Feedback

libs/langchain/langchain/embeddings/__init__.py

@@ -14,6 +14,7 @@
 from typing import TYPE_CHECKING, Any
 
 from langchain._api import create_importer
+from langchain.embeddings.base import init_embeddings
 from langchain.embeddings.cache import CacheBackedEmbeddings
 
 if TYPE_CHECKING:
@@ -221,4 +222,5 @@ def __getattr__(name: str) -> Any:
     "VertexAIEmbeddings",
     "VoyageEmbeddings",
     "XinferenceEmbeddings",
+    "init_embeddings",
 ]

libs/langchain/langchain/embeddings/base.py

@@ -2,6 +2,7 @@
 from importlib import util
 from typing import Any, List, Optional, Tuple, Union
 
+from langchain_core._api import beta
 from langchain_core.embeddings import Embeddings
 from langchain_core.runnables import Runnable
 
@@ -35,11 +36,13 @@ def _parse_model_string(model_name: str) -> Tuple[str, str]:
     Returns:
         A tuple of (provider, model_name)
 
-    Examples:
-        >>> _parse_model_string("openai:text-embedding-3-small")
-        ("openai", "text-embedding-3-small")
-        >>> _parse_model_string("bedrock:amazon.titan-embed-text-v1")
-        ("bedrock", "amazon.titan-embed-text-v1")
+    .. code-block:: python
+
+        _parse_model_string("openai:text-embedding-3-small")
+        # Returns: ("openai", "text-embedding-3-small")
+
+        _parse_model_string("bedrock:amazon.titan-embed-text-v1")
+        # Returns: ("bedrock", "amazon.titan-embed-text-v1")
 
     Raises:
         ValueError: If the model string is not in the correct format or
@@ -89,7 +92,7 @@ def _infer_model_and_provider(
             "Must specify either:\n"
             "1. A model string in format 'provider:model-name'\n"
             "   Example: 'openai:text-embedding-3-small'\n"
-            "2. Or explicitly set model_provider from: "
+            "2. Or explicitly set provider from: "
             f"{providers}"
         )
 
@@ -102,13 +105,13 @@ def _infer_model_and_provider(
     return provider, model_name
 
 
-def _init_embedding_model_helper(
+def _init_embeddings_helper(
     model: str, *, provider: Optional[str] = None, **kwargs: Any
 ) -> Embeddings:
     """Initialize an Embeddings model from the model name and provider.
 
     Internal helper function that handles the actual model initialization.
-    Use init_embedding_model() instead of calling this directly.
+    Use init_embeddings() instead of calling this directly.
     """
     provider, model_name = _infer_model_and_provider(model, provider=provider)
     pkg = _SUPPORTED_PROVIDERS[provider]
@@ -150,7 +153,7 @@ def _init_embedding_model_helper(
         )
 
 
-@functools.lru_cache
+@functools.lru_cache(maxsize=len(_SUPPORTED_PROVIDERS))
 def _check_pkg(pkg: str) -> None:
     """Check if a package is installed."""
     if not util.find_spec(pkg):
@@ -160,23 +163,23 @@ def _check_pkg(pkg: str) -> None:
         )
 
 
-def embedding_model(
+@beta()
+def init_embeddings(
     model: str,
     *,
-    model_provider: Optional[str] = None,
+    provider: Optional[str] = None,
     **kwargs: Any,
 ) -> Union[Embeddings, Runnable[Any, List[float]]]:
-    f"""Initialize an embeddings model from a model name and optional provider.
+    """Initialize an embeddings model from a model name and optional provider.
 
-    This function creates an embeddings model instance from either:
-    1. A model string in the format 'provider:model-name'
-    2. A model name and explicit provider
+    **Note:** Must have the integration package corresponding to the model provider
+    installed.
 
     Args:
         model: Name of the model to use. Can be either:
             - A model string like "openai:text-embedding-3-small"
-            - Just the model name if model_provider is specified
-        model_provider: Optional explicit provider name. If not specified,
+            - Just the model name if provider is specified
+        provider: Optional explicit provider name. If not specified,
             will attempt to parse from the model string. Supported providers
             and their required packages:
 
@@ -192,22 +195,29 @@ def embedding_model(
         ValueError: If the model provider is not supported or cannot be determined
         ImportError: If the required provider package is not installed
 
-    Examples:
-        >>> # Using a model string
-        >>> model = init_embedding_model("openai:text-embedding-3-small")
-        >>> model.embed_query("Hello, world!")
-        
-        >>> # Using explicit provider
-        >>> model = init_embedding_model(
-        ...     model="text-embedding-3-small",
-        ...     model_provider="openai"
-        ... )
-        
-        >>> # With additional parameters
-        >>> model = init_embedding_model(
-        ...     "openai:text-embedding-3-small",
-        ...     api_key="sk-..."
-        ... )
+    .. dropdown:: Example Usage
+        :open:
+
+        .. code-block:: python
+
+            # Using a model string
+            model = init_embeddings("openai:text-embedding-3-small")
+            model.embed_query("Hello, world!")
+
+            # Using explicit provider
+            model = init_embeddings(
+                model="text-embedding-3-small",
+                provider="openai"
+            )
+            model.embed_documents(["Hello, world!", "Goodbye, world!"])
+
+            # With additional parameters
+            model = init_embeddings(
+                "openai:text-embedding-3-small",
+                api_key="sk-..."
+            )
+
+    .. versionadded:: 0.3.9
     """
     if not model:
         providers = _SUPPORTED_PROVIDERS.keys()
@@ -216,10 +226,10 @@ def embedding_model(
             f"Supported providers are: {', '.join(providers)}"
         )
 
-    return _init_embedding_model_helper(model, model_provider=model_provider, **kwargs)
+    return _init_embeddings_helper(model, provider=provider, **kwargs)
 
 
 __all__ = [
-    "embedding_model",
+    "init_embeddings",
     "Embeddings",  # This one is for backwards compatibility
 ]

libs/langchain/tests/integration_tests/embeddings/test_base.py

@@ -5,7 +5,7 @@
 import pytest
 from langchain_core.embeddings import Embeddings
 
-from langchain.embeddings.base import _SUPPORTED_PROVIDERS, embedding_model
+from langchain.embeddings.base import _SUPPORTED_PROVIDERS, init_embeddings
 
 
 @pytest.mark.parametrize(
@@ -24,12 +24,12 @@ async def test_init_embedding_model(provider: str, model: str) -> None:
     except ImportError:
         pytest.skip(f"Package {package} is not installed")
 
-    model_colon = embedding_model(f"{provider}:{model}")
+    model_colon = init_embeddings(f"{provider}:{model}")
     assert isinstance(model_colon, Embeddings)
 
-    model_explicit = embedding_model(
+    model_explicit = init_embeddings(
         model=model,
-        model_provider=provider,
+        provider=provider,
     )
     assert isinstance(model_explicit, Embeddings)
 

libs/langchain/tests/unit_tests/embeddings/test_imports.py

@@ -55,6 +55,7 @@
     "JohnSnowLabsEmbeddings",
     "VoyageEmbeddings",
     "BookendEmbeddings",
+    "init_embeddings",
 ]