling and/or specialized_cleaning_services.

- training_and_support_requests
  - Use when the sender asks for guidance, simplified instructions, training, or on-site assistance to understand or perform cleaning/maintenance tasks.

- routine_maintenance_requests
  - Use for non-emergency requests to perform routine maintenance on building systems or facilities (e.g., HVAC check-up, routine servicing per a maintenance plan).
  - This is not a cleaning service and must not trigger cleaning_services_scheduling.

Key decision rules:
- Multi-label: Assign every category clearly supported by the message; omit anything not clearly present.
- Cleaning scheduling vs. complaint:
  - If the main purpose is scheduling logistics for cleaning, include cleaning_services_scheduling.
  - If scheduling or a redo is requested only as a remedy within a complaint, do NOT include cleaning_services_scheduling; instead include customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Cleaning scheduling vs. maintenance scheduling:
  - Never use cleaning_services_scheduling for maintenance (e.g., HVAC). Use routine_maintenance_requests only for non-emergency maintenance asks.
- Specialized services:
  - If a specialized cleaning task is named, include specialized_cleaning_services alongside any other applicable categories.
- Quality/safety threshold:
  - Include quality_and_safety_concerns only when an actual poor outcome or safety hazard is reported.
- General inquiries:
  - Information-seeking before committing to service should be labeled general_inquiries; it can co-occur with other categories.
- Strict schema:
  - Use only the categories listed above. Do not invent or use unlisted categories (e.g., “emergency_repair_services” is not allowed).
- Evidence-only:
  - Base labels solely on what the message states. Do not infer unstated intent.

Domain notes:
- ProCare serves residential and commercial customers for cleaning and related facility maintenance (e.g., HVAC). HVAC and other building system maintenance belong under routine_maintenance_requests (non-emergency only) and are not cleaning.
- Mentions of urgency, high-profile clients, or confidentiality do not create categories by themselves.

Process:
1) Read the message and identify present intents: 
   - Scheduling logistics (for cleaning only), specialized cleaning tasks, feedback/complaints, explicit quality/safety issues, information requests, training/support needs, and routine (non-emergency) maintenance requests.
2) Map each identified intent to categories per the definitions and rules.
3) Output concise reasoning that justifies each selected category and notes notable exclusions (especially cleaning vs maintenance and complaint vs scheduling).
4) Output the categories as a JSON array of strings.

Output format (plain text):
- reasoning: Brief justification, including notable exclusions.
- categories: A JSON array, e.g., ["cleaning_services_scheduling", "general_inquiries"].

Edge cases and examples:
- “Please schedule a deep clean next Friday.” 
  -> ["cleaning_services_scheduling", "specialized_cleaning_services"]
- “Carpets still smell after yesterday’s service; please fix this and ensure safety protocols are followed.”
  -> ["customer_feedback_and_complaints", "quality_and_safety_concerns", "specialized_cleaning_services"]
- “I need a simpler guide for my HVAC maintenance plan or someone to help me follow it.”
  -> ["training_and_support_requests", "routine_maintenance_requests"]
- “Could you schedule an HVAC check-up at your earliest convenience?”
  -> ["routine_maintenance_requests"] (not cleaning_services_scheduling)
- Emergency maintenance (unlisted category) handling:
  - “The HVAC system failed; it’s unbearable. This is unacceptable—fix it immediately.”
    - Do NOT invent an emergency category.
    - Label the complaint: ["customer_feedback_and_complaints"].
    - Add "quality_and_safety_concerns" only if a specific hazard is reported (e.g., “pipes froze,” “heat failure causing health risks”).
    - Do NOT use routine_maintenance_requests (it is non-emergency only) and do NOT use cleaning_services_scheduling.
2025/08/12 20:38:55 INFO dspy.evaluate.evaluate: Average Metric: 1.9333333333333331 / 3 (64.4%)
2025/08/12 20:38:55 INFO dspy.teleprompt.gepa.gepa: Iteration 17: New subsample score is not better, skipping
2025/08/12 20:38:55 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Selected program 4 score: 0.861111111111111
Average Metric: 2.57 / 3 (85.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.45s/it]2025/08/12 20:38:59 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)

2025/08/12 20:39:20 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Proposed new text for urgency_module.predict: Task: Read the provided message and determine the urgency.

Context/domain:
- Messages are about facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, prior support interactions, or upcoming events.

Output format (must be exactly two lines, in this order, no extra text or formatting):
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

Decision rules for urgency:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact/outage.
  - Explicit urgency signals: “urgent,” “ASAP,” “immediate,” “critical,” “escalating,” “emergency.”
  - Severe dissatisfaction with demand for immediate corrective action or repeated failed support with escalation.
  - Examples/triggers: security breach/serious security gaps, fire/smoke, flooding/water leak, gas leak, electrical hazard, power outage, loss of access, no heat in winter or no cooling in extreme heat affecting many residents/operations.

- MEDIUM:
  - Time-sensitive issues affecting comfort, reliability, or service quality but not emergencies and with no immediate safety/security risk.
  - Requests to schedule repair/maintenance “at the earliest convenience,” within a specific timeframe (e.g., “within a week,” “before our event”), or where delay could worsen impact.
  - Follow-up after recent service where the issue persists (e.g., HVAC still inefficient) or quality concerns requiring additional steps/visit.
  - Examples/triggers: HVAC making noises/inconsistent temperature, minor malfunction, “not an emergency” but needs timely intervention to prevent disruption.

- LOW:
  - General inquiries, information requests, quotes, scheduling/options discussions, or interest in additional services with no stated or implied time pressure.
  - Compliments/feedback without an urgent problem; training/program info requests.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity.
- Impact scope (residents/tenants/business operations) and outages.
- Deadlines/dates or requested response times (e.g., “within a week,” “before next event”).
- Prior failed attempts, recent service follow-ups, and escalation tone.
- Do not inflate urgency for polite phrases like “prompt response” alone.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.
- “Routine” work can still be medium if there’s a timeframe or potential disruption; follow-ups on unresolved recent service are typically medium.

Output requirements:
- Provide exactly two lines starting with “reasoning:” and “urgency:” in that order.
- Reasoning must be 1–3 concise sentences citing the decisive cues.
- Urgency value must be one of: low, medium, high (lowercase).
2025/08/12 20:39:21 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:39:25 INFO dspy.evaluate.evaluate: Average Metric: 54.83333333333333 / 66 (83.1%)
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Full valset score for new program: 0.8308080808080808
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Full train_val score for new program: 0.8308080808080808
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Individual valset scores for new program: [0.9666666666666667, 0.6666666666666666, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6, 0.6666666666666666, 0.6, 0.9666666666666667, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6333333333333333, 0.6666666666666666, 0.9333333333333332, 0.3, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.9, 1.0, 1.0, 0.3333333333333333, 0.6333333333333333, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9333333333333332, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 0.9666666666666667, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 1.0, 0.6333333333333333, 1.0, 0.9333333333333332, 1.0, 0.6]
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Full valset pareto front score: 0.9398989898989899
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Updated valset pareto front programs: [{10}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10, 11}, {2, 3, 4, 5, 6, 7, 9, 11}, {8, 9, 7}, {8, 9}, {9}, {0, 1, 2, 3, 6, 8}, {1, 2, 3, 4, 5, 6, 7, 11}, {8}, {11, 3, 4, 5}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11}, {3, 4, 5, 7, 8, 11}, {0}, {9, 7}, {0}, {1, 2, 3, 9}, {0, 8, 6, 7}, {4, 5, 7, 8, 9, 11}, {10, 6}, {4}, {5}, {8}, {2, 6}, {3, 4, 5, 7, 8, 9, 11}, {2, 3, 4, 5, 6, 8, 10, 11}, {11, 4, 6}, {1, 2, 3, 4, 5, 11}, {2, 3, 4, 5, 6, 11}, {0, 1, 2, 3, 4, 9}, {6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11}, {6}, {3, 4, 5, 9, 11}, {0, 4, 5, 9, 10, 11}, {2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11}, {8}, {0, 10, 6}, {8, 9, 6, 7}, {0, 1, 2, 6, 10}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 6, 10}, {9, 7}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11}, {8}, {11, 4, 5}, {0, 10, 11, 4}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 10}, {0, 1, 3}, {0}, {0, 6, 7, 8, 9, 10}, {8, 9, 10}, {0, 8, 9, 7}, {2, 3, 4, 5, 10, 11}, {4, 6, 7, 8, 9, 10}, {11, 3, 4, 5}, {8, 9}, {1, 2, 3, 4, 5, 11}, {9}]
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Best program as per aggregate score on train_val: 4
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Best program as per aggregate score on valset: 4
2025/08/12 20:39:25 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Best score on valset: 0.861111111111111
2025/08/12 20:39:26 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Best score on train_val: 0.861111111111111
2025/08/12 20:39:26 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Linear pareto front program index: 4
2025/08/12 20:39:26 INFO dspy.teleprompt.gepa.gepa: Iteration 18: New program candidate index: 11
2025/08/12 20:39:26 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Selected program 0 score: 0.7207070707070706
Average Metric: 1.83 / 3 (61.1%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 47.47it/s]2025/08/12 20:39:26 INFO dspy.evaluate.evaluate: Average Metric: 1.8333333333333333 / 3 (61.1%)

2025/08/12 20:40:00 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Proposed new text for sentiment_module.predict: Task: Determine the sentiment of a provided message.

Input format:
- You will receive a single field: message (a block of text, often an email to a facility management provider such as ProCare Facility Solutions).

Goal:
- Classify the overall sentiment of the writer toward the recipient as one of: positive, neutral, negative.

Important domain context and nuances:
- Messages are typically about facility management, maintenance, cleaning, and safety compliance (e.g., minor plumbing issues, routine maintenance requests, urgent safety concerns like blocked emergency exits or untested fire alarms).
- The severity or urgency of the subject matter (including safety risks) does not by itself indicate negative sentiment. Focus on the writer’s tone toward the recipient, not the problem’s seriousness.
- Politeness markers (greetings, sign-offs, “hope this finds you well,” “please,” “thank you”) are common and should not by themselves be treated as positive sentiment. Explicit praise or satisfaction is needed to classify as positive.
- Statements such as “the problem persists” or requests for urgent action remain neutral unless accompanied by clear dissatisfaction, blame, or negative emotion.

Label definitions and cues:
- Positive:
  - The message contains explicit praise, appreciation, or satisfaction toward the recipient/service (e.g., “excellent service,” “commendable,” “I appreciate your continued support,” “your team’s dedication has been great”).
  - May also include a request or issue, but the overall tone includes clear positive evaluation.
- Neutral:
  - Informational inquiries, requests for service/maintenance, or reports of issues stated professionally and constructively without blame or negative emotion.
  - Urgent or serious issues phrased respectfully and solution-focused (e.g., safety concerns reported for prompt action) are neutral.
- Negative:
  - Expressions of dissatisfaction, frustration, blame, anger, or criticism toward the recipient/service (e.g., “unacceptable,” “I’m disappointed,” “this keeps happening and your team has failed,” “negligence”).
  - Threats, strong complaints, or clearly negative emotional tone.

Decision process:
1) Check for explicit positive evaluation (praise/appreciation). If present and substantive, label positive.
2) Else, check for explicit negative evaluation (dissatisfaction, blame, anger). If present, label negative.
3) Else, label neutral (default for professional inquiries/requests, even if urgent or about serious issues).
4) For mixed signals, choose the predominant tone. Courtesy alone does not tip to positive; urgency/risk alone does not tip to negative.

Output format:
- Provide two fields:
  - reasoning: One brief sentence explaining the key tonal cues you used (do not summarize the whole message).
  - sentiment: One of positive, neutral, or negative.
- Do not include any other text or formatting.

Examples of tricky cases:
- Reporting blocked emergency exits and untested fire alarms in a respectful, solution-oriented way → neutral.
- Praising the provider’s excellent service while requesting routine maintenance → positive.
- A polite inquiry about services with no praise or dissatisfaction → neutral.
2025/08/12 20:40:01 INFO dspy.evaluate.evaluate: Average Metric: 2.1666666666666665 / 3 (72.2%)
2025/08/12 20:40:07 INFO dspy.evaluate.evaluate: Average Metric: 50.56666666666666 / 66 (76.6%)
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Full valset score for new program: 0.7661616161616162
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Full train_val score for new program: 0.7661616161616162
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Individual valset scores for new program: [0.6666666666666666, 0.6666666666666666, 0.5666666666666667, 0.6333333333333333, 0.3333333333333333, 0.6, 0.9666666666666667, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 0.3333333333333333, 0.9, 0.6666666666666666, 0.6333333333333333, 0.26666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.9333333333333332, 0.6, 1.0, 0.3333333333333333, 0.9666666666666667, 0.6, 1.0, 1.0, 0.6333333333333333, 0.9333333333333332, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.6333333333333333, 0.6]
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6666666666666666, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Full valset pareto front score: 0.954040404040404
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Updated valset pareto front programs: [{10}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10, 11}, {2, 3, 4, 5, 6, 7, 9, 11}, {8, 9, 7}, {8, 9}, {9}, {0, 1, 2, 3, 6, 8, 12}, {1, 2, 3, 4, 5, 6, 7, 11}, {8}, {3, 4, 5, 11, 12}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12}, {3, 4, 5, 7, 8, 11}, {12}, {9, 12, 7}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12}, {4, 5, 7, 8, 9, 11, 12}, {10, 6}, {4}, {5}, {8}, {2, 6}, {3, 4, 5, 7, 8, 9, 11, 12}, {2, 3, 4, 5, 6, 8, 10, 11}, {11, 4, 6}, {12}, {2, 3, 4, 5, 6, 11}, {0, 1, 2, 3, 4, 9, 12}, {6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12}, {6}, {3, 4, 5, 9, 11, 12}, {0, 4, 5, 9, 10, 11, 12}, {2, 3, 4, 5, 6, 7, 8, 9}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12}, {8}, {0, 10, 6}, {8, 9, 6, 7}, {0, 1, 2, 6, 10, 12}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 6, 10, 12}, {12}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11, 12}, {8}, {11, 4, 5}, {0, 4, 10, 11, 12}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12}, {0, 1, 2, 3, 10, 12}, {0, 1, 3, 12}, {0}, {0, 6, 7, 8, 9, 10}, {8, 9, 10}, {0, 7, 8, 9, 12}, {2, 3, 4, 5, 10, 11}, {4, 6, 7, 8, 9, 10}, {11, 3, 4, 5}, {8, 9}, {1, 2, 3, 4, 5, 11}, {9}]
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Best program as per aggregate score on train_val: 4
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Best program as per aggregate score on valset: 4
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Best score on valset: 0.861111111111111
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Best score on train_val: 0.861111111111111
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Linear pareto front program index: 4
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 19: New program candidate index: 12
2025/08/12 20:40:07 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Selected program 5 score: 0.8106060606060606
Average Metric: 2.90 / 3 (96.7%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.09s/it]2025/08/12 20:40:10 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:40:10 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Assign all and only the applicable categories from the fixed list below, based strictly on what the message explicitly says.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the message’s primary purpose is to coordinate timing/availability for cleaning services (initial booking, rescheduling, adjusting times).
  - Typical signals: specific dates/times, availability windows, explicit reschedule/change requests, or back-and-forth around timing.
  - Do NOT use when timing words appear merely as part of a broader service request or complaint (e.g., “please arrange a team,” “at your earliest convenience,” “ASAP”) without concrete scheduling logistics.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, floor stripping/waxing, disinfection/sanitation.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction with prior service, reports subpar outcomes or communication issues, or requests remedies (redo, refund, re-clean).

- quality_and_safety_concerns
  - Use when the message raises quality shortfalls (e.g., areas still dirty, stains remaining) or safety/health risks (e.g., mold, hazards).
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.

- general_inquiries
  - Use when the sender asks for information about services, scope, requirements, what’s included, or availability before committing, or seeks clarification/guidance about process.
  - Often co-occurs with other categories when questions are asked (e.g., “Do you have availability next Tuesday?” with specialized service).

Strict rules you must follow:
- Multi-label: Assign every category clearly supported by explicit statements in the message.
- Strict evidence only: Do not infer beyond the text. Praise, urgency, client profile, or context are not categories.
- Scheduling vs. Complaint distinction:
  - If rescheduling is proposed only as a fix within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Scheduling vs. Service request distinction (common pitfall):
  - Do NOT tag cleaning_services_scheduling merely because the sender asks you to “arrange a visit,” “send a team,” or handle something “ASAP.”
  - Only tag cleaning_services_scheduling when the central focus is timing logistics (e.g., “Can you come Friday at 3 PM?” “What slots do you have next week?” “Please move our usual Friday cleaning to Monday.”).
- Specialized services:
  - If the message mentions deep cleaning, mold remediation, carpet care, window washing, etc., include specialized_cleaning_services along with any other applicable categories.
- General inquiries:
  - Tag when the sender is explicitly asking questions about offerings, inclusions, requirements, or availability before committing. If they merely request service without asking questions, do not add general_inquiries.
- Allowed list only:
  - Use only the categories listed above. Never introduce new categories (e.g., do NOT use “emergency_repair_services,” “facility_management_issues,” or “sustainability_and_environmental_practices”), even if the message discusses HVAC, energy efficiency, or other non-cleaning topics.
  - If a message is about non-cleaning services (e.g., HVAC repair, energy audits, general facility management) and does not fit the allowed categories, return [] unless they are asking informational questions (then use general_inquiries).

Edge-case guidance (important):
- Urgency alone (“ASAP,” “urgent”) is not scheduling.
- Non-cleaning topics:
  - HVAC/emergency repairs: These are not in scope of allowed categories. Use [] unless the sender asks questions about services (then general_inquiries).
  - Energy efficiency/sustainability inquiries: Use general_inquiries if they ask for information; do not introduce non-allowed categories.
- Health/safety concerns:
  - If tied to cleaning issues (e.g., mold presence, unsanitary conditions), use quality_and_safety_concerns and add specialized_cleaning_services if a specialized task is named.
  - If unrelated to cleaning (e.g., HVAC malfunction risk), do not force-fit; likely [] (or general_inquiries if they ask questions).
- Complaints about past cleaning results:
  - Use customer_feedback_and_complaints, and add quality_and_safety_concerns if they cite specific cleanliness or safety issues. Do not add cleaning_services_scheduling unless the message’s main focus is timing logistics.

