fix: CLI conveniences (add-on to #674) (#675)

* for openai, check for key and if missing allow user to pass it, for azure, throw error if the key isn't present

* correct prior checking of azure to be more strict, added similar checks at the embedding endpoint config stage

* forgot to override value in config before saving

* clean up the valuerrors from missing keys so that no stacktrace gets printed, make success text green to match others

memgpt/cli/cli_config.py

@@ -52,13 +52,36 @@ def configure_llm_endpoint(config: MemGPTConfig):
 
     # set: model_endpoint_type, model_endpoint
     if provider == "openai":
+        # check for key
+        if config.openai_key is None:
+            # allow key to get pulled from env vars
+            openai_api_key = os.getenv("OPENAI_API_KEY", None)
+            if openai_api_key is None:
+                # if we still can't find it, ask for it as input
+                while openai_api_key is None or len(openai_api_key) == 0:
+                    # Ask for API key as input
+                    openai_api_key = questionary.text(
+                        "Enter your OpenAI API key (starts with 'sk-', see https://platform.openai.com/api-keys):"
+                    ).ask()
+                config.openai_key = openai_api_key
+                config.save()
+
         model_endpoint_type = "openai"
         model_endpoint = "https://api.openai.com/v1"
         model_endpoint = questionary.text("Override default endpoint:", default=model_endpoint).ask()
         provider = "openai"
+
     elif provider == "azure":
+        # check for necessary vars
+        azure_creds = get_azure_credentials()
+        if not all([azure_creds["azure_key"], azure_creds["azure_endpoint"], azure_creds["azure_version"]]):
+            raise ValueError(
+                "Missing environment variables for Azure (see https://memgpt.readme.io/docs/endpoints#azure-openai). Please set then run `memgpt configure` again."
+            )
+
         model_endpoint_type = "azure"
-        model_endpoint = get_azure_credentials()["azure_endpoint"]
+        model_endpoint = azure_creds["azure_endpoint"]
+
     else:  # local models
         backend_options = ["webui", "webui-legacy", "llamacpp", "koboldcpp", "ollama", "lmstudio", "lmstudio-legacy", "vllm", "openai"]
         default_model_endpoint_type = None
@@ -178,14 +201,38 @@ def configure_embedding_endpoint(config: MemGPTConfig):
     embedding_provider = questionary.select(
         "Select embedding provider:", choices=["openai", "azure", "hugging-face", "local"], default=default_embedding_endpoint_type
     ).ask()
+
     if embedding_provider == "openai":
+        # check for key
+        if config.openai_key is None:
+            # allow key to get pulled from env vars
+            openai_api_key = os.getenv("OPENAI_API_KEY", None)
+            if openai_api_key is None:
+                # if we still can't find it, ask for it as input
+                while openai_api_key is None or len(openai_api_key) == 0:
+                    # Ask for API key as input
+                    openai_api_key = questionary.text(
+                        "Enter your OpenAI API key (starts with 'sk-', see https://platform.openai.com/api-keys):"
+                    ).ask()
+                config.openai_key = openai_api_key
+                config.save()
+
         embedding_endpoint_type = "openai"
         embedding_endpoint = "https://api.openai.com/v1"
         embedding_dim = 1536
+
     elif embedding_provider == "azure":
+        # check for necessary vars
+        azure_creds = get_azure_credentials()
+        if not all([azure_creds["azure_key"], azure_creds["azure_embedding_endpoint"], azure_creds["azure_embedding_version"]]):
+            raise ValueError(
+                "Missing environment variables for Azure (see https://memgpt.readme.io/docs/endpoints#azure-openai). Please set then run `memgpt configure` again."
+            )
+
         embedding_endpoint_type = "azure"
-        embedding_endpoint = get_azure_credentials()["azure_embedding_endpoint"]
+        embedding_endpoint = azure_creds["azure_embedding_endpoint"]
         embedding_dim = 1536
+
     elif embedding_provider == "hugging-face":
         # configure hugging face embedding endpoint (https://github.com/huggingface/text-embeddings-inference)
         # supports custom model/endpoints
@@ -292,32 +339,19 @@ def configure():
     openai_key = get_openai_credentials()
     azure_creds = get_azure_credentials()
 
-    if not openai_key and all(value is None or value == "" for value in azure_creds.values()):
-        raise ValueError(
-            "Missing environment variables (see https://memgpt.readme.io/docs/endpoints). Please set them and run `memgpt configure` again."
-        )
-    else:  # Detecting non-empty configurations for Azure or OpenAI
-        detected_services = []
-
-        if all([azure_creds["azure_key"], azure_creds["azure_endpoint"], azure_creds["azure_version"]]):
-            detected_services.append("Azure")
-
-        if openai_key:
-            detected_services.append("OpenAI")
-
-        if detected_services:
-            detected_services_message = ", ".join(detected_services)
-            typer.secho(f"Detected {detected_services_message} configuration.", fg=typer.colors.YELLOW)
-
     MemGPTConfig.create_config_dir()
 
     # Will pre-populate with defaults, or what the user previously set
     config = MemGPTConfig.load()
-    model_endpoint_type, model_endpoint = configure_llm_endpoint(config)
-    model, model_wrapper, context_window = configure_model(config, model_endpoint_type)
-    embedding_endpoint_type, embedding_endpoint, embedding_dim, embedding_model = configure_embedding_endpoint(config)
-    default_preset, default_persona, default_human, default_agent = configure_cli(config)
-    archival_storage_type, archival_storage_uri, archival_storage_path = configure_archival_storage(config)
+    try:
+        model_endpoint_type, model_endpoint = configure_llm_endpoint(config)
+        model, model_wrapper, context_window = configure_model(config, model_endpoint_type)
+        embedding_endpoint_type, embedding_endpoint, embedding_dim, embedding_model = configure_embedding_endpoint(config)
+        default_preset, default_persona, default_human, default_agent = configure_cli(config)
+        archival_storage_type, archival_storage_uri, archival_storage_path = configure_archival_storage(config)
+    except ValueError as e:
+        typer.secho(str(e), fg=typer.colors.RED)
+        return
 
     config = MemGPTConfig(
         # model configs
@@ -348,7 +382,7 @@ def configure():
         archival_storage_uri=archival_storage_uri,
         archival_storage_path=archival_storage_path,
     )
-    print(f"Saving config to {config.config_path}")
+    typer.secho(f"📖 Saving config to {config.config_path}", fg=typer.colors.GREEN)
     config.save()