Update app.py

app.py

@@ -153,4 +153,70 @@ else:
 
 # Add a footer
 st.markdown("---")
-st.markdown("By AI Planet")
+st.markdown("By AI Planet")
+
+from trulens_eval import Tru
+from trulens_eval.tru_custom_app import instrument
+from trulens_eval import Feedback, Select
+from trulens_eval.feedback.provider.openai import OpenAI
+import numpy as np
+from trulens_eval import TruCustomApp
+tru = Tru()
+tru.reset_database()
+
+def append_to_csv(user_query, provider, retriever, csv_path='relevance_scores.csv'):
+
+    # provider = OpenAI(model_engine="gpt-4o")
+    provider = provider
+    # Define a groundedness feedback function
+    f_groundedness = (
+        Feedback(provider.groundedness_measure_with_cot_reasons, name = "Groundedness")
+        .on(Select.RecordCalls.retrieve.rets.collect())
+        .on_output()
+    )
+    # Question/answer relevance between overall question and answer.
+    f_answer_relevance = (
+        Feedback(provider.relevance_with_cot_reasons, name = "Answer Relevance")
+        .on_input()
+        .on_output()
+    )
+    
+    # Context relevance between question and each context chunk.
+    f_context_relevance = (
+        Feedback(provider.context_relevance_with_cot_reasons, name = "Context Relevance")
+        .on_input()
+        .on(Select.RecordCalls.retrieve.rets[:])
+        .aggregate(np.mean) # choose a different aggregation method if you wish
+    )
+
+    tru_rag = TruCustomApp(retriever,
+        app_id = 'RAG v1',
+        feedbacks = [f_groundedness, f_answer_relevance, f_context_relevance])
+    
+    with tru_rag as recording:
+        llm_response = retriever.get_relevant_documents(user_query)
+        
+    rec = recording.get()
+    feedback_results = rec.wait_for_feedback_results()
+    
+    # Collect feedback results
+    feedback_scores = {feedback.name: feedback_result.result for feedback, feedback_result in feedback_results.items()}
+
+    data = {
+        "User Query": [user_query],
+        "LLM Response": [llm_response],
+        "Answer Relevance": [feedback_scores.get("Answer Relevance")],
+        "Context Relevance": [feedback_scores.get("Context Relevance")],
+        "Groundedness": [feedback_scores.get("Groundedness")]
+    }
+    df = pd.DataFrame(data)
+    
+    # Check if the CSV file exists
+    if not os.path.isfile(csv_path):
+        df.to_csv(csv_path, index=False)
+    else:
+        df.to_csv(csv_path, mode='a', header=False, index=False)
+
+
+
+append_to_csv(prompt,OpenAI(model_engine="gpt-4o"),retriever)