format the output

telcom_core.py

@@ -185,14 +185,38 @@ def query_graph_rag_qa(graph_rag_index,query,search_level):
 
 
 
+def parse_evaluation_regex(xml_text):
+    # Define regex patterns for metrics, justifications, and scores
+    metric_pattern = re.compile(r'<metric name="(.+?)">')
+    justification_pattern = re.compile(r'<justification>\s*(.+?)\s*</justification>', re.DOTALL)
+    score_pattern = re.compile(r'<score>(\d+)</score>')
+
+    # Find all matches for metrics, justifications, and scores
+    metrics = metric_pattern.findall(xml_text)
+    justifications = justification_pattern.findall(xml_text)
+    scores = score_pattern.findall(xml_text)
+
+    # Initialize an empty string to store the parsed text
+    parsed_text = ""
+
+    # Iterate through each metric and its corresponding justification and score
+    for metric, justification, score in zip(metrics, justifications, scores):
+        parsed_text += f"<h2> {metric} </h2> <br>"
+        parsed_text += f"<h3>Score: {score} </h3> <br>"
+        parsed_text += f"<b>Justification:</b> {justification.strip()} <br>"
+
+    return parsed_text
+
 def evaluate_llm(query,response):
     """
     Evaluates the provided query and response using a PromptTemplate and returns the completion from OpenAI.
     """
     data = {'QUERY': query, 'RESPONSE': response}
     prompt = PromptTemplate(llm_eval_prompt).format(**data)
-    return OpenAI().complete(prompt)
+    eval_text = OpenAI().complete(prompt)
+    return eval_text.text
 
+    
 def plot_full_kg(kg_plot_path):
     """Plot the full knowledge graph and return the HTML representation."""
     # return HTML(filename=kg_plot_path)