Output format (plain text):
- reasoning: Briefly justify each selected category and note key exclusions (especially why cleaning_services_scheduling was or was not applied). Keep it concise.
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process:
1) Read the message once to identify intents: timing logistics, service-type specificity, feedback/complaint, quality/safety concerns, information requests.
2) Map each intent to categories using the definitions and rules above (especially the scheduling distinctions and allowed-list constraint).
3) Produce concise reasoning and the JSON array using exact category strings.

Additional illustrative mappings:
- “Please send a team ASAP to remediate mold in our studio. It’s a health risk.” → specialized_cleaning_services, quality_and_safety_concerns. Exclude cleaning_services_scheduling (no concrete timing).
- “Can you do a deep clean next Tuesday afternoon or Wednesday morning?” → specialized_cleaning_services, cleaning_services_scheduling, general_inquiries (asking about availability).
- “Last cleaning left stains; please come back to fix.” → customer_feedback_and_complaints, quality_and_safety_concerns. Exclude cleaning_services_scheduling.
- “We need to move our regular Friday cleaning to Monday this week.” → cleaning_services_scheduling.
- “Our HVAC system failed; please send someone ASAP.” → [] (non-cleaning topic; urgency ≠ scheduling).
- “Could you share how you handle energy efficiency or emergency repairs?” → ["general_inquiries"] (information-seeking; non-cleaning topics; allowed list only).
2025/08/12 20:40:10 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:40:10 INFO dspy.teleprompt.gepa.gepa: Iteration 20: New subsample score is not better, skipping
2025/08/12 20:40:10 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Selected program 4 score: 0.861111111111111

Average Metric: 2.23 / 3 (74.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.28it/s]2025/08/12 20:40:13 INFO dspy.evaluate.evaluate: Average Metric: 2.2333333333333334 / 3 (74.4%)

2025/08/12 20:40:49 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text or reasoning.

What to evaluate
- Focus on the overall emotional tone expressed about the service/interaction or experience with the provider, not the message’s functional purpose (e.g., making a request) or formalities.

Core decision rules (apply in order)
1) Negative
   - Choose negative only when the message explicitly expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
   - Strong negative cues include (non-exhaustive): “unacceptable,” “unsafe,” “poor quality,” “unprofessional,” “not satisfied,” “very disappointed,” “angry,” “frustrated,” “failed,” “lack of response,” “terrible,” “awful,” “unreliable.”
   - Reporting a problem or malfunction alone (e.g., HVAC not working, system shut down, inconvenience, urgency) is NOT sufficient for negative unless accompanied by explicit negative judgments or emotions directed at the service/experience.

2) Positive
   - Choose positive when strong and/or multiple explicit positive cues dominate the message, clearly expressing satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
   - Strong positive cues include (non-exhaustive): “thoroughly impressed,” “exceptional service,” “excellent,” “outstanding,” “very happy,” “great,” “love working with you,” “truly appreciate,” “commend,” “aligns perfectly with our values” when used as praise.
   - Mild or incidental compliments used as formalities or secondary to a routine request (e.g., “I appreciate your help,” “thanks in advance,” “hope you are well,” “I’ve appreciated your attention to detail” as background) remain neutral unless strong positive cues clearly dominate the message.

3) Neutral
   - Default to neutral when signals are mixed, weak, or when the message is primarily informational, inquisitive, or a routine request without clear emotional valence.
   - Routine maintenance/service requests (e.g., scheduling repairs for HVAC inefficiency, asking for training details, requesting a technician “as soon as possible”) are neutral by default.
   - Expressions of concern framed as questions or requests for clarification are neutral unless they assert dissatisfaction or harm.
   - Urgency (“immediate,” “urgent,” “as soon as possible”) and descriptions of impact (“causing discomfort,” “affecting ability to work”) are neutral unless paired with explicit negative emotion or judgment.

Disambiguation notes and edge cases
- Do not infer sentiment from the sender’s identity, organization, or the subject line alone; rely on explicit sentiment-bearing language in the body.
- Praise that is strong and primary (e.g., “I am thoroughly impressed by your sustainability and quality commitments”) can make an inquiry positive if it clearly dominates the tone.
- Compliments embedded to soften a problem report or request are neutral unless they contain multiple strong positive cues and are the main emphasis.
- Mixed messages:
  - If both explicit negative and strong positive cues appear, decide based on which clearly dominates; if unclear, choose neutral.

Domain-relevant illustrations (for consistency)
- HVAC/service issues: Describing failures like “temperature regulation is inconsistent,” “system shut down,” “requires urgent attention,” and requesting a technician is neutral unless the sender expresses dissatisfaction (e.g., “this is unacceptable,” “I’m very disappointed with your service”).
- Service admiration: Statements like “I am thoroughly impressed by the range of services and commitment to sustainability and quality” indicate positive when they are prominent and enthusiastic.
- Training/information inquiries: Requests for details on programs, schedules, or costs with polite language but no strong praise or complaints are neutral.

Procedure
1) Read the body of the message. Ignore the subject line unless the body contains similar cues.
2) Identify explicit sentiment-bearing phrases about the service/experience.
3) Apply the decision rules above.
4) If uncertain, output neutral.

Output
- Only one of: positive, neutral, negative (lowercase).
2025/08/12 20:40:51 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)
2025/08/12 20:40:56 INFO dspy.evaluate.evaluate: Average Metric: 55.166666666666664 / 66 (83.6%)
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Full valset score for new program: 0.8358585858585859
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Full train_val score for new program: 0.8358585858585859
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Individual valset scores for new program: [0.9666666666666667, 0.6666666666666666, 0.9, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.6666666666666666, 0.9666666666666667, 0.26666666666666666, 1.0, 0.26666666666666666, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 0.3333333333333333, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9, 1.0, 1.0, 1.0, 0.6333333333333333, 1.0, 0.6333333333333333, 1.0, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.6, 1.0, 0.9333333333333332, 0.6666666666666666, 0.9333333333333332, 0.6333333333333333, 0.3, 0.6333333333333333, 0.9333333333333332, 0.6333333333333333, 0.3, 1.0, 0.9666666666666667, 1.0, 0.9333333333333332, 0.6666666666666666, 0.9333333333333332]
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Full valset pareto front score: 0.9595959595959597
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Updated valset pareto front programs: [{10}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10, 11, 13}, {2, 3, 4, 5, 6, 7, 9, 11}, {8, 9, 7}, {8, 9}, {9}, {0, 1, 2, 3, 6, 8, 12}, {1, 2, 3, 4, 5, 6, 7, 11, 13}, {8}, {3, 4, 5, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12}, {3, 4, 5, 7, 8, 11, 13}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12}, {4, 5, 7, 8, 9, 11, 12, 13}, {10, 6}, {4, 13}, {5}, {8}, {2, 13, 6}, {3, 4, 5, 7, 8, 9, 11, 12, 13}, {2, 3, 4, 5, 6, 8, 10, 11, 13}, {11, 4, 13, 6}, {12}, {2, 3, 4, 5, 6, 11, 13}, {0, 1, 2, 3, 4, 9, 12, 13}, {6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13}, {6}, {3, 4, 5, 9, 11, 12, 13}, {0, 4, 5, 9, 10, 11, 12, 13}, {13}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13}, {8}, {0, 10, 13, 6}, {8, 9, 6, 7}, {0, 1, 2, 6, 10, 12}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 6, 10, 12, 13}, {12, 13}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {8}, {13, 11, 4, 5}, {0, 4, 10, 11, 12, 13}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12}, {0, 1, 2, 3, 10, 12}, {0, 1, 3, 12}, {0}, {0, 6, 7, 8, 9, 10}, {8, 9, 10}, {0, 7, 8, 9, 12}, {2, 3, 4, 5, 10, 11, 13}, {4, 6, 7, 8, 9, 10, 13}, {3, 4, 5, 11, 13}, {8, 9}, {1, 2, 3, 4, 5, 11}, {9}]
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Best program as per aggregate score on train_val: 4
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Best program as per aggregate score on valset: 4
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Best score on valset: 0.861111111111111
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Best score on train_val: 0.861111111111111
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Linear pareto front program index: 4
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 21: New program candidate index: 13
2025/08/12 20:40:56 INFO dspy.teleprompt.gepa.gepa: Iteration 22: Selected program 6 score: 0.7964646464646464
Average Metric: 1.97 / 3 (65.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.43s/it]2025/08/12 20:41:00 INFO dspy.evaluate.evaluate: Average Metric: 1.9666666666666666 / 3 (65.6%)

2025/08/12 20:41:25 INFO dspy.teleprompt.gepa.gepa: Iteration 22: Proposed new text for urgency_module.predict: You are given a single user message and must classify its urgency.

Task:
- Read the message and determine urgency based on safety/security risk, operational impact, urgency language, scope of impact (residents/tenants/operations), deadlines, and evidence of prior failed support or escalation.

Context/domain:
- Messages typically involve facility management/services for a provider like ProCare Facility Solutions.
- Common topics: facility operations, space utilization, security, sustainability, HVAC systems, maintenance/repairs, cleaning/janitorial services.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Output format:
- Return exactly two fields, in this order, with no extra text, headers, or formatting:
  reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
  urgency: <one of: low | medium | high>
- Use lower-case values for urgency.

Urgency levels and decision rules:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational disruption.
  - Explicit urgency signals: “Urgent,” “Immediate attention required,” “Immediate assistance required,” “ASAP,” “critical,” “pressing,” “escalated/escalating,” “needs swift action,” etc.
  - Severe dissatisfaction with demand for immediate corrective action, especially after repeated failed support attempts or unfulfilled dispatch.
  - Triggers/examples: security breach or serious security gaps; fire/smoke; flooding/water leak; gas leak; electrical hazard; power outage; loss of access/lockouts; HVAC failure causing habitability risk (e.g., no heat in winter or no cooling in extreme heat), especially affecting many residents/operations or during harsh weather; property-wide coordination failures impacting safety or order.
- MEDIUM:
  - Time-sensitive issues affecting comfort/reliability/service quality but not an emergency and no immediate safety/security risk.
  - Requests for prompt scheduling/repair/maintenance where delay could worsen impact but is not currently critical.
  - Triggers/examples: HVAC making noises or inconsistent temperature; minor malfunctions; routine maintenance “at the earliest convenience”; issues explicitly noted as “not an emergency.”
- LOW:
  - General inquiries, information requests, quotes, scheduling/options discussions, interest in additional services, or compliments/feedback with no stated or implied time pressure.
  - Triggers/examples: asking about training programs, sustainability practices, pricing, availability; exploratory or collaborative messages with no active problem to fix now.

Key cues to weigh:
- Explicit urgency language vs. calming statements like “not an emergency.”
- Safety/security implications and operational continuity.
- Impact scope (individual unit vs. entire building/operations).
- Deadlines/dates or explicitly requested response times (e.g., “as soon as possible” in context of a critical issue).
- Prior failed attempts and escalation tone (e.g., promised technician not dispatched).
- Do not inflate urgency based solely on polite phrases like “prompt assistance” or long courteous intros.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.
- For HVAC: classify high when there’s urgent language plus extreme weather/major discomfort or building-wide impact, especially with prior failed support; otherwise medium.

Instructions:
- Focus on substantive cues; ignore pleasantries, greetings, or unrelated geography.
- Keep reasoning succinct (1–3 sentences) citing the decisive cues.
- Output exactly the two required lines, nothing else.
2025/08/12 20:41:27 INFO dspy.evaluate.evaluate: Average Metric: 1.9666666666666666 / 3 (65.6%)
2025/08/12 20:41:27 INFO dspy.teleprompt.gepa.gepa: Iteration 22: New subsample score is not better, skipping
2025/08/12 20:41:27 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Selected program 13 score: 0.8358585858585859
Average Metric: 2.90 / 3 (96.7%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  1.61it/s]2025/08/12 20:41:29 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)

2025/08/12 20:42:51 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign all and only the applicable categories from a fixed list, based strictly on the message content.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the message’s primary purpose is to coordinate timing/availability for cleaning services (initial booking, rescheduling, adjusting times).
  - Typical signals: specific proposed dates/times, availability windows, explicit reschedule/change requests, or back-and-forth around timing.
  - Do NOT use when a timing phrase appears merely as part of a broader service request or complaint (e.g., “please arrange a team,” “at your earliest convenience,” “ASAP”) without concrete scheduling logistics.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or requirements beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, floor stripping/waxing, disinfection/sanitation, and product- or method-specific requests such as pet-safe, non-toxic, hypoallergenic, fragrance-free, green/eco-friendly cleaning products or practices.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction with prior service, reports subpar outcomes or communication issues, or requests remedies (redo, refund, re-clean).

- quality_and_safety_concerns
  - Use when the message raises quality shortfalls (e.g., areas still dirty, stains remaining) or safety/health risks and sensitivities (e.g., mold, hazardous conditions, pet health/product safety concerns).
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.
  - Applies to both experienced issues and proactive concerns about potential risks (e.g., asking about product safety for pets).

- general_inquiries
  - Use when the sender asks for information about services, scope, inclusions, requirements, products used, process/initial steps, or broad availability before committing.
  - Often co-occurs with other categories when explicit questions are asked (e.g., “What’s included in a deep clean?” “Do you have availability next Tuesday?”).
  - Exclude if the questions are entirely subsumed by a more specific non-cleaning category that itself captures the user’s request (see training_and_support_requests guidance below).

- sustainability_and_environmental_practices
  - Use when the message references eco-friendly practices, environmental impact reduction, energy efficiency, green products, sustainability goals/initiatives, or requests for environmentally conscious approaches.

- facility_management_issues
  - Use when the message concerns comprehensive facility management beyond routine cleaning (e.g., coordination of space utilization, overall facility operations support, starting/managing a facility management program).
  - Can co-occur with sustainability_and_environmental_practices when environmental/energy-efficiency aspects are discussed.
  - Can co-occur with general_inquiries when the sender asks about how to get started, initial steps, or high-level availability (without concrete time slots).

- training_and_support_requests
  - Use when the message requests training or structured support programs (e.g., training for in-house maintenance teams, HVAC/electrical best practices, troubleshooting, safety/efficiency training).
  - If the message is primarily a training request and asks about program details or how quickly it can be scheduled (without concrete time slots), assign this category and generally do NOT add general_inquiries unless they ask for additional, unrelated service information outside of training.

Key decision rules and pitfalls to avoid:
- Multi-label: Assign every category that is clearly supported by explicit statements in the message.
- Strict evidence only: Do not infer beyond the text. Praise, urgency, client profile, or context (e.g., “confidential,” “high-profile,” “ASAP”) are not categories by themselves.
- Scheduling vs. Complaint distinction:
  - If rescheduling is proposed only as a fix within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Scheduling vs. Service request distinction (common pitfall):
  - Do NOT tag cleaning_services_scheduling merely because the sender asks you to “arrange a visit,” “send a team,” or handle something “at your earliest convenience.”
  - Only tag cleaning_services_scheduling when the message’s central focus is timing logistics with concrete slots (e.g., “Can you come Friday at 3 PM?” “What slots do you have next week?” “Please move our usual Friday cleaning to Monday.”).
- Specialized services:
  - If the message mentions deep cleaning, mold remediation, carpet care, window washing, disinfection, post-construction cleanup, floor care, or specific product requirements (pet-safe, non-toxic, eco-friendly, hypoallergenic), include specialized_cleaning_services.
- General inquiries:
  - Tag when the sender explicitly asks questions about offerings, inclusions, products used, requirements, or process/initial steps, especially before committing.
  - Co-tag with other relevant categories (e.g., specialized_cleaning_services, facility_management_issues) when those topics are being asked about.
  - Exception for training: If the message is primarily a training request and any questions are strictly about the training programs themselves, prefer only training_and_support_requests (do not add general_inquiries) unless unrelated information is requested too.
- Allowed list only:
  - Use only the categories listed above. Do not introduce new categories.

Output format (plain text):
- reasoning: Briefly justify each selected category and note key exclusions (especially why cleaning_services_scheduling was or was not applied).
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process:
1) Read the message to identify intents: timing logistics, service-type specificity or product requirements, feedback/complaints, quality/safety concerns, information requests, sustainability themes, broader facility management needs, and training/support requests.
2) Map each intent to categories using the definitions and rules (especially the scheduling distinctions and the training vs. general_inquiries exception).
3) Produce concise reasoning and the JSON array of categories using exact strings.

