ea4all-gradio-agents-mcp-hackathon-ui-retrofit

ea4all/ea4all_mcp.py

@@ -65,19 +65,14 @@
 ## Gradio Agents MCP Hackathon: retrofit to expose EA4ALL Agentic System Agents only
 from langchain.callbacks.tracers import LangChainTracer
 from langchain.callbacks.tracers.langchain import wait_for_all_tracers
-from langchain_community.document_loaders import ConfluenceLoader
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableConfig
 
 from ea4all.src.shared.configuration import BaseConfiguration, APM_MOCK_QNA, PMO_MOCK_QNA
-from ea4all.src.shared import vectorstore
 from ea4all.src.ea4all_gra.configuration import AgentConfiguration as gra
-from ea4all.src.ea4all_indexer.configuration import IndexConfiguration
-import ea4all.src.ea4all_apm.graph as e4a
 import ea4all.src.ea4all_vqa.graph as e4v
 import ea4all.src.ea4all_gra.graph as e4t
 import ea4all.src.shared.utils as e4u
-from ea4all.src.ea4all_indexer.graph import indexer_graph
 from ea4all.src.graph import super_graph
 #from ea4all.src.pmo_crew.crew_runner import run_pmo_crew
 

ea4all/utils/utils.py

@@ -2,7 +2,16 @@ import ea4all.src.ea4all_apm.graph as e4a
 import ea4all.src.ea4all_vqa.graph as e4v
 import ea4all.src.ea4all_gra.graph as e4t
 import ea4all.src.shared.utils as e4u
+from ea4all.src.ea4all_gra.configuration import AgentConfiguration as gra
 from ea4all.src.shared import vectorstore
+from ea4all.src.shared.configuration import BaseConfiguration
+from ea4all.src.ea4all_indexer.configuration import IndexConfiguration
+from ea4all.src.ea4all_indexer.graph import indexer_graph
+
+
+from langchain_community.document_loaders import ConfluenceLoader
+from langchain_core.messages import ChatMessage
+from langsmith import Client
 
 import uuid
 import os
@@ -85,13 +94,17 @@ def get_user_feedback(evt: gr.SelectData, request:gr.Request):
 #Set initial state of apm, llm and capture user-ip
 async def ea4all_agent_init(request:gr.Request):
 
+    agentic_qna_desc="""Hi,
+        improve effieciency, knowledge sharing, and get valuable insights from your IT landscape using natural language.
+        As an Enterprise Architect Agentic System I can answer questions related to Enterprise Architecture, Technology, plus the following IT Landscape sample dataset: """
+
     #capture user IP address
     #ea4all_user = e4u.get_user_identification(request)
     gr.Info("Thank you for connecting! I'd love to hear your feedback! Thumbs up or Thumbs down. LinkedIn comment.")
 
     # Set initial landscape vectorstore
 
-    await indexer_graph.ainvoke(input={"docs":[]}, config=config)
+    #await indexer_graph.ainvoke(input={"docs":[]}, config=config)
 
     #set chatbot description w/ user apm columns
     df = vectorstore.apm_dataframe_loader(e4u._join_paths(BaseConfiguration.ea4all_store, IndexConfiguration.apm_catalogue))
@@ -136,18 +149,6 @@ def load_dbr(file):
 def unload_dbr():
     return gr.TextArea(visible=False)
 
-def on_dbrtext(file):
-    if file:
-        return gr.TextArea(visible=True)
-    return gr.TextArea(visible=False)
-
-#Upload & clear business requirement
-def load_dbr(file):
-    return file.decode()
-
-def unload_dbr():
-    return gr.TextArea(visible=False)
-
 #vqa_chatbot (ChatInterface -> Chatbot)
 def add_message(message, history):
     if message["text"] is not None: