mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
117 lines
6.1 KiB
Python
117 lines
6.1 KiB
Python
"""**Embedding models** are wrappers around embedding models
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from different APIs and services.
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**Embedding models** can be LLMs or not.
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**Class hierarchy:**
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.. code-block::
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Embeddings --> <name>Embeddings # Examples: OpenAIEmbeddings, HuggingFaceEmbeddings
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"""
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import importlib
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import logging
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from typing import Any
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_module_lookup = {
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"AlephAlphaAsymmetricSemanticEmbedding": "langchain_community.embeddings.aleph_alpha", # noqa: E501
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"AlephAlphaSymmetricSemanticEmbedding": "langchain_community.embeddings.aleph_alpha", # noqa: E501
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"AwaEmbeddings": "langchain_community.embeddings.awa",
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"AzureOpenAIEmbeddings": "langchain_community.embeddings.azure_openai",
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"BaichuanTextEmbeddings": "langchain_community.embeddings.baichuan",
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"BedrockEmbeddings": "langchain_community.embeddings.bedrock",
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"BookendEmbeddings": "langchain_community.embeddings.bookend",
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"ClarifaiEmbeddings": "langchain_community.embeddings.clarifai",
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"CohereEmbeddings": "langchain_community.embeddings.cohere",
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"DashScopeEmbeddings": "langchain_community.embeddings.dashscope",
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"DatabricksEmbeddings": "langchain_community.embeddings.databricks",
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"DeepInfraEmbeddings": "langchain_community.embeddings.deepinfra",
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"DeterministicFakeEmbedding": "langchain_community.embeddings.fake",
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"EdenAiEmbeddings": "langchain_community.embeddings.edenai",
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"ElasticsearchEmbeddings": "langchain_community.embeddings.elasticsearch",
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"EmbaasEmbeddings": "langchain_community.embeddings.embaas",
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"ErnieEmbeddings": "langchain_community.embeddings.ernie",
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"FakeEmbeddings": "langchain_community.embeddings.fake",
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"FastEmbedEmbeddings": "langchain_community.embeddings.fastembed",
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"GPT4AllEmbeddings": "langchain_community.embeddings.gpt4all",
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"GooglePalmEmbeddings": "langchain_community.embeddings.google_palm",
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"GradientEmbeddings": "langchain_community.embeddings.gradient_ai",
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"HuggingFaceBgeEmbeddings": "langchain_community.embeddings.huggingface",
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"HuggingFaceEmbeddings": "langchain_community.embeddings.huggingface",
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"HuggingFaceHubEmbeddings": "langchain_community.embeddings.huggingface_hub",
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"HuggingFaceInferenceAPIEmbeddings": "langchain_community.embeddings.huggingface",
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"HuggingFaceInstructEmbeddings": "langchain_community.embeddings.huggingface",
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"InfinityEmbeddings": "langchain_community.embeddings.infinity",
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"InfinityEmbeddingsLocal": "langchain_community.embeddings.infinity_local",
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"JavelinAIGatewayEmbeddings": "langchain_community.embeddings.javelin_ai_gateway",
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"JinaEmbeddings": "langchain_community.embeddings.jina",
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"JohnSnowLabsEmbeddings": "langchain_community.embeddings.johnsnowlabs",
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"LLMRailsEmbeddings": "langchain_community.embeddings.llm_rails",
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"LaserEmbeddings": "langchain_community.embeddings.laser",
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"LlamaCppEmbeddings": "langchain_community.embeddings.llamacpp",
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"LlamafileEmbeddings": "langchain_community.embeddings.llamafile",
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"LocalAIEmbeddings": "langchain_community.embeddings.localai",
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"MiniMaxEmbeddings": "langchain_community.embeddings.minimax",
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"MlflowAIGatewayEmbeddings": "langchain_community.embeddings.mlflow_gateway",
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"MlflowCohereEmbeddings": "langchain_community.embeddings.mlflow",
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"MlflowEmbeddings": "langchain_community.embeddings.mlflow",
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"ModelScopeEmbeddings": "langchain_community.embeddings.modelscope_hub",
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"MosaicMLInstructorEmbeddings": "langchain_community.embeddings.mosaicml",
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"NLPCloudEmbeddings": "langchain_community.embeddings.nlpcloud",
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"NeMoEmbeddings": "langchain_community.embeddings.nemo",
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"OCIGenAIEmbeddings": "langchain_community.embeddings.oci_generative_ai",
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"OctoAIEmbeddings": "langchain_community.embeddings.octoai_embeddings",
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"OllamaEmbeddings": "langchain_community.