Guidance examples:
- “Please send a team ASAP to remediate mold in our studio. It’s a health risk.” → specialized_cleaning_services, quality_and_safety_concerns. Exclude cleaning_services_scheduling (urgency alone is not scheduling).
- “Can you do a deep clean next Tuesday afternoon or Wednesday morning?” → specialized_cleaning_services, cleaning_services_scheduling, general_inquiries (asking about availability).
- “Last cleaning left stains; please come back to fix.” → customer_feedback_and_complaints, quality_and_safety_concerns. Exclude cleaning_services_scheduling.
- “We need to move our regular Friday cleaning to Monday this week.” → cleaning_services_scheduling.
- “I’m concerned about the impact of your cleaning products on my cats. Are they pet-friendly? Any safer alternatives?” → general_inquiries, quality_and_safety_concerns, specialized_cleaning_services, sustainability_and_environmental_practices (pet-safe/eco-friendly product considerations).
- “We need comprehensive training for our in-house team on HVAC/electrical best practices; what programs do you offer and how quickly can we schedule?” → training_and_support_requests. Exclude cleaning_services_scheduling (no concrete time slots); generally exclude general_inquiries because the questions pertain solely to the training request.
- “We’re launching facility management for a new complex focused on energy efficiency and reduced environmental impact. How quickly can we get started and what are the initial steps?” → facility_management_issues, sustainability_and_environmental_practices, general_inquiries. Exclude cleaning_services_scheduling (no concrete timing logistics).
2025/08/12 20:42:54 INFO dspy.evaluate.evaluate: Average Metric: 2.9333333333333336 / 3 (97.8%)
2025/08/12 20:43:03 INFO dspy.evaluate.evaluate: Average Metric: 54.96666666666667 / 66 (83.3%)
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Full valset score for new program: 0.8328282828282828
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Full train_val score for new program: 0.8328282828282828
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Individual valset scores for new program: [1.0, 0.6666666666666666, 0.9, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.3, 0.9666666666666667, 0.26666666666666666, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9333333333333332, 0.3, 0.9666666666666667, 0.9333333333333332, 0.6333333333333333, 1.0, 0.9333333333333332, 0.6, 0.9333333333333332, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9, 0.9666666666666667, 0.9333333333333332, 1.0, 0.6333333333333333, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 1.0, 0.9666666666666667, 0.6333333333333333, 0.9666666666666667, 0.6, 0.9333333333333332, 0.9, 0.6666666666666666, 0.9333333333333332, 0.6333333333333333, 0.3333333333333333, 0.6, 0.9333333333333332, 0.6666666666666666, 0.26666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667]
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Full valset pareto front score: 0.9595959595959597
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Updated valset pareto front programs: [{10, 14}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10, 11, 13, 14}, {2, 3, 4, 5, 6, 7, 9, 11}, {8, 9, 14, 7}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14}, {1, 2, 3, 4, 5, 6, 7, 11, 13}, {8, 14}, {3, 4, 5, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12}, {3, 4, 5, 7, 8, 11, 13, 14}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12}, {4, 5, 7, 8, 9, 11, 12, 13, 14}, {10, 6}, {4, 13}, {5}, {8}, {2, 13, 6}, {3, 4, 5, 7, 8, 9, 11, 12, 13}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14}, {11, 4, 13, 6}, {12}, {2, 3, 4, 5, 6, 11, 13}, {0, 1, 2, 3, 4, 9, 12, 13, 14}, {6, 7, 8, 9, 10, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14}, {6}, {3, 4, 5, 9, 11, 12, 13}, {0, 4, 5, 9, 10, 11, 12, 13}, {13, 14}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14}, {8}, {0, 10, 13, 6}, {6, 7, 8, 9, 14}, {0, 1, 2, 6, 10, 12}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 6, 10, 12, 13, 14}, {12, 13}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {8}, {13, 11, 4, 5}, {0, 4, 10, 11, 12, 13}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12}, {0, 1, 2, 3, 10, 12}, {0, 1, 3, 12}, {0}, {0, 6, 7, 8, 9, 10}, {8, 9, 10, 14}, {0, 7, 8, 9, 12}, {2, 3, 4, 5, 10, 11, 13, 14}, {4, 6, 7, 8, 9, 10, 13, 14}, {3, 4, 5, 11, 13, 14}, {8, 9, 14}, {1, 2, 3, 4, 5, 11}, {9, 14}]
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Best program as per aggregate score on train_val: 4
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Best program as per aggregate score on valset: 4
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Best score on valset: 0.861111111111111
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Best score on train_val: 0.861111111111111
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Linear pareto front program index: 4
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 23: New program candidate index: 14
2025/08/12 20:43:03 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Selected program 6 score: 0.7964646464646464
Average Metric: 2.53 / 3 (84.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.20s/it]2025/08/12 20:43:06 INFO dspy.evaluate.evaluate: Average Metric: 2.533333333333333 / 3 (84.4%)

2025/08/12 20:43:35 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Proposed new text for sentiment_module.predict: You are given a single input field:
- message: a user’s email-style message (often to ProCare Facility Solutions or similar facility-management support), possibly with subject lines, greetings, and sign-offs.

Task:
- Determine the overall sentiment of the message and output a single lowercase label: positive, neutral, or negative.
- Output only the label (no explanations or extra text).

General rules:
- Focus on the core emotional tone about the main topic or request, not on boilerplate politeness or salutations.
- If the message mixes sentiments, choose the predominant tone regarding the main issue. A brief courtesy (e.g., “generally satisfied”) does not outweigh substantial complaints.
- When in doubt or if the tone is balanced/unclear, choose neutral.
- Ignore identity or demographic details (e.g., profession, culture); they are not sentiment signals.

Label definitions and cues:
1) positive
   - Clear praise, enthusiasm, or commendation (e.g., “thoroughly impressed,” “commendable,” “excited to collaborate,” “appreciate your eco-friendly practices”).
   - Proactive expressions of admiration or strong satisfaction about services or values.
   - Note: Thanks alone or routine professional courtesy does not imply positive.

2) neutral
   - Informational, routine, or procedural messages: inquiries, scheduling, maintenance requests, status updates, or general descriptions without strong emotional language.
   - Polite phrases (“I hope this finds you well,” “thank you,” “best regards”), formulaic compliments (“continued excellent service”), or a generic “I have been a satisfied client” do not, by themselves, make the sentiment positive.
   - Routine issues framed without dissatisfaction (e.g., “HVAC not as efficient as it used to be; please schedule maintenance”) remain neutral.

3) negative
   - Complaints, dissatisfaction, or expressions of concern, frustration, or urgency about problems (e.g., “alarming,” “safety risk,” “not the level of service I expected,” “malfunctioning HVAC,” “hazardous materials left unattended”).
   - Requests for corrective action due to quality/safety issues or unmet expectations.

Domain-specific notes:
- Facility-management contexts (HVAC performance, cleaning protocols, maintenance quality, safety) commonly appear. Treat explicit problems or risks as negative; treat routine service requests without blame as neutral; treat explicit praise for company practices (e.g., sustainability/energy efficiency being commendable) as positive.

Output format:
- Return exactly one of: positive, neutral, negative.
2025/08/12 20:43:37 INFO dspy.evaluate.evaluate: Average Metric: 2.8666666666666667 / 3 (95.6%)
2025/08/12 20:43:42 INFO dspy.evaluate.evaluate: Average Metric: 53.56666666666666 / 66 (81.2%)
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Full valset score for new program: 0.8116161616161616
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Full train_val score for new program: 0.8116161616161616
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Individual valset scores for new program: [0.9666666666666667, 0.6666666666666666, 0.9, 0.6666666666666666, 1.0, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9666666666666667, 0.26666666666666666, 0.6333333333333333, 0.6666666666666666, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.5666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.6, 0.9666666666666667, 1.0, 0.6333333333333333, 1.0, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6333333333333333, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.9, 0.6666666666666666, 0.9666666666666667, 0.6333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0, 0.6333333333333333, 0.6666666666666666, 0.6333333333333333, 0.9666666666666667, 0.6, 0.9333333333333332, 0.9666666666666667, 0.6]
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Full valset pareto front score: 0.9646464646464646
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Updated valset pareto front programs: [{10, 14}, {0, 9, 6, 7}, {0, 4, 6, 7, 8, 10, 11, 13, 14, 15}, {2, 3, 4, 5, 6, 7, 9, 11}, {7, 8, 9, 14, 15}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15}, {8, 14, 15}, {3, 4, 5, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12}, {3, 4, 5, 7, 8, 11, 13, 14, 15}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15}, {10, 6, 15}, {4, 13}, {5}, {8}, {2, 13, 6, 15}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15}, {4, 6, 11, 13, 15}, {12}, {2, 3, 4, 5, 6, 11, 13, 15}, {0, 1, 2, 3, 4, 9, 12, 13, 14}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15}, {6, 15}, {3, 4, 5, 9, 11, 12, 13}, {0, 4, 5, 9, 10, 11, 12, 13}, {13, 14, 15}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {8}, {0, 10, 13, 6}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 6, 10, 12, 13, 14}, {12, 13}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {8}, {13, 11, 4, 5}, {0, 4, 10, 11, 12, 13}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15}, {0, 1, 2, 3, 10, 12}, {0, 1, 3, 12}, {0}, {0, 6, 7, 8, 9, 10, 15}, {8, 9, 10, 14}, {0, 7, 8, 9, 12, 15}, {2, 3, 4, 5, 10, 11, 13, 14}, {4, 6, 7, 8, 9, 10, 13, 14, 15}, {3, 4, 5, 11, 13, 14}, {8, 9, 14}, {1, 2, 3, 4, 5, 11}, {9, 14}]
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Best program as per aggregate score on train_val: 4
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Best program as per aggregate score on valset: 4
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Best score on valset: 0.861111111111111
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Best score on train_val: 0.861111111111111
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Linear pareto front program index: 4
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 24: New program candidate index: 15
2025/08/12 20:43:42 INFO dspy.teleprompt.gepa.gepa: Iteration 25: Selected program 12 score: 0.7661616161616162
Average Metric: 2.33 / 3 (77.8%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  3.86it/s]2025/08/12 20:43:43 INFO dspy.evaluate.evaluate: Average Metric: 2.333333333333333 / 3 (77.8%)

2025/08/12 20:44:17 INFO dspy.teleprompt.gepa.gepa: Iteration 25: Proposed new text for categories_module.predict: You are a classifier that reads a single free-text message (often an email with a Subject and body) and returns all applicable service categories from a fixed, predefined taxonomy used by a facilities services company (e.g., ProCare Facility Solutions).

Task
- Determine which category or categories from the allowed list best describe the user’s request or intent in the message.
- Use multi-label classification: include every applicable category, but do not invent new ones.
- If none apply, return an empty list.

Allowed categories (use exact lowercase snake_case names)
- emergency_repair_services
  - Urgent, immediate-response repairs for critical system failures impacting operations, comfort, or safety.
  - Typical signals: “urgent,” “ASAP,” “immediate assistance,” “dispatch a technician,” “system failed,” “not working,” “outage,” “critical issue,” “beyond our capability.”
  - Common systems: HVAC (air conditioning, heating, furnace), electrical, plumbing, security, other building-critical systems.
- training_and_support_requests
  - Requests for training, guidance, best practices, courses, workshops, certifications, or program details to improve facility management or maintenance processes.
  - Typical signals: “training programs,” “best practices,” “how to get started,” “professional guidance,” “formal training.”
- cleaning_services_scheduling
  - Requests to schedule, reschedule, or book cleaning services (one-time, recurring, or deep cleaning).
  - Typical signals: “schedule,” “set up a time,” “book,” “arrange,” “next week,” “flexible on date/time.”
- specialized_cleaning_services
  - Requests for non-routine, specialty, or deep-clean tasks beyond standard janitorial work.
  - Typical examples: deep cleaning, carpet shampooing/steam cleaning, window washing, floor waxing/buffing, post-construction cleaning, sanitation-focused cleans.

Classification rules and edge cases
- Include all categories that clearly apply; be precise and avoid overclassification.
- Emergency vs. non-emergency repairs:
  - If the message conveys urgency or system failure needing immediate dispatch, use emergency_repair_services.
  - Do not add scheduling categories for emergencies unless the message primarily centers on booking routine cleaning.
- Cleaning overlap:
  - If the message is booking cleaning generally, include cleaning_services_scheduling.
  - If it specifically asks for deep/specialty cleaning (e.g., carpets, windows), also include specialized_cleaning_services alongside cleaning_services_scheduling.
  - If only routine janitorial cleaning is implied (no specialty/deep tasks), do not include specialized_cleaning_services.
- Training:
  - Use training_and_support_requests only when the user is seeking training/guidance/program info.
- Do not invent categories. Mentions of topics like general feedback, sustainability, safety concerns (without urgent repair), or generic inquiries should not be categorized unless they clearly match one of the allowed categories.
- If ambiguous, choose the minimal set of categories that are directly supported by explicit phrases in the message.

Input format
- You will receive a single field named “message” that contains the subject and body text. No other structured fields are guaranteed.

Output format
- Return two top-level keys:
  - reasoning: 1–3 sentences explaining why the selected categories apply (or why none apply). Keep it concise and reference key phrases from the message.
  - categories: a list of category strings (use the exact names listed above). If none apply, return [].
- Example structure:
  - reasoning: The message requests a deep clean with carpets and windows and asks to set a time next week, indicating cleaning scheduling plus specialized cleaning.
  - categories: ['cleaning_services_scheduling', 'specialized_cleaning_services']

Helpful cues by category (non-exhaustive)
- emergency_repair_services: “HVAC failed,” “air conditioning not working,” “no heat,” “breaker tripped and won’t reset,” “flood/leak,” “immediate attention required,” “send a technician ASAP.”
- training_and_support_requests: “interested in training programs,” “best practices for facility management,” “how to get started with training,” “workshops/certifications.”
- cleaning_services_scheduling: “schedule a cleaning,” “set up a time,” “book for next week,” “weekly cleaning,” “deep clean timing.”
- specialized_cleaning_services: “deep clean,” “carpet cleaning/shampoo,” “window washing,” “floor waxing,” “post-construction clean,” “sanitization.”

Quality checks before finalizing
- Ensure every category you output is in the allowed list.
- If no category clearly applies, output an empty list for categories and explain briefly in reasoning.
- Keep reasoning concise and factual; do not reference categories that you did not output.
2025/08/12 20:44:19 INFO dspy.evaluate.evaluate: Average Metric: 2.333333333333333 / 3 (77.8%)
2025/08/12 20:44:19 INFO dspy.teleprompt.gepa.gepa: Iteration 25: New subsample score is not better, skipping
2025/08/12 20:44:19 INFO dspy.teleprompt.gepa.gepa: Iteration 26: Selected program 0 score: 0.7207070707070706
Average Metric: 2.67 / 3 (88.9%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 51.92it/s]2025/08/12 20:44:19 INFO dspy.evaluate.evaluate: Average Metric: 2.6666666666666665 / 3 (88.9%)

2025/08/12 20:44:52 INFO dspy.teleprompt.gepa.gepa: Iteration 26: Proposed new text for categories_module.predict: Task
You are given a single inbound message addressed to ProCare Facility Solutions’ support team. Your job is to read the entire message (subject and body) and return all applicable categories from the defined taxonomy below.

Taxonomy (use only these categories; exact strings, lowercase snake_case)
- routine_maintenance_requests
  - Use when the sender requests scheduled or non-emergency service, diagnosis, or repair for facility systems (e.g., HVAC, plumbing, electrical) as part of routine upkeep or a maintenance plan. Indicators include requests to arrange a technician visit, schedule maintenance, or address performance issues that are not urgent emergencies.

- specialized_cleaning_services
  - Use when the sender asks about or requests non-standard cleaning work such as deep cleaning, carpet maintenance, specialized protocols for controlled environments (e.g., labs), or eco/green cleaning options.

- sustainability_and_environmental_practices
  - Use when the sender references eco-friendly/green products, sustainability goals, environmental health, or asks specifically about environmentally responsible practices.

- general_inquiries
  - Use when the sender is gathering information (e.g., how services work, scheduling options, pricing/costs, case studies, references) rather than placing a concrete service order. This can be applied alongside a service-specific category if the message is primarily informational.

General rules
- Include every applicable category; do not add categories that are not supported by the message.
- If the message both asks about a specific service and seeks general information (process, options, pricing, references), include both the relevant service category and general_inquiries.
- If the message requests help with ongoing system issues (e.g., inconsistent HVAC temperatures, unusual noises) and asks for a technician without indicating an immediate hazard, classify as routine_maintenance_requests.
- Mentions of eco-friendly or sustainable products/practices warrant sustainability_and_environmental_practices; if tied to cleaning offerings, also include specialized_cleaning_services.
- When uncertain between a specific service and a general question, favor adding general_inquiries if the message is exploratory or preliminary.
- Order of categories does not matter. Use each category at most once.

Output format
Return a JSON object with:
- reasoning: 1–3 concise sentences explaining why the chosen categories apply.
- categories: an array of strings containing the selected category identifiers from the taxonomy.

Example output shape (values are illustrative):
{
  "reasoning": "The sender requests non-emergency HVAC servicing and asks to schedule a technician visit.",
  "categories": ["routine_maintenance_requests"]
}
2025/08/12 20:44:54 INFO dspy.evaluate.evaluate: Average Metric: 2.6666666666666665 / 3 (88.9%)
2025/08/12 20:44:54 INFO dspy.teleprompt.gepa.gepa: Iteration 26: New subsample score is not better, skipping
2025/08/12 20:44:54 INFO dspy.teleprompt.gepa.gepa: Iteration 27: Selected program 14 score: 0.8328282828282828
Average Metric: 2.90 / 3 (96.7%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00,  1.85s/it]2025/08/12 20:44:59 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)

2025/08/12 20:45:38 INFO dspy.teleprompt.gepa.gepa: Iteration 27: Proposed new text for urgency_module.predict: Task: Read the provided message and determine its urgency.

Context/domain:
- Messages typically relate to facility management and services for a provider like ProCare Facility Solutions (e.g., facility operations, space utilization, security/access control, sustainability, HVAC systems, maintenance/repairs, cleaning/janitorial).
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, inspections, or prior support interactions.

How to assess:
1) Scan for explicit urgency language: “urgent,” “ASAP,” “immediate attention required,” “critical,” “escalating,” “emergency,” all-caps variants, deadlines/dates.
2) Identify safety/security risks: fire/smoke, flooding/water leak/burst pipe, sewage backup, gas leak/odor, electrical hazard/sparking/burning smell, power outage, carbon monoxide alarms, elevator entrapment, security breach/serious access control failure, loss of access to critical areas, structural hazards.
3) Evaluate operational/comfort impact and scope: affects multiple residents/tenants/operations vs a single room; no heat in winter or no cooling during extreme heat; outages disrupting business continuity.
4) Consider time sensitivity without immediate danger: ongoing malfunctions, reliability issues, upcoming inspections/events with near-term deadlines.
5) Weigh explicit de-escalators: “not an emergency,” “no rush,” “flexible timing,” “at your earliest convenience.”
6) Ignore pleasantries, bios, compliments, and unrelated personal details; focus on the problem, risk, scope, timing, and prior failed attempts.
7) If multiple issues are mentioned, classify by the highest applicable urgency.

Urgency levels and decision rules:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency signals (“Urgent,” “ASAP,” “immediate,” “critical,” “escalating”) with a significant issue.
  - Severe dissatisfaction with demand for immediate corrective action, especially after repeated failed support/escalation.
  - Triggers/examples: security breach or serious door/lock/access control failure; fire/smoke; flooding/active water leak/burst pipe; gas leak/odor; electrical hazard/sparking/burning smell; power outage; elevator entrapment; carbon monoxide alarm; sewage backup; loss of access to critical areas; no heat in winter or no cooling during extreme heat affecting many residents/operations.
- MEDIUM:
  - Time-sensitive issues affecting comfort/reliability/service quality without immediate safety/security risk.
  - Requests for prompt scheduling/repair where delay could worsen impact but it’s not currently critical.
  - Triggers/examples: HVAC making noises, inconsistent temperature, minor malfunction; small/non-escalating leak; door closer misaligned but area still secured; pest sightings; upcoming inspection/event within days requiring fixes; “at the earliest convenience” when an active issue exists; issues noted as “not an emergency” but affecting residents/operations.
- LOW:
  - General inquiries, information requests, quotes, options/scheduling discussions, or interest in additional services with no stated or implied time pressure.
  - Routine/preventive maintenance when no current problem exists, especially with “no rush” or flexible timing.
  - Compliments/feedback without an urgent problem.
  - Triggers/examples: asking about specialized/deep/eco-friendly cleaning; pricing/availability; proactive HVAC tune-up with systems running fine; “schedule when convenient.”

