add: docs for utils

docs/utils.md

@@ -0,0 +1,3 @@
+# Utils
+
+::: guardrails_genie.utils

guardrails_genie/utils.py

@@ -16,6 +16,20 @@ def get_markdown_from_pdf_url(url: str) -> str:
 
 
 class EvaluationCallManager:
+    """
+    Manages the evaluation calls for a specific project and entity in Weave.
+
+    This class is responsible for initializing and managing evaluation calls associated with a 
+    specific project and entity. It provides functionality to collect guardrail guard calls 
+    from evaluation predictions and scores, and render these calls into a structured format 
+    suitable for display in Streamlit.
+
+    Args:
+        entity (str): The entity name.
+        project (str): The project name.
+        call_id (str): The call id.
+        max_count (int): The maximum number of guardrail guard calls to collect from the evaluation.
+    """
     def __init__(self, entity: str, project: str, call_id: str, max_count: int = 10):
         self.base_call = weave.init(f"{entity}/{project}").get_call(call_id=call_id)
         self.max_count = max_count
@@ -23,6 +37,21 @@ class EvaluationCallManager:
         self.call_list = []
 
     def collect_guardrail_guard_calls_from_eval(self):
+        """
+        Collects guardrail guard calls from evaluation predictions and scores.
+
+        This function iterates through the children calls of the base evaluation call, 
+        extracting relevant guardrail guard calls and their associated scores. It stops 
+        collecting calls if it encounters an "Evaluation.summarize" operation or if the 
+        maximum count of guardrail guard calls is reached. The collected calls are stored 
+        in a list of dictionaries, each containing the input prompt, outputs, and score.
+
+        Returns:
+            list: A list of dictionaries, each containing:
+                - input_prompt (str): The input prompt for the guard call.
+                - outputs (dict): The outputs of the guard call.
+                - score (dict): The score of the guard call.
+        """
         guard_calls, count = [], 0
         for eval_predict_and_score_call in self.base_call.children():
             if "Evaluation.summarize" in eval_predict_and_score_call._op_name:
@@ -44,6 +73,23 @@ class EvaluationCallManager:
         return guard_calls
 
     def render_calls_to_streamlit(self):
+        """
+        Renders the collected guardrail guard calls into a pandas DataFrame suitable for
+        display in Streamlit.
+
+        This function processes the collected guardrail guard calls stored in `self.call_list` and 
+        organizes them into a dictionary format that can be easily converted into a pandas DataFrame. 
+        The DataFrame contains columns for the input prompts, the safety status of the outputs, and 
+        the correctness of the predictions for each guardrail.
+
+        The structure of the DataFrame is as follows:
+        - The first column contains the input prompts.
+        - Subsequent columns contain the safety status and prediction correctness for each guardrail.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing the input prompts, safety status, and prediction 
+                          correctness for each guardrail.
+        """
         dataframe = {
             "input_prompt": [
                 call["input_prompt"] for call in self.call_list[0]["calls"]

mkdocs.yml

@@ -59,5 +59,6 @@ extra_javascript:
 
 nav:
   - Home: 'index.md'
+  - Utils: 'utils.md'
 
 repo_url: https://github.com/soumik12345/guardrails-genie