Update app.py

app.py

@@ -329,21 +329,17 @@ textarea::placeholder {
 """
 
 def process_question(question: str) -> Iterator[str]:
-    """
-    Process the question and return a response generator for streaming.
-    """
     if question in question_cache:
-        yield question_cache[question][0]
+        response, docs = question_cache[question]
+        yield response + "\nSources:\n" + "\n".join([doc.page_content for doc in docs])
         return
-
+        
     relevant_docs = retriever(question)
     context = "\n".join([doc.page_content for doc in relevant_docs])
-
     prompt = prompt_template.format_messages(
         context=context,
         question=question
     )
-
     full_response = ""
     try:
         for chunk in llm.stream(prompt):
@@ -351,14 +347,49 @@ def process_question(question: str) -> Iterator[str]:
                 current_chunk = chunk
             else:
                 current_chunk = chunk.content
-
             full_response += current_chunk
-            yield full_response  # Send the updated response in streaming
-
-        question_cache[question] = (full_response, context)
+            # sources = "\n".join(set([doc.metadata.get("source") for doc in relevant_docs]))
+            # sources = [os.path.splitext(source[1])[0] for source in sources] 
+            # yield full_response + "\n\n\nالمصادر المحتملة :\n" + "".join(sources)
+            sources = [doc.metadata.get("source") for doc in relevant_docs]
+            sources = list(set([os.path.splitext(source)[0] for source in sources]))
+            yield full_response + "\n\n\nالمصادر المحتملة :\n" + "\n".join(sources)
+            # yield full_response + "\n\n\nالمصادر المحتملة:\n" + "\n".join([doc.metadata.get("source") for doc in relevant_docs])
+        question_cache[question] = (full_response, relevant_docs)
     except Exception as e:
         yield f"Erreur lors du traitement : {str(e)}"
 
+# def process_question(question: str) -> Iterator[str]:
+#     """
+#     Process the question and return a response generator for streaming.
+#     """
+#     if question in question_cache:
+#         yield question_cache[question][0]
+#         return
+
+#     relevant_docs = retriever(question)
+#     context = "\n".join([doc.page_content for doc in relevant_docs])
+
+#     prompt = prompt_template.format_messages(
+#         context=context,
+#         question=question
+#     )
+
+#     full_response = ""
+#     try:
+#         for chunk in llm.stream(prompt):
+#             if isinstance(chunk, str):
+#                 current_chunk = chunk
+#             else:
+#                 current_chunk = chunk.content
+
+#             full_response += current_chunk
+#             yield full_response  # Send the updated response in streaming
+
+#         question_cache[question] = (full_response, context)
+#     except Exception as e:
+#         yield f"Erreur lors du traitement : {str(e)}"
+
 # def process_question(question: str) -> Iterator[str]:
 #     """
 #     Process the question and return a response generator for streaming, including sources.