Tie-breakers and nuances:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- Do not inflate urgency based solely on polite phrases like “prompt assistance” if no urgent risk is present.
- Consider impact scope: issues affecting many residents/tenants/operations may raise urgency, but only to the level justified by risk/time sensitivity.
- Hard, near-term deadlines that meaningfully impact operations suggest medium; escalate to high only if missing the deadline implies safety/security/major operational risk.
- If unclear and no risk or time pressure is indicated, default to low.

Output format:
- Provide exactly two fields, in this order, with no extra text, headers, or formatting:
  reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
  urgency: <one of: low | medium | high>
- Use lowercase for urgency value.

Reasoning guidance:
- Reference the decisive cues: explicit urgency or “not an emergency,” presence/absence of safety/security risk, scope of impact (residents/tenants/operations), and any deadlines/timing statements.

Examples (for alignment):
- Routine HVAC maintenance, no issues, flexible/no rush → low.
- Inquiry about cleaning services details/pricing → low.
- HVAC noisy and inconsistent temp, “not an emergency,” affects residents, asks for prompt visit → medium.
2025/08/12 20:45:39 INFO dspy.evaluate.evaluate: Average Metric: 2.9 / 3 (96.7%)
2025/08/12 20:45:39 INFO dspy.teleprompt.gepa.gepa: Iteration 27: New subsample score is not better, skipping
2025/08/12 20:45:39 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Selected program 15 score: 0.8116161616161616
Average Metric: 2.87 / 3 (95.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:06<00:00,  2.07s/it]2025/08/12 20:45:45 INFO dspy.evaluate.evaluate: Average Metric: 2.8666666666666667 / 3 (95.6%)

2025/08/12 20:46:59 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Proposed new text for categories_module.predict: Task: Classify a single customer message sent to ProCare Facility Solutions by assigning every applicable label from a fixed category list. Use only evidence in the text. Multi-label is allowed.

Allowed categories and definitions:
- cleaning_services_scheduling
  - Use only when the primary intent is to schedule, reschedule, adjust, or inquire about dates/times for cleaning services specifically.
  - Includes: booking a cleaning, changing cleaning times, checking availability for cleaning, aligning cleaning schedules.
  - Exclude for any non-cleaning work (e.g., HVAC maintenance), even if the sender says “please schedule a visit.”

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, carpet cleaning/maintenance, window washing, floor stripping/waxing, pressure washing.
  - Do NOT use for general preferences (e.g., “eco-friendly products”) unless a specialized cleaning task is explicitly named.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction about prior or ongoing service, reports subpar work or communication, requests a redo/refund/remedy, or otherwise complains about service outcomes (cleaning or maintenance).

- quality_and_safety_concerns
  - Use when the message reports explicit quality shortfalls or safety hazards (e.g., still dirty/stained, performance declined after service, tasks overlooked, hazardous materials left).
  - Can co-occur with customer_feedback_and_complaints when the sender is unhappy with outcomes.
  - Do not use for general anxiety or preferences without an actual reported issue.

- general_inquiries
  - Use when the sender asks for information or clarification (e.g., what’s included, requirements, process, pricing, availability) before committing to any service.
  - Can co-occur with cleaning_services_scheduling or specialized_cleaning_services when exploratory.

- training_and_support_requests
  - Use when the sender asks for guidance, simplified instructions, training, or on-site assistance to understand or perform cleaning/maintenance tasks (e.g., “provide a simpler guide,” “send someone to help me follow the maintenance plan”).

- routine_maintenance_requests
  - Use for non-emergency requests to perform routine maintenance on building systems or facilities (e.g., HVAC check-up, routine servicing per a maintenance plan).
  - Note: HVAC maintenance falls here. This is not a cleaning service and should not trigger cleaning_services_scheduling.

Key decision rules:
- Multi-label: Assign every category clearly supported by the text.
- Evidence-only: Do not infer intents not supported by the message.
- Strict schema: Use ONLY the categories above. Do not invent or use unlisted categories (e.g., “facility_management_issues,” “sustainability_and_environmental_practices” are NOT allowed).
- Cleaning scheduling vs. complaint:
  - If the main purpose is logistics around timing/availability for cleaning services, include cleaning_services_scheduling.
  - If scheduling or a redo is requested only as a remedy within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints and quality_and_safety_concerns (and specialized_cleaning_services if specific cleaning tasks are referenced).
- Cleaning scheduling vs. maintenance scheduling:
  - Do not use cleaning_services_scheduling for maintenance (e.g., HVAC). Use routine_maintenance_requests (and training_and_support_requests if they also seek guidance/help).
- Specialized services:
  - Include specialized_cleaning_services if a specialized cleaning type/task is explicitly named (deep clean, carpet, windows, floor care, pressure washing).
  - Do not treat “eco-friendly/green products” alone as specialized_cleaning_services unless tied to a specific cleaning task (e.g., “use green solvents for our carpet cleaning”).
- Quality/safety threshold:
  - Include quality_and_safety_concerns when the message reports actual poor outcomes or hazards (e.g., “technician overlooked the HVAC check and performance declined,” “area still stained after cleaning,” “slippery floor left without signage”).
- Sustainability/environmental mentions (mapping guidance within this schema):
  - Complaints about lack of eco-friendly practices → customer_feedback_and_complaints (add quality_and_safety_concerns only if a concrete quality/safety issue is reported).
  - Requests for a plan/info on eco-friendly products or energy-saving approaches without committing → general_inquiries.
  - Only add specialized_cleaning_services if a specialized cleaning task is explicitly named alongside sustainability preferences.
- Facility management/space utilization mentions (mapping guidance within this schema):
  - If asking for help/guidance to improve practices/processes → training_and_support_requests (and general_inquiries if asking for info).
  - If expressing dissatisfaction with service delivery (e.g., poor coordination) → customer_feedback_and_complaints (add quality_and_safety_concerns if explicit issues reported).
  - Do not add unlisted categories like “facility_management_issues.”

Domain notes:
- ProCare handles residential and commercial cleaning and related facility maintenance requests (e.g., HVAC routine maintenance).
- HVAC-related requests map to routine_maintenance_requests; they are not cleaning services.
- Mentions of urgency, client profile, or confidentiality do not create categories by themselves.

Process:
1) Read the message and identify intents:
   - Scheduling logistics for cleaning only.
   - Specialized cleaning tasks named.
   - Feedback/complaints about past/ongoing service.
   - Explicit quality/safety issues described.
   - Information requests before committing to service.
   - Training/support needs to perform cleaning/maintenance tasks.
   - Routine maintenance requests (e.g., HVAC).
2) Map each intent to categories per definitions and rules above.
3) Produce the required output.

Output format (plain text):
- Provide two top-level keys:
  - reasoning: Briefly justify each selected category and note notable exclusions (especially cleaning vs. maintenance scheduling and complaint vs. scheduling). Keep it concise and evidence-based.
  - categories: A JSON array of the selected category strings, e.g., ["cleaning_services_scheduling", "general_inquiries"].

Clarifying examples:
- “Please schedule a deep clean next Friday.” → ["cleaning_services_scheduling", "specialized_cleaning_services"]
- “Carpets still smell after yesterday’s service; please fix this and ensure safety protocols are followed.” → ["customer_feedback_and_complaints", "quality_and_safety_concerns", "specialized_cleaning_services"]
- “I need a simpler guide for my HVAC maintenance plan or someone to help me follow it.” → ["training_and_support_requests", "routine_maintenance_requests"]
- “Could you schedule an HVAC check-up at your earliest convenience?” → ["routine_maintenance_requests"]
- “The technician overlooked the HVAC check and performance dropped after the visit; please send someone back.” → ["customer_feedback_and_complaints", "quality_and_safety_concerns", "routine_maintenance_requests"] (do NOT add cleaning_services_scheduling)
- “You promised eco-friendly cleaning but haven’t provided it. Send a plan for using green products.” → ["customer_feedback_and_complaints", "general_inquiries"] (add specialized_cleaning_services only if a specific cleaning task like carpet/window/floor care is named)
- “I need guidance to streamline space use and improve energy-saving practices at home.” → ["training_and_support_requests"] (add general_inquiries if also requesting info; do NOT invent categories)
2025/08/12 20:47:02 INFO dspy.evaluate.evaluate: Average Metric: 2.8 / 3 (93.3%)
2025/08/12 20:47:02 INFO dspy.teleprompt.gepa.gepa: Iteration 28: New subsample score is not better, skipping
2025/08/12 20:47:02 INFO dspy.teleprompt.gepa.gepa: Iteration 29: Selected program 14 score: 0.8328282828282828
Average Metric: 2.97 / 3 (98.9%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00,  1.68s/it]2025/08/12 20:47:07 INFO dspy.evaluate.evaluate: Average Metric: 2.966666666666667 / 3 (98.9%)

2025/08/12 20:47:38 INFO dspy.teleprompt.gepa.gepa: Iteration 29: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment toward the service/interaction or experience with the provider as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text or reasoning.

What to evaluate
- Focus on the overall emotional tone expressed about the service/interaction or experience with the provider, not the message’s functional purpose (e.g., making a request) or formalities.

Core decision rules (apply in order)
1) Negative
   - Choose negative only when the message explicitly expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
   - Strong negative cues include (non-exhaustive): “unacceptable,” “unsafe,” “poor quality,” “unprofessional,” “not satisfied,” “very disappointed,” “angry,” “frustrated,” “failed,” “lack of response,” “terrible,” “awful,” “unreliable.”
   - Reporting a problem or malfunction alone (e.g., HVAC not working, system shut down, inconvenience, urgency) is NOT sufficient for negative unless accompanied by explicit negative judgments or emotions directed at the service/experience.

2) Positive
   - Choose positive when strong and/or multiple explicit positive cues dominate the message, clearly expressing satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
   - Strong positive cues include (non-exhaustive): “thoroughly impressed,” “exceptional service,” “excellent,” “outstanding,” “fantastic job,” “very happy,” “great,” “love working with you,” “truly appreciate,” “commend,” “above and beyond,” “means the world to me” (as praise).
   - Mild or incidental compliments used as formalities or secondary to a routine request (e.g., “I appreciate your help,” “thanks in advance,” “hope you are well,” “have been quite satisfied so far” as background) remain neutral unless strong positive cues clearly dominate the message.

3) Neutral
   - Default to neutral when signals are mixed, weak, or when the message is primarily informational, inquisitive, or a routine request without clear emotional valence.
   - Routine maintenance/service requests (e.g., scheduling repairs for HVAC inefficiency, asking for training details, requesting a technician “as soon as possible”) are neutral by default.
   - Expressions of concern framed as questions or requests for clarification are neutral unless they assert dissatisfaction or harm.
   - Urgency (“immediate,” “urgent,” “as soon as possible”) and descriptions of impact (“causing discomfort,” “affecting ability to work,” “risk to equipment/collections”) are neutral unless paired with explicit negative emotion or judgment.

Disambiguation notes and edge cases
- Evaluate the body of the message; ignore the subject line unless the body contains similar sentiment cues.
- Do not infer sentiment from the sender’s identity, organization, loyalty, or role; rely on explicit sentiment-bearing language.
- Praise that is strong and primary (e.g., “Your team has always done a fantastic job and I truly appreciate it”) can make an otherwise problem-focused message positive if the praise clearly dominates and there are no explicit complaints.
- Compliments embedded to soften a problem report or request (e.g., “quite satisfied so far,” “appreciate your support”) are neutral unless they include multiple strong positive cues and are the main emphasis.
- Mixed messages:
  - If both explicit negative and strong positive cues appear, decide based on which clearly dominates; if unclear, choose neutral.

Domain-relevant illustrations (for consistency)
- HVAC/service issues: Describing failures like “temperature regulation is inconsistent,” “system shut down,” “urgent repair needed,” and requesting a technician is neutral unless the sender expresses dissatisfaction (e.g., “this is unacceptable,” “I’m very disappointed with your service”).
- Service admiration: Statements like “Your team has always done a fantastic job” and “I truly appreciate the dedication and expertise” indicate positive when they are prominent and enthusiastic.
- Training/information inquiries: Requests for details on programs, schedules, or costs with polite language but no strong praise or complaints are neutral.

Procedure
1) Read the body of the message.
2) Identify explicit sentiment-bearing phrases about the service/experience.
3) Apply the decision rules above.
4) If uncertain, output neutral.

Output
- Only one of: positive, neutral, negative (lowercase).
2025/08/12 20:47:41 INFO dspy.evaluate.evaluate: Average Metric: 2.966666666666667 / 3 (98.9%)
2025/08/12 20:47:41 INFO dspy.teleprompt.gepa.gepa: Iteration 29: New subsample score is not better, skipping
2025/08/12 20:47:41 INFO dspy.teleprompt.gepa.gepa: Iteration 30: Selected program 9 score: 0.8282828282828283
Average Metric: 2.53 / 3 (84.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:06<00:00,  2.02s/it]2025/08/12 20:47:47 INFO dspy.evaluate.evaluate: Average Metric: 2.533333333333333 / 3 (84.4%)

2025/08/12 20:48:20 INFO dspy.teleprompt.gepa.gepa: Iteration 30: Proposed new text for urgency_module.predict: Task: Read the provided client message and determine its urgency.

Context/domain:
- Messages relate to facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning services) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, exhibits/artifacts, or prior support interactions.

Output format (strict):
- Provide exactly two fields, in this order, no extra text, bullets, or formatting:
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

How to decide urgency (apply the highest level that fits any part of the message):
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency signals (e.g., “Urgent,” “ASAP,” “immediate attention,” “critical,” “escalating,” “cannot operate,” “shutdown”).
  - Severe dissatisfaction with demand for immediate corrective action or evidence of repeated failed support plus escalation.
  - Triggers/examples: security breach or serious access control gaps; fire/smoke; flooding/water leak; gas smell/leak; electrical hazard/sparking; power outage; loss of access/lockout affecting operations; no heat in winter or no cooling in extreme heat affecting many residents/operations.
- MEDIUM:
  - Time-sensitive issues affecting comfort, reliability, preservation/quality, or service continuity but not an emergency and with no immediate safety/security risk.
  - Requests for prompt scheduling/repair/maintenance where delay could worsen impact but is not currently critical.
  - Triggers/examples: HVAC making noises or inconsistent temperatures; humidity/temperature control concerns for exhibits/artifacts without immediate threat; cleaning/maintenance quality issues needing adjustment; routine maintenance requested “at the earliest convenience”; scheduling a fix soon to avoid disruption.
- LOW:
  - General inquiries, information requests, quotes, or scheduling/options discussions with no stated or implied time pressure.
  - Compliments/feedback without an urgent problem.
  - Triggers/examples: asking about training programs/modules/schedules; requesting pricing/availability; adjusting a cleaning schedule (even if prior portal attempts failed) without urgent impact.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity (scope: individual vs. residents/tenants/business operations).
- Deadlines/dates or requested response times tied to operational needs or events.
- Prior failed attempts and escalation tone (only raise urgency if there’s clear escalation or immediate impact).
- Do not inflate urgency based solely on polite phrases like “prompt response,” “appreciate your attention,” or general concern if no urgent risk is present.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.

Reasoning guidance:
- Keep reasoning to 1–3 sentences, citing the most decisive cues (e.g., safety risk, operational impact, explicit urgency words, scope).
- Do not quote the entire message; summarize the cues.
2025/08/12 20:48:22 INFO dspy.evaluate.evaluate: Average Metric: 2.533333333333333 / 3 (84.4%)
2025/08/12 20:48:22 INFO dspy.teleprompt.gepa.gepa: Iteration 30: New subsample score is not better, skipping
2025/08/12 20:48:22 INFO dspy.teleprompt.gepa.gepa: Iteration 31: Selected program 9 score: 0.8282828282828283
Average Metric: 2.63 / 3 (87.8%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00,  1.69s/it]2025/08/12 20:48:27 INFO dspy.evaluate.evaluate: Average Metric: 2.6333333333333333 / 3 (87.8%)

2025/08/12 20:49:14 INFO dspy.teleprompt.gepa.gepa: Iteration 31: Proposed new text for sentiment_module.predict: Task
- You will receive a single input field named "message" containing a professional email or support inquiry (often related to facility management topics such as services, maintenance, sustainability/energy efficiency, training programs, or in-house team development).
- Your job is to determine the overall sentiment of the message toward the recipient/company/services.

Output format
- Return only one lowercase label: positive, neutral, or negative.
- Do not include explanations, reasoning, or any other text unless explicitly requested.

General approach
1) Determine the primary intent of the message (e.g., request/inquiry vs. praise/complaint).
2) Identify explicit sentiment directed at the recipient/company/services.
3) Weigh the intensity and centrality of the sentiment (is it strong and central, or mild/incidental to a request?).
4) If both positive and negative cues appear, choose the predominant one; if they balance out or are weak/incidental, choose neutral.

Label definitions and rules
1) Neutral
   - Most professional inquiries/requests for information or support.
   - Messages that are polite or appreciative in a routine way (e.g., “I hope this finds you well,” “Thank you for your time,” “Looking forward to your response”).
   - Messages that include mild or brief positive statements as context but are primarily utilitarian requests (e.g., “we’re satisfied so far,” “we appreciate your services,” “reputation for excellence,” “could be a great fit”).
   - Interest in training, maintenance plans, or sustainability practices is neutral by default unless the message’s core purpose is to praise.
   - Past satisfaction stated briefly in a setup paragraph followed by an information/request focus remains neutral.

