Knowledge Retention

The knowledge retention metric determines whether your LLM is able to retain factual information presented throughout a conversation.

caution

This is deepeval's first experimental conversational metric. A conversational metric is a metric that evaluates an entire conversation.

Required Arguments

To use the KnowledgeRetentionMetric, you'll have to provide the following arguments when creating an ConversationalTestCase:

• messages (list[LLMTestCase])

info

Here's an example of how to create a ConversationalTestCase:

from deepeval.test_case import ConversationalTestCase, LLMTestCase

message1 = LLMTestCase(input="...", actual_output="...")
message2 = LLMTestCase(input="...", actual_output="...")
message3 = LLMTestCase(input="...", actual_output="...")

conversational_test_case = ConversationalTestCase(messages=[message1, message2, message3])

Example

Let's take this conversation as an example:

from deepeval.test_case import LLMTestCase

messages = [
    LLMTestCase(
        input="",
        actual_output="Hello! I'm here to assist you with opening a new bank account. To start, may I have your full name, please?"
    ),
    LLMTestCase(
        input="Sure, it's Alex Johnson. I also have a dog called Jacky.",
        actual_output="Great, Alex! Now, could you please provide your current address?"
    ),
    LLMTestCase(
        input="123 Maple Street, Springfield.",
        actual_output="Is that all?"
    ),
    LLMTestCase(
        input="I also have another address at 123 Broadway, NYC.",
        actual_output="Wonderful. Next, I'll need your bank account number where we can link this new account."
    ),
    LLMTestCase(
        input="456789123",
        actual_output="This account number seems invalid, can you please double-check?"
    ),
    LLMTestCase(
        input="Sorry you're right, its 456789124.",
        actual_output="Thank you. And for our next step, could you tell me your date of birth?"
    ),
    LLMTestCase(
        input="It's July 9th.",
        actual_output="What about the year?"
    ),
    LLMTestCase(
        input="1990",
        actual_output="Got it. Now, for security purposes, could you share your mother's maiden name?"
    ),
    LLMTestCase(
        input="It's Smith.",
        actual_output="Excellent. Just a few more details. What is your phone number?"
    ),
    LLMTestCase(
        input="555-0102",
        actual_output="Great, we're almost done. Could you remind me of your full name for the account documentation?"
    ),
    LLMTestCase(
        input="Didn't I tell you already? It's Alex Johnson.",
        actual_output="What is your bank account number?"
    ),
    LLMTestCase(
        input="Yes, I did... It's 456789124. Are you not keeping track of this?",
        actual_output="One last question, what is the country code of your phone number?"
    ),
    LLMTestCase(
        input="+44",
        actual_output="Thank you, Alex, for bearing with me. We now have all the information we need to proceed with opening your new bank account. I appreciate your cooperation and patience throughout this process."
    )
]

You can use the KnowledgeRetentionMetric as follows:

from deepeval.metrics import KnowledgeRetentionMetric
from deepeval.test_case import ConversationalTestCase
...

test_case = ConversationalTestCase(messages=messages)
metric = KnowledgeRetentionMetric(threshold=0.5)

metric.measure(test_case)
print(metric.score)
print(metric.reason)

There are three optional parameters when creating a KnowledgeRetentionMetric:

• [Optional] threshold: a float representing the maximum passing threshold, defaulted to 0.5.
• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4-turbo'.
• [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
• [Optional] 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 0. Defaulted to False.

How Is It Calculated?

The KnowledgeRetentionMetric score is calculated according to the following equation:

$$\text{Knowledge Retention} = \frac{\text{Number of Messages without Knowledge Attritions}}{\text{Total Number of Messages}}$$

The KnowledgeRetentionMetric first uses an LLM to extract knowledge gained throughout messages, before using the same LLM to determine whether each corresponding LLM responses indicates an inability to recall said knowledge.

info

Unlike other metrics, the KnowledgeRetentionMetric is still in beta, and we would love to hear any suggestions on our discord channel.