Improve LLMs and Embedding models resources experience (#21)

* Fix inconsistent default values
* Disallow LLM's empty name. Handle LLM creation error on UI

libs/kotaemon/kotaemon/embeddings/openai.py

@@ -19,7 +19,7 @@ class BaseOpenAIEmbeddings(BaseEmbeddings):
 
     _dependencies = ["openai"]
 
-    api_key: str = Param(help="API key", required=True)
+    api_key: str = Param(None, help="API key", required=True)
     timeout: Optional[float] = Param(None, help="Timeout for the API request.")
     max_retries: Optional[int] = Param(
         None, help="Maximum number of retries for the API request."
@@ -88,6 +88,7 @@ class OpenAIEmbeddings(BaseOpenAIEmbeddings):
     base_url: Optional[str] = Param(None, help="OpenAI base URL")
     organization: Optional[str] = Param(None, help="OpenAI organization")
     model: str = Param(
+        None,
         help=(
             "ID of the model to use. You can go to [Model overview](https://platform."
             "openai.com/docs/models/overview) to see the available models."
@@ -131,14 +132,16 @@ class OpenAIEmbeddings(BaseOpenAIEmbeddings):
 
 class AzureOpenAIEmbeddings(BaseOpenAIEmbeddings):
     azure_endpoint: str = Param(
+        None,
         help=(
             "HTTPS endpoint for the Azure OpenAI model. The azure_endpoint, "
             "azure_deployment, and api_version parameters are used to construct "
             "the full URL for the Azure OpenAI model."
-        )
+        ),
+        required=True,
     )
-    azure_deployment: str = Param(help="Azure deployment name", required=True)
-    api_version: str = Param(help="Azure model version", required=True)
+    azure_deployment: str = Param(None, help="Azure deployment name", required=True)
+    api_version: str = Param(None, help="Azure model version", required=True)
     azure_ad_token: Optional[str] = Param(None, help="Azure AD token")
     azure_ad_token_provider: Optional[str] = Param(None, help="Azure AD token provider")
 

libs/ktem/ktem/llms/manager.py

@@ -140,8 +140,17 @@ class LLMManager:
 
     def add(self, name: str, spec: dict, default: bool):
         """Add a new model to the pool"""
+        if not name:
+            raise ValueError("Name must not be empty")
+
         try:
             with Session(engine) as session:
+
+                if default:
+                    # turn all models to non-default
+                    session.query(LLMTable).update({"default": False})
+                    session.commit()
+
                 item = LLMTable(name=name, spec=spec, default=default)
                 session.add(item)
                 session.commit()
@@ -164,6 +173,9 @@ class LLMManager:
 
     def update(self, name: str, spec: dict, default: bool):
         """Update a model in the pool"""
+        if not name:
+            raise ValueError("Name must not be empty")
+
         try:
             with Session(engine) as session:
 

libs/ktem/ktem/llms/ui.py

@@ -140,7 +140,7 @@ class LLMManagement(BasePage):
             self.create_llm,
             inputs=[self.name, self.llm_choices, self.spec, self.default],
             outputs=None,
-        ).then(self.list_llms, inputs=None, outputs=[self.llm_list],).then(
+        ).success(self.list_llms, inputs=None, outputs=[self.llm_list]).success(
             lambda: ("", None, "", False, self.spec_desc_default),
             outputs=[
                 self.name,
@@ -229,7 +229,7 @@ class LLMManagement(BasePage):
             llms.add(name, spec=spec, default=default)
             gr.Info(f"LLM {name} created successfully")
         except Exception as e:
-            gr.Error(f"Failed to create LLM {name}: {e}")
+            raise gr.Error(f"Failed to create LLM {name}: {e}")
 
     def list_llms(self):
         """List the LLMs"""