2) Positive
   - The clear, central purpose is to express praise, approval, enthusiasm, or strong satisfaction with the recipient/services.
   - Contains explicit, non-incidental positive language with intensity, especially multiple or emphatic phrases (e.g., “thoroughly impressed,” “top-notch,” “exceptional service,” “always impressed,” “fantastic,” “excellent,” “truly made a difference,” “commendable,” “we love your service,” “we’re excited to collaborate”).
   - Indicators of strong/central praise include:
     - Two or more sentences of direct praise or repeated commendations.
     - Use of superlatives/intensifiers (“always,” “exceptional,” “top-notch,” “truly,” “thoroughly”).
     - Expressions of strong trust/confidence tied to the company’s performance (“I trust your expertise,” “confident it will be handled with excellence”).
   - Even if the message includes a request (e.g., scheduling maintenance), classify as positive when such strong praise is prominent and central.

3) Negative
   - Expresses dissatisfaction, complaint, frustration, disappointment, or concern about problems with the recipient/services.
   - Uses terms such as “not satisfied,” “disappointed,” “frustrated,” “issue,” “problem,” “concerned,” “unacceptable,” where the negative emotion is directed at the recipient/services and is more than a neutral problem description.

Facility-management-specific heuristics
- Emails to providers like ProCare Facility Solutions commonly include formal greetings, polite closings, and mild appreciation; treat these as neutral unless there is strong, central praise or a clear complaint.
- References to general reputation (“reputation for excellence”) or forward-looking fit (“could be a great fit”) within an inquiry are typically neutral.
- Past satisfaction introduced briefly before asking for more services/info (e.g., “very pleased with your services” followed by a request) is neutral unless the praise is extensive, emphatic, and central.
- Do not infer sentiment from the sender’s role or the topic itself; rely only on expressed sentiment toward the recipient/services.

Tie-breakers
- If sentiment cues are weak, generic, or incidental to a primarily informational/request-oriented message, choose neutral.
- If unsure between positive and neutral due to a single mild compliment embedded in a request, choose neutral.

Quality checklist before labeling
- Is the main purpose to request information/support? If yes, default to neutral unless strong, central praise or a clear complaint dominates.
- Are there multiple, strong positive commendations with intensifiers and explicit trust/confidence? If yes, label positive (even if there is a request).
- Are there explicit complaints or negative emotions directed at the service? If yes, label negative.
2025/08/12 20:49:16 INFO dspy.evaluate.evaluate: Average Metric: 2.3 / 3 (76.7%)
2025/08/12 20:49:16 INFO dspy.teleprompt.gepa.gepa: Iteration 31: New subsample score is not better, skipping
2025/08/12 20:49:16 INFO dspy.teleprompt.gepa.gepa: Iteration 32: Selected program 5 score: 0.8106060606060606
Average Metric: 2.20 / 3 (73.3%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.56s/it]2025/08/12 20:49:21 INFO dspy.evaluate.evaluate: Average Metric: 2.1999999999999997 / 3 (73.3%)

2025/08/12 20:50:21 INFO dspy.teleprompt.gepa.gepa: Iteration 32: Proposed new text for urgency_module.predict: Task: Read the provided message (subject and body) and determine the urgency.

Context/domain:
- Messages are typically about facility management/services for a provider like ProCare Facility Solutions (facility operations, space utilization, security/access control, sustainability/compliance, HVAC, electrical, plumbing, elevators, maintenance, cleaning/janitorial).
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Output format (must be exactly two lines, in this order, no extra text or formatting):
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

Decision process (apply in order):
1) Safety/security risks or major operational impact.
   - High if there is a clear or implied immediate risk to people, property, or security, or a major outage/disruption.
2) Explicit urgency signals.
   - Phrases like “Urgent,” “ASAP,” “immediate attention,” “critical,” “cannot wait,” “send someone today,” or hard deadlines that imply immediacy increase urgency.
3) Scope and impact.
   - Affecting many residents/tenants/operations or causing loss of access increases urgency.
4) Service history and tone.
   - Repeated failed attempts to resolve, escalation language, or severe dissatisfaction demanding immediate corrective action raises urgency.
5) If no risk and no time pressure are evident, default to low.

Urgency levels and rules:
- HIGH:
  - Clear or implied immediate safety/security risk or major operational impact.
  - Explicit urgent language requiring immediate action or same-day response.
  - Severe dissatisfaction paired with a demand for immediate corrective action or evidence of repeated failed support/escalation.
  - Examples/triggers: security breach or access control failure, fire/smoke, flooding/water leak, gas/electrical hazard, power outage, building/area lockout or loss of access, elevator entrapment/outage affecting many, no heat in winter or no cooling in extreme heat affecting many occupants/operations, blocked emergency exits, untested/nonfunctional life-safety systems (e.g., fire alarms).

- MEDIUM:
  - Time-sensitive issues affecting comfort, reliability, compliance, or service quality, but not emergencies and with no immediate safety/security risk.
  - Active service problems where delay could worsen impact or trust (e.g., recurring but non-hazardous malfunctions, policy/compliance concerns, sustainability practice lapses).
  - Requests to schedule/repair/maintain “as soon as possible” without emergency conditions.
  - Active requests to dispatch/schedule a technician within a specified window (e.g., “sometime next week,” “mid-week preferred”) for essential systems (HVAC, plumbing, electrical) even if routine/preventive.
  - Examples/triggers: HVAC noise or inconsistent temperatures; routine HVAC maintenance requested for next week; recurring cleaning quality issues; use of non-compliant/chemical cleaners contrary to sustainability commitments; requests for prompt adjustments to ongoing services.

- LOW:
  - General inquiries, quotes, pricing, capabilities, technical documentation/case studies, or exploratory discussions with no operational problem and no stated time pressure.
  - Routine portal/account help or schedule adjustments that do not block operations and have no defined timeframe.
  - Compliments/feedback without urgency.
  - Examples/triggers: evaluating services and asking for details/methodologies/case studies; “not an emergency” with no other risk indicators; “when convenient” with no timeframe and no active service/dispatch request.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity for residents/tenants/business operations.
- Deadlines/dates or requested response times, including requested service windows (e.g., “this week,” “mid-week”).
- Prior failed attempts and escalation tone (including threats to cancel/escalate).

Important clarifications:
- Do not inflate urgency based solely on polite phrases like “prompt response” or “at your earliest convenience” when no risk or specific time window is present.
- If the subject/body includes “Urgent,” “high-priority,” or “cannot wait,” and the context implies operational impact, treat as higher urgency.
- Active requests to schedule on-site work with an explicit timeframe for essential systems (e.g., “send a tech next week for routine HVAC maintenance”) are at least medium even if no fault is present.
- If a message explicitly says it’s not an emergency and no serious risk/time pressure is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.

Formatting rules:
- Output exactly two lines: “reasoning:” then “urgency:”.
- Keep reasoning to 1–3 concise sentences citing the strongest cues (risk, urgency language/deadlines, scope/impact, escalation).
2025/08/12 20:50:23 INFO dspy.evaluate.evaluate: Average Metric: 2.1999999999999997 / 3 (73.3%)
2025/08/12 20:50:23 INFO dspy.teleprompt.gepa.gepa: Iteration 32: New subsample score is not better, skipping
2025/08/12 20:50:23 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Selected program 0 score: 0.7207070707070706
Average Metric: 2.60 / 3 (86.7%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.22s/it]2025/08/12 20:50:27 INFO dspy.evaluate.evaluate: Average Metric: 2.6 / 3 (86.7%)

2025/08/12 20:50:56 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Proposed new text for urgency_module.predict: Task: Read the provided message (subject and body) and determine the urgency.

Domain context:
- Messages typically relate to facility services (e.g., ProCare Facility Solutions): cleaning, specialized remediation (e.g., mold), maintenance, scheduling, and training programs.
- Some scenarios involve health/safety risks (mold growth, contamination) or operational disruptions (missed/failed scheduling impacting business activities).
- Others are informational/planning inquiries (e.g., asking about training curricula) with no immediate consequences.

How to assess urgency:
1) Identify explicit urgency cues:
   - Words/phrases: urgent, immediately, ASAP, emergency, “immediate attention required,” “earliest possible,” “as soon as possible.”
   - Escalation indicators: repeated failed attempts to resolve, significant disruptions already occurring.
2) Evaluate impact and risk:
   - Health/safety hazards or risk of damage (e.g., mold remediation, contamination, hazardous conditions, sensitive equipment at risk) → true emergencies.
   - Active operational disruption or business continuity risk (missed cleanings causing significant impact, outages/failures affecting ongoing work).
   - Time sensitivity with near-term deadlines but no hazard.
   - Purely informational/planning with no stated deadline or impact.
3) Map to a single urgency label:
   - High: Any health/safety risk or potential damage; active incident disrupting operations; explicit immediate/urgent request supported by context; escalations with ongoing significant impact.
     Examples: Mold remediation needed to protect health/equipment; severe scheduling failures already disrupting business and explicitly requiring immediate action.
   - Medium: Needs attention soon (days) but no emergency; time-bound or near-term impact but not hazardous; corrective actions desired before the next scheduled service without severe current disruption.
     Examples: Rescheduling or fixing coordination issues that could impact upcoming work but aren’t currently causing severe harm.
   - Low: Informational/planning inquiries or general requests with no time pressure, no hazard, and no stated impact.
     Examples: Asking for training program details (curriculum, duration, prerequisites); general service information.
4) Resolve ambiguity by prioritizing concrete impact/risk over tone alone:
   - Do not elevate to High based solely on stern tone or the word “urgent” if the content shows no immediate risk or disruption.
   - Phrases like “earliest convenience” are not High by themselves; consider the context (e.g., with mold hazard → High).
   - If uncertain and there is no evidence of hazard, disruption, or deadline, classify as Low.

Output format (plain text, no extra formatting):
reasoning: 1–3 concise sentences explaining the key cues (risk, impact, time sensitivity) that led to the label.
urgency: one of low, medium, high

Example of output:
reasoning: The message reports mold growth posing health risks and potential equipment damage, and requests the earliest possible intervention. This is an active hazard requiring prompt professional remediation.
urgency: high
2025/08/12 20:50:58 INFO dspy.evaluate.evaluate: Average Metric: 2.9333333333333336 / 3 (97.8%)
2025/08/12 20:51:02 INFO dspy.evaluate.evaluate: Average Metric: 51.56666666666666 / 66 (78.1%)
2025/08/12 20:51:02 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Full valset score for new program: 0.7813131313131313
2025/08/12 20:51:02 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Full train_val score for new program: 0.7813131313131313
2025/08/12 20:51:02 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Individual valset scores for new program: [1.0, 1.0, 0.9, 0.9666666666666667, 0.3333333333333333, 0.6, 0.9666666666666667, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 0.6333333333333333, 0.3333333333333333, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9, 0.3333333333333333, 0.6333333333333333, 0.9333333333333332, 0.3333333333333333, 1.0, 0.6666666666666666, 0.6, 0.9333333333333332, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 0.6333333333333333, 0.6, 1.0, 0.6666666666666666, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 0.9666666666666667, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.3333333333333333, 1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 1.0, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.9666666666666667, 0.6]
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: New valset pareto front scores: [1.0, 1.0, 0.9, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Full valset pareto front score: 0.9646464646464646
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Updated valset pareto front programs: [{16, 10, 14}, {0, 6, 7, 9, 16}, {0, 4, 6, 7, 8, 10, 11, 13, 14, 15, 16}, {2, 3, 4, 5, 6, 7, 9, 11}, {7, 8, 9, 14, 15}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15}, {8, 14, 15}, {3, 4, 5, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16}, {3, 4, 5, 7, 8, 11, 13, 14, 15}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15}, {16, 10, 6, 15}, {4, 13}, {5}, {8}, {2, 13, 6, 15}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16}, {4, 6, 11, 13, 15}, {12}, {2, 3, 4, 5, 6, 11, 13, 15}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16}, {6, 15}, {3, 4, 5, 9, 11, 12, 13}, {0, 4, 5, 9, 10, 11, 12, 13, 16}, {13, 14, 15}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16}, {8}, {0, 6, 10, 13, 16}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12, 16}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16}, {0, 1, 2, 6, 10, 12, 13, 14, 16}, {12, 13}, {11, 3, 4, 7}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {8}, {13, 11, 4, 5}, {0, 4, 10, 11, 12, 13, 16}, {3, 4, 5, 8, 11}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16}, {0, 1, 2, 3, 10, 12, 16}, {0, 1, 3, 12}, {0, 16}, {0, 6, 7, 8, 9, 10, 15, 16}, {8, 9, 10, 14, 16}, {0, 7, 8, 9, 12, 15}, {2, 3, 4, 5, 10, 11, 13, 14, 16}, {4, 6, 7, 8, 9, 10, 13, 14, 15}, {3, 4, 5, 11, 13, 14}, {8, 9, 14}, {1, 2, 3, 4, 5, 11}, {9, 14}]
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Best valset aggregate score so far: 0.861111111111111
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Best program as per aggregate score on train_val: 4
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Best program as per aggregate score on valset: 4
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Best score on valset: 0.861111111111111
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Best score on train_val: 0.861111111111111
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Linear pareto front program index: 4
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 33: New program candidate index: 16
2025/08/12 20:51:03 INFO dspy.teleprompt.gepa.gepa: Iteration 34: Selected program 13 score: 0.8358585858585859
Average Metric: 2.67 / 3 (88.9%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:08<00:00,  2.70s/it]2025/08/12 20:51:11 INFO dspy.evaluate.evaluate: Average Metric: 2.6666666666666665 / 3 (88.9%)

2025/08/12 20:51:56 INFO dspy.teleprompt.gepa.gepa: Iteration 34: Proposed new text for urgency_module.predict: Title: Urgency classification for facility management messages (ProCare Facility Solutions)

Task:
- Read the sender’s message (including subject line if present) and determine the urgency.

Context/domain:
- Messages typically relate to facility management and services for a provider like ProCare Facility Solutions.
- Common topics: facility operations, space utilization, security/access control, sustainability, HVAC systems, electrical, plumbing, maintenance/repairs, cleaning/janitorial, and related services.
- Senders may be residential or commercial clients and may reference residents, tenants, staff, property operations, or prior support interactions.

Output format (strict):
- Provide exactly two fields, in this order, no extra text, headers, or formatting:
  reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
  urgency: <one of: low | medium | high>

Definitions and decision rules:

HIGH urgency when any of the following apply:
- Safety/security risk or major operational impact (explicit or implied):
  - Security breaches, doors/gates/access control failures, threats to confidentiality/privacy with active disruption, fire/smoke, flooding/water leak, gas leak, electrical hazard/sparks, power outage, elevator outage, loss of access/lockouts, structural hazard.
  - HVAC failure causing no heat in winter or no cooling in extreme heat, or widespread/systemic shutdown affecting many residents/tenants or operations.
- Explicit urgency signals:
  - Subject/body includes “Urgent,” “Emergency,” “ASAP,” “Immediate,” “Critical,” “Escalating,” “right away,” “immediate attention required,” “high priority.”
- Severe dissatisfaction paired with a demand for immediate corrective action, evidence of repeated failed support/escalations, or a deteriorating situation.
- Time-sensitive operational disruptions with strong urgency language (e.g., urgent schedule changes that are actively disrupting residents/tenants or confidentiality).
Notes:
- If “urgent” or equivalent appears, default to high unless the message explicitly says it is not an emergency and no serious risk exists.

MEDIUM urgency when:
- Time-sensitive issues affecting comfort, reliability, or service quality but not emergencies and no immediate safety/security risk.
- Requests to schedule/repair/adjust services “as soon as possible” or “at your earliest convenience” where delay could worsen impact but is not currently critical.
- Examples: HVAC noisy/inconsistent but still operating; minor malfunctions; cleaning schedule adjustments needed promptly without explicit “urgent”; upcoming event deadlines; service reliability issues noted as “not an emergency.”

LOW urgency when:
- General inquiries, quotes, information requests, availability/scheduling discussions without active problems or deadlines.
- Interest in additional services, routine questions, compliments/feedback, or exploratory messages with no time pressure.

Key cues to weigh:
- Explicit urgency terms vs. statements like “not an emergency.”
- Safety/security implications; operational continuity and scope (one unit vs. building-wide or business-critical).
- Evidence of outages/shutdowns, leaks, hazards, or loss of access.
- Impact on residents/tenants/operations or confidentiality/privacy.
- Deadlines/dates or requested response times.
- Prior failed attempts and escalation tone.
- Do not inflate urgency based solely on polite phrases like “prompt attention” or “appreciate a quick reply.”

Procedure:
1) Scan subject and body for explicit urgency markers; if present, set to high unless explicitly “not an emergency” and no serious risk (then consider medium).
2) Identify any safety/security hazards, outages, or access issues; if present, set to high.
3) If time-sensitive but not hazardous, set to medium.
4) If neither risk nor time pressure is indicated, set to low.
5) When ambiguous and no clear risk/deadline exists, default to low.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high (use medium if time-sensitive, else low).
- Worsening conditions, shutdowns, or repeated failed attempts + request for immediate action push toward high.

Examples mapping (for consistency):
- “Urgent: HVAC inconsistent and has shut down a few times; please send tech ASAP” → high (explicit urgency + partial outage/worsening).
- “Urgent cleaning schedule change needed; current times disrupting resident privacy” → high (explicit urgency + active disruption/confidentiality risk).
- “Inquiry about deep cleaning availability for upcoming guests” → low (general scheduling info, no urgency).

Output reminder:
- Return only:
  reasoning: <1–3 concise sentences>
  urgency: <low|medium|high>
2025/08/12 20:51:58 INFO dspy.evaluate.evaluate: Average Metric: 2.6666666666666665 / 3 (88.9%)
2025/08/12 20:51:58 INFO dspy.teleprompt.gepa.gepa: Iteration 34: New subsample score is not better, skipping
2025/08/12 20:51:58 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Selected program 4 score: 0.861111111111111
Average Metric: 2.63 / 3 (87.8%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.44it/s]2025/08/12 20:52:00 INFO dspy.evaluate.evaluate: Average Metric: 2.6333333333333333 / 3 (87.8%)

2025/08/12 20:53:09 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Proposed new text for categories_module.predict: You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign all and only the applicable categories from a fixed list, based strictly on the message content.

Allowed categories and precise definitions:
- cleaning_services_scheduling
  - Use only when the message’s primary purpose is to coordinate timing/availability for cleaning services (initial booking, rescheduling, adjusting times).
  - Typical signals: specific dates/times, availability windows, explicit reschedule/change requests, or back-and-forth around timing for cleaning crews.
  - Do NOT use for maintenance scheduling or when a timing phrase appears merely as part of a broader service request or complaint (e.g., “arrange a team,” “at your earliest convenience,” “within the next week”) without concrete, central timing logistics for cleaning.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, floor stripping/waxing, disinfection/sanitation.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction with prior service, reports subpar outcomes or communication issues, or requests remedies (redo, refund, re-clean).

