Update app.py

app.py

@@ -160,15 +160,31 @@ def search_glossary(query):
     # 🕵️‍♂️ Searching the glossary for: query
     all_results = ""
     st.markdown(f"- {query}")
-    
+
+    #database_choice Literal['Semantic Search', 'Arxiv Search - Latest - (EXPERIMENTAL)'] Default: "Semantic Search"
+    #llm_model_picked Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] Default: "mistralai/Mistral-7B-Instruct-v0.2"
+
     # 🔍 Run 1 - ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
     client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
     response2 = client.predict(
-        query,	# str  in 'parameter_13' Textbox component
-        "google/gemma-7b-it",	# LLM Model Dropdown component
-        True,	# Stream output Checkbox component
-        api_name="/ask_llm"
+        message=query,	# str  in 'parameter_13' Textbox component
+        llm_results_use=5,
+		database_choice="Semantic Search",
+		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
+        api_name="/update_with_rag_md"
+    )
+
+    #llm_model_picked Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] Default: "mistralai/Mistral-7B-Instruct-v0.2"
+
+
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    result = client.predict(
+		prompt=query,
+		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
+		stream_outputs=True,
+		api_name="/ask_llm"
     )
+
     st.write('🔍 Run of Multi-Agent System Paper Summary Spec is Complete')
     st.markdown(response2)