feat: MongoDBAtlasEmbeddingRetriever

integrations/mongodb_atlas/src/haystack_integrations/components/retrievers/mongodb_atlas/__init__.py

@@ -0,0 +1,3 @@
+from haystack_integrations.components.retrievers.mongodb_atlas.embedding_retriever import MongoDBAtlasEmbeddingRetriever
+
+__all__ = ["MongoDBAtlasEmbeddingRetriever"]

integrations/mongodb_atlas/src/haystack_integrations/components/retrievers/mongodb_atlas/embedding_retriever.py

@@ -0,0 +1,83 @@
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <[email protected]>
+#
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, List, Optional
+
+from haystack import component, default_from_dict, default_to_dict
+from haystack.dataclasses import Document
+from haystack.utils import deserialize_secrets_inplace
+from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+
+
+@component
+class MongoDBAtlasEmbeddingRetriever:
+    """
+    Retrieves documents from the MongoDBAtlasDocumentStore by embedding similarity.
+
+    Needs to be connected to the MongoDBAtlasDocumentStore.
+    """
+
+    def __init__(
+        self,
+        *,
+        document_store: MongoDBAtlasDocumentStore,
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: int = 10,
+    ):
+        """
+        Create the MongoDBAtlasDocumentStore component.
+
+        :param document_store: An instance of MongoDBAtlasDocumentStore.
+        :param filters: Filters applied to the retrieved Documents. Defaults to None.
+        :param top_k: Maximum number of Documents to return, defaults to 10.
+        """
+        if not isinstance(document_store, MongoDBAtlasDocumentStore):
+            msg = "document_store must be an instance of MongoDBAtlasDocumentStore"
+            raise ValueError(msg)
+
+        self.document_store = document_store
+        self.filters = filters or {}
+        self.top_k = top_k
+
+    def to_dict(self) -> Dict[str, Any]:
+        return default_to_dict(
+            self,
+            filters=self.filters,
+            top_k=self.top_k,
+            document_store=self.document_store.to_dict(),
+        )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "MongoDBAtlasEmbeddingRetriever":
+        deserialize_secrets_inplace(
+            data["init_parameters"]["document_store"]["init_parameters"], keys=["mongo_connection_string"]
+        )
+        data["init_parameters"]["document_store"] = default_from_dict(
+            MongoDBAtlasDocumentStore, data["init_parameters"]["document_store"]
+        )
+        return default_from_dict(cls, data)
+
+    @component.output_types(documents=List[Document])
+    def run(
+        self,
+        query_embedding: List[float],
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: Optional[int] = None,
+    ):
+        """
+        Retrieve documents from the MongoDBAtlasDocumentStore, based on their embeddings.
+
+        :param query_embedding: Embedding of the query.
+        :param filters: Filters applied to the retrieved Documents.
+        :param top_k: Maximum number of Documents to return.
+        :return: List of Documents similar to `query_embedding`.
+        """
+        filters = filters or self.filters
+        top_k = top_k or self.top_k
+
+        docs = self.document_store.embedding_retrieval(
+            query_embedding=query_embedding,
+            filters=filters,
+            top_k=top_k,
+        )
+        return {"documents": docs}

integrations/mongodb_atlas/src/haystack_integrations/document_stores/mongodb_atlas/document_store.py

@@ -5,9 +5,10 @@
 import re
 from typing import Any, Dict, List, Optional, Union
 
+import numpy as np
 from haystack import default_from_dict, default_to_dict
 from haystack.dataclasses.document import Document
-from haystack.document_stores.errors import DuplicateDocumentError
+from haystack.document_stores.errors import DocumentStoreError, DuplicateDocumentError
 from haystack.document_stores.types import DuplicatePolicy
 from haystack.utils import Secret, deserialize_secrets_inplace
 from haystack_integrations.document_stores.mongodb_atlas.filters import haystack_filters_to_mongo
@@ -25,7 +26,7 @@ def __init__(
         mongo_connection_string: Secret = Secret.from_env_var("MONGO_CONNECTION_STRING"),  # noqa: B008
         database_name: str,
         collection_name: str,
-        recreate_collection: bool = False,
+        vector_search_index: str,
     ):
         """
         Creates a new MongoDBAtlasDocumentStore instance.
@@ -37,8 +38,11 @@ def __init__(
             This can be obtained on the MongoDB Atlas Dashboard by clicking on the `CONNECT` button.
             This value will be read automatically from the env var "MONGO_CONNECTION_STRING".
         :param database_name: Name of the database to use.
-        :param collection_name: Name of the collection to use.
-        :param recreate_collection: Whether to recreate the collection when initializing the document store.
+        :param collection_name: Name of the collection to use. To use this document store for embedding retrieval,
+            this collection needs to have a vector search index set up on the `embedding` field.
+        :param vector_search_index: The name of the vector search index to use for vector search operations.
+            Create a vector_search_index in the Atlas web UI and specify the init params of MongoDBAtlasDocumentStore. \
+            See https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#std-label-avs-create-index
         """
         if collection_name and not bool(re.match(r"^[a-zA-Z0-9\-_]+$", collection_name)):
             msg = f'Invalid collection name: "{collection_name}". It can only contain letters, numbers, -, or _.'
@@ -49,21 +53,16 @@ def __init__(
 
         self.database_name = database_name
         self.collection_name = collection_name
-        self.recreate_collection = recreate_collection
+        self.vector_search_index = vector_search_index
 
         self.connection: MongoClient = MongoClient(
             resolved_connection_string, driver=DriverInfo(name="MongoDBAtlasHaystackIntegration")
         )
         database = self.connection[self.database_name]
 