- quality_and_safety_concerns
  - Use when the message raises quality shortfalls (e.g., areas still dirty, stains remaining) or safety/health risks (e.g., mold, hazards), or questions/challenges the provider’s quality/safety standards or protocols.
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.

- general_inquiries
  - Use when the sender explicitly asks for information about services, scope, what’s included, requirements, pricing, or availability before committing, or seeks clarification/guidance about process.
  - Important restriction from prior feedback: If the inquiry is solely about quality/safety standards (e.g., protocols, certifications, safety practices) without broader questions about offerings/availability/process, tag only quality_and_safety_concerns and do NOT add general_inquiries.

- facility_management_issues
  - Use when the message concerns systemic/process coordination of facilities (e.g., space utilization, overlapping bookings, scheduling system design, policy/process improvements) rather than cleaning outcomes or specific service timing.
  - Do NOT add cleaning_services_scheduling when the focus is reviewing/improving a scheduling system rather than coordinating a specific cleaning appointment.

- routine_maintenance_requests
  - Use when the message requests non-emergency facility maintenance (e.g., HVAC performance issues, minor plumbing, electrical checks, general upkeep) rather than cleaning.
  - Mentioning a desire to schedule a maintenance visit does not trigger cleaning_services_scheduling; that category is reserved for cleaning timing logistics.

Key decision rules and pitfalls to avoid:
- Multi-label: Assign every category that is clearly supported by explicit statements in the message.
- Strict evidence only: Do not infer beyond the text. Praise, urgency (“ASAP”), client profile, or confidentiality are not categories by themselves.
- Scheduling vs. Complaint distinction:
  - If rescheduling is proposed only as a fix within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Scheduling vs. Service request distinction:
  - Do NOT tag cleaning_services_scheduling merely because the sender asks you to “arrange a visit,” “send a team,” or handle something “at your earliest convenience.”
  - Only tag cleaning_services_scheduling when the message’s central focus is concrete timing logistics for cleaning (e.g., “Can you come Friday at 3 PM?” “What slots do you have next week?” “Please move our usual Friday cleaning to Monday.”).
  - For maintenance timing (HVAC, plumbing, etc.), use routine_maintenance_requests and do not add cleaning_services_scheduling.
- Specialized services:
  - If the message mentions deep cleaning, mold remediation, carpet care, window washing, etc., include specialized_cleaning_services alongside any other applicable categories.
- General inquiries:
  - Tag when the sender asks about offerings, inclusions, requirements, pricing, or availability before committing. If they merely request service without asking questions, do not add general_inquiries.
  - Do not add general_inquiries when the only questions are about quality/safety standards; use quality_and_safety_concerns alone in that case.

Input:
- A single free-text customer message (often includes a subject and body).

Output format (plain text, exact structure):
- reasoning: Briefly justify each selected category and note key exclusions (especially why cleaning_services_scheduling was or was not applied).
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process to follow:
1) Read the message once to identify intents: timing logistics (cleaning only), service-type specificity, feedback/complaint, quality/safety concerns, information requests, facility management/process issues, routine maintenance.
2) Map each intent to categories using the definitions and rules above (especially the scheduling distinctions and the quality/safety vs general_inquiries nuance).
3) Produce concise reasoning and the JSON array of categories using the exact strings from the Allowed categories list.

Edge-case guidance from prior evaluations:
- System/process coordination about space usage or overlapping bookings → facility_management_issues only. Exclude cleaning_services_scheduling.
- Inquiry focused on quality/safety standards and protocols (certifications, how safety is ensured) → quality_and_safety_concerns only. Exclude general_inquiries unless broader service/process/availability questions are asked.
- Non-urgent HVAC/plumbing visit request → routine_maintenance_requests. Exclude cleaning_services_scheduling (not a cleaning appointment).
2025/08/12 20:53:12 INFO dspy.evaluate.evaluate: Average Metric: 2.6666666666666665 / 3 (88.9%)
2025/08/12 20:53:20 INFO dspy.evaluate.evaluate: Average Metric: 56.93333333333333 / 66 (86.3%)
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: New program is on the linear pareto front
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Full valset score for new program: 0.8626262626262626
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Full train_val score for new program: 0.8626262626262626
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Individual valset scores for new program: [1.0, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 1.0, 0.9333333333333332, 0.9666666666666667, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.6333333333333333, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6333333333333333, 0.9333333333333332, 0.6333333333333333, 0.9666666666666667, 0.6, 0.6666666666666666, 1.0, 1.0, 0.5666666666666667, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 0.6, 1.0, 1.0, 0.6333333333333333, 0.9333333333333332, 0.6666666666666666, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9333333333333332, 0.9666666666666667, 1.0, 0.9, 1.0, 0.9333333333333332, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.5666666666666667]
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: New valset pareto front scores: [1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Full valset pareto front score: 0.9651515151515152
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Updated valset pareto front programs: [{16, 17, 10, 14}, {0, 6, 7, 9, 16}, {17}, {2, 3, 4, 5, 6, 7, 9, 11}, {7, 8, 9, 14, 15, 17}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16, 17}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15, 17}, {8, 17, 14, 15}, {3, 4, 5, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 17}, {3, 4, 5, 7, 8, 11, 13, 14, 15, 17}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17}, {16, 10, 6, 15}, {4, 13}, {5}, {8}, {2, 13, 6, 15}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17}, {4, 6, 11, 13, 15, 17}, {12}, {2, 3, 4, 5, 6, 11, 13, 15, 17}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16, 17}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17}, {6, 15}, {3, 4, 5, 9, 11, 12, 13}, {0, 4, 5, 9, 10, 11, 12, 13, 16}, {13, 14, 15}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17}, {8, 17}, {0, 6, 10, 13, 16}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12, 16}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16, 17}, {0, 1, 2, 6, 10, 12, 13, 14, 16}, {12, 13}, {3, 4, 7, 11, 17}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {8}, {4, 5, 11, 13, 17}, {0, 4, 10, 11, 12, 13, 16}, {3, 4, 5, 8, 11, 17}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16}, {0, 1, 2, 3, 10, 12, 16}, {0, 1, 3, 12, 17}, {0, 16}, {0, 6, 7, 8, 9, 10, 15, 16}, {8, 9, 10, 14, 16, 17}, {0, 7, 8, 9, 12, 15, 17}, {2, 3, 4, 5, 10, 11, 13, 14, 16, 17}, {4, 6, 7, 8, 9, 10, 13, 14, 15, 17}, {3, 4, 5, 11, 13, 14}, {8, 9, 14}, {1, 2, 3, 4, 5, 11}, {9, 14}]
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Best valset aggregate score so far: 0.8626262626262626
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Best program as per aggregate score on train_val: 17
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Best program as per aggregate score on valset: 17
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Best score on valset: 0.8626262626262626
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Best score on train_val: 0.8626262626262626
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Linear pareto front program index: 17
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 35: New program candidate index: 17
2025/08/12 20:53:20 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Selected program 13 score: 0.8358585858585859
Average Metric: 2.27 / 3 (75.6%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.43s/it]2025/08/12 20:53:24 INFO dspy.evaluate.evaluate: Average Metric: 2.2666666666666666 / 3 (75.6%)

2025/08/12 20:54:24 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Proposed new text for sentiment_module.predict: Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text or reasoning.

What to evaluate
- Focus on the overall emotional tone expressed about the service/interaction or experience with the provider, not the message’s functional purpose (e.g., making a request) or formalities.

Core decision rules (apply in order)
1) Negative
   - Choose negative only when the message explicitly expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
   - Strong negative cues include (non-exhaustive): “unacceptable,” “unsafe,” “poor quality,” “unprofessional,” “not satisfied,” “very disappointed,” “angry,” “frustrated,” “failed,” “lack of response,” “terrible,” “awful,” “unreliable.”
   - Reporting a problem or malfunction alone (e.g., HVAC not working, system shut down, emergency leak, inconvenience, urgency) is NOT sufficient for negative unless accompanied by explicit negative judgments or emotions directed at the service/experience.

2) Positive
   - Choose positive when strong and/or multiple explicit positive cues dominate the message, clearly expressing satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
   - Strong positive cues include (non-exhaustive): “thoroughly impressed,” “exceptional service,” “excellent,” “outstanding,” “very happy,” “great,” “love working with you,” “truly appreciate,” “deeply appreciate,” “commend,” “top-notch,” “fantastic,” “high standards,” “highly valued,” “aligns perfectly with our values” when used as praise.
   - Mild or incidental compliments used as formalities or secondary to a routine request (e.g., “I appreciate your help,” “thanks in advance,” “hope you are well”) remain neutral unless strong positive cues clearly dominate.
   - Heuristic: If there are multiple strong positive phrases about the provider’s service (e.g., past satisfaction, explicit praise, valued expertise) that are a prominent theme, classify as positive even if the message also reports an issue or makes a request.

3) Neutral
   - Default to neutral when signals are mixed, weak, or when the message is primarily informational, inquisitive, or a routine request without clear emotional valence.
   - Routine maintenance/service requests (e.g., scheduling HVAC maintenance, optimization advice, requesting a technician “as soon as possible”) are neutral by default.
   - Expressions of concern framed as questions or requests for clarification are neutral unless they assert dissatisfaction or harm.
   - Urgency (“immediate,” “urgent,” “as soon as possible”) and descriptions of impact (“causing discomfort,” “affecting ability to work”) are neutral unless paired with explicit negative emotion or judgment.

Disambiguation notes and edge cases
- Do not infer sentiment from the sender’s identity, organization, or the subject line alone; rely on explicit sentiment-bearing language in the body. If the body is present, ignore the subject unless it contains similar cues reflected in the body.
- Praise that is strong and primary (e.g., “I am thoroughly impressed by your service,” “your team is top-notch and I truly appreciate your professionalism,” “overall management has been commendable, and your expertise is highly valued”) can make an inquiry or problem report positive if it clearly dominates the tone.
- Compliments embedded to soften a problem report or request are neutral unless they include multiple strong positive cues and are the main emphasis.
- Mixed messages:
  - If both explicit negative and strong positive cues appear, decide based on which clearly dominates; if unclear, choose neutral.

Domain-relevant illustrations (for consistency with facility services/HVAC contexts)
- HVAC/service issues: Describing failures like “temperature regulation is inconsistent,” “system shut down,” “severe leak,” “requires urgent attention,” and requesting a technician is neutral unless the sender expresses dissatisfaction with the provider (e.g., “this is unacceptable,” “I’m very disappointed with your service”).
- Service admiration: Statements like “I’ve been enjoying the fantastic services,” “your team has always been top-notch,” “thank you for providing such excellent service,” “I have been a satisfied client for two years,” “overall management has been commendable,” “your expertise is highly valued,” and “I am confident in your support” indicate positive when they are prominent and enthusiastic.
- Training/information inquiries: Requests for details on programs, schedules, or costs with polite language but no strong praise or complaints are neutral.

Procedure
1) Read the body of the message. Ignore the subject line unless the body contains similar cues; do not rely on subject-only sentiment if a body is present.
2) Identify explicit sentiment-bearing phrases about the provider’s service/experience.
3) Apply the decision rules above, prioritizing explicit negative > strong positive > default neutral.
4) If uncertain, output neutral.

Output
- Only one of: positive, neutral, negative (lowercase).
2025/08/12 20:54:26 INFO dspy.evaluate.evaluate: Average Metric: 2.6 / 3 (86.7%)
2025/08/12 20:54:32 INFO dspy.evaluate.evaluate: Average Metric: 56.166666666666664 / 66 (85.1%)
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Full valset score for new program: 0.851010101010101
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Full train_val score for new program: 0.851010101010101
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Individual valset scores for new program: [0.9666666666666667, 1.0, 0.9, 1.0, 0.9666666666666667, 0.9333333333333332, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6333333333333333, 1.0, 1.0, 0.9666666666666667, 0.26666666666666666, 0.6666666666666666, 0.6, 0.9666666666666667, 0.9666666666666667, 1.0, 0.6333333333333333, 0.9666666666666667, 0.6666666666666666, 0.9333333333333332, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9, 1.0, 1.0, 0.6666666666666666, 0.6333333333333333, 1.0, 0.6333333333333333, 1.0, 0.9333333333333332, 0.6666666666666666, 0.9, 0.9333333333333332, 1.0, 1.0, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 1.0, 0.9333333333333332, 1.0, 0.9333333333333332, 0.6333333333333333, 0.3, 0.6333333333333333, 0.9333333333333332, 0.6333333333333333, 0.6333333333333333, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6, 1.0, 0.6]
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: New valset pareto front scores: [1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Full valset pareto front score: 0.9651515151515152
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Updated valset pareto front programs: [{16, 17, 10, 14}, {0, 6, 7, 9, 16, 18}, {17}, {2, 3, 4, 5, 6, 7, 9, 11, 18}, {7, 8, 9, 14, 15, 17}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16, 17}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15, 17, 18}, {8, 17, 14, 15}, {3, 4, 5, 11, 12, 13, 14, 18}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 17, 18}, {3, 4, 5, 7, 8, 11, 13, 14, 15, 17, 18}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18}, {16, 10, 6, 15}, {18, 4, 13}, {5}, {8}, {2, 6, 13, 15, 18}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 18}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17, 18}, {4, 6, 11, 13, 15, 17, 18}, {12}, {2, 3, 4, 5, 6, 11, 13, 15, 17, 18}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16, 17, 18}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18}, {6, 15}, {3, 4, 5, 9, 11, 12, 13, 18}, {0, 4, 5, 9, 10, 11, 12, 13, 16, 18}, {13, 14, 15}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18}, {8, 17}, {0, 6, 10, 13, 16, 18}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12, 16}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16, 17, 18}, {0, 1, 2, 6, 10, 12, 13, 14, 16, 18}, {18, 12, 13}, {3, 4, 7, 11, 17}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18}, {8}, {4, 5, 11, 13, 17, 18}, {0, 4, 10, 11, 12, 13, 16, 18}, {3, 4, 5, 8, 11, 17, 18}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16}, {0, 1, 2, 3, 10, 12, 16}, {0, 1, 3, 12, 17}, {0, 16}, {0, 6, 7, 8, 9, 10, 15, 16}, {8, 9, 10, 14, 16, 17}, {0, 7, 8, 9, 12, 15, 17}, {2, 3, 4, 5, 10, 11, 13, 14, 16, 17, 18}, {4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18}, {3, 4, 5, 11, 13, 14}, {8, 9, 14}, {1, 2, 3, 4, 5, 11, 18}, {9, 14}]
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Best valset aggregate score so far: 0.8626262626262626
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Best program as per aggregate score on train_val: 17
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Best program as per aggregate score on valset: 17
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Best score on valset: 0.8626262626262626
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Best score on train_val: 0.8626262626262626
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Linear pareto front program index: 17
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 36: New program candidate index: 18
2025/08/12 20:54:32 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Selected program 16 score: 0.7813131313131313
Average Metric: 2.23 / 3 (74.4%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.40it/s]2025/08/12 20:54:33 INFO dspy.evaluate.evaluate: Average Metric: 2.2333333333333334 / 3 (74.4%)

2025/08/12 20:55:08 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Proposed new text for sentiment_module.predict: You are given a single input field:
- message: a professional email or message, typically addressed to ProCare Facility Solutions (facility management, maintenance, sustainability, energy efficiency). Content may include praise, inquiries, urgent assistance requests, descriptions of issues, or general information.

Your task:
- Read the message and classify its sentiment as one of: positive, neutral, negative.
- Provide a brief one-sentence reasoning followed by the sentiment label.

Output format (plain text, exactly these keys):
reasoning: <one short sentence justifying the label>
sentiment: <positive|neutral|negative>

Decision rules:
1) Focus on the sender’s attitude toward the recipient (ProCare/its team), not merely the situation described.
   - Describing a problem, urgency, or inconvenience does NOT by itself make the sentiment negative.
   - If the sender remains appreciative, confident, or collaborative toward ProCare while reporting an issue, classify as positive.

2) Positive when the tone shows appreciation, praise, confidence, interest in collaboration, or optimism, even if requesting urgent help or describing problems.
   - Positive cues: “impressed,” “appreciate,” “thank you,” “look forward,” “confident,” “valued partner,” “exceptional service,” “highly valued,” expressions of eagerness to work together.

3) Negative when the sender expresses dissatisfaction or negative emotion directed at ProCare/the recipient:
   - Cues: blame or criticism of ProCare (“your team failed,” “unacceptable service,” “disappointed in you”), anger/frustration toward them, threats/escalations, demands for compensation due to ProCare’s actions.

4) Neutral when the message is informational or an inquiry without clear praise or complaint:
   - Polite professionalism alone (“Hello,” “Best regards,” “I hope this finds you well,” “please provide info”) is neutral unless combined with explicit appreciation/praise/optimism.

5) Mixed content:
   - Prioritize sentiment directed at ProCare. If the complaint targets a third party or a general situation while the tone toward ProCare is appreciative/confident, choose positive.
   - If both praise and complaint are directed at ProCare, choose the dominant explicit sentiment. If unclear, default to neutral.

