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fix: upgrade MongoDB vector store from deprecated knnVector to GA vectorSearch (#3995)

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Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Varun Chawla
2026-03-22 23:50:17 -07:00
committed by GitHub
parent 5dabf24809
commit 7cebaba0a2
2 changed files with 21 additions and 23 deletions
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mem0/vector_stores/mongodb.py
+11 -12
View File
@@ -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):
"$vectorSearch": {
"index": self.index_name,
"limit": limit,
"numCandidates": limit,
"numCandidates": min(limit * 20, 10000),
"queryVector": vectors,
"path": "embedding",
}
tests/vector_stores/test_mongodb.py
+10 -11
View File
@@ -48,17 +48,16 @@ def test_initalize_create_col(mongo_vector_fixture):
search_index_model = args[0].document
assert search_index_model == {
"name": "test_collection_vector_index",
"type": "vectorSearch",
"definition": {
"mappings": {
"dynamic": False,
"fields": {
"embedding": {
"type": "knnVector",
"dimensions": 1536,
"similarity": "cosine",
}
},
}
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536,
"similarity": "cosine",
}
]
},
}
assert mongo_vector.collection == mock_collection
@@ -95,7 +94,7 @@ def test_search(mongo_vector_fixture):
"$vectorSearch": {
"index": "test_collection_vector_index",
"limit": 2,
"numCandidates": 2,
"numCandidates": 40,
"queryVector": query_vector,
"path": "embedding",
},