Update app.py

app.py

@@ -153,4 +153,72 @@ 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)