Response Suggestion

Response suggestion task for customer support interactions.

This module provides a predefined task for generating suggested responses to customer inquiries, helping support agents provide consistent, helpful, and professional communication.

Example

Basic usage with BatchResponses:

from openai import OpenAI
from openaivec.responses import BatchResponses
from openaivec.task import customer_support

client = OpenAI()
responder = BatchResponses.of_task(
    client=client,
    model_name="gpt-4o-mini",
    task=customer_support.RESPONSE_SUGGESTION
)

inquiries = [
    "I can't access my account. I've tried resetting my password but the email never arrives.",
    "I'm really disappointed with your service. This is the third time I've had issues.",
    "Thank you for your help yesterday! The problem is now resolved."
]
responses = responder.parse(inquiries)

for response in responses:
    print(f"Suggested Response: {response.suggested_response}")
    print(f"Tone: {response.tone}")
    print(f"Priority: {response.priority}")
    print(f"Follow-up: {response.follow_up_required}")

With pandas integration:

import pandas as pd
from openaivec import pandas_ext  # Required for .ai accessor
from openaivec.task import customer_support

df = pd.DataFrame({"inquiry": [
    "I can't access my account. I've tried resetting my password but the email never arrives.",
    "I'm really disappointed with your service. This is the third time I've had issues."
]})
df["response"] = df["inquiry"].ai.task(customer_support.RESPONSE_SUGGESTION)

# Extract response components
extracted_df = df.ai.extract("response")
print(extracted_df[["inquiry", "response_suggested_response", "response_tone", "response_priority"]])

Attributes:

Name	Type	Description
RESPONSE_SUGGESTION	PreparedTask	A prepared task instance configured for response suggestion with temperature=0.0 and top_p=1.0 for deterministic output.

response_suggestion(response_style='professional', company_name='our company', business_context='general customer support', temperature=0.0, top_p=1.0)

Create a configurable response suggestion task.

Parameters:

Name	Type	Description	Default
response_style	str	Style of response (professional, friendly, empathetic, formal).	'professional'
company_name	str	Name of the company for personalization.	'our company'
business_context	str	Business context for responses.	'general customer support'
temperature	float	Sampling temperature (0.0-1.0).	0.0
top_p	float	Nucleus sampling parameter (0.0-1.0).	1.0

Returns:

Type	Description
PreparedTask	PreparedTask configured for response suggestions.

Source code in src/openaivec/task/customer_support/response_suggestion.py

def response_suggestion(
    response_style: str = "professional",
    company_name: str = "our company",
    business_context: str = "general customer support",
    temperature: float = 0.0,
    top_p: float = 1.0
) -> PreparedTask:
    """Create a configurable response suggestion task.

    Args:
        response_style: Style of response (professional, friendly, empathetic, formal).
        company_name: Name of the company for personalization.
        business_context: Business context for responses.
        temperature: Sampling temperature (0.0-1.0).
        top_p: Nucleus sampling parameter (0.0-1.0).

    Returns:
        PreparedTask configured for response suggestions.
    """

    style_instructions = {
        "professional": "Maintain professional tone with clear, direct communication",
        "friendly": "Use warm, approachable language while remaining professional",
        "empathetic": "Show understanding and compassion for customer concerns",
        "formal": "Use formal business language appropriate for official communications"
    }

    instructions = f"""Generate a professional, helpful response suggestion for the customer inquiry that addresses their needs effectively.

Business Context: {business_context}
Company Name: {company_name}
Response Style: {style_instructions.get(response_style, style_instructions['professional'])}

Response Guidelines:
1. Address the customer's main concern directly
2. Use appropriate tone based on customer sentiment
3. Provide clear next steps or solutions
4. Include empathy when dealing with frustrated customers
5. Maintain professional standards while being human
6. Offer specific help rather than generic responses
7. Set appropriate expectations for resolution time
8. Include any necessary disclaimers or policy information

Tone Selection:
- empathetic: For frustrated, disappointed, or upset customers
- professional: For business inquiries, formal requests, or complex issues
- friendly: For positive interactions, thank you messages, or simple questions
- apologetic: For service failures, bugs, or company mistakes
- solution_focused: For technical issues requiring specific steps

Response Types:
- acknowledgment: Confirming receipt and understanding of the inquiry
- solution: Providing direct answers or resolution steps
- escalation: Transferring to appropriate team or management
- information_request: Asking for additional details to help resolve
- closure: Confirming resolution and checking customer satisfaction

Priority Levels:
- immediate: Critical issues requiring instant response
- high: Urgent problems needing quick attention
- medium: Standard inquiries with normal response time
- low: General questions or feedback with flexible timing

Key Elements to Include:
- Acknowledge the customer's specific issue
- Show understanding of their frustration or needs
- Provide clear, actionable next steps
- Set realistic expectations for resolution
- Offer additional assistance if needed
- Include relevant contact information or resources

Response Structure:
1. Opening: Acknowledge and thank the customer
2. Empathy: Show understanding of their situation
3. Solution: Provide specific help or next steps
4. Follow-up: Offer continued assistance
5. Closing: Professional sign-off

Personalization Considerations:
- Use customer's name if provided
- Reference specific details from their inquiry
- Acknowledge their loyalty or relationship length
- Tailor language to their communication style
- Consider their apparent technical expertise level

Avoid:
- Generic, templated responses
- Overly technical language for non-technical customers
- Making promises that can't be kept
- Dismissing customer concerns
- Lengthy responses that don't address the main issue

IMPORTANT: Generate responses in the same language as the input text, except for the predefined categorical fields (tone, priority, response_type, estimated_resolution_time) which must use the exact English values specified above. For example, if the input is in Italian, provide suggested_response, key_points, alternative_responses, and personalization_notes in Italian, but use English values like "empathetic" for tone.

Generate helpful, professional response that moves toward resolution while maintaining positive customer relationship."""

    return PreparedTask(
        instructions=instructions,
        response_format=ResponseSuggestion,
        temperature=temperature,
        top_p=top_p
    )