This guide shows you how to leverage HoneyHive’s evaluation capabilities even if your interaction logs already exist in external systems like Excel spreadsheets, CSV files, or database tables. The core idea is to load these external logs into a suitable format and then run HoneyHive evaluators on them.

This is particularly useful when you want to:

  • Evaluate the quality of historical interactions.
  • Benchmark different versions of prompts or models using past data.
  • Apply new evaluation metrics to existing logs without rerunning the original generation process.

This guide assumes you are familiar with how experiments function in HoneyHive. If you need a refresher, please visit the Experiment’s Introduction page.

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Overview

For this example, we will use a set of examples from the CNN / DailyMail dataset, to simulate a summarization task.

The dataset contains two key components:

  • article: Contains the full text of news articles, which serves as our input
  • highlights: Contains human-written bullet-point summaries of each article, which we’ll use to simulate the expected output from our LLM summarization task

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Step-by-Step Implementation

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Full code example

Here’s a minimal example assuming you’ve loaded your external data into a list format:

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Creating the Dataset

To evaluate your model’s performance, you’ll need to transform your external log data into a structured format that the evaluation framework can process. The framework expects a Python list of dictionaries, where each dictionary represents a single interaction containing:

  • Request inputs
  • Generated outputs
  • Ground truth information (if available)

For instance, if your logs are stored in a CSV file, you can load them into a Pandas DataFrame and convert the data using df.to_dict(‘records’). Each dictionary represents a single logged interaction. Then, you use the evaluate function with your dataset and defined evaluators.

For the purposes of our example, we’ll assume our data has already been transformed into this required format:

Python
dataset = [
    {
        'inputs': {
            'article': '(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based. The Palestinians signed the ICC\'s founding Rome Statute in January...',
        },
        'ground_truths': {
            'highlights': 'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June.\nIsrael and the United States opposed the move, which could open the door to war crimes investigations against Israelis.'
        }
    },
    {
        'inputs': {
            'article': '(CNN)Never mind cats having nine lives. A stray pooch in Washington State has used up at least three of her own after being hit by a car, apparently whacked on the head with a hammer in a misguided mercy killing and then buried in a field -- only to survive. That\'s according to Washington State University, where the dog -- a friendly white-and-black bully breed mix now named Theia -- has been receiving care at the Veterinary Teaching Hospital...',
        },
        'ground_truths': {
            'highlights': 'Theia, a bully breed mix, was apparently hit by a car, whacked with a hammer and buried in a field.\n"She\'s a true miracle dog and she deserves a good life," says Sara Mellado, who is looking for a home for Theia.'
        }
    }
]

This guide demonstrates defining the dataset directly within the script. Alternatively, you can upload your dataset (in JSON, JSONL, or CSV format) to the HoneyHive platform and then pass its dataset_id when running the experiment. For instructions on uploading and managing datasets within HoneyHive, please refer to the Upload Dataset page.

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Defining the Evaluators

To assess the quality of our summarizations, we’ll implement two key evaluators: compression ratio and keyword overlap. These metrics help us understand both the length efficiency and content preservation of our summaries.

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Compression ratio

The compression ratio evaluator measures how concise our summary is compared to the original article:

Python
@evaluator()
def compression_ratio(outputs, inputs, ground_truths):
    return len(outputs)/len(inputs["article"])

This simple metric returns a value between 0 and 1, where lower values indicate more aggressive summarization. For example, a ratio of 0.25 means our summary is one-quarter the length of the original article.

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Keyword overlap

The keyword overlap evaluator assesses how well our summary preserves the main topics and key information from the original text:

Python
def extract_keywords(text, top_n=10):
    # Use TfidfVectorizer to calculate TF-IDF scores
    vectorizer = TfidfVectorizer(stop_words='english')
    tfidf_matrix = vectorizer.fit_transform([text])
    feature_names = vectorizer.get_feature_names_out()
    tfidf_scores = tfidf_matrix.toarray()[0]

    # Get top N keywords based on TF-IDF scores
    keywords = sorted(
        zip(feature_names, tfidf_scores),
        key=lambda x: x[1],
        reverse=True
    )[:top_n]
    return set([keyword for keyword, score in keywords])

@evaluator()
def keyword_overlap(outputs, inputs, ground_truths):
    article_keywords = extract_keywords(inputs["article"])
    highlights_keywords = extract_keywords(outputs)
    return len(article_keywords.intersection(highlights_keywords))/len(article_keywords)

This evaluator works in two steps:

First, it extracts the top 10 keywords from both the original article and the generated summary using TF-IDF (Term Frequency-Inverse Document Frequency) scoring. Then, it calculates the overlap ratio between these keyword sets, returning a score between 0 and 1. A higher score indicates better preservation of key concepts from the original article.

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The evaluated function

The evaluated function is traditionally the function that will generate an output based on the input, like an LLM, whose outputs we want to evalute. In this case, we alreavy have our outputs in our logs, so we can define a simple pass-through function to use the highglights column as our output:

Python
    def pass_through_logged_data(inputs, ground_truths):
        return ground_truths["highlights"]

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Running the Experiment

Finally, we can run the experiment by passing our dataset, function and evaluators to the evaluation harness:

Python
if __name__ == "__main__":
    # Run experiment
    evaluate(
        function = pass_through_logged_data,               # Function to be evaluated
        hh_api_key = HH_API_KEY,
        hh_project = HH_PROJECT,
        name = 'External Logs',
        dataset = dataset,                      # to be passed for json_list
        evaluators=[compression_ratio, keyword_overlap],                 # to compute client-side metrics on each run
    )

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Dashboard View

Once the script runs, HoneyHive ingests each log entry as a trace, along with the computed client-side evaluator metrics. Navigate to your project in the HoneyHive dashboard to view the results. You can analyze distributions, filter by metadata, and compare metrics across your dataset.

Image: Example evaluation view in HoneyHive.

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Conclusion

By mapping your existing external logs to the HoneyHive evaluate function’s expected format, you can apply powerful client-side and server-side evaluations without rerunning the original AI/LLM calls. This provides a flexible way to assess performance, track quality over time, and gain insights from historical data.

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Next Steps

Introduction to Evaluators

Deep dive into HoneyHive’s evaluation framework, including custom evaluators.

Server-Side Evaluators

Learn about configuring evaluators that run asynchronously on HoneyHive’s infrastructure.

Managing Datasets

Explore how HoneyHive helps manage datasets for evaluations and experiments.