Add support for Langchain VectorStores (#2518)

tests/vector_stores/test_faiss.py

@@ -39,14 +39,14 @@ def test_create_col(faiss_instance, mock_faiss_index):
     # Test creating a collection with euclidean distance
     with patch('faiss.IndexFlatL2', return_value=mock_faiss_index) as mock_index_flat_l2:
         with patch('faiss.write_index'):
-            faiss_instance.create_col(name="new_collection", vector_size=256)
-            mock_index_flat_l2.assert_called_once_with(256)
+            faiss_instance.create_col(name="new_collection")
+            mock_index_flat_l2.assert_called_once_with(faiss_instance.embedding_model_dims)
     
     # Test creating a collection with inner product distance
     with patch('faiss.IndexFlatIP', return_value=mock_faiss_index) as mock_index_flat_ip:
         with patch('faiss.write_index'):
-            faiss_instance.create_col(name="new_collection", vector_size=256, distance="inner_product")
-            mock_index_flat_ip.assert_called_once_with(256)
+            faiss_instance.create_col(name="new_collection", distance="inner_product")
+            mock_index_flat_ip.assert_called_once_with(faiss_instance.embedding_model_dims)
 
 
 def test_insert(faiss_instance, mock_faiss_index):

tests/vector_stores/test_langchain_vector_store.py

@@ -0,0 +1,101 @@
+from unittest.mock import Mock, patch
+
+import pytest
+from langchain_community.vectorstores import VectorStore
+
+from mem0.vector_stores.langchain import Langchain
+
+
+@pytest.fixture
+def mock_langchain_client():
+    with patch("langchain_community.vectorstores.VectorStore") as mock_client:
+        yield mock_client
+
+@pytest.fixture
+def langchain_instance(mock_langchain_client):
+    mock_client = Mock(spec=VectorStore)
+    return Langchain(client=mock_client, collection_name="test_collection")
+
+def test_insert_vectors(langchain_instance):
+    # Test data
+    vectors = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+    payloads = [{"data": "text1", "name": "vector1"}, {"data": "text2", "name": "vector2"}]
+    ids = ["id1", "id2"]
+
+    # Test with add_embeddings method
+    langchain_instance.client.add_embeddings = Mock()
+    langchain_instance.insert(vectors=vectors, payloads=payloads, ids=ids)
+    langchain_instance.client.add_embeddings.assert_called_once_with(
+        embeddings=vectors,
+        metadatas=payloads,
+        ids=ids
+    )
+
+    # Test with add_texts method
+    delattr(langchain_instance.client, "add_embeddings")  # Remove attribute completely
+    langchain_instance.client.add_texts = Mock()
+    langchain_instance.insert(vectors=vectors, payloads=payloads, ids=ids)
+    langchain_instance.client.add_texts.assert_called_once_with(
+        texts=["text1", "text2"],
+        metadatas=payloads,
+        ids=ids
+    )
+
+    # Test with empty payloads
+    langchain_instance.client.add_texts.reset_mock()
+    langchain_instance.insert(vectors=vectors, payloads=None, ids=ids)
+    langchain_instance.client.add_texts.assert_called_once_with(
+        texts=["", ""],
+        metadatas=None,
+        ids=ids
+    )
+
+def test_search_vectors(langchain_instance):
+    # Mock search results
+    mock_docs = [
+        Mock(metadata={"name": "vector1"}, id="id1"),
+        Mock(metadata={"name": "vector2"}, id="id2")
+    ]
+    langchain_instance.client.similarity_search_by_vector.return_value = mock_docs
+
+    # Test search without filters
+    vectors = [[0.1, 0.2, 0.3]]
+    results = langchain_instance.search(query="", vectors=vectors, limit=2)
+    
+    langchain_instance.client.similarity_search_by_vector.assert_called_once_with(
+        embedding=vectors,
+        k=2
+    )
+    
+    assert len(results) == 2
+    assert results[0].id == "id1"
+    assert results[0].payload == {"name": "vector1"}
+    assert results[1].id == "id2"
+    assert results[1].payload == {"name": "vector2"}
+
+    # Test search with filters
+    filters = {"name": "vector1"}
+    langchain_instance.search(query="", vectors=vectors, limit=2, filters=filters)
+    langchain_instance.client.similarity_search_by_vector.assert_called_with(
+        embedding=vectors,
+        k=2,
+        filter=filters
+    )
+
+def test_get_vector(langchain_instance):
+    # Mock get result
+    mock_doc = Mock(metadata={"name": "vector1"}, id="id1")
+    langchain_instance.client.get_by_ids.return_value = [mock_doc]
+
+    # Test get existing vector
+    result = langchain_instance.get("id1")
+    langchain_instance.client.get_by_ids.assert_called_once_with(["id1"])
+    
+    assert result is not None
+    assert result.id == "id1"
+    assert result.payload == {"name": "vector1"}
+
+    # Test get non-existent vector
+    langchain_instance.client.get_by_ids.return_value = []
+    result = langchain_instance.get("non_existent_id")
+    assert result is None