246 lines
8.7 KiB
Python
246 lines
8.7 KiB
Python
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"""Factory functions for embeddings."""
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import functools
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from importlib import util
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from typing import Any
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from langchain_core.embeddings import Embeddings
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_SUPPORTED_PROVIDERS = {
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"azure_openai": "langchain_openai",
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"bedrock": "langchain_aws",
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"cohere": "langchain_cohere",
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"google_vertexai": "langchain_google_vertexai",
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"huggingface": "langchain_huggingface",
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"mistralai": "langchain_mistralai",
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"ollama": "langchain_ollama",
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"openai": "langchain_openai",
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}
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def _get_provider_list() -> str:
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"""Get formatted list of providers and their packages."""
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return "\n".join(f" - {p}: {pkg.replace('_', '-')}" for p, pkg in _SUPPORTED_PROVIDERS.items())
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def _parse_model_string(model_name: str) -> tuple[str, str]:
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"""Parse a model string into provider and model name components.
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The model string should be in the format 'provider:model-name', where provider
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is one of the supported providers.
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Args:
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model_name: A model string in the format 'provider:model-name'
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Returns:
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A tuple of (provider, model_name)
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```python
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_parse_model_string("openai:text-embedding-3-small")
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# Returns: ("openai", "text-embedding-3-small")
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_parse_model_string("bedrock:amazon.titan-embed-text-v1")
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# Returns: ("bedrock", "amazon.titan-embed-text-v1")
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```
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Raises:
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ValueError: If the model string is not in the correct format or
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the provider is unsupported
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"""
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if ":" not in model_name:
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providers = _SUPPORTED_PROVIDERS
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msg = (
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f"Invalid model format '{model_name}'.\n"
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f"Model name must be in format 'provider:model-name'\n"
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f"Example valid model strings:\n"
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f" - openai:text-embedding-3-small\n"
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f" - bedrock:amazon.titan-embed-text-v1\n"
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f" - cohere:embed-english-v3.0\n"
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f"Supported providers: {providers}"
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)
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raise ValueError(msg)
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provider, model = model_name.split(":", 1)
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provider = provider.lower().strip()
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model = model.strip()
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if provider not in _SUPPORTED_PROVIDERS:
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msg = (
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f"Provider '{provider}' is not supported.\n"
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f"Supported providers and their required packages:\n"
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f"{_get_provider_list()}"
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)
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raise ValueError(msg)
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if not model:
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msg = "Model name cannot be empty"
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raise ValueError(msg)
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return provider, model
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def _infer_model_and_provider(
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model: str,
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*,
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provider: str | None = None,
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) -> tuple[str, str]:
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if not model.strip():
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msg = "Model name cannot be empty"
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raise ValueError(msg)
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if provider is None and ":" in model:
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provider, model_name = _parse_model_string(model)
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else:
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model_name = model
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if not provider:
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providers = _SUPPORTED_PROVIDERS
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msg = (
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"Must specify either:\n"
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"1. A model string in format 'provider:model-name'\n"
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" Example: 'openai:text-embedding-3-small'\n"
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"2. Or explicitly set provider from: "
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f"{providers}"
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)
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raise ValueError(msg)
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if provider not in _SUPPORTED_PROVIDERS:
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msg = (
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f"Provider '{provider}' is not supported.\n"
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f"Supported providers and their required packages:\n"
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f"{_get_provider_list()}"
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)
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raise ValueError(msg)
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return provider, model_name
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@functools.lru_cache(maxsize=len(_SUPPORTED_PROVIDERS))
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def _check_pkg(pkg: str) -> None:
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"""Check if a package is installed."""
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if not util.find_spec(pkg):
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msg = f"Could not import {pkg} python package. Please install it with `pip install {pkg}`"
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raise ImportError(msg)
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def init_embeddings(
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model: str,
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*,
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provider: str | None = None,
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**kwargs: Any,
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) -> Embeddings:
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"""Initialize an embedding model from a model name and optional provider.
