Init embeddings (#28370)

libs/langchain/langchain/embeddings/__init__.py

@@ -14,6 +14,7 @@ import logging
 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 @@ __all__ = [
     "VertexAIEmbeddings",
     "VoyageEmbeddings",
     "XinferenceEmbeddings",
+    "init_embeddings",
 ]

libs/langchain/langchain/embeddings/base.py

@@ -1,4 +1,224 @@
-from langchain_core.embeddings import Embeddings
+import functools
+from importlib import util
+from typing import Any, List, Optional, Tuple, Union
 
-# This is for backwards compatibility
+from langchain_core._api import beta
-__all__ = ["Embeddings"]
+from langchain_core.embeddings import Embeddings
+from langchain_core.runnables import Runnable
+
+_SUPPORTED_PROVIDERS = {
+    "azure_openai": "langchain_openai",
+    "bedrock": "langchain_aws",
+    "cohere": "langchain_cohere",
+    "google_vertexai": "langchain_google_vertexai",
+    "huggingface": "langchain_huggingface",
+    "mistralai": "langchain_mistralai",
+    "openai": "langchain_openai",
+}
+
+
+def _get_provider_list() -> str:
+    """Get formatted list of providers and their packages."""
+    return "\n".join(
+        f"  - {p}: {pkg.replace('_', '-')}" for p, pkg in _SUPPORTED_PROVIDERS.items()
+    )
+
+
+def _parse_model_string(model_name: str) -> Tuple[str, str]:
+    """Parse a model string into provider and model name components.
+
+    The model string should be in the format 'provider:model-name', where provider
+    is one of the supported providers.
+
+    Args:
+        model_name: A model string in the format 'provider:model-name'
+
+    Returns:
+        A tuple of (provider, model_name)
+
+    .. 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
+            the provider is unsupported
+    """
+    if ":" not in model_name:
+        providers = _SUPPORTED_PROVIDERS
+        raise ValueError(
+            f"Invalid model format '{model_name}'.\n"
+            f"Model name must be in format 'provider:model-name'\n"
+            f"Example valid model strings:\n"
+            f"  - openai:text-embedding-3-small\n"
+            f"  - bedrock:amazon.titan-embed-text-v1\n"
+            f"  - cohere:embed-english-v3.0\n"
+            f"Supported providers: {providers}"
+        )
+
+    provider, model = model_name.split(":", 1)
+    provider = provider.lower().strip()
+    model = model.strip()
+
+    if provider not in _SUPPORTED_PROVIDERS:
+        raise ValueError(
+            f"Provider '{provider}' is not supported.\n"
+            f"Supported providers and their required packages:\n"
+            f"{_get_provider_list()}"
+        )
+    if not model:
+        raise ValueError("Model name cannot be empty")
+    return provider, model
+
+
+def _infer_model_and_provider(
+    model: str, *, provider: Optional[str] = None
+) -> Tuple[str, str]:
+    if not model.strip():
+        raise ValueError("Model name cannot be empty")
+    if provider is None and ":" in model:
+        provider, model_name = _parse_model_string(model)
+    else:
+        provider = provider
+        model_name = model
+
+    if not provider:
+        providers = _SUPPORTED_PROVIDERS
+        raise ValueError(
+            "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 provider from: "
+            f"{providers}"
+        )
+
+    if provider not in _SUPPORTED_PROVIDERS:
+        raise ValueError(
+            f"Provider '{provider}' is not supported.\n"
+            f"Supported providers and their required packages:\n"
+            f"{_get_provider_list()}"
+        )
+    return provider, model_name
+
+
+@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):
+        raise ImportError(
+            f"Could not import {pkg} python package. "
+            f"Please install it with `pip install {pkg}`"
+        )
+
+
+@beta()
+def init_embeddings(
+    model: str,
+    *,
+    provider: Optional[str] = None,
+    **kwargs: Any,
+) -> Union[Embeddings, Runnable[Any, List[float]]]:
+    """Initialize an embeddings model from a model name and optional 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 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:
+
+            {_get_provider_list()}
+
+        **kwargs: Additional model-specific parameters passed to the embedding model.
+            These vary by provider, see the provider-specific documentation for details.
+
+    Returns:
+        An Embeddings instance that can generate embeddings for text.
+
+    Raises:
+        ValueError: If the model provider is not supported or cannot be determined
+        ImportError: If the required provider package is not installed
+
+    .. 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()
+        raise ValueError(
+            "Must specify model name. "
+            f"Supported providers are: {', '.join(providers)}"
+        )
+
+    provider, model_name = _infer_model_and_provider(model, provider=provider)
+    pkg = _SUPPORTED_PROVIDERS[provider]
+    _check_pkg(pkg)
+
+    if provider == "openai":
+        from langchain_openai import OpenAIEmbeddings
+
+        return OpenAIEmbeddings(model=model_name, **kwargs)
+    elif provider == "azure_openai":
+        from langchain_openai import AzureOpenAIEmbeddings
+
+        return AzureOpenAIEmbeddings(model=model_name, **kwargs)
+    elif provider == "google_vertexai":
+        from langchain_google_vertexai import VertexAIEmbeddings
+
+        return VertexAIEmbeddings(model=model_name, **kwargs)
+    elif provider == "bedrock":
+        from langchain_aws import BedrockEmbeddings
+
+        return BedrockEmbeddings(model_id=model_name, **kwargs)
+    elif provider == "cohere":
+        from langchain_cohere import CohereEmbeddings
+
+        return CohereEmbeddings(model=model_name, **kwargs)
+    elif provider == "mistralai":
+        from langchain_mistralai import MistralAIEmbeddings
+
+        return MistralAIEmbeddings(model=model_name, **kwargs)
+    elif provider == "huggingface":
+        from langchain_huggingface import HuggingFaceEmbeddings
+
+        return HuggingFaceEmbeddings(model_name=model_name, **kwargs)
+    else:
+        raise ValueError(
+            f"Provider '{provider}' is not supported.\n"
+            f"Supported providers and their required packages:\n"
+            f"{_get_provider_list()}"
+        )
+
+
+__all__ = [
+    "init_embeddings",
+    "Embeddings",  # This one is for backwards compatibility
+]

