Fixed s3 vectors memory initialization issue from configuration (#3481)

docs/components/vectordbs/dbs/s3_vectors.mdx

@@ -28,7 +28,7 @@ config = {
         "provider": "s3_vectors",
         "config": {
             "vector_bucket_name": "my-mem0-vector-bucket",
-            "index_name": "my-memories-index",
+            "collection_name": "my-memories-index",
             "embedding_model_dims": 1536,
             "distance_metric": "cosine",
             "region_name": "us-east-1"
@@ -50,13 +50,13 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 
 Here are the available parameters for the `s3_vectors` config:
 
-| Parameter              | Description                                                          | Default Value |
+| Parameter              | Description                                                                      | Default Value                         |
-| ---------------------- | -------------------------------------------------------------------- | ------------- |
+| ---------------------- | -------------------------------------------------------------------------------- | ------------------------------------- |
-| `vector_bucket_name`   | The name of the S3 Vector bucket to use. It will be created if it doesn't exist. | Required      |
+| `vector_bucket_name`   | The name of the S3 Vector bucket to use. It will be created if it doesn't exist. | Required                              |
-| `index_name`           | The name of the vector index within the bucket.                        | `mem0`        |
+| `collection_name`      | The name of the vector index within the bucket.                                  | `mem0`                                |
-| `embedding_model_dims` | Dimensions of the embedding model. Must match your embedder.         | `1536`        |
+| `embedding_model_dims` | Dimensions of the embedding model. Must match your embedder.                     | `1536`                                |
-| `distance_metric`      | Distance metric for similarity search. Options: `cosine`, `euclidean`. | `cosine`      |
+| `distance_metric`      | Distance metric for similarity search. Options: `cosine`, `euclidean`.           | `cosine`                              |
-| `region_name`          | The AWS region where the bucket and index reside.                    | `None` (uses default from AWS config) |
+| `region_name`          | The AWS region where the bucket and index reside.                                | `None` (uses default from AWS config) |
 
 ### IAM Permissions
 
@@ -64,14 +64,14 @@ Your AWS identity (user or role) needs permissions to perform actions on S3 Vect
 
 ```json
 {
-    "Version": "2012-10-17",
+  "Version": "2012-10-17",
-    "Statement": [
+  "Statement": [
-        {
+    {
-            "Effect": "Allow",
+      "Effect": "Allow",
-            "Action": "s3vectors:*",
+      "Action": "s3vectors:*",
-            "Resource": "*"
+      "Resource": "*"
-        }
+    }
-    ]
+  ]
 }
 ```
 

mem0/configs/vector_stores/s3_vectors.py

@@ -5,17 +5,13 @@ from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 class S3VectorsConfig(BaseModel):
     vector_bucket_name: str = Field(description="Name of the S3 Vector bucket")
-    index_name: str = Field("mem0", description="Name of the vector index")
+    collection_name: str = Field("mem0", description="Name of the vector index")
-    embedding_model_dims: int = Field(
+    embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector")
-        1536, description="Dimension of the embedding vector"
-    )
     distance_metric: str = Field(
         "cosine",
         description="Distance metric for similarity search. Options: 'cosine', 'euclidean'",
     )
-    region_name: Optional[str] = Field(
+    region_name: Optional[str] = Field(None, description="AWS region for the S3 Vectors client")
-        None, description="AWS region for the S3 Vectors client"
-    )
 
     @model_validator(mode="before")
     @classmethod

mem0/vector_stores/s3_vectors.py

@@ -25,14 +25,14 @@ class S3Vectors(VectorStoreBase):
     def __init__(
         self,
         vector_bucket_name: str,
-        index_name: str,
+        collection_name: str,
         embedding_model_dims: int,
         distance_metric: str = "cosine",
         region_name: Optional[str] = None,
     ):
         self.client = boto3.client("s3vectors", region_name=region_name)
         self.vector_bucket_name = vector_bucket_name
-        self.collection_name = index_name
+        self.collection_name = collection_name
         self.embedding_model_dims = embedding_model_dims
         self.distance_metric = distance_metric
 

tests/vector_stores/test_s3_vectors.py

@@ -1,6 +1,8 @@
+from mem0.configs.vector_stores.s3_vectors import S3VectorsConfig
 import pytest
 from botocore.exceptions import ClientError
 
+from mem0.memory.main import Memory
 from mem0.vector_stores.s3_vectors import S3Vectors
 
 BUCKET_NAME = "test-bucket"
@@ -17,20 +19,42 @@ def mock_boto_client(mocker):
     return mock_client
 
