> ## Documentation Index
> Fetch the complete documentation index at: https://pype-db52d533.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Conversation Completeness

The ConversationCompletenessMetric is a conversational metric designed to evaluate whether an LLM chatbot can complete an end-to-end conversation by satisfying user needs throughout the interaction. It serves as a proxy to measure user satisfaction in conversational AI applications.

### **Required Arguments**

* turns: A list of ModelTestCase instances representing the conversation turns. Each ModelTestCase requires:
* input: The user's input to the chatbot.
* actual\_output: The chatbot's response to the user's input.

### **Optional Arguments**

* threshold: A float representing the minimum passing threshold, defaulted to 0.5.
* model: A string specifying which of OpenAI's GPT models to use, or any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
* include\_reason: A boolean which, when set to True, includes a reason for its evaluation score. Defaulted to True.
* strict\_mode: A boolean which, when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
* async\_mode: A boolean which, when set to True, enables concurrent execution within the measure() method. Defaulted to True.
* verbose\_mode: A boolean which, when set to True, prints the intermediate steps used to calculate the metric to the console. Defaulted to False.

### **Usage Example**

```python theme={null}
from agensight.eval.test_case import ModelTestCase, ConversationalTestCase
from agensight.eval.metrics import ConversationCompletenessMetric

convo_test_case = ConversationalTestCase(
    turns=[
        ModelTestCase(input="Hello, how can I help you today?", actual_output="I need assistance with my account."),
        ModelTestCase(input="Sure, I can help with that. Can you please provide your account number?", actual_output="My account number is 123456.")
    ]
)
metric = ConversationCompletenessMetric(threshold=0.5)

metric.measure(convo_test_case)
print(metric.score, metric.reason)
```