fix(embeddings/lmstudio): honor LMSTUDIO_BASE_URL env var

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
This commit is contained in:
kartik-mem0
2026-08-13 17:36:59 +05:30
parent 96d45b78c7
commit 08e9cc476c
3 changed files with 35 additions and 3 deletions
+3 -2
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@@ -34,7 +34,7 @@ class BaseEmbedderConfig(ABC):
# Gemini specific
output_dimensionality: Optional[str] = None,
# LM Studio specific
lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
lmstudio_base_url: Optional[str] = None,
# AWS Bedrock specific
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
@@ -70,7 +70,8 @@ class BaseEmbedderConfig(ABC):
:type memory_update_embedding_type: Optional[str], optional
:param memory_search_embedding_type: The type of embedding to use for the search memory action, defaults to None
:type memory_search_embedding_type: Optional[str], optional
:param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
:param lmstudio_base_url: LM Studio base URL to be use, defaults to None
(resolved by the provider from LMSTUDIO_BASE_URL, then "http://localhost:1234/v1")
:type lmstudio_base_url: Optional[str], optional
"""
+4
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@@ -1,3 +1,4 @@
import os
from typing import Literal, Optional
from openai import OpenAI
@@ -13,6 +14,9 @@ class LMStudioEmbedding(EmbeddingBase):
self.config.model = self.config.model or "nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
self.config.embedding_dims = self.config.embedding_dims or 1536
self.config.api_key = self.config.api_key or "lm-studio"
self.config.lmstudio_base_url = (
self.config.lmstudio_base_url or os.getenv("LMSTUDIO_BASE_URL") or "http://localhost:1234/v1"
)
self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
+28 -1
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@@ -6,7 +6,6 @@ from mem0.configs.embeddings.base import BaseEmbedderConfig
from mem0.embeddings.lmstudio import LMStudioEmbedding
@pytest.fixture
def mock_lm_studio_client():
with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
@@ -80,3 +79,31 @@ def test_embed_batch_count_mismatch_raises(mock_lm_studio_client):
with pytest.raises(ValueError, match="returned 1 embeddings for 2 texts"):
embedder.embed_batch(["first text", "second text"])
def test_base_url_honors_lmstudio_base_url_env(mock_lm_studio_client, monkeypatch):
monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
LMStudioEmbedding(BaseEmbedderConfig())
assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
def test_base_url_prefers_explicit_config_over_env(mock_lm_studio_client, monkeypatch):
monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
LMStudioEmbedding(BaseEmbedderConfig(lmstudio_base_url="http://explicit:1234/v1"))
assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"
def test_base_url_falls_back_to_localhost_default(mock_lm_studio_client, monkeypatch):
monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)
with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
embedder = LMStudioEmbedding(BaseEmbedderConfig())
assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
assert embedder.config.lmstudio_base_url == "http://localhost:1234/v1"