Replace memgpt run flags error with warning + remove custom embeddi…

…ng endpoint option + add agent create time (#364)
letta-ai · Nov 9, 2023 · 7d74aad · 7d74aad
1 parent 350e4af
commit 7d74aad
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Showing 2 changed files with 33 additions and 14 deletions.
diff --git a/memgpt/cli/cli.py b/memgpt/cli/cli.py
@@ -107,11 +107,18 @@ def run(
         # persistence_manager = LocalStateManager(agent_config).load() # TODO: implement load
         # TODO: load prior agent state
         if persona and persona != agent_config.persona:
-            raise ValueError(f"Cannot override {agent_config.name} existing persona {agent_config.persona} with {persona}")
+            typer.secho(f"Warning: Overriding existing persona {agent_config.persona} with {persona}", fg=typer.colors.YELLOW)
+            agent_config.persona = persona
+            # raise ValueError(f"Cannot override {agent_config.name} existing persona {agent_config.persona} with {persona}")
         if human and human != agent_config.human:
-            raise ValueError(f"Cannot override {agent_config.name} existing human {agent_config.human} with {human}")
+            typer.secho(f"Warning: Overriding existing human {agent_config.human} with {human}", fg=typer.colors.YELLOW)
+            agent_config.human = human
+            # raise ValueError(f"Cannot override {agent_config.name} existing human {agent_config.human} with {human}")
         if model and model != agent_config.model:
-            raise ValueError(f"Cannot override {agent_config.name} existing model {agent_config.model} with {model}")
+            typer.secho(f"Warning: Overriding existing model {agent_config.model} with {model}", fg=typer.colors.YELLOW)
+            agent_config.model = model
+            # raise ValueError(f"Cannot override {agent_config.name} existing model {agent_config.model} with {model}")
+        agent_config.save()
 
         # load existing agent
         memgpt_agent = AgentAsync.load_agent(memgpt.interface, agent_config)

diff --git a/memgpt/cli/cli_config.py b/memgpt/cli/cli_config.py
@@ -66,21 +66,24 @@ def configure():
     # TODO: configure local model
 
     # configure provider
-    use_local = not use_openai and os.getenv("OPENAI_API_BASE")
-    endpoint_options = []
+    model_endpoint_options = []
     if os.getenv("OPENAI_API_BASE") is not None:
-        endpoint_options.append(os.getenv("OPENAI_API_BASE"))
+        model_endpoint_options.append(os.getenv("OPENAI_API_BASE"))
     if use_azure:
-        endpoint_options += ["azure"]
+        model_endpoint_options += ["azure"]
     if use_openai:
-        endpoint_options += ["openai"]
+        model_endpoint_options += ["openai"]
 
-    assert len(endpoint_options) > 0, "No endpoints found. Please enable OpenAI, Azure, or set OPENAI_API_BASE."
-    default_endpoint = questionary.select("Select default inference endpoint:", endpoint_options).ask()
+    assert len(model_endpoint_options) > 0, "No endpoints found. Please enable OpenAI, Azure, or set OPENAI_API_BASE."
+    default_endpoint = questionary.select("Select default inference endpoint:", model_endpoint_options).ask()
 
     # configure embedding provider
-    endpoint_options.append("local")  # can compute embeddings locally
-    default_embedding_endpoint = questionary.select("Select default embedding endpoint:", endpoint_options).ask()
+    embedding_endpoint_options = ["local"]  # cannot configure custom endpoint (too confusing)
+    if use_azure:
+        model_endpoint_options += ["azure"]
+    if use_openai:
+        model_endpoint_options += ["openai"]
+    default_embedding_endpoint = questionary.select("Select default embedding endpoint:", embedding_endpoint_options).ask()
 
     # configure embedding dimentions
     default_embedding_dim = 1536
@@ -159,11 +162,20 @@ def list(option: str):
     if option == "agents":
         """List all agents"""
         table = PrettyTable()
-        table.field_names = ["Name", "Model", "Persona", "Human", "Data Source"]
+        table.field_names = ["Name", "Model", "Persona", "Human", "Data Source", "Create Time"]
         for agent_file in utils.list_agent_config_files():
             agent_name = os.path.basename(agent_file).replace(".json", "")
             agent_config = AgentConfig.load(agent_name)
-            table.add_row([agent_name, agent_config.model, agent_config.persona, agent_config.human, ",".join(agent_config.data_sources)])
+            table.add_row(
+                [
+                    agent_name,
+                    agent_config.model,
+                    agent_config.persona,
+                    agent_config.human,
+                    ",".join(agent_config.data_sources),
+                    agent_config.create_time,
+                ]
+            )
         print(table)
     elif option == "humans":
         """List all humans"""