From 7cebaba0a22c9b438e601dd6491f14f459706d2c Mon Sep 17 00:00:00 2001
From: Varun Chawla <34209028+veeceey@users.noreply.github.com>
Date: Sun, 22 Mar 2026 23:50:17 -0700
Subject: [PATCH] fix: upgrade MongoDB vector store from deprecated knnVector
 to GA vectorSearch (#3995)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 mem0/vector_stores/mongodb.py       | 23 +++++++++++------------
 tests/vector_stores/test_mongodb.py | 21 ++++++++++-----------
 2 files changed, 21 insertions(+), 23 deletions(-)

diff --git a/mem0/vector_stores/mongodb.py b/mem0/vector_stores/mongodb.py
index 2bdebf293..7d1e82bb6 100644
--- a/mem0/vector_stores/mongodb.py
+++ b/mem0/vector_stores/mongodb.py
@@ -26,7 +26,7 @@ class OutputData(BaseModel):
 
 
 class MongoDB(VectorStoreBase):
-    VECTOR_TYPE = "knnVector"
+    VECTOR_TYPE = "vector"
     SIMILARITY_METRIC = "cosine"
 
     def __init__(self, db_name: str, collection_name: str, embedding_model_dims: int, mongo_uri: str):
@@ -69,17 +69,16 @@ class MongoDB(VectorStoreBase):
             else:
                 search_index_model = SearchIndexModel(
                     name=self.index_name,
+                    type="vectorSearch",
                     definition={
-                        "mappings": {
-                            "dynamic": False,
-                            "fields": {
-                                "embedding": {
-                                    "type": self.VECTOR_TYPE,
-                                    "dimensions": self.embedding_model_dims,
-                                    "similarity": self.SIMILARITY_METRIC,
-                                }
-                            },
-                        }
+                        "fields": [
+                            {
+                                "type": self.VECTOR_TYPE,
+                                "path": "embedding",
+                                "numDimensions": self.embedding_model_dims,
+                                "similarity": self.SIMILARITY_METRIC,
+                            }
+                        ]
                     },
                 )
                 collection.create_search_index(search_index_model)
@@ -141,7 +140,7 @@ class MongoDB(VectorStoreBase):
                     "$vectorSearch": {
                         "index": self.index_name,
                         "limit": limit,
-                        "numCandidates": limit,
+                        "numCandidates": min(limit * 20, 10000),
                         "queryVector": vectors,
                         "path": "embedding",
                     }
diff --git a/tests/vector_stores/test_mongodb.py b/tests/vector_stores/test_mongodb.py
index bb4b3f69f..d75a05846 100644
--- a/tests/vector_stores/test_mongodb.py
+++ b/tests/vector_stores/test_mongodb.py
@@ -48,17 +48,16 @@ def test_initalize_create_col(mongo_vector_fixture):
     search_index_model = args[0].document
     assert search_index_model == {
         "name": "test_collection_vector_index",
+        "type": "vectorSearch",
         "definition": {
-            "mappings": {
-                "dynamic": False,
-                "fields": {
-                    "embedding": {
-                        "type": "knnVector",
-                        "dimensions": 1536,
-                        "similarity": "cosine",
-                    }
-                },
-            }
+            "fields": [
+                {
+                    "type": "vector",
+                    "path": "embedding",
+                    "numDimensions": 1536,
+                    "similarity": "cosine",
+                }
+            ]
         },
     }
     assert mongo_vector.collection == mock_collection
@@ -95,7 +94,7 @@ def test_search(mongo_vector_fixture):
                 "$vectorSearch": {
                     "index": "test_collection_vector_index",
                     "limit": 2,
-                    "numCandidates": 2,
+                    "numCandidates": 40,
                     "queryVector": query_vector,
                     "path": "embedding",
                 },