Update app.py

app.py

@@ -200,11 +200,57 @@ def search_glossary(query):
     # Dropdown for database selection
     database_options = ['Semantic Search', 'Arxiv Search - Latest - (EXPERIMENTAL)']
     database_choice = st.selectbox('📚 Select Database', options=database_options, index=0)
+
+
     
     # Run Button with Emoji
     if st.button("🚀 Run"):
-        # ArXiv searcher - Paper Summary & Ask LLM
+        
+        # 🕵️‍♂️ 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(
+            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"
+        )
+        st.code(response2, language="python", *, line_numbers=True, wrap_lines=False)
+
+        #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"
+        # ArXiv searcher ~-<>-~ Paper References - Update with RAG
+        # 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.code(result, language="python", *, line_numbers=True, wrap_lines=False)
+
+        
+        # ArXiv searcher ~-<>-~ Paper References - Update with RAG
+        # client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+        response1 = client.predict(
+            query,	
+            10,	
+            "Semantic Search - up to 10 Mar 2024",	# Search Source Dropdown component
+            "mistralai/Mixtral-8x7B-Instruct-v0.1",	# LLM Model Dropdown component
+            api_name="/update_with_rag_md"
+        )
+        st.code(response1, language="python", *, line_numbers=True, wrap_lines=False)
+
+        
+        # ArXiv searcher - Paper Summary & Ask LLM
+        # client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
         
         response1 = client.predict(
             message=query,
@@ -234,6 +280,8 @@ def search_glossary(query):
         # Save both responses to Cosmos DB
         save_to_cosmos_db(query, response1, response2)
 
+
+        
     
 
 # 🔍 Search Glossary function