fix: bug with storing embedding info in metadata store

memgpt/cli/cli_load.py

@@ -96,7 +96,12 @@ def load_directory(
             user_id = uuid.UUID(config.anon_clientid)
 
         ms = MetadataStore(config)
-        source = Source(name=name, user_id=user_id)
+        source = Source(
+            name=name,
+            user_id=user_id,
+            embedding_model=config.default_embedding_config.embedding_model,
+            embedding_dim=config.default_embedding_config.embedding_dim,
+        )
         ms.create_source(source)
         passage_storage = StorageConnector.get_storage_connector(TableType.PASSAGES, config, user_id)
         # TODO: also get document store
@@ -209,7 +214,12 @@ def load_vector_database(
             user_id = uuid.UUID(config.anon_clientid)
 
         ms = MetadataStore(config)
-        source = Source(name=name, user_id=user_id)
+        source = Source(
+            name=name,
+            user_id=user_id,
+            embedding_model=config.default_embedding_config.embedding_model,
+            embedding_dim=config.default_embedding_config.embedding_dim,
+        )
         ms.create_source(source)
         passage_storage = StorageConnector.get_storage_connector(TableType.PASSAGES, config, user_id)
         # TODO: also get document store

memgpt/data_sources/connectors.py

@@ -24,6 +24,12 @@ def load_data(
     document_store: Optional[StorageConnector] = None,
 ):
     """Load data from a connector (generates documents and passages) into a specified source_id, associatedw with a user_id."""
+    assert (
+        source.embedding_model == embedding_config.embedding_model
+    ), f"Source and embedding config models must match, got: {source.embedding_model} and {embedding_config.embedding_model}"
+    assert (
+        source.embedding_dim == embedding_config.embedding_dim
+    ), f"Source and embedding config dimensions must match, got: {source.embedding_dim} and {embedding_config.embedding_dim}."
 
     # embedding model
     embed_model = embedding_model(embedding_config)
@@ -55,8 +61,8 @@ def load_data(
                 metadata_=passage_metadata,
                 user_id=source.user_id,
                 data_source=source.name,
-                embedding_dim=embedding_config.embedding_dim,
-                embedding_model=embedding_config.embedding_model,
+                embedding_dim=source.embedding_dim,
+                embedding_model=source.embedding_model,
                 embedding=embedding,
             )
 

memgpt/main.py

@@ -131,14 +131,13 @@ def run_agent_loop(memgpt_agent, config: MemGPTConfig, first, ms: MetadataStore,
                         ):
                             valid_options.append(source.name)
                         else:
+                            # print warning about invalid sources
+                            typer.secho(
+                                f"Source {source.name} exists but has embedding dimentions {source.embedding_dim} from model {source.embedding_model}, while the agent uses embedding dimentions {memgpt_agent.agent_state.embedding_config.embedding_dim} and model {memgpt_agent.agent_state.embedding_config.embedding_model}",
+                                fg=typer.colors.YELLOW,
+                            )
                             invalid_options.append(source.name)
 
-                    # print warning about invalid sources
-                    typer.secho(
-                        f"Warning: the following sources are not compatible with this agent's embedding model and dimension: {invalid_options}",
-                        fg=typer.colors.YELLOW,
-                    )
-
                     # prompt user for data source selection
                     data_source = questionary.select("Select data source", choices=valid_options).ask()
 

memgpt/server/server.py

@@ -1,4 +1,5 @@
 import json
+from datetime import datetime
 import logging
 import uuid
 from abc import abstractmethod
@@ -1023,7 +1024,12 @@ def create_api_key_for_user(self, user_id: uuid.UUID) -> Token:
 
     def create_source(self, name: str, user_id: uuid.UUID) -> Source:  # TODO: add other fields
         """Create a new data source"""
-        source = Source(name=name, user_id=user_id)
+        source = Source(
+            name=name,
+            user_id=user_id,
+            embedding_model=self.config.default_embedding_config.embedding_model,
+            embedding_dim=self.config.default_embedding_config.embedding_dim,
+        )
         self.ms.create_source(source)
         return source