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refactor(embedding): remove env fallbacks from BaseEmbeddingModel; pass credentials in tests

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This commit is contained in:
dengfuping 2026-05-20 14:33:59 +08:00
parent b10c41a7da
commit 1a5b64c6dc
3 changed files with 9 additions and 24 deletions
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24
reme/core/embedding/base_embedding_model.py
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@ -6,7 +6,6 @@ Defines the abstract base class and standard API for all embedding model impleme
import asyncio
import hashlib
import json
import os
import time
from abc import ABC
from collections import OrderedDict
@ -56,8 +55,8 @@ class BaseEmbeddingModel(ABC):
enable_cache: Whether to enable embedding cache
**kwargs: Additional model-specific parameters
"""
self._api_key: str | None = api_key
self._base_url: str | None = base_url
self.api_key: str | None = api_key
self.base_url: str | None = base_url
self.model_name = model_name
self.dimensions = dimensions
self.use_dimensions = use_dimensions
@ -78,25 +77,6 @@ class BaseEmbeddingModel(ABC):
self.cache_path: Path = Path(self.cache_dir)
self.cache_path.mkdir(parents=True, exist_ok=True)
@property
def api_key(self) -> str | None:
"""Get API key from environment variable."""
return (
os.getenv("REME_EMBEDDING_API_KEY")
or os.getenv("EMBEDDING_API_KEY")
or os.getenv("OPENAI_API_KEY")
or self._api_key
)
@property
def base_url(self) -> str | None:
"""Get base URL from environment variable."""
return (
os.getenv("REME_EMBEDDING_BASE_URL")
or os.getenv("EMBEDDING_BASE_URL")
or self._base_url
)
def _truncate_text(self, text: str) -> str:
"""Truncate text to max_input_length if it exceeds the limit."""
if len(text) > self.max_input_length:

2
tests/test_file_store.py
View file

@ -243,7 +243,7 @@ def create_file_store(store_type: str) -> BaseFileStore:
"""
config = TestConfig()
# Initialize embedding model (api_key/base_url from env: EMBEDDING_* or OPENAI_API_KEY)
# api_key/base_url from env (same as Application.embedding_api_key in application.py)
embedding_api_key = os.environ.get("EMBEDDING_API_KEY") or os.environ.get("OPENAI_API_KEY")
embedding_base_url = os.environ.get("EMBEDDING_BASE_URL") or os.environ.get("OPENAI_BASE_URL")
# use_dimensions=True so API returns 64-dim, matching collection/table schema for all backends

7
tests/test_vector_store.py
View file

@ -278,11 +278,16 @@ def create_vector_store(store_type: str, collection_name: str) -> BaseVectorStor
"""
config = TestConfig()
# Initialize embedding model (use_dimensions=True so API output matches HNSW dim / vector column)
# api_key/base_url from env (Application layer does the same via embedding_api_key in application.py)
embedding_api_key = os.environ.get("EMBEDDING_API_KEY") or os.environ.get("OPENAI_API_KEY")
embedding_base_url = os.environ.get("EMBEDDING_BASE_URL") or os.environ.get("OPENAI_BASE_URL")
# use_dimensions=True so API output matches HNSW dim / vector column
embedding_model = OpenAIEmbeddingModel(
model_name=config.EMBEDDING_MODEL_NAME,
dimensions=config.EMBEDDING_DIMENSIONS,
use_dimensions=True,
api_key=embedding_api_key or None,
base_url=embedding_base_url or None,
)
if store_type == "local":