kartik-mem0
08e9cc476c
BaseEmbedderConfig baked "http://localhost:1234/v1" into the lmstudio_base_url default, so the attribute was never falsy and the embedder had no way to tell "unset" from "explicitly localhost". Setting LMSTUDIO_BASE_URL had no effect. The default moves to the provider, which now resolves explicit config, then LMSTUDIO_BASE_URL, then the localhost default, matching how the OpenAI embedder already reads OPENAI_BASE_URL. The resolved value is written back to config.lmstudio_base_url, so the attribute still reads the same after construction. Closes #6692
110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
from unittest.mock import Mock, patch
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import pytest
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.lmstudio import LMStudioEmbedding
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@pytest.fixture
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def mock_lm_studio_client():
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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mock_client = Mock()
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mock_client.embeddings.create.return_value = Mock(data=[Mock(embedding=[0.1, 0.2, 0.3, 0.4, 0.5])])
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mock_openai.return_value = mock_client
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yield mock_client
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def test_embed_text(mock_lm_studio_client):
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config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
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embedder = LMStudioEmbedding(config)
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text = "Sample text to embed."
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embedding = embedder.embed(text)
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mock_lm_studio_client.embeddings.create.assert_called_once_with(
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input=["Sample text to embed."], model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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)
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assert embedding == [0.1, 0.2, 0.3, 0.4, 0.5]
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def test_embed_batch_single_call(mock_lm_studio_client):
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config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
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embedder = LMStudioEmbedding(config)
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mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
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mock_item1 = Mock(index=1, embedding=[0.4, 0.5, 0.6])
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mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0, mock_item1])
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texts = ["First text.", "Second text."]
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embeddings = embedder.embed_batch(texts)
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mock_lm_studio_client.embeddings.create.assert_called_once_with(
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input=texts, model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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)
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assert embeddings == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
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def test_embed_batch_empty_list(mock_lm_studio_client):
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config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
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embedder = LMStudioEmbedding(config)
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result = embedder.embed_batch([])
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assert result == []
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mock_lm_studio_client.embeddings.create.assert_not_called()
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def test_embed_batch_strips_newlines(mock_lm_studio_client):
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config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
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embedder = LMStudioEmbedding(config)
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mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
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mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0])
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embedder.embed_batch(["line one\nline two"])
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mock_lm_studio_client.embeddings.create.assert_called_once_with(
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input=["line one line two"], model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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)
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def test_embed_batch_count_mismatch_raises(mock_lm_studio_client):
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config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
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embedder = LMStudioEmbedding(config)
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mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
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mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0])
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with pytest.raises(ValueError, match="returned 1 embeddings for 2 texts"):
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embedder.embed_batch(["first text", "second text"])
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def test_base_url_honors_lmstudio_base_url_env(mock_lm_studio_client, monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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LMStudioEmbedding(BaseEmbedderConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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def test_base_url_prefers_explicit_config_over_env(mock_lm_studio_client, monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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LMStudioEmbedding(BaseEmbedderConfig(lmstudio_base_url="http://explicit:1234/v1"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"
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def test_base_url_falls_back_to_localhost_default(mock_lm_studio_client, monkeypatch):
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monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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embedder = LMStudioEmbedding(BaseEmbedderConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
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assert embedder.config.lmstudio_base_url == "http://localhost:1234/v1"
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