docarray
diff --git a/‎.pre-commit-config.yaml
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diff --git a/‎README.md
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diff --git a/‎docarray/index/__init__.py
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@@ -21,7 +21,7 @@ repos:
         exclude: ^(docarray/proto/pb/docarray_pb2.py|docarray/proto/pb/docarray_pb2.py|docs/|docarray/resources/)
 
 - repo: https://github.com/charliermarsh/ruff-pre-commit
-  rev: v0.0.243
+  rev: v0.0.250
   hooks:
     - id: ruff
 
 
@@ -22,7 +22,7 @@ DocArray is a Python library expertly crafted for the [representation](#represen
 
 - :fire: Offers native support for **[NumPy](https://github.com/numpy/numpy)**, **[PyTorch](https://github.com/pytorch/pytorch)**, **[TensorFlow](https://github.com/tensorflow/tensorflow)**, and **[JAX](https://github.com/google/jax)**, catering specifically to **model training scenarios**.
 - :zap: Based on **[Pydantic](https://github.com/pydantic/pydantic)**, and instantly compatible with web and microservice frameworks like **[FastAPI](https://github.com/tiangolo/fastapi/)** and **[Jina](https://github.com/jina-ai/jina/)**.
-- :package: Provides support for vector databases such as **[Weaviate](https://weaviate.io/), [Qdrant](https://qdrant.tech/), [ElasticSearch](https://www.elastic.co/de/elasticsearch/), [Redis](https://redis.io/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**.
+- :package: Provides support for vector databases such as **[Weaviate](https://weaviate.io/), [Qdrant](https://qdrant.tech/), [ElasticSearch](https://www.elastic.co/de/elasticsearch/), **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**.
 - :chains: Allows data transmission as JSON over **HTTP** or as **[Protobuf](https://protobuf.dev/)** over **[gRPC](https://grpc.io/)**.
 
 ## Installation
@@ -350,7 +350,7 @@ This is useful for:
 - :mag: **Neural search** applications
 - :bulb: **Recommender systems**
 
-Currently, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**,  **[Redis](https://redis.io/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come!
+Currently, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**,  **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come!
 
 The Document Index interface lets you index and retrieve Documents from multiple vector databases, all with the same user interface.
 
@@ -421,7 +421,7 @@ They are now called **Document Indexes** and offer the following improvements (s
 - **Production-ready:** The new Document Indexes are a much thinner wrapper around the various vector DB libraries, making them more robust and easier to maintain
 - **Increased flexibility:** We strive to support any configuration or setting that you could perform through the DB's first-party client
 
-For now, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**,  Exact Nearest Neighbour search and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come.
+For now, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, **[Redis](https://redis.io/)**, **[Mongo Atlas](https://www.mongodb.com/)**, Exact Nearest Neighbour search and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come.
 
 </details>
 
@@ -844,6 +844,7 @@ Currently, DocArray supports the following vector databases:
 - [Milvus](https://milvus.io)
 - ExactNNMemorySearch as a local alternative with exact kNN search.
 - [HNSWlib](https://github.com/nmslib/hnswlib) as a local-first ANN alternative
+- [Mongo Atlas](https://www.mongodb.com/)
 
 An integration of [OpenSearch](https://opensearch.org/) is currently in progress.
 
@@ -874,6 +875,7 @@ from langchain.embeddings.openai import OpenAIEmbeddings
 
 embeddings = OpenAIEmbeddings()
 
+
 # Define a document schema
 class MovieDoc(BaseDoc):
     title: str
@@ -903,6 +905,7 @@ from docarray.index import (
     QdrantDocumentIndex,
     ElasticDocIndex,
     RedisDocumentIndex,
+    MongoDBAtlasDocumentIndex,
 )
 
 # Select a suitable backend and initialize it with data
 
@@ -13,6 +13,9 @@
     from docarray.index.backends.epsilla import EpsillaDocumentIndex  # noqa: F401
     from docarray.index.backends.hnswlib import HnswDocumentIndex  # noqa: F401
     from docarray.index.backends.milvus import MilvusDocumentIndex  # noqa: F401
+    from docarray.index.backends.mongodb_atlas import (  # noqa: F401
+        MongoDBAtlasDocumentIndex,
+    )
     from docarray.index.backends.qdrant import QdrantDocumentIndex  # noqa: F401
     from docarray.index.backends.redis import RedisDocumentIndex  # noqa: F401
     from docarray.index.backends.weaviate import WeaviateDocumentIndex  # noqa: F401
@@ -26,6 +29,7 @@
     'WeaviateDocumentIndex',
     'RedisDocumentIndex',
     'MilvusDocumentIndex',
+    'MongoDBAtlasDocumentIndex',
 ]
 
 
@@ -55,6 +59,9 @@ def __getattr__(name: str):
     elif name == 'RedisDocumentIndex':
         import_library('redis', raise_error=True)
         import docarray.index.backends.redis as lib
+    elif name == 'MongoDBAtlasDocumentIndex':
+        import_library('pymongo', raise_error=True)
+        import docarray.index.backends.mongodb_atlas as lib
     else:
         raise ImportError(
             f'cannot import name \'{name}\' from \'{_get_path_from_docarray_root_level(__file__)}\''