 
+@pytest.fixture
+def mock_embedder(mocker):
+    mock_embedder = mocker.MagicMock()
+    mock_embedder.return_value.embed.return_value = [0.1, 0.2, 0.3]
+    mocker.patch("mem0.utils.factory.EmbedderFactory.create", mock_embedder)
+
+    return mock_embedder
+
+
+@pytest.fixture
+def mock_llm(mocker):
+    mock_llm = mocker.MagicMock()
+    mocker.patch("mem0.utils.factory.LlmFactory.create", mock_llm)
+    mocker.patch("mem0.memory.storage.SQLiteManager", mocker.MagicMock())
+
+    return mock_llm
+
+
 def test_initialization_creates_resources(mock_boto_client):
     """Test that bucket and index are created if they don't exist."""
-    not_found_error = ClientError({"Error": {"Code": "NotFoundException"}}, "OperationName")
+    not_found_error = ClientError(
+        {"Error": {"Code": "NotFoundException"}}, "OperationName"
+    )
     mock_boto_client.get_vector_bucket.side_effect = not_found_error
     mock_boto_client.get_index.side_effect = not_found_error
 
     S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
         region_name=REGION,
     )
 
-    mock_boto_client.create_vector_bucket.assert_called_once_with(vectorBucketName=BUCKET_NAME)
+    mock_boto_client.create_vector_bucket.assert_called_once_with(
+        vectorBucketName=BUCKET_NAME
+    )
     mock_boto_client.create_index.assert_called_once_with(
         vectorBucketName=BUCKET_NAME,
         indexName=INDEX_NAME,
@@ -47,7 +71,7 @@ def test_initialization_uses_existing_resources(mock_boto_client):
 
     S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
         region_name=REGION,
     )
@@ -56,11 +80,41 @@ def test_initialization_uses_existing_resources(mock_boto_client):
     mock_boto_client.create_index.assert_not_called()
 
 
+def test_memory_initialization_with_config(mock_boto_client, mock_llm, mock_embedder):
+    """Test Memory initialization with S3Vectors from config."""
+
+    # check that Attribute error is not raised
+    mock_boto_client.get_vector_bucket.return_value = {}
+    mock_boto_client.get_index.return_value = {}
+
+    config = {
+        "vector_store": {
+            "provider": "s3_vectors",
+            "config": {
+                "vector_bucket_name": BUCKET_NAME,
+                "collection_name": INDEX_NAME,
+                "embedding_model_dims": EMBEDDING_DIMS,
+                "distance_metric": "cosine",
+                "region_name": REGION,
+            },
+        }
+    }
+
+    try:
+        memory = Memory.from_config(config)
+
+        assert memory.vector_store is not None
+        assert isinstance(memory.vector_store, S3Vectors)
+        assert isinstance(memory.config.vector_store.config, S3VectorsConfig)
+    except AttributeError:
+        pytest.fail("Memory initialization failed")
+
+
 def test_insert(mock_boto_client):
     """Test inserting vectors."""
     store = S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
     )
     vectors = [[0.1, 0.2], [0.3, 0.4]]
@@ -94,7 +148,7 @@ def test_search(mock_boto_client):
     }
     store = S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
     )
     query_vector = [0.1, 0.2]
@@ -108,10 +162,12 @@ def test_search(mock_boto_client):
 
 def test_get(mock_boto_client):
     """Test retrieving a vector by ID."""
-    mock_boto_client.get_vectors.return_value = {"vectors": [{"key": "id1", "metadata": {"meta": "data1"}}]}
+    mock_boto_client.get_vectors.return_value = {
+        "vectors": [{"key": "id1", "metadata": {"meta": "data1"}}]
+    }
     store = S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
     )
     result = store.get("id1")
@@ -131,7 +187,7 @@ def test_delete(mock_boto_client):
     """Test deleting a vector."""
     store = S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
     )
     store.delete("id1")
@@ -144,13 +200,15 @@ def test_delete(mock_boto_client):
 def test_reset(mock_boto_client):
     """Test resetting the vector index."""
     # GIVEN: The index does not exist, so it gets created on init and reset
-    not_found_error = ClientError({"Error": {"Code": "NotFoundException"}}, "OperationName")
+    not_found_error = ClientError(
+        {"Error": {"Code": "NotFoundException"}}, "OperationName"
+    )
     mock_boto_client.get_index.side_effect = not_found_error
 
     # WHEN: The store is initialized
     store = S3Vectors(
         vector_bucket_name=BUCKET_NAME,
-        index_name=INDEX_NAME,
+        collection_name=INDEX_NAME,
         embedding_model_dims=EMBEDDING_DIMS,
     )
 
@@ -161,5 +219,7 @@ def test_reset(mock_boto_client):
     store.reset()
 
     # THEN: The index is deleted and then created again
-    mock_boto_client.delete_index.assert_called_once_with(vectorBucketName=BUCKET_NAME, indexName=INDEX_NAME)
+    mock_boto_client.delete_index.assert_called_once_with(
+        vectorBucketName=BUCKET_NAME, indexName=INDEX_NAME
+    )
     assert mock_boto_client.create_index.call_count == 2