Use ~/.memgpt/config to set questionary defaults in memgpt configure

memgpt/cli/cli_config.py

@@ -26,8 +26,11 @@ def configure():
 
     MemGPTConfig.create_config_dir()
 
+    # Will pre-populate with defaults, or what the user previously set
+    config = MemGPTConfig.load()
+
     # openai credentials
-    use_openai = questionary.confirm("Do you want to enable MemGPT with Open AI?").ask()
+    use_openai = questionary.confirm("Do you want to enable MemGPT with Open AI?", default=True).ask()
     if use_openai:
         # search for key in enviornment
         openai_key = os.getenv("OPENAI_API_KEY")
@@ -37,7 +40,7 @@ def configure():
             # openai_key = questionary.text("Open AI API keys not found in enviornment - please enter:").ask()
 
     # azure credentials
-    use_azure = questionary.confirm("Do you want to enable MemGPT with Azure?", default=False).ask()
+    use_azure = questionary.confirm("Do you want to enable MemGPT with Azure?", default=(config.azure_key is not None)).ask()
     use_azure_deployment_ids = False
     if use_azure:
         # search for key in enviornment
@@ -69,49 +72,58 @@ def configure():
     model_endpoint_options = []
     if os.getenv("OPENAI_API_BASE") is not None:
         model_endpoint_options.append(os.getenv("OPENAI_API_BASE"))
-    if use_azure:
-        model_endpoint_options += ["azure"]
     if use_openai:
         model_endpoint_options += ["openai"]
-
+    if use_azure:
+        model_endpoint_options += ["azure"]
     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()
+    valid_default_model = config.model_endpoint in model_endpoint_options
+    default_endpoint = questionary.select(
+        "Select default inference endpoint:",
+        model_endpoint_options,
+        default=config.model_endpoint if valid_default_model else model_endpoint_options[0],
+    ).ask()
 
     # configure embedding provider
     embedding_endpoint_options = ["local"]  # cannot configure custom endpoint (too confusing)
     if use_azure:
-        model_endpoint_options += ["azure"]
+        embedding_endpoint_options += ["azure"]
     if use_openai:
-        model_endpoint_options += ["openai"]
-    default_embedding_endpoint = questionary.select("Select default embedding endpoint:", embedding_endpoint_options).ask()
+        embedding_endpoint_options += ["openai"]
+    valid_default_embedding = config.embedding_model in embedding_endpoint_options
+    default_embedding_endpoint = questionary.select(
+        "Select default embedding endpoint:",
+        embedding_endpoint_options,
+        default=config.embedding_model if valid_default_embedding else embedding_endpoint_options[-1],
+    ).ask()
 
     # configure embedding dimentions
-    default_embedding_dim = 1536
+    default_embedding_dim = config.embedding_dim
     if default_embedding_endpoint == "local":
         # HF model uses lower dimentionality
         default_embedding_dim = 384
 
     # configure preset
-    default_preset = questionary.select("Select default preset:", preset_options, default=DEFAULT_PRESET).ask()
+    default_preset = questionary.select("Select default preset:", preset_options, default=config.preset).ask()
 
     # default model
     if use_openai or use_azure:
         model_options = []
         if use_openai:
             model_options += ["gpt-4", "gpt-4-1106-preview", "gpt-3.5-turbo-16k"]
         default_model = questionary.select(
-            "Select default model (recommended: gpt-4):", choices=["gpt-4", "gpt-4-1106-preview", "gpt-3.5-turbo-16k"], default="gpt-4"
+            "Select default model (recommended: gpt-4):", choices=["gpt-4", "gpt-4-1106-preview", "gpt-3.5-turbo-16k"], default=config.model
         ).ask()
     else:
         default_model = "local"  # TODO: figure out if this is ok? this is for local endpoint
 
     # defaults
     personas = [os.path.basename(f).replace(".txt", "") for f in utils.list_persona_files()]
     print(personas)
-    default_persona = questionary.select("Select default persona:", personas, default="sam_pov").ask()
+    default_persona = questionary.select("Select default persona:", personas, default=config.default_persona).ask()
     humans = [os.path.basename(f).replace(".txt", "") for f in utils.list_human_files()]
     print(humans)
-    default_human = questionary.select("Select default human:", humans, default="cs_phd").ask()
+    default_human = questionary.select("Select default human:", humans, default=config.default_human).ask()
 
     # TODO: figure out if we should set a default agent or not
     default_agent = None
@@ -126,11 +138,14 @@ def configure():
 
     # Configure archival storage backend
     archival_storage_options = ["local", "postgres"]
-    archival_storage_type = questionary.select("Select storage backend for archival data:", archival_storage_options, default="local").ask()
+    archival_storage_type = questionary.select(
+        "Select storage backend for archival data:", archival_storage_options, default=config.archival_storage_type
+    ).ask()
     archival_storage_uri = None
     if archival_storage_type == "postgres":
         archival_storage_uri = questionary.text(
-            "Enter postgres connection string (e.g. postgresql+pg8000://{user}:{password}@{ip}:5432/{database}):"
+            "Enter postgres connection string (e.g. postgresql+pg8000://{user}:{password}@{ip}:5432/{database}):",
+            default=config.archival_storage_uri if config.archival_storage_uri else "",
         ).ask()
 
     # TODO: allow configuring embedding model

memgpt/connectors/db.py

@@ -70,7 +70,7 @@ def __init__(self, name: Optional[str] = None, agent_config: Optional[AgentConfi
         # create table
         self.uri = config.archival_storage_uri
         if config.archival_storage_uri is None:
-            raise ValueError(f"Must specifiy archival_storage_uri in config")
+            raise ValueError(f"Must specifiy archival_storage_uri in config {config.config_path}")
         self.db_model = get_db_model(self.table_name)
         self.engine = create_engine(self.uri)
         Base.metadata.create_all(self.engine)  # Create the table if it doesn't exist

tests/test_storage.py

@@ -20,9 +20,9 @@ def test_postgres_openai():
     if os.getenv("OPENAI_API_KEY") is None:
         return  # soft pass
 
-    os.environ["MEMGPT_CONFIG_FILE"] = "./config"
-    config = MemGPTConfig()
-    config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")  # the URI for a postgres DB w/ the pgvector extension
+    # os.environ["MEMGPT_CONFIG_PATH"] = "./config"
+    config = MemGPTConfig(archival_storage_type="postgres", archival_storage_uri=os.getenv("PGVECTOR_TEST_DB_URL"))
+    print(config.config_path)
     assert config.archival_storage_uri is not None
     config.archival_storage_uri = config.archival_storage_uri.replace(
         "postgres://", "postgresql://"
@@ -56,10 +56,15 @@ def test_postgres_openai():
 
 def test_postgres_local():
     assert os.getenv("PGVECTOR_TEST_DB_URL") is not None
-    os.environ["MEMGPT_CONFIG_FILE"] = "./config"
-
-    config = MemGPTConfig(embedding_model="local", embedding_dim=384)  # use HF model
-    config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")  # the URI for a postgres DB w/ the pgvector extension
+    # os.environ["MEMGPT_CONFIG_PATH"] = "./config"
+
+    config = MemGPTConfig(
+        archival_storage_type="postgres",
+        archival_storage_uri=os.getenv("PGVECTOR_TEST_DB_URL"),
+        embedding_model="local",
+        embedding_dim=384,  # use HF model
+    )
+    print(config.config_path)
     assert config.archival_storage_uri is not None
     config.archival_storage_uri = config.archival_storage_uri.replace(
         "postgres://", "postgresql://"