Inquiry Summary

Inquiry summary task for customer support interactions.

This module provides a predefined task for summarizing customer inquiries, extracting key information, and creating concise summaries for support agents and management reporting.

Example

Basic usage with BatchResponses:

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

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

inquiries = [
    '''Hi there, I've been having trouble with my account for the past week. 
    Every time I try to log in, it says my password is incorrect, but I'm sure 
    it's right. I tried resetting it twice but the email never arrives. 
    I'm getting really frustrated because I need to access my files for work tomorrow.''',

    '''I love your product! It's been incredibly helpful for my team. 
    However, I was wondering if there's any way to get more storage space? 
    We're running out and would like to upgrade our plan.'''
]
summaries = summarizer.parse(inquiries)

for summary in summaries:
    print(f"Summary: {summary.summary}")
    print(f"Issue: {summary.main_issue}")
    print(f"Actions Taken: {summary.actions_taken}")
    print(f"Resolution Status: {summary.resolution_status}")

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": [long_inquiry_text]})
df["summary"] = df["inquiry"].ai.task(customer_support.INQUIRY_SUMMARY)

# Extract summary components
extracted_df = df.ai.extract("summary")
print(extracted_df[["inquiry", "summary_main_issue", "summary_resolution_status"]])

Attributes:

Name	Type	Description
INQUIRY_SUMMARY	PreparedTask	A prepared task instance configured for inquiry summarization with temperature=0.0 and top_p=1.0 for deterministic output.

inquiry_summary(summary_length='concise', business_context='general customer support', temperature=0.0, top_p=1.0)

Create a configurable inquiry summary task.

Parameters:

Name	Type	Description	Default
summary_length	str	Length of summary (concise, detailed, bullet_points).	'concise'
business_context	str	Business context for summary.	'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 inquiry summarization.

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

def inquiry_summary(
    summary_length: str = "concise",
    business_context: str = "general customer support",
    temperature: float = 0.0,
    top_p: float = 1.0
) -> PreparedTask:
    """Create a configurable inquiry summary task.

    Args:
        summary_length: Length of summary (concise, detailed, bullet_points).
        business_context: Business context for summary.
        temperature: Sampling temperature (0.0-1.0).
        top_p: Nucleus sampling parameter (0.0-1.0).

    Returns:
        PreparedTask configured for inquiry summarization.
    """

    length_instructions = {
        "concise": "Write a concise 2-3 sentence summary that captures the essence of the inquiry",
        "detailed": "Write a detailed 4-6 sentence summary that includes comprehensive context",
        "bullet_points": "Create a bullet-point summary with key facts and actions"
    }

    instructions = f"""Create a comprehensive summary of the customer inquiry that captures all essential information for support agents and management.

Business Context: {business_context}
Summary Style: {length_instructions.get(summary_length, length_instructions['concise'])}

Summary Guidelines:
1. {length_instructions.get(summary_length, length_instructions['concise'])}
2. Identify the primary issue or request clearly
3. Note any secondary issues that may need attention
4. Extract relevant customer background or context
5. List any troubleshooting steps the customer has already tried
6. Include timeline information about when issues started
7. Describe the business or personal impact on the customer
8. Assess current resolution status based on the inquiry
9. Extract key technical details, error messages, or specific information
10. Determine if follow-up communication will be needed

Resolution Status Categories:
- not_started: New inquiry, no resolution attempts yet
- in_progress: Customer has tried some solutions, but issue persists
- needs_escalation: Complex issue requiring specialized attention
- resolved: Issue appears to be resolved based on customer feedback

Key Details to Extract:
- Error messages or codes
- Product versions or configurations
- Account information (without sensitive data)
- Technical specifications
- Business impact details
- Deadline or time constraints
- Previous ticket references

Impact Assessment:
- Business operations affected
- Revenue implications
- User experience degradation
- Time-sensitive requirements
- Reputation concerns

Focus on:
- Factual information over emotional content
- Actionable details that help resolution
- Context that aids in prioritization
- Clear distinction between symptoms and root causes
- Relevant background without unnecessary details

IMPORTANT: Provide summary responses in the same language as the input text, except for the predefined categorical field (resolution_status) which must use the exact English values specified above (not_started, in_progress, needs_escalation, resolved). For example, if the input is in German, provide all summary content in German, but use English values like "in_progress" for resolution_status.

Provide accurate, actionable summary that enables efficient support resolution."""

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