Store user_id in vectordb (#2466)

mem0/configs/base.py

@@ -6,9 +6,12 @@ from pydantic import BaseModel, Field
 from mem0.embeddings.configs import EmbedderConfig
 from mem0.graphs.configs import GraphStoreConfig
 from mem0.llms.configs import LlmConfig
-from mem0.memory.setup import mem0_dir
 from mem0.vector_stores.configs import VectorStoreConfig
 
+# Set up the directory path
+home_dir = os.path.expanduser("~")
+mem0_dir = os.environ.get("MEM0_DIR") or os.path.join(home_dir, ".mem0")
+
 
 class MemoryItem(BaseModel):
     id: str = Field(..., description="The unique identifier for the text data")

mem0/configs/vector_stores/azure_ai_search.py

@@ -7,7 +7,7 @@ class AzureAISearchConfig(BaseModel):
     collection_name: str = Field("mem0", description="Name of the collection")
     service_name: str = Field(None, description="Azure AI Search service name")
     api_key: str = Field(None, description="API key for the Azure AI Search service")
-    embedding_model_dims: int = Field(None, description="Dimension of the embedding vector")
+    embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector")
     compression_type: Optional[str] = Field(
         None, description="Type of vector compression to use. Options: 'scalar', 'binary', or None"
     )