Update main.py

main.py

@@ -48,13 +48,7 @@ async def VectorDatabase(categorie):
 @cl.step(type="retrieval")
 async def Retriever(categorie):
     vectorstore = await VectorDatabase(categorie)
-    if categorie == "bibliographie-OPP-DGDIN":
-        retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 150,"filter": {'categorie': {'$eq': categorie}}})
-    elif categorie == "year":
-        retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 6,"filter": {'year': {'$gte': 2019}}})
-    elif categorie == "skills":
-        retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 200,"filter": {'file': {'$eq': 'competences-master-CFA.csv'}}})
-    elif categorie == "videosTC":
+    if categorie == "videosTC":
         retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .7, "k": 200,"filter": {"title": {"$eq": "videos-confinement-timeline"}}})
     return retriever
 
@@ -128,7 +122,7 @@ async def set_starters():
 async def on_message(message: cl.Message):
     await cl.Message(f"> EVENTIA").send()
     model = await LLModel()
-    retriever = await Retriever(cl.user_session.get("selectRequest"))
+    retriever = await Retriever("videosTC")
     ########## Chain with streaming ##########
     message_history = ChatMessageHistory()
     memory = ConversationBufferMemory(memory_key="chat_history",output_key="answer",chat_memory=message_history,return_messages=True)
@@ -174,24 +168,6 @@ async def on_message(message: cl.Message):
     
     #search = vectorstore.similarity_search(message.content,k=50, filter={"categorie": {"$eq": "bibliographie-OPP-DGDIN"}})
     search = await Search(message.content, "videosTC")
-
-    #os.environ["GOOGLE_CSE_ID"] = os.getenv('GOOGLE_CSE_ID')
-    #os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')
-    #searchAPI = GoogleSearchAPIWrapper()
-    #def top5_results(query):
-    #    return searchAPI.results(query, 5)
-
-    #tool = Tool(
-    #    name="Google Search Snippets",
-    #    description="Search Google for recent results.",
-    #    func=top5_results,
-    #)
-    #query = str(message.content)
-    #ref_text = tool.run(query)
-    #if 'Result' not in ref_text[0].keys():
-    #    print(ref_text)
-    #else:
-    #    print('None')
     
     sources = [
         cl.Text(name="Sources", content=search[0], display="inline")