Add LM Studio support (#2425)

tests/embeddings/test_lm_studio_embeddings.py

@@ -0,0 +1,41 @@
+import pytest
+from unittest.mock import Mock, patch
+from mem0.embeddings.lmstudio import LMStudioEmbedding
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+
+
+@pytest.fixture
+def mock_lm_studio_client():
+    with patch("mem0.embeddings.lmstudio.Client") as mock_lm_studio:
+        mock_client = Mock()
+        mock_client.list.return_value = {"models": [{"name": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"}]}
+        mock_lm_studio.return_value = mock_client
+        yield mock_client
+
+
+def test_embed_text(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    mock_response = {"embedding": [0.1, 0.2, 0.3, 0.4, 0.5]}
+    mock_lm_studio_client.embeddings.return_value = mock_response
+
+    text = "Sample text to embed."
+    embedding = embedder.embed(text)
+
+    mock_lm_studio_client.embeddings.assert_called_once_with(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", prompt=text)
+
+    assert embedding == [0.1, 0.2, 0.3, 0.4, 0.5]
+
+
+def test_ensure_model_exists(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    mock_lm_studio_client.pull.assert_not_called()
+
+    mock_lm_studio_client.list.return_value = {"models": []}
+
+    embedder._ensure_model_exists()
+
+    mock_lm_studio_client.pull.assert_called_once_with("nomic-embed-text")

tests/llms/test_lm_studio.py

@@ -0,0 +1,34 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.lmstudio import LMStudioLLM
+
+
+@pytest.fixture
+def mock_lm_studio_client():
+    with patch("mem0.llms.lmstudio.Client") as mock_lm_studio:
+        mock_client = Mock()
+        mock_client.list.return_value = {"models": [{"name": "lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"}]}
+        mock_lm_studio.return_value = mock_client
+        yield mock_client
+
+
+def test_generate_response_without_tools(mock_lm_studio_client):
+    config = BaseLlmConfig(model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", temperature=0.7, max_tokens=100, top_p=1.0)
+    llm = LMStudioLLM(config)
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello, how are you?"},
+    ]
+
+    mock_response = {"message": {"content": "I'm doing well, thank you for asking!"}}
+    mock_lm_studio_client.chat.return_value = mock_response
+
+    response = llm.generate_response(messages)
+
+    mock_lm_studio_client.chat.assert_called_once_with(
+        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", messages=messages, options={"temperature": 0.7, "num_predict": 100, "top_p": 1.0}
+    )
+    assert response == "I'm doing well, thank you for asking!"