Murnitur AI allows developers to run programmatic evaluations, providing a flexible and automated way to assess Large Language Models (LLMs) through code.

Note: Before running programmatic evaluations, you need to set up the Murnitur SDK with your API key. Detailed instructions on how to set up the SDK can be found here.

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Example

import murnitur
from murnitur.loaders import Loader
from murnitur.annotation.eval import Evaluation

# initialize evaluation
murnitur.set_api_key("mt-ey...")
key = "sk-..."

eval = Evaluation(openai_key=key, murnitur_key=murnitur.get_api_key())

# Load datasets from csv
data = Loader.load_csv("https://murnitur.github.io/murnix-kube/sample-dataset.csv")

results = eval.run_suite(metrics=["hallucination", "pii"], dataset=data)

print(results)

Each run is saved in the UI. You can view the results on the AI Evaluations page.

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Knowledge Retention Evaluation

To evaluate how well your model retains information across interactions, use Murnitur’s knowledge_retention method. You can also set up a base threshold to define acceptable retention levels.

1. Import the Required Module:

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   from murnitur.annotation.eval import Evaluation
   

2. Prepare Your Dataset:

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   data = [
     {
       "input": "How often should I run webinars to gain traction for Murnitur?",
       "output": "Once a month to start. This gives you time to prepare quality content."
     },
     {
       "input": "What topics should I cover in these webinars?",
       "output": "Focus on unique features like tracing and monitoring, and AI safety with Murnitur Shield."
     }
   ]
   

3. Set Up a Base Threshold (Optional):

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   base_threshold = 0.2  # Define your acceptable retention level
   

4. Run the Evaluation:

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   eval = Evaluation()
   results = eval.knowledge_retention(dataset=data, save_output=True, threshold=base_threshold)
   print(results)
   

5. Sample Output:

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   {
     "score": 0.1,
     "reason": "The model retains information well with minimal loss."
   }
   

This method returns a JSON with a score (0-1) indicating the level of knowledge retention, where closer to 0 means better retention. By setting a base threshold, you can determine if the retention meets your desired standards.

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Creating dataset

Side Note: If you are creating a dataset from a dictionary, use the following data structure:

dataset = [
    {
        "input": "",
        "output": "",
        "ground_truth": "",
        "context": [""],
        "retrieval_context": [""],
    }
]

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Loading dataset

Use any of the following functions to load the dataset:

from murnitur.loaders import Loader

dataset_from_json = Loader.load_json(path_to_json_file)

dataset_from_csv = Loader.load_csv(path_to_csv_document)

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Extra Params

Extra parameters that can be passed to run_suite

• save_output: A boolean. Set to false if you do not want to store the results in Murnitur AI.
• async_mode: Indicates whether the requests should run asynchronously.

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Evaluation Metrics

We currently support the following evaluation metrics:

• hallucination: Measures the accuracy of the model’s output, identifying any fabricated or incorrect information.
• faithfulness: Evaluates how well the model’s output adheres to the provided context or source material.
• relevancy: Assesses the relevance of the model’s response to the input query or prompt.
• bias: Detects any unfair or discriminatory tendencies in the model’s predictions.
• context-relevancy: Determines how relevant the input or output is in relation to the given context.
• context-precision: Measures the precision of the model’s output within the specific context provided.
• toxicity: Identifies harmful or offensive language in the model’s responses.
• summarization: Evaluates the quality and accuracy of summaries generated by the model.
• pii: Detects the presence of personally identifiable information in the model’s output.

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Metrics & Required Fields