Allow users to add LLM within the UI (#6)

* Rename AzureChatOpenAI to LCAzureChatOpenAI
* Provide vanilla ChatOpenAI and AzureChatOpenAI
* Remove the highest accuracy, lowest cost criteria

These criteria are unnecessary. The users, not pipeline creators, should choose
which LLM to use. Furthermore, it's cumbersome to input this information,
really degrades user experience.

* Remove the LLM selection in simple reasoning pipeline
* Provide a dedicated stream method to generate the output
* Return placeholder message to chat if the text is empty

libs/ktem/ktem/pages/admin/__init__.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from ktem.app import BasePage
 from ktem.db.models import User, engine
+from ktem.llms.ui import LLMManagement
 from sqlmodel import Session, select
 
 from .user import UserManagement
@@ -16,6 +17,9 @@ class AdminPage(BasePage):
             with gr.Tab("User Management", visible=False) as self.user_management_tab:
                 self.user_management = UserManagement(self._app)
 
+        with gr.Tab("LLM Management") as self.llm_management_tab:
+            self.llm_management = LLMManagement(self._app)
+
     def on_subscribe_public_events(self):
         if self._app.f_user_management:
             self._app.subscribe_event(

libs/ktem/ktem/pages/chat/__init__.py

@@ -9,6 +9,8 @@ from ktem.db.models import Conversation, engine
 from sqlmodel import Session, select
 from theflow.settings import settings as flowsettings
 
+from kotaemon.base import Document
+
 from .chat_panel import ChatPanel
 from .chat_suggestion import ChatSuggestion
 from .common import STATE
@@ -189,6 +191,7 @@ class ChatPage(BasePage):
                 self.chat_control.conversation_rn,
                 self.chat_panel.chatbot,
                 self.info_panel,
+                self.chat_state,
             ]
             + self._indices_input,
             show_progress="hidden",
@@ -220,6 +223,7 @@ class ChatPage(BasePage):
                 self.chat_control.conversation_rn,
                 self.chat_panel.chatbot,
                 self.info_panel,
+                self.chat_state,
             ]
             + self._indices_input,
             show_progress="hidden",
@@ -392,7 +396,7 @@ class ChatPage(BasePage):
 
         return pipeline, reasoning_state
 
-    async def chat_fn(self, conversation_id, chat_history, settings, state, *selecteds):
+    def chat_fn(self, conversation_id, chat_history, settings, state, *selecteds):
         """Chat function"""
         chat_input = chat_history[-1][0]
         chat_history = chat_history[:-1]
@@ -403,52 +407,43 @@ class ChatPage(BasePage):
         pipeline, reasoning_state = self.create_pipeline(settings, state, *selecteds)
         pipeline.set_output_queue(queue)
 
-        asyncio.create_task(pipeline(chat_input, conversation_id, chat_history))
         text, refs = "", ""
-
-        len_ref = -1  # for logging purpose
         msg_placeholder = getattr(
             flowsettings, "KH_CHAT_MSG_PLACEHOLDER", "Thinking ..."
         )
-
         print(msg_placeholder)
-        while True:
-            try:
-                response = queue.get_nowait()
-            except Exception:
-                state[pipeline.get_info()["id"]] = reasoning_state["pipeline"]
-                yield chat_history + [
-                    (chat_input, text or msg_placeholder)
-                ], refs, state
+        yield chat_history + [(chat_input, text or msg_placeholder)], refs, state
+
+        len_ref = -1  # for logging purpose
+
+        for response in pipeline.stream(chat_input, conversation_id, chat_history):
+
+            if not isinstance(response, Document):
                 continue
 
-            if response is None:
-                queue.task_done()
-                print("Chat completed")
-                break
+            if response.channel is None:
+                continue
 
-            if "output" in response:
-                if response["output"] is None:
+            if response.channel == "chat":
+                if response.content is None:
                     text = ""
                 else:
-                    text += response["output"]
+                    text += response.content
 
-            if "evidence" in response:
-                if response["evidence"] is None:
+            if response.channel == "info":
+                if response.content is None:
                     refs = ""
                 else:
-                    refs += response["evidence"]
+                    refs += response.content
 
             if len(refs) > len_ref:
                 print(f"Len refs: {len(refs)}")
                 len_ref = len(refs)
 
-        state[pipeline.get_info()["id"]] = reasoning_state["pipeline"]
-        yield chat_history + [(chat_input, text)], refs, state
+            state[pipeline.get_info()["id"]] = reasoning_state["pipeline"]
+            yield chat_history + [(chat_input, text or msg_placeholder)], refs, state
 
-    async def regen_fn(
-        self, conversation_id, chat_history, settings, state, *selecteds
-    ):
+    def regen_fn(self, conversation_id, chat_history, settings, state, *selecteds):
         """Regen function"""
         if not chat_history:
             gr.Warning("Empty chat")
@@ -456,12 +451,11 @@ class ChatPage(BasePage):
             return
 
         state["app"]["regen"] = True
-        async for chat, refs, state in self.chat_fn(
+        for chat, refs, state in self.chat_fn(
             conversation_id, chat_history, settings, state, *selecteds
         ):
             new_state = deepcopy(state)
             new_state["app"]["regen"] = False
             yield chat, refs, new_state
-        else:
-            state["app"]["regen"] = False
-            yield chat_history, "", state
+
+        state["app"]["regen"] = False