# Cosine Similarity

The Cosine Similarity metric is a **span-level metric** used to measure the similarity between the **response** and the **ground truth** in an Agentic application. This metric is ideal for evaluating how closely a model's response aligns with the expected output.

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#### **Schema**

The schema for the Cosine Similarity metric includes:

* **Ground Truth**: The expected output for the given input.
* **Response**: The model's actual output.

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#### **How to Run the Metric**

1. **Access the Dataset**
   * Navigate to the dataset where you want to evaluate responses.
   * Click on the **Evaluate** button.
2. **Select the Metric**
   * Choose **Cosine Similarity-Alteryx** from the list of metrics.
   * You can rename the metric for clarity, if needed.
3. **Choose the Evaluation Type**
   * Select the component type you wish to evaluate:
     * **LLM**: Evaluate spans related to language model outputs.
     * **Agent**: Evaluate agent-level interactions.
     * **Tool**: Evaluate tool-related outputs.
4. **Define the Schema**
   * Specify the **span name** you want to evaluate.
   * Choose the parameter for evaluation, such as:
     * `input`
     * `output`
     * `ground truth`
5. **Configure the Model**
   * Select the **model configuration** to be used for evaluation.
6. **Set Passing Criteria**
   * Define the pass/fail **threshold** for the metric.
7. **Run the Metric**
   * Click on **Run** to start the evaluation process.

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#### **When to Run the Cosine Similarity Metric?**

* **To Evaluate Response Accuracy**:\
  Use this metric when you want to determine how closely the system's response matches the expected ground truth.
* **Span-Level Analysis**:\
  Cosine Similarity is particularly useful for evaluating specific spans or segments of a trace where text similarity is critical.
* **For Model Comparison**:\
  Employ this metric to compare outputs from different models or configurations and identify which aligns better with the ground truth.