Update main.py

main.py

@@ -29,7 +29,19 @@ import datetime
 
 os.environ["TOKENIZERS_PARALLELISM"] = os.environ["TOKENIZERS_PARALLELISM"]
 os.environ['ANTHROPIC_API_KEY'] = os.environ['ANTHROPIC_API_KEY']
-        
+def retriever_to_cache():
+    index_name = os.environ['PINECONE_INDEX_NAME']
+    embeddings = HuggingFaceEmbeddings()
+    pinecone.init(
+        api_key=os.environ['PINECONE_API_KEY'],
+        environment=os.environ['PINECONE_ENVIRONMENT']
+    )
+    vectorstore = Pinecone.from_existing_index(
+        index_name=index_name, embedding=embeddings
+    )
+    retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 60,"filter": {'categorie': {'$eq': 'OF'}}})
+    return retriever
+
 @cl.author_rename
 def rename(orig_author: str):
     rename_dict = {"ConversationalRetrievalChain": "💬 Assistant conversationnel", "Retriever": "Agent conversationnel", "StuffDocumentsChain": "Chaîne de documents", "LLMChain": "Agent", "ChatAnthropic": "🤖 IA"}
@@ -78,20 +90,6 @@ async def on_action(action):
         cl.Action(name="close_button", value="closed", label="Fermer", description="Fermer le volet d'information!")
     ]
     await cl.Message(author="🌐🌐🌐",content="Fermer le panneau d'information", actions=others).send()
-
-@cl.cache
-def retriever_to_cache():
-    index_name = os.environ['PINECONE_INDEX_NAME']
-    embeddings = HuggingFaceEmbeddings()
-    pinecone.init(
-        api_key=os.environ['PINECONE_API_KEY'],
-        environment=os.environ['PINECONE_ENVIRONMENT']
-    )
-    vectorstore = Pinecone.from_existing_index(
-        index_name=index_name, embedding=embeddings
-    )
-    retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 60,"filter": {'categorie': {'$eq': 'OF'}}})
-    return retriever
     
 @cl.cache
 def to_cache(file):