-        if self.recreate_collection and self.collection_name in database.list_collection_names():
-            database[self.collection_name].drop()
-
-        # Implicitly create the collection if it doesn't exist
         if collection_name not in database.list_collection_names():
-            database.create_collection(self.collection_name)
-            database[self.collection_name].create_index("id", unique=True)
-
+            msg = f"Collection '{collection_name}' does not exist in database '{database_name}'."
+            raise ValueError(msg)
         self.collection = database[self.collection_name]
 
     def to_dict(self) -> Dict[str, Any]:
@@ -75,7 +74,7 @@ def to_dict(self) -> Dict[str, Any]:
             mongo_connection_string=self.mongo_connection_string.to_dict(),
             database_name=self.database_name,
             collection_name=self.collection_name,
-            recreate_collection=self.recreate_collection,
+            vector_search_index=self.vector_search_index,
         )
 
     @classmethod
@@ -157,3 +156,65 @@ def delete_documents(self, document_ids: List[str]) -> None:
         if not document_ids:
             return
         self.collection.delete_many(filter={"id": {"$in": document_ids}})
+
+    def embedding_retrieval(
+        self,
+        query_embedding: np.ndarray,
+        filters: Optional[Dict[str, Any]] = None,
+        top_k: int = 10,
+    ) -> List[Document]:
+        """
+        Find the documents that are most similar to the provided `query_emb` by using a vector similarity metric.
+
+        :param query_emb: Embedding of the query
+        :param filters: optional filters (see get_all_documents for description).
+        :param top_k: How many documents to return.
+        """
+        if not query_embedding:
+            msg = "Query embedding must not be empty"
+            raise ValueError(msg)
+
+        query_embedding = np.array(query_embedding).astype(np.float32)
+
+        filters = haystack_filters_to_mongo(filters)
+        pipeline = [
+            {
+                "$vectorSearch": {
+                    "index": self.vector_search_index,
+                    "path": "embedding",
+                    "queryVector": query_embedding.tolist(),
+                    "numCandidates": 100,
+                    "limit": top_k,
+                    # "filter": filters,
+                }
+            },
+            {
+                "$project": {
+                    "_id": 0,
+                    "content": 1,
+                    "dataframe": 1,
+                    "blob": 1,
+                    "meta": 1,
+                    "embedding": 1,
+                    "score": {"$meta": "vectorSearchScore"},
+                }
+            },
+        ]
+        try:
+            documents = list(self.collection.aggregate(pipeline))
+        except Exception as e:
+            msg = f"Retrieval of documents from MongoDB Atlas failed: {e}"
+            raise DocumentStoreError(msg) from e
+
+        documents = [self.mongo_doc_to_haystack_doc(doc) for doc in documents]
+        return documents
+
+    def mongo_doc_to_haystack_doc(self, mongo_doc: Dict[str, Any]) -> Document:
+        """
+        Converts the dictionary coming out of MongoDB into a Haystack document
+
+        :param mongo_doc: A dictionary representing a document as stored in MongoDB
+        :return: A Haystack Document object
+        """
+        mongo_doc.pop("_id", None)
+        return Document.from_dict(mongo_doc)

integrations/mongodb_atlas/tests/test_document_store.py

@@ -2,7 +2,6 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 import os
-from unittest.mock import patch
 from uuid import uuid4
 
 import pytest
@@ -13,16 +12,31 @@
 from haystack.utils import Secret
 from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
 from pandas import DataFrame
+from pymongo import MongoClient  # type: ignore
+from pymongo.driver_info import DriverInfo  # type: ignore
 
 
 @pytest.fixture
-def document_store(request):
+def document_store():
+    database_name = "haystack_integration_test"
+    collection_name = "test_collection_" + str(uuid4())
+
+    connection: MongoClient = MongoClient(
+        os.environ["MONGO_CONNECTION_STRING"], driver=DriverInfo(name="MongoDBAtlasHaystackIntegration")
+    )
+    database = connection[database_name]
+    if collection_name in database.list_collection_names():
+        database[collection_name].drop()
+    database.create_collection(collection_name)
+    database[collection_name].create_index("id", unique=True)
+
     store = MongoDBAtlasDocumentStore(
-        database_name="haystack_integration_test",
-        collection_name=request.node.name + str(uuid4()),
+        database_name=database_name,
+        collection_name=collection_name,
+        vector_search_index="cosine_index",
     )
     yield store
-    store.collection.drop()
+    database[collection_name].drop()
 
 
 @pytest.mark.skipif(
@@ -51,13 +65,10 @@ def test_write_dataframe(self, document_store: MongoDBAtlasDocumentStore):
         retrieved_docs = document_store.filter_documents()
         assert retrieved_docs == docs
 