Conventions:
- Use only lowercase labels: positive, neutral, negative.
- Keep reasoning concise and specific (one sentence).
2025/08/12 20:55:08 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)
2025/08/12 20:55:13 INFO dspy.evaluate.evaluate: Average Metric: 48.9 / 66 (74.1%)
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Full valset score for new program: 0.7409090909090909
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Full train_val score for new program: 0.7409090909090909
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Individual valset scores for new program: [0.6666666666666666, 1.0, 0.5666666666666667, 0.6333333333333333, 0.3333333333333333, 0.6, 0.9666666666666667, 1.0, 0.6333333333333333, 1.0, 0.6666666666666666, 1.0, 0.6333333333333333, 0.3333333333333333, 0.6333333333333333, 0.3333333333333333, 0.6333333333333333, 0.6666666666666666, 0.6666666666666666, 1.0, 0.5666666666666667, 0.3333333333333333, 0.6333333333333333, 0.9333333333333332, 0.3333333333333333, 1.0, 0.6666666666666666, 0.6, 0.9333333333333332, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 0.6666666666666666, 0.9666666666666667, 0.6, 1.0, 0.6666666666666666, 1.0, 0.6, 1.0, 0.6333333333333333, 0.9333333333333332, 1.0, 1.0, 1.0, 0.3333333333333333, 0.6333333333333333, 0.6333333333333333, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 1.0, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 1.0, 0.6333333333333333, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.6]
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: New valset pareto front scores: [1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Full valset pareto front score: 0.9702020202020202
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Updated valset pareto front programs: [{16, 17, 10, 14}, {0, 6, 7, 9, 16, 18, 19}, {17}, {2, 3, 4, 5, 6, 7, 9, 11, 18}, {7, 8, 9, 14, 15, 17}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16, 17, 19}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15, 17, 18}, {8, 14, 15, 17, 19}, {3, 4, 5, 11, 12, 13, 14, 18}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 17, 18, 19}, {3, 4, 5, 7, 8, 11, 13, 14, 15, 17, 18}, {12}, {13}, {0, 12}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18}, {6, 10, 15, 16, 19}, {18, 4, 13}, {5}, {8}, {2, 6, 13, 15, 18}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 18}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19}, {4, 6, 11, 13, 15, 17, 18}, {12}, {2, 3, 4, 5, 6, 11, 13, 15, 17, 18}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16, 17, 18}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19}, {6, 15}, {3, 4, 5, 9, 11, 12, 13, 18}, {0, 4, 5, 9, 10, 11, 12, 13, 16, 18}, {13, 14, 15}, {12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19}, {8, 17}, {0, 6, 10, 13, 16, 18, 19}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12, 16, 19}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16, 17, 18, 19}, {0, 1, 2, 6, 10, 12, 13, 14, 16, 18, 19}, {18, 19, 12, 13}, {3, 4, 7, 11, 17}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18}, {8}, {4, 5, 11, 13, 17, 18, 19}, {0, 4, 10, 11, 12, 13, 16, 18, 19}, {3, 4, 5, 8, 11, 17, 18, 19}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16, 19}, {19}, {0, 1, 3, 12, 17}, {0, 16}, {0, 6, 7, 8, 9, 10, 15, 16}, {8, 9, 10, 14, 16, 17, 19}, {0, 7, 8, 9, 12, 15, 17, 19}, {2, 3, 4, 5, 10, 11, 13, 14, 16, 17, 18, 19}, {4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18}, {3, 4, 5, 11, 13, 14}, {8, 9, 19, 14}, {1, 2, 3, 4, 5, 11, 18}, {9, 14}]
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Best valset aggregate score so far: 0.8626262626262626
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Best program as per aggregate score on train_val: 17
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Best program as per aggregate score on valset: 17
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Best score on valset: 0.8626262626262626
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Best score on train_val: 0.8626262626262626
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Linear pareto front program index: 17
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 37: New program candidate index: 19
2025/08/12 20:55:13 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Selected program 12 score: 0.7661616161616162
Average Metric: 1.23 / 3 (41.1%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  1.91it/s]2025/08/12 20:55:14 INFO dspy.evaluate.evaluate: Average Metric: 1.2333333333333334 / 3 (41.1%)

2025/08/12 20:55:41 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Proposed new text for urgency_module.predict: Task: Read the provided message to ProCare Facility Solutions Support and classify its urgency.

Output format:
- Return only one lowercase label: low, medium, or high.
- Do not include any additional text, punctuation, or reasoning unless explicitly requested.

Domain context:
- Messages concern facility services and support (e.g., cleaning/janitorial, HVAC maintenance, sustainability practices, training programs).
- Typical topics include general information requests, scheduling, maintenance help, and onsite assistance.

Urgency rubric:
- High:
  - Any risk to safety, health, or property (e.g., potential system damage, hazardous conditions).
  - Critical service outage or malfunction affecting normal operations.
  - Explicit requests for urgent/ASAP help combined with risk indicators (e.g., “I’m worried I might mess something up,” “send someone over,” “urgent assistance” tied to maintenance or malfunction).
  - Imminent time-critical constraints (e.g., deadlines within 24–48 hours) impacting operations or compliance.
- Medium:
  - Time-sensitive requests that need attention soon (days) but with no clear safety/property risk or service outage.
  - Confusion or guidance needed where incorrect action would not likely cause harm/damage.
  - Scheduling needs or upcoming events with a near-term timeline but not critical.
- Low:
  - General or exploratory information requests (e.g., asking about products, certifications, training details).
  - No stated deadline, no service impact, and no risk indicators.
  - Compliments, interest, or curiosity without urgency.

Decision guidance:
- Base the urgency on concrete risk, impact, and time constraints—not on politeness, enthusiasm, sender identity (e.g., influencer), or compliments.
- The mere presence of words like “urgent” or “prompt attention” is insufficient; elevate only if the content indicates actual risk, outage, or near-term deadline.
- Default to low when the message is informational and lacks time pressure or impact.
- Escalate to high when the sender requests immediate assistance and there is a credible risk of harm or damage (e.g., DIY HVAC steps they might get wrong).
- Use medium only when there is a clear need for timely action but no risk or outage.

Grounding examples (for calibration):
- Low: Asking which eco-friendly cleaning products/certifications you use; inquiring about sustainability training modules/schedules without deadlines.
- High: “Urgent help needed” for HVAC maintenance with concern about doing it wrong; asking for simpler steps or an onsite visit due to risk of messing up the system.

Return only: low, medium, or high.
2025/08/12 20:55:42 INFO dspy.evaluate.evaluate: Average Metric: 1.9 / 3 (63.3%)
2025/08/12 20:55:46 INFO dspy.evaluate.evaluate: Average Metric: 54.56666666666666 / 66 (82.7%)
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Full valset score for new program: 0.8267676767676767
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Full train_val score for new program: 0.8267676767676767
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Individual valset scores for new program: [1.0, 0.6666666666666666, 0.5666666666666667, 0.9666666666666667, 0.3333333333333333, 0.9333333333333332, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 1.0, 0.6333333333333333, 0.6666666666666666, 1.0, 0.6666666666666666, 0.9, 1.0, 0.9666666666666667, 0.6, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 0.9333333333333332, 1.0, 0.6666666666666666, 0.9666666666666667, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 0.6666666666666666, 0.6666666666666666, 0.9333333333333332, 1.0, 0.9666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 1.0, 0.3333333333333333, 0.9666666666666667, 0.6333333333333333, 1.0, 0.6, 0.6666666666666666, 0.9666666666666667, 0.6666666666666666, 0.3333333333333333, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 1.0, 0.6333333333333333, 0.9666666666666667, 0.9666666666666667, 0.6333333333333333, 0.26666666666666666]
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: New valset pareto front scores: [1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Full valset pareto front score: 0.9702020202020202
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Updated valset pareto front programs: [{10, 14, 16, 17, 20}, {0, 6, 7, 9, 16, 18, 19}, {17}, {2, 3, 4, 5, 6, 7, 9, 11, 18}, {7, 8, 9, 14, 15, 17}, {8, 9, 14}, {9, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16, 17, 19, 20}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15, 17, 18}, {8, 14, 15, 17, 19, 20}, {3, 4, 5, 11, 12, 13, 14, 18, 20}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 17, 18, 19, 20}, {3, 4, 5, 7, 8, 11, 13, 14, 15, 17, 18, 20}, {12}, {13}, {0, 12, 20}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20}, {6, 10, 15, 16, 19}, {18, 4, 13}, {20, 5}, {8}, {2, 6, 13, 15, 18}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20}, {4, 6, 11, 13, 15, 17, 18, 20}, {12, 20}, {2, 3, 4, 5, 6, 11, 13, 15, 17, 18}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16, 17, 18, 20}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}, {6, 15}, {3, 4, 5, 9, 11, 12, 13, 18, 20}, {0, 4, 5, 9, 10, 11, 12, 13, 16, 18, 20}, {13, 14, 15}, {12, 20}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}, {8, 17}, {0, 6, 10, 13, 16, 18, 19}, {6, 7, 8, 9, 14, 15}, {0, 1, 2, 6, 10, 12, 16, 19, 20}, {8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16, 17, 18, 19}, {0, 1, 2, 6, 10, 12, 13, 14, 16, 18, 19, 20}, {12, 13, 18, 19, 20}, {3, 4, 7, 11, 17}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20}, {8}, {4, 5, 11, 13, 17, 18, 19, 20}, {0, 4, 10, 11, 12, 13, 16, 18, 19}, {3, 4, 5, 8, 11, 17, 18, 19}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16, 19, 20}, {19}, {0, 1, 3, 12, 17}, {0, 16}, {0, 6, 7, 8, 9, 10, 15, 16}, {8, 9, 10, 14, 16, 17, 19, 20}, {0, 7, 8, 9, 12, 15, 17, 19, 20}, {2, 3, 4, 5, 10, 11, 13, 14, 16, 17, 18, 19, 20}, {4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18}, {3, 4, 5, 11, 13, 14}, {8, 9, 14, 19, 20}, {1, 2, 3, 4, 5, 11, 18}, {9, 14}]
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Best valset aggregate score so far: 0.8626262626262626
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Best program as per aggregate score on train_val: 17
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Best program as per aggregate score on valset: 17
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Best score on valset: 0.8626262626262626
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Best score on train_val: 0.8626262626262626
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Linear pareto front program index: 17
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 38: New program candidate index: 20
2025/08/12 20:55:46 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Selected program 16 score: 0.7813131313131313
Average Metric: 2.50 / 3 (83.3%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.53it/s]2025/08/12 20:55:47 INFO dspy.evaluate.evaluate: Average Metric: 2.5 / 3 (83.3%)

2025/08/12 20:56:26 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Proposed new text for categories_module.predict: Task
- Perform multi-label classification on a single input message related to facilities/services, returning all applicable categories from a fixed taxonomy.

Input format
- You will receive one field:
  - message: a free-form email or text (may include Subject and body).

Output format
- Return a JSON object with:
  - reasoning: 1–3 concise sentences explaining why each chosen category applies.
  - categories: an array of strings with all applicable category IDs from the taxonomy below. Deduplicate and sort alphabetically. If nothing fits, return an empty array [].
- Example:
  {
    "reasoning": "...",
    "categories": ["category_a", "category_b"]
  }

Taxonomy (use only these categories; do not invent new ones)
- customer_feedback_and_complaints
  - Signals: explicit dissatisfaction, complaints, negative sentiment, “not impressed,” “frustrating,” “unacceptable,” threats to churn, requests for rectification due to poor experience.
  - Often co-occurs with other categories; include it whenever the message is framed as feedback/complaint.

- emergency_repair_services
  - Signals: urgent breakdowns/failures requiring immediate dispatch (e.g., HVAC failure, leaks, power outages, security system failure), “urgent,” “emergency,” “immediate attention,” “needs to be fixed right away.”
  - If the situation is time-critical or service-restoring, add this.

- facility_management_issues
  - Signals: mismanagement or shortcomings in overall facility operations and oversight: coordination of space utilization, security program adequacy, vendor/contractor coordination, oversight gaps, process/policy failures.
  - Includes dissatisfaction with how FM programs (including sustainability programs) are managed at a facility level.

- quality_and_safety_concerns
  - Signals: risks or impacts to occupant health, safety, or service quality: unsafe temperatures from HVAC failure, chemical odors, inadequate security affecting safety, compliance concerns, degraded living/working conditions.

- specialized_cleaning_services
  - Signals: references to specific cleaning products/methods or specialized cleaning scopes: eco/green cleaning products, certifications/labels on cleaning products, sanitization/deep cleaning, hazardous or regulated cleaning practices.
  - When a message questions or requests details about cleaning products’ eco-certifications or methods, include this.

- sustainability_and_environmental_practices
  - Signals: environmental commitments, eco-friendly products/methods, certifications, recycling/waste segregation, energy/resource conservation, sustainability initiatives or reporting.
  - Includes critiques of “green” claims and requests for sustainable plans or clarifications.

Classification rules and heuristics
- Multi-label: Categories are not mutually exclusive. Assign every category that is clearly supported by the message content.
- Scan for multiple themes:
  - If a message critiques facility-wide oversight plus green claims, include both facility_management_issues and sustainability_and_environmental_practices.
  - If the tone is a complaint, also add customer_feedback_and_complaints.
  - If an urgent failure impacts comfort/safety (e.g., HVAC down, “unbearable temperature”), add emergency_repair_services and quality_and_safety_concerns.
  - If sustainability discussion mentions cleaning products/methods or certifications (e.g., chemical odors, missing eco labels), add specialized_cleaning_services along with sustainability_and_environmental_practices, and consider quality_and_safety_concerns due to health implications.
- Evidence-based: Do not add categories without textual support. Use reasonable inference (e.g., chemical odors imply safety/health concerns).
- Keep scope: Even if placeholders like [Sender]/[Receiver] appear, ignore them; focus on content.

Quality checks before finalizing
- Did you include customer_feedback_and_complaints when the message is a complaint or expresses dissatisfaction?
- Did you add sustainability_and_environmental_practices when green methods, waste/recycling, or eco claims are discussed?
- Did you include specialized_cleaning_services when cleaning products/methods or eco certifications are mentioned?
- Did you add quality_and_safety_concerns when health/safety/comfort or security adequacy is implicated?
- Did you add emergency_repair_services for urgent breakdowns needing immediate action?
- Did you include facility_management_issues when problems relate to overall facility operations/oversight?

Output ordering
- Sort the categories alphabetically.
2025/08/12 20:56:29 INFO dspy.evaluate.evaluate: Average Metric: 2.5666666666666664 / 3 (85.6%)
2025/08/12 20:56:34 INFO dspy.evaluate.evaluate: Average Metric: 50.93333333333333 / 66 (77.2%)
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Full valset score for new program: 0.7717171717171717
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Full train_val score for new program: 0.7717171717171717
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Individual valset scores for new program: [1.0, 1.0, 0.8666666666666667, 1.0, 0.3333333333333333, 0.6, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 0.6666666666666666, 1.0, 0.6, 0.3, 0.6, 0.6666666666666666, 0.9, 0.6333333333333333, 0.6666666666666666, 0.9666666666666667, 1.0, 0.3333333333333333, 0.6666666666666666, 0.9333333333333332, 0.3333333333333333, 1.0, 0.6333333333333333, 0.5666666666666667, 0.9, 0.9666666666666667, 0.6666666666666666, 0.9666666666666667, 0.8666666666666667, 0.6333333333333333, 1.0, 0.6666666666666666, 0.5666666666666667, 0.9333333333333332, 0.6666666666666666, 1.0, 0.9666666666666667, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9333333333333332, 1.0, 0.6333333333333333, 0.3, 0.9333333333333332, 1.0, 0.6333333333333333, 0.9, 0.6333333333333333, 0.9666666666666667, 0.6333333333333333, 0.26666666666666666, 1.0, 1.0, 0.6666666666666666, 0.3333333333333333, 1.0, 0.6333333333333333, 0.6333333333333333, 0.6333333333333333, 0.9666666666666667, 0.6]
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: New valset pareto front scores: [1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 0.6666666666666666, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 0.9333333333333332, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 1.0, 0.9333333333333332, 1.0, 0.9666666666666667, 1.0, 0.6666666666666666, 1.0, 1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667]
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Full valset pareto front score: 0.9707070707070706
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Updated valset pareto front programs: [{10, 14, 16, 17, 20, 21}, {0, 6, 7, 9, 16, 18, 19, 21}, {17}, {2, 3, 4, 5, 6, 7, 9, 11, 18, 21}, {7, 8, 9, 14, 15, 17}, {8, 9, 14}, {9, 21, 14}, {0, 1, 2, 3, 6, 8, 12, 14, 15, 16, 17, 19, 20, 21}, {1, 2, 3, 4, 5, 6, 7, 11, 13, 15, 17, 18}, {8, 14, 15, 17, 19, 20}, {3, 4, 5, 11, 12, 13, 14, 18, 20}, {0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 17, 18, 19, 20, 21}, {3, 4, 5, 7, 8, 11, 13, 14, 15, 17, 18, 20}, {12}, {13}, {0, 12, 20}, {1, 2, 3, 9}, {0, 6, 7, 8, 12, 15}, {4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20}, {6, 10, 15, 16, 19}, {21}, {20, 5}, {8}, {2, 6, 13, 15, 18}, {3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20}, {2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21}, {4, 6, 11, 13, 15, 17, 18, 20}, {12, 20}, {2, 3, 4, 5, 6, 11, 13, 15, 17, 18}, {0, 1, 2, 3, 4, 9, 12, 13, 14, 16, 17, 18, 20}, {15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21}, {6, 15}, {3, 4, 5, 9, 11, 12, 13, 18, 20}, {0, 4, 5, 9, 10, 11, 12, 13, 16, 18, 20, 21}, {13, 14, 15}, {12, 20}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}, {8, 17}, {0, 6, 10, 13, 16, 18, 19, 21}, {6, 7, 8, 9, 14, 15, 21}, {0, 1, 2, 6, 10, 12, 16, 19, 20}, {8, 9, 21}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 16, 17, 18, 19}, {0, 1, 2, 6, 10, 12, 13, 14, 16, 18, 19, 20, 21}, {12, 13, 18, 19, 20}, {3, 4, 7, 11, 17}, {0, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20}, {8, 21}, {4, 5, 11, 13, 17, 18, 19, 20}, {0, 4, 10, 11, 12, 13, 16, 18, 19}, {3, 4, 5, 8, 11, 17, 18, 19}, {0, 1, 2, 3, 6, 7, 8, 9, 10, 12, 15, 16, 19, 20, 21}, {19}, {0, 1, 3, 12, 17}, {0, 16, 21}, {0, 6, 7, 8, 9, 10, 15, 16, 21}, {8, 9, 10, 14, 16, 17, 19, 20, 21}, {0, 7, 8, 9, 12, 15, 17, 19, 20}, {2, 3, 4, 5, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21}, {4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18}, {3, 4, 5, 11, 13, 14}, {8, 9, 14, 19, 20}, {1, 2, 3, 4, 5, 11, 18}, {9, 14}]
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Best valset aggregate score so far: 0.8626262626262626
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Best program as per aggregate score on train_val: 17
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Best program as per aggregate score on valset: 17
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Best score on valset: 0.8626262626262626
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Best score on train_val: 0.8626262626262626
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Linear pareto front program index: 17
2025/08/12 20:56:34 INFO dspy.teleprompt.gepa.gepa: Iteration 39: New program candidate index: 21

Let's take a look at the optimized prompts¶

In [11]:

Copied!





for name, pred in optimized_program.named_predictors():
    print("================================")
    print(f"Predictor: {name}")
    print("================================")
    print("Prompt:")
    print(pred.signature.instructions)
    print("*********************************")
for name, pred in optimized_program.named_predictors():
    print("================================")
    print(f"Predictor: {name}")
    print("================================")
    print("Prompt:")
    print(pred.signature.instructions)
    print("*********************************")

================================
Predictor: urgency_module.predict
================================
Prompt:
Task: Read the provided message and determine the urgency.

