Update app.py

app.py

@@ -40,16 +40,18 @@ def respond(
 
     print(system_message)
     
-#    retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": score})
-    retriever = vector_db.as_retriever(search_type="mmr")
+    retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": score})
+#    retriever = vector_db.as_retriever(search_type="mmr")
     documents = retriever.invoke(message)
 
     spacer = " \n"
     context = ""
+
+    print(message)
     print(len(documents))
 
     for doc in documents:
-        case_text = df[df["case_url"] == doc.metadata["case_url"]].case_text.values[0]
+        #case_text = df[df["case_url"] == doc.metadata["case_url"]].case_text.values[0]
 
         context += "#######" + spacer
         context += "# Case number: " + doc.metadata["case_nb"] + spacer
@@ -58,6 +60,9 @@ def respond(
         context += "# Case url: " + doc.metadata["case_url"] + spacer
         context += "# Case text: " + doc.page_content + spacer
         #context += "Case text: " + case_text[:8000] + spacer
+
+        print("# Case number: " + doc.metadata["case_nb"] + spacer)
+        print("# Case url: " + doc.metadata["case_url"] + spacer)
     
     
     message = f"""
@@ -74,8 +79,6 @@ Otherwise, if relevant cases were found, start by summarizing these cases in the
 Always answer the user using the language used in his question: {message}
     """
     
-    print(message)
-
 #    for val in history:
 #        if val[0]:
 #            messages.append({"role": "user", "content": val[0]})