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!!! note
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Requires the integration package for the chosen model provider to be installed.
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See the `model_provider` parameter below for specific package names
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(e.g., `pip install langchain-openai`).
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Refer to the [provider integration's API reference](https://docs.langchain.com/oss/python/integrations/providers)
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for supported model parameters to use as `**kwargs`.
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Args:
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model: The name of the model, e.g. `'openai:text-embedding-3-small'`.
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You can also specify model and model provider in a single argument using
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`'{model_provider}:{model}'` format, e.g. `'openai:text-embedding-3-small'`.
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provider: The model provider if not specified as part of the model arg
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(see above).
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Supported `provider` values and the corresponding integration package
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are:
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- `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
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- `azure_openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
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- `bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)
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- `cohere` -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere)
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- `google_vertexai` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)
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- `huggingface` -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface)
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- `mistraiai` -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai)
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- `ollama` -> [`langchain-ollama`](https://docs.langchain.com/oss/python/integrations/providers/ollama)
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**kwargs: Additional model-specific parameters passed to the embedding model.
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These vary by provider. Refer to the specific model provider's
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[integration reference](https://reference.langchain.com/python/integrations/)
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for all available parameters.
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Returns:
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An `Embeddings` instance that can generate embeddings for text.
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Raises:
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ValueError: If the model provider is not supported or cannot be determined
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ImportError: If the required provider package is not installed
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???+ example
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```python
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# pip install langchain langchain-openai
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# Using a model string
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model = init_embeddings("openai:text-embedding-3-small")
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model.embed_query("Hello, world!")
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# Using explicit provider
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model = init_embeddings(model="text-embedding-3-small", provider="openai")
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model.embed_documents(["Hello, world!", "Goodbye, world!"])
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# With additional parameters
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model = init_embeddings("openai:text-embedding-3-small", api_key="sk-...")
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```
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!!! version-added "Added in `langchain` 0.3.9"
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"""
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if not model:
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providers = _SUPPORTED_PROVIDERS.keys()
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msg = f"Must specify model name. Supported providers are: {', '.join(providers)}"
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raise ValueError(msg)
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provider, model_name = _infer_model_and_provider(model, provider=provider)
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pkg = _SUPPORTED_PROVIDERS[provider]
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_check_pkg(pkg)
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if provider == "openai":
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from langchain_openai import OpenAIEmbeddings
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return OpenAIEmbeddings(model=model_name, **kwargs)
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if provider == "azure_openai":
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from langchain_openai import AzureOpenAIEmbeddings
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return AzureOpenAIEmbeddings(model=model_name, **kwargs)
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if provider == "google_vertexai":
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from langchain_google_vertexai import VertexAIEmbeddings
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return VertexAIEmbeddings(model=model_name, **kwargs)
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if provider == "bedrock":
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from langchain_aws import BedrockEmbeddings
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return BedrockEmbeddings(model_id=model_name, **kwargs)
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if provider == "cohere":
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from langchain_cohere import CohereEmbeddings
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return CohereEmbeddings(model=model_name, **kwargs)
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if provider == "mistralai":
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from langchain_mistralai import MistralAIEmbeddings
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return MistralAIEmbeddings(model=model_name, **kwargs)
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if provider == "huggingface":
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from langchain_huggingface import HuggingFaceEmbeddings
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return HuggingFaceEmbeddings(model_name=model_name, **kwargs)
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if provider == "ollama":
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from langchain_ollama import OllamaEmbeddings
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return OllamaEmbeddings(model=model_name, **kwargs)
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msg = (
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f"Provider '{provider}' is not supported.\n"
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f"Supported providers and their required packages:\n"
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f"{_get_provider_list()}"
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)
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raise ValueError(msg)
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__all__ = [
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"Embeddings", # This one is for backwards compatibility
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"init_embeddings",
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]
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