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

@@ -0,0 +1,44 @@
+"""Test embeddings base module."""
+
+import importlib
+
+import pytest
+from langchain_core.embeddings import Embeddings
+
+from langchain.embeddings.base import _SUPPORTED_PROVIDERS, init_embeddings
+
+
+@pytest.mark.parametrize(
+    "provider, model",
+    [
+        ("openai", "text-embedding-3-large"),
+        ("google_vertexai", "text-embedding-gecko@003"),
+        ("bedrock", "amazon.titan-embed-text-v1"),
+        ("cohere", "embed-english-v2.0"),
+    ],
+)
+async def test_init_embedding_model(provider: str, model: str) -> None:
+    package = _SUPPORTED_PROVIDERS[provider]
+    try:
+        importlib.import_module(package)
+    except ImportError:
+        pytest.skip(f"Package {package} is not installed")
+
+    model_colon = init_embeddings(f"{provider}:{model}")
+    assert isinstance(model_colon, Embeddings)
+
+    model_explicit = init_embeddings(
+        model=model,
+        provider=provider,
+    )
+    assert isinstance(model_explicit, Embeddings)
+
+    text = "Hello world"
+
+    embedding_colon = await model_colon.aembed_query(text)
+    assert isinstance(embedding_colon, list)
+    assert all(isinstance(x, float) for x in embedding_colon)
+
+    embedding_explicit = await model_explicit.aembed_query(text)
+    assert isinstance(embedding_explicit, list)
+    assert all(isinstance(x, float) for x in embedding_explicit)