-    @patch("haystack_integrations.document_stores.mongodb_atlas.document_store.MongoClient")
-    def test_to_dict(self, _):
-        document_store = MongoDBAtlasDocumentStore(
-            database_name="database_name",
-            collection_name="collection_name",
-        )
-        assert document_store.to_dict() == {
+    def test_to_dict(self, document_store):
+        serialized_store = document_store.to_dict()
+        assert serialized_store["init_parameters"].pop("collection_name").startswith("test_collection_")
+        assert serialized_store == {
             "type": "haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore",
             "init_parameters": {
                 "mongo_connection_string": {
@@ -67,14 +78,12 @@ def test_to_dict(self, _):
                     "strict": True,
                     "type": "env_var",
                 },
-                "database_name": "database_name",
-                "collection_name": "collection_name",
-                "recreate_collection": False,
+                "database_name": "haystack_integration_test",
+                "vector_search_index": "cosine_index",
             },
         }
 
-    @patch("haystack_integrations.document_stores.mongodb_atlas.document_store.MongoClient")
-    def test_from_dict(self, _):
+    def test_from_dict(self):
         docstore = MongoDBAtlasDocumentStore.from_dict(
             {
                 "type": "haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore",
@@ -86,13 +95,13 @@ def test_from_dict(self, _):
                         "strict": True,
                         "type": "env_var",
                     },
-                    "database_name": "database_name",
-                    "collection_name": "collection_name",
-                    "recreate_collection": True,
+                    "database_name": "haystack_integration_test",
+                    "collection_name": "test_embeddings_collection",
+                    "vector_search_index": "cosine_index",
                 },
             }
         )
         assert docstore.mongo_connection_string == Secret.from_env_var("MONGO_CONNECTION_STRING")
-        assert docstore.database_name == "database_name"
-        assert docstore.collection_name == "collection_name"
-        assert docstore.recreate_collection
+        assert docstore.database_name == "haystack_integration_test"
+        assert docstore.collection_name == "test_embeddings_collection"
+        assert docstore.vector_search_index == "cosine_index"

integrations/mongodb_atlas/tests/test_embedding_retrieval.py

@@ -0,0 +1,75 @@
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <[email protected]>
+#
+# SPDX-License-Identifier: Apache-2.0
+import os
+from typing import List
+
+import pytest
+from haystack.document_stores.errors import DocumentStoreError
+from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+
+
+@pytest.mark.skipif(
+    "MONGO_CONNECTION_STRING" not in os.environ,
+    reason="No MongoDB Atlas connection string provided",
+)
+class TestEmbeddingRetrieval:
+
+    def test_embedding_retrieval_cosine_similarity(self):
+        document_store = MongoDBAtlasDocumentStore(
+            database_name="haystack_integration_test",
+            collection_name="test_embeddings_collection",
+            vector_search_index="cosine_index",
+        )
+        query_embedding = [0.1] * 768
+        results = document_store.embedding_retrieval(query_embedding=query_embedding, top_k=2, filters={})
+        assert len(results) == 2
+        assert results[0].content == "Document A"
+        assert results[1].content == "Document B"
+        assert results[0].score > results[1].score
+
+    def test_embedding_retrieval_dot_product(self):
+        document_store = MongoDBAtlasDocumentStore(
+            database_name="haystack_integration_test",
+            collection_name="test_embeddings_collection",
+            vector_search_index="dotProduct_index",
+        )
+        query_embedding = [0.1] * 768
+        results = document_store.embedding_retrieval(query_embedding=query_embedding, top_k=2, filters={})
+        assert len(results) == 2
+        assert results[0].content == "Document A"
+        assert results[1].content == "Document B"
+        assert results[0].score > results[1].score
+
+    def test_embedding_retrieval_euclidean(self):
+        document_store = MongoDBAtlasDocumentStore(
+            database_name="haystack_integration_test",
+            collection_name="test_embeddings_collection",
+            vector_search_index="euclidean_index",
+        )
+        query_embedding = [0.1] * 768
+        results = document_store.embedding_retrieval(query_embedding=query_embedding, top_k=2, filters={})
+        assert len(results) == 2
+        assert results[0].content == "Document C"
+        assert results[1].content == "Document B"
+        assert results[0].score > results[1].score
+
+    def test_empty_query_embedding(self):
+        document_store = MongoDBAtlasDocumentStore(
+            database_name="haystack_integration_test",
+            collection_name="test_embeddings_collection",
+            vector_search_index="cosine_index",
+        )
+        query_embedding: List[float] = []
+        with pytest.raises(ValueError):
+            document_store.embedding_retrieval(query_embedding=query_embedding)
+
+    def test_query_embedding_wrong_dimension(self):
+        document_store = MongoDBAtlasDocumentStore(
+            database_name="haystack_integration_test",
+            collection_name="test_embeddings_collection",
+            vector_search_index="cosine_index",
+        )
+        query_embedding = [0.1] * 4
+        with pytest.raises(DocumentStoreError):
+            document_store.embedding_retrieval(query_embedding=query_embedding)