Update app.py

intfloat/multilingual-e5-large-instruct

app.py

@@ -21,9 +21,9 @@ client = InferenceClient(model)
 
 folder = snapshot_download(repo_id="umaiku/faiss_index", repo_type="dataset", local_dir=os.getcwd())
 
-embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-small")
+embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large-instruct")
 
-vector_db = FAISS.load_local("faiss_index_full", embeddings, allow_dangerous_deserialization=True)
+vector_db = FAISS.load_local("faiss_index_large", embeddings, allow_dangerous_deserialization=True)
 
 df = pd.read_csv("faiss_index/bger_cedh_db 1954-2024.csv")
 
@@ -110,7 +110,7 @@ demo = gr.ChatInterface(
     additional_inputs=[
         gr.Textbox(value="You are an assistant in Swiss Jurisprudence cases.", label="System message"),
         gr.Slider(minimum=1, maximum=24000, value=5000, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
@@ -118,7 +118,7 @@ demo = gr.ChatInterface(
             step=0.05,
             label="Top-p (nucleus sampling)",
         ),
-        gr.Slider(minimum=0, maximum=1, value=0.7, step=0.1, label="Score Threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.75, step=0.05, label="Score Threshold"),
     ],
     description="# 📜 ALexI: Artificial Legal Intelligence for Swiss Jurisprudence",
 )