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

@@ -0,0 +1,111 @@
+"""Test embeddings base module."""
+
+import pytest
+
+from langchain.embeddings.base import (
+    _SUPPORTED_PROVIDERS,
+    _infer_model_and_provider,
+    _parse_model_string,
+)
+
+
+def test_parse_model_string() -> None:
+    """Test parsing model strings into provider and model components."""
+    assert _parse_model_string("openai:text-embedding-3-small") == (
+        "openai",
+        "text-embedding-3-small",
+    )
+    assert _parse_model_string("bedrock:amazon.titan-embed-text-v1") == (
+        "bedrock",
+        "amazon.titan-embed-text-v1",
+    )
+    assert _parse_model_string("huggingface:BAAI/bge-base-en:v1.5") == (
+        "huggingface",
+        "BAAI/bge-base-en:v1.5",
+    )
+
+
+def test_parse_model_string_errors() -> None:
+    """Test error cases for model string parsing."""
+    with pytest.raises(ValueError, match="Model name must be"):
+        _parse_model_string("just-a-model-name")
+
+    with pytest.raises(ValueError, match="Invalid model format "):
+        _parse_model_string("")
+
+    with pytest.raises(ValueError, match="is not supported"):
+        _parse_model_string(":model-name")
+
+    with pytest.raises(ValueError, match="Model name cannot be empty"):
+        _parse_model_string("openai:")
+
+    with pytest.raises(
+        ValueError, match="Provider 'invalid-provider' is not supported"
+    ):
+        _parse_model_string("invalid-provider:model-name")
+
+    for provider in _SUPPORTED_PROVIDERS:
+        with pytest.raises(ValueError, match=f"{provider}"):
+            _parse_model_string("invalid-provider:model-name")
+
+
+def test_infer_model_and_provider() -> None:
+    """Test model and provider inference from different input formats."""
+    assert _infer_model_and_provider("openai:text-embedding-3-small") == (
+        "openai",
+        "text-embedding-3-small",
+    )
+
+    assert _infer_model_and_provider(
+        model="text-embedding-3-small", provider="openai"
+    ) == ("openai", "text-embedding-3-small")
+
+    assert _infer_model_and_provider(
+        model="ft:text-embedding-3-small", provider="openai"
+    ) == ("openai", "ft:text-embedding-3-small")
+
+    assert _infer_model_and_provider(model="openai:ft:text-embedding-3-small") == (
+        "openai",
+        "ft:text-embedding-3-small",
+    )
+
+
+def test_infer_model_and_provider_errors() -> None:
+    """Test error cases for model and provider inference."""
+    # Test missing provider
+    with pytest.raises(ValueError, match="Must specify either"):
+        _infer_model_and_provider("text-embedding-3-small")
+
+    # Test empty model
+    with pytest.raises(ValueError, match="Model name cannot be empty"):
+        _infer_model_and_provider("")
+
+    # Test empty provider with model
+    with pytest.raises(ValueError, match="Must specify either"):
+        _infer_model_and_provider("model", provider="")
+
+    # Test invalid provider
+    with pytest.raises(ValueError, match="is not supported"):
+        _infer_model_and_provider("model", provider="invalid")
+
+    # Test provider list is in error
+    with pytest.raises(ValueError) as exc:
+        _infer_model_and_provider("model", provider="invalid")
+    for provider in _SUPPORTED_PROVIDERS:
+        assert provider in str(exc.value)
+
+
+@pytest.mark.parametrize(
+    "provider",
+    sorted(_SUPPORTED_PROVIDERS.keys()),
+)
+def test_supported_providers_package_names(provider: str) -> None:
+    """Test that all supported providers have valid package names."""
+    package = _SUPPORTED_PROVIDERS[provider]
+    assert "-" not in package
+    assert package.startswith("langchain_")
+    assert package.islower()
+
+
+def test_is_sorted() -> None:
+    assert list(_SUPPORTED_PROVIDERS) == sorted(_SUPPORTED_PROVIDERS.keys())

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

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