feat: add description to source (#1175)

memgpt/cli/cli_config.py

@@ -775,7 +775,7 @@ def list(arg: Annotated[ListChoice, typer.Argument]):
         """List all data sources"""
 
         # create table
-        table.field_names = ["Name", "Embedding Model", "Embedding Dim", "Created At", "Agents"]
+        table.field_names = ["Name", "Description", "Embedding Model", "Embedding Dim", "Created At", "Agents"]
         # TODO: eventually look accross all storage connections
         # TODO: add data source stats
         # TODO: connect to agents
@@ -788,7 +788,14 @@ def list(arg: Annotated[ListChoice, typer.Argument]):
             agent_names = [agent_state.name for agent_state in agent_states if agent_state is not None]
 
             table.add_row(
-                [source.name, source.embedding_model, source.embedding_dim, utils.format_datetime(source.created_at), ",".join(agent_names)]
+                [
+                    source.name,
+                    source.description,
+                    source.embedding_model,
+                    source.embedding_dim,
+                    utils.format_datetime(source.created_at),
+                    ",".join(agent_names),
+                ]
             )
 
         print(table)

memgpt/cli/cli_load.py

@@ -88,6 +88,7 @@ def load_directory(
     recursive: Annotated[bool, typer.Option(help="Recursively search for files in directory.")] = False,
     extensions: Annotated[str, typer.Option(help="Comma separated list of file extensions to load")] = default_extensions,
     user_id: Annotated[Optional[uuid.UUID], typer.Option(help="User ID to associate with dataset.")] = None,  # TODO: remove
+    description: Annotated[Optional[str], typer.Option(help="Description of the source.")] = None,
 ):
     try:
         connector = DirectoryConnector(input_files=input_files, input_directory=input_dir, recursive=recursive, extensions=extensions)
@@ -101,6 +102,7 @@ def load_directory(
             user_id=user_id,
             embedding_model=config.default_embedding_config.embedding_model,
             embedding_dim=config.default_embedding_config.embedding_dim,
+            description=description,
         )
         ms.create_source(source)
         passage_storage = StorageConnector.get_storage_connector(TableType.PASSAGES, config, user_id)

memgpt/data_types.py

@@ -506,6 +506,7 @@ class Source:
         self,
         user_id: uuid.UUID,
         name: str,
+        description: Optional[str] = None,
         created_at: Optional[datetime] = None,
         id: Optional[uuid.UUID] = None,
         # embedding info
@@ -521,6 +522,7 @@ class Source:
 
         self.name = name
         self.user_id = user_id
+        self.description = description
         self.created_at = created_at if created_at is not None else get_utc_time()
 
         # embedding info (optional)

memgpt/metadata.py

@@ -202,6 +202,7 @@ class SourceModel(Base):
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     embedding_dim = Column(BIGINT)
     embedding_model = Column(String)
+    description = Column(String)
 
     # TODO: add num passages
 
@@ -216,6 +217,7 @@ class SourceModel(Base):
             created_at=self.created_at,
             embedding_dim=self.embedding_dim,
             embedding_model=self.embedding_model,
+            description=self.description,
         )
 
 

memgpt/models/pydantic_models.py

@@ -102,6 +102,7 @@ class SourceModel(SQLModel, table=True):
     user_id: uuid.UUID = Field(..., description="The unique identifier of the user associated with the source.")
     created_at: datetime = Field(default_factory=get_utc_time, description="The unix timestamp of when the source was created.")
     id: uuid.UUID = Field(default_factory=uuid.uuid4, description="The unique identifier of the source.", primary_key=True)
+    description: Optional[str] = Field(None, description="The description of the source.")
     # embedding info
     # embedding_config: EmbeddingConfigModel = Field(..., description="The embedding configuration used by the source.")
     embedding_config: Optional[EmbeddingConfigModel] = Field(