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Fix: add insert mode MongoDB (#7394)

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* add dropdown inser mode, create method __insert_mode

* fix unit_test mongodb

* add info to index_name

* to overwrite, delete_many from collection

* create verify_search_index

* fix SIMILARITY_OPTIONS

* fix documentation components-vector-stores.md
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Gustavo Costa 2025-04-03 16:03:17 -03:00 • committed by GitHub
commit f9a7c9bcef
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3 changed files with 136 additions and 58 deletions
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64
src/backend/tests/unit/components/vectorstores/test_mongodb_atlas.py
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@ -1,12 +1,12 @@
import os
import time
from typing import Any
import pytest
from langchain_community.embeddings.fake import DeterministicFakeEmbedding
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langflow.components.vectorstores.mongodb_atlas import MongoVectorStoreComponent
from langflow.schema.data import Data
from pymongo.operations import SearchIndexModel
from pymongo.collection import Collection
from tests.base import ComponentTestBaseWithoutClient, VersionComponentMapping
@ -30,6 +30,13 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
"index_name": "test_index",
"enable_mtls": False,
"embedding": DeterministicFakeEmbedding(size=8),
"index_field": "embedding",
"filter_field": "text",
"number_dimensions": 8,
"similarity": "cosine",
"quantization": "scalar",
"insert_mode": "append",
"ingest_data": [Data(data={"text": "test data 1"}), Data(data={"text": "test data 2"})],
}
@pytest.fixture
@ -41,42 +48,18 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
{"version": "1.1.1", "module": "vectorstores", "file_name": "mongodb_atlas"},
]
def __create_search_index(self, vector_store: MongoDBAtlasVectorSearch, default_kwargs: dict[str, Any]) -> None:
def __create_search_index(
self, component_class: type[MongoVectorStoreComponent], collection: Collection, default_kwargs: dict[str, Any]
) -> None:
"""Create a vector search index if it doesn't exist."""
try:
index_definition = SearchIndexModel(
definition={
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 8,
"similarity": "cosine",
"quantization": "scalar",
},
{"type": "filter", "path": "text"},
]
},
name=default_kwargs["index_name"],
type="vectorSearch",
)
component_class().set(**default_kwargs).verify_search_index(collection)
vector_store._collection.create_search_index(index_definition)
# Wait for index to be ready
import time
time.sleep(40) # Give some time for index to be ready
# Verify index was created
indexes = vector_store._collection.list_search_indexes()
index_names = [idx["name"] for idx in indexes]
assert default_kwargs["index_name"] in index_names
except Exception as e:
# Index might already exist, which is fine
if "AlreadyExists" not in str(e):
raise
# Verify index was created
indexes = collection.list_search_indexes()
index_names = {idx["name"]: idx["type"] for idx in indexes}
index_type = index_names.get(default_kwargs["index_name"])
assert default_kwargs["index_name"] in index_names
assert index_type == "vectorSearch"
def test_create_db(self, component_class: type[MongoVectorStoreComponent], default_kwargs: dict[str, Any]) -> None:
"""Test creating a MongoDB Atlas vector store."""
@ -93,6 +76,7 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
"""Test creating a collection with data."""
test_texts = ["test data 1", "test data 2", "something completely different"]
default_kwargs["ingest_data"] = [Data(data={"text": text}) for text in test_texts]
default_kwargs["insert_mode"] = "overwrite"
component: MongoVectorStoreComponent = component_class().set(**default_kwargs)
vector_store = component.build_vector_store()
@ -115,6 +99,7 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
]
default_kwargs["ingest_data"] = [Data(data={"text": text, "metadata": {}}) for text in test_data]
default_kwargs["number_of_results"] = 2
default_kwargs["insert_mode"] = "overwrite"
# Create and initialize the component
component: MongoVectorStoreComponent = component_class().set(**default_kwargs)
@ -131,7 +116,7 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
assert isinstance(doc["embedding"], list)
assert len(doc["embedding"]) == 8 # Should match our embedding size
self.__create_search_index(vector_store, default_kwargs)
self.__create_search_index(component_class, vector_store._collection, default_kwargs)
# Verify index was created
indexes = vector_store._collection.list_search_indexes()
@ -141,6 +126,7 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
# Test similarity search through the component
component.set(search_query="dog")
results = component.search_documents()
time.sleep(5) # wait the results come from API
assert len(results) == 2, "Expected 2 results for 'lazy dog' query"
# The most relevant results should be about dogs
@ -168,8 +154,8 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
self, component_class: type[MongoVectorStoreComponent], default_kwargs: dict[str, Any]
) -> None:
"""Test search with empty query."""
default_kwargs["insert_mode"] = "overwrite"
component: MongoVectorStoreComponent = component_class().set(**default_kwargs)
component.build_vector_store()
# Test with empty search query
component.set(search_query="")
@ -191,7 +177,7 @@ class TestMongoVectorStoreComponent(ComponentTestBaseWithoutClient):
component: MongoVectorStoreComponent = component_class().set(**default_kwargs)
vector_store = component.build_vector_store()
self.__create_search_index(vector_store, default_kwargs)
self.__create_search_index(component_class, vector_store._collection, default_kwargs)
# Test search and verify metadata is preserved
component.set(search_query="Document", number_of_results=2)