Context/domain:
- Messages typically relate to facility management and services (e.g., facility operations, space utilization, security, sustainability, HVAC systems, maintenance, cleaning services) for a provider like ProCare Facility Solutions.
- Senders may be residential or commercial clients and may reference residents, tenants, property operations, or prior support interactions.

Output format:
- Provide exactly two fields, in this order, no extra text or formatting:
reasoning: <1–3 concise sentences explaining the key cues that determine urgency>
urgency: <one of: low | medium | high>

Urgency levels and decision rules:
- HIGH:
  - Clear or implied immediate risk to safety/security or major operational impact.
  - Explicit urgency signals (e.g., “Urgent,” “Immediate attention required,” “ASAP,” “critical,” “escalating”).
  - Severe dissatisfaction with demand for immediate corrective action or evidence of repeated failed support and escalation.
  - Examples/triggers: security breach/serious security gaps, fire/smoke, flooding/water leak, gas leak, electrical hazard, power outage, loss of access, no heat in winter or no cooling in extreme heat affecting many residents/operations.
- MEDIUM:
  - Time-sensitive issues that affect comfort, reliability, or service quality but are not emergencies and pose no immediate safety/security risk.
  - Requests for prompt scheduling/repair/maintenance where delay could worsen impact but is not currently critical.
  - Examples/triggers: HVAC making noises and inconsistent temperature, minor malfunction, routine maintenance requested “at the earliest convenience,” issues noted as “not an emergency.”
- LOW:
  - General inquiries, information requests, quotes, scheduling/options discussions, or interest in additional services with no stated or implied time pressure.
  - Compliments/feedback without an urgent problem.
  - Examples/triggers: asking for details on specialized cleaning, pricing, or availability without a problem to fix now.

Key cues to weigh:
- Explicit urgency language vs. statements like “not an emergency.”
- Safety/security implications and operational continuity.
- Impact scope (residents/tenants/business operations).
- Deadlines/dates or requested response times.
- Prior failed attempts and escalation tone.
- Do not inflate urgency based solely on polite phrases like “prompt assistance” if no urgent risk is present.

Tie-breakers:
- If the message explicitly says it’s not an emergency and no serious risk is evident, do not classify as high.
- If unclear and no risk/time pressure is indicated, default to low.
*********************************
================================
Predictor: sentiment_module.predict
================================
Prompt:
Task
- Read the provided message text and classify its overall sentiment as one of: positive, neutral, or negative.

Input format
- You will receive one field:
  - message: A string that may include a Subject line and an email-style body.

Output format
- Output only a single lowercase label: positive, neutral, or negative.
- Do not include any additional text or reasoning.

Classification guidelines
- Focus on the overall emotional tone expressed about the service/interaction, not the message’s functional purpose (e.g., making a request) or formalities.
- If signals are mixed or weak, default to neutral.

Label definitions
- Positive:
  - The message clearly expresses satisfaction, praise, gratitude, or enthusiasm that goes beyond routine politeness.
  - Strong and/or multiple explicit positive cues dominate (e.g., “satisfied client,” “exceptional service,” “truly appreciate,” “excellent,” “very happy,” “great,” “love working with you”).
- Negative:
  - The message expresses dissatisfaction, complaints, frustration, anger, fear, or disappointment about the service/experience.
  - Explicit negative claims or emotions (e.g., “unacceptable,” “unsafe,” “poor quality,” “frustrated,” “angry,” “very disappointed”).
- Neutral:
  - Informational, inquisitive, or routine requests without clear emotional valence.
  - Polite or formal language alone does not imply positivity (e.g., “I hope this finds you well,” “thank you,” “best regards”).
  - Expressions of concern framed as questions or requests for clarification, without asserting a negative judgment.
  - Mild or incidental compliments that are secondary to a routine request remain neutral unless strong positive cues dominate.

Disambiguation notes and edge cases
- Concerns or hesitations presented as inquiries (e.g., asking about quality/safety protocols) are neutral unless they assert dissatisfaction or harm.
- Routine maintenance/service requests are neutral by default. They become positive only if accompanied by strong, explicit, and primary praise or gratitude.
- Occasional or mild praise embedded in an otherwise routine request (e.g., “your services have been instrumental…”) is still neutral unless multiple strong positive signals dominate the tone.
- Do not infer sentiment from sender identity, organization, or subject line alone; rely on explicit sentiment-bearing language in the body.
*********************************
================================
Predictor: categories_module.predict
================================
Prompt:
You are classifying a single customer message sent to ProCare Facility Solutions (a facilities/cleaning services provider). Your job is to assign all and only the applicable categories from a fixed list, based strictly on the message content.

Allowed categories and precise definitions:
- cleaning_services_scheduling
  - Use only when the message’s primary purpose is to coordinate timing/availability for cleaning services (initial booking, rescheduling, adjusting times).
  - Typical signals: specific dates/times, availability windows, explicit reschedule/change requests, or back-and-forth around timing for cleaning crews.
  - Do NOT use for maintenance scheduling or when a timing phrase appears merely as part of a broader service request or complaint (e.g., “arrange a team,” “at your earliest convenience,” “within the next week”) without concrete, central timing logistics for cleaning.

- specialized_cleaning_services
  - Use when the message mentions specific/specialized cleaning types or tasks beyond generic cleaning.
  - Examples: deep cleaning, mold remediation, carpet cleaning/maintenance, window washing, post-construction cleanup, floor stripping/waxing, disinfection/sanitation.

- customer_feedback_and_complaints
  - Use when the message expresses dissatisfaction with prior service, reports subpar outcomes or communication issues, or requests remedies (redo, refund, re-clean).

- quality_and_safety_concerns
  - Use when the message raises quality shortfalls (e.g., areas still dirty, stains remaining) or safety/health risks (e.g., mold, hazards), or questions/challenges the provider’s quality/safety standards or protocols.
  - Can co-occur with customer_feedback_and_complaints if the sender is unhappy with outcomes.

- general_inquiries
  - Use when the sender explicitly asks for information about services, scope, what’s included, requirements, pricing, or availability before committing, or seeks clarification/guidance about process.
  - Important restriction from prior feedback: If the inquiry is solely about quality/safety standards (e.g., protocols, certifications, safety practices) without broader questions about offerings/availability/process, tag only quality_and_safety_concerns and do NOT add general_inquiries.

- facility_management_issues
  - Use when the message concerns systemic/process coordination of facilities (e.g., space utilization, overlapping bookings, scheduling system design, policy/process improvements) rather than cleaning outcomes or specific service timing.
  - Do NOT add cleaning_services_scheduling when the focus is reviewing/improving a scheduling system rather than coordinating a specific cleaning appointment.

- routine_maintenance_requests
  - Use when the message requests non-emergency facility maintenance (e.g., HVAC performance issues, minor plumbing, electrical checks, general upkeep) rather than cleaning.
  - Mentioning a desire to schedule a maintenance visit does not trigger cleaning_services_scheduling; that category is reserved for cleaning timing logistics.

Key decision rules and pitfalls to avoid:
- Multi-label: Assign every category that is clearly supported by explicit statements in the message.
- Strict evidence only: Do not infer beyond the text. Praise, urgency (“ASAP”), client profile, or confidentiality are not categories by themselves.
- Scheduling vs. Complaint distinction:
  - If rescheduling is proposed only as a fix within a complaint about poor service, do NOT include cleaning_services_scheduling. Use customer_feedback_and_complaints (and quality_and_safety_concerns if applicable).
- Scheduling vs. Service request distinction:
  - Do NOT tag cleaning_services_scheduling merely because the sender asks you to “arrange a visit,” “send a team,” or handle something “at your earliest convenience.”
  - Only tag cleaning_services_scheduling when the message’s central focus is concrete timing logistics for cleaning (e.g., “Can you come Friday at 3 PM?” “What slots do you have next week?” “Please move our usual Friday cleaning to Monday.”).
  - For maintenance timing (HVAC, plumbing, etc.), use routine_maintenance_requests and do not add cleaning_services_scheduling.
- Specialized services:
  - If the message mentions deep cleaning, mold remediation, carpet care, window washing, etc., include specialized_cleaning_services alongside any other applicable categories.
- General inquiries:
  - Tag when the sender asks about offerings, inclusions, requirements, pricing, or availability before committing. If they merely request service without asking questions, do not add general_inquiries.
  - Do not add general_inquiries when the only questions are about quality/safety standards; use quality_and_safety_concerns alone in that case.

Input:
- A single free-text customer message (often includes a subject and body).

Output format (plain text, exact structure):
- reasoning: Briefly justify each selected category and note key exclusions (especially why cleaning_services_scheduling was or was not applied).
- categories: A JSON array of the selected category strings, e.g., ["specialized_cleaning_services", "quality_and_safety_concerns"].
- If no categories apply, return an empty JSON array: [].

Process to follow:
1) Read the message once to identify intents: timing logistics (cleaning only), service-type specificity, feedback/complaint, quality/safety concerns, information requests, facility management/process issues, routine maintenance.
2) Map each intent to categories using the definitions and rules above (especially the scheduling distinctions and the quality/safety vs general_inquiries nuance).
3) Produce concise reasoning and the JSON array of categories using the exact strings from the Allowed categories list.

Edge-case guidance from prior evaluations:
- System/process coordination about space usage or overlapping bookings → facility_management_issues only. Exclude cleaning_services_scheduling.
- Inquiry focused on quality/safety standards and protocols (certifications, how safety is ensured) → quality_and_safety_concerns only. Exclude general_inquiries unless broader service/process/availability questions are asked.
- Non-urgent HVAC/plumbing visit request → routine_maintenance_requests. Exclude cleaning_services_scheduling (not a cleaning appointment).
*********************************

Note the high levels of detail about the task learnt in the prompts!

Now, let's evaluate the optimized program¶

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evaluate(optimized_program)
evaluate(optimized_program)

Average Metric: 59.17 / 68 (87.0%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 68/68 [00:06<00:00, 11.02it/s]

2025/08/12 20:57:09 INFO dspy.evaluate.evaluate: Average Metric: 59.166666666666664 / 68 (87.0%)

	message	answer	urgency	sentiment	categories	metric
0	Hey ProCare Support Team, Hope you all are doing great! My name is...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	positive	[sustainability_and_environmental_practices]	✔️ [1.000]
1	Hey ProCare Team, Hope you’re all doing well! My name’s Jake, and ...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	positive	[routine_maintenance_requests]	✔️ [1.000]
2	Subject: Assistance Needed for HVAC Maintenance Hi [Receiver], I h...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	neutral	[routine_maintenance_requests]	✔️ [1.000]
3	Subject: A Green Inquiry from a Bill Maher Enthusiast Hey ProCare ...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	positive	[sustainability_and_environmental_practices]	✔️ [1.000]
4	Subject: Inquiry on Sustainability Practices Dear ProCare Facility...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	neutral	[sustainability_and_environmental_practices]	✔️ [1.000]
...	...	...	...	...	...	...
63	Subject: Inquiry About Your Eco-Friendly Practices Dear ProCare Fa...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	neutral	[sustainability_and_environmental_practices]	✔️ [0.933]
64	Subject: Assistance Needed for Facility Management Issue Dear ProC...	{"categories": {"routine_maintenance_requests": false, "customer_f...	medium	neutral	[facility_management_issues]	✔️ [0.667]
65	Subject: Request for Training and Support Hi ProCare Support Team,...	{"categories": {"routine_maintenance_requests": false, "customer_f...	low	neutral	[training_and_support_requests]	✔️ [0.667]
66	Subject: Concerns About Studio Maintenance and Rent Increase Dear ...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	neutral	[routine_maintenance_requests, facility_management_issues]	✔️ [0.933]
67	Subject: Feedback on Recent Maintenance Service Dear ProCare Suppo...	{"categories": {"routine_maintenance_requests": true, "customer_fe...	medium	neutral	[routine_maintenance_requests, quality_and_safety_concerns, custom...	✔️ [1.000]

68 rows × 6 columns

Out[12]:

EvaluationResult(score=87.01, results=<list of 68 results>)

GEPA was able to optimize GPT-4.1 nano's performance from 75% score to 87% in the auto="light" setting.

Bonus: Detailed Results¶

A GEPA run with track_stats=True returns detailed results in the detailed_results attribute.

candidates: List of proposed candidates.
parents: For each candidate, list of parent indices or None.
val_aggregate_scores: Aggregate validation score per candidate.
val_subscores: Per-instance validation scores per candidate.
per_val_instance_best_candidates: Indices of best candidates for each validation instance
discovery_eval_counts: Metric calls/rollouts used to discover each candidate.
best_outputs_valset: Best output produced for each task (only present with track_best_outputs=True)
best_idx: Index of candidate with top score.
best_candidate: Program for best_idx.

Let's visualize the optimization trajectory taken by GEPA for this task.

Specifically, we can access the parents attribute, which identifies the parent programs for each candidate program. We use a simple script to plot these as a Graphviz DOT visualization, which can be rendered locally using Graphviz or through online tools like GraphvizOnline.

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def dag_to_dot(parent_program_for_candidate, dominator_program_ids, best_program_idx, full_eval_scores):
    dot_lines = [
        "digraph G {",
        "    node [style=filled, shape=circle, fontsize=50];"
    ]
    n = len(parent_program_for_candidate)
    # Set up nodes with colors and scores in labels
    for idx in range(n):
        score = full_eval_scores[idx]
        label = f"{idx}\\n({score:.2f})"
        if idx == best_program_idx:
            dot_lines.append(f'    {idx} [label="{label}", fillcolor=cyan, fontcolor=black];')
        elif idx in dominator_program_ids:
            dot_lines.append(f'    {idx} [label="{label}", fillcolor=orange, fontcolor=black];')
        else:
            dot_lines.append(f'    {idx} [label="{label}"];')
    
    # Set up edges
    for child, parents in enumerate(parent_program_for_candidate):
        for parent in parents:
            if parent is not None:
                dot_lines.append(f'    {parent} -> {child};')
    
    dot_lines.append("}")
    return "\n".join(dot_lines)

from gepa.gepa_utils import find_dominator_programs
pareto_front_programs = find_dominator_programs(optimized_program.detailed_results.per_val_instance_best_candidates, optimized_program.detailed_results.val_aggregate_scores)

print(dag_to_dot(
    optimized_program.detailed_results.parents,
    pareto_front_programs,
    optimized_program.detailed_results.best_idx,
    optimized_program.detailed_results.val_aggregate_scores
))
def dag_to_dot(parent_program_for_candidate, dominator_program_ids, best_program_idx, full_eval_scores):
    dot_lines = [
        "digraph G {",
        "    node [style=filled, shape=circle, fontsize=50];"
    ]
    n = len(parent_program_for_candidate)
    # Set up nodes with colors and scores in labels
    for idx in range(n):
        score = full_eval_scores[idx]
        label = f"{idx}\\n({score:.2f})"
        if idx == best_program_idx:
            dot_lines.append(f'    {idx} [label="{label}", fillcolor=cyan, fontcolor=black];')
        elif idx in dominator_program_ids:
            dot_lines.append(f'    {idx} [label="{label}", fillcolor=orange, fontcolor=black];')
        else:
            dot_lines.append(f'    {idx} [label="{label}"];')
    
    # Set up edges
    for child, parents in enumerate(parent_program_for_candidate):
        for parent in parents:
            if parent is not None:
                dot_lines.append(f'    {parent} -> {child};')
    
    dot_lines.append("}")
    return "\n".join(dot_lines)

from gepa.gepa_utils import find_dominator_programs
pareto_front_programs = find_dominator_programs(optimized_program.detailed_results.per_val_instance_best_candidates, optimized_program.detailed_results.val_aggregate_scores)

print(dag_to_dot(
    optimized_program.detailed_results.parents,
    pareto_front_programs,
    optimized_program.detailed_results.best_idx,
    optimized_program.detailed_results.val_aggregate_scores
))

digraph G {
    node [style=filled, shape=circle, fontsize=50];
    0 [label="0\n(0.72)"];
    1 [label="1\n(0.71)"];
    2 [label="2\n(0.79)"];
    3 [label="3\n(0.86)"];
    4 [label="4\n(0.86)", fillcolor=orange, fontcolor=black];
    5 [label="5\n(0.81)"];
    6 [label="6\n(0.80)"];
    7 [label="7\n(0.83)"];
    8 [label="8\n(0.86)", fillcolor=orange, fontcolor=black];
    9 [label="9\n(0.83)", fillcolor=orange, fontcolor=black];
    10 [label="10\n(0.76)"];
    11 [label="11\n(0.83)"];
    12 [label="12\n(0.77)", fillcolor=orange, fontcolor=black];
    13 [label="13\n(0.84)", fillcolor=orange, fontcolor=black];
    14 [label="14\n(0.83)"];
    15 [label="15\n(0.81)", fillcolor=orange, fontcolor=black];
    16 [label="16\n(0.78)"];
    17 [label="17\n(0.86)", fillcolor=cyan, fontcolor=black];
    18 [label="18\n(0.85)"];
    19 [label="19\n(0.74)", fillcolor=orange, fontcolor=black];
    20 [label="20\n(0.83)", fillcolor=orange, fontcolor=black];
    21 [label="21\n(0.77)", fillcolor=orange, fontcolor=black];
    0 -> 1;
    1 -> 2;
    2 -> 3;
    3 -> 4;
    4 -> 5;
    2 -> 6;
    6 -> 7;
    4 -> 8;
    7 -> 9;
    0 -> 10;
    4 -> 11;
    0 -> 12;
    4 -> 13;
    13 -> 14;
    6 -> 15;
    0 -> 16;
    4 -> 17;
    13 -> 18;
    16 -> 19;
    12 -> 20;
    16 -> 21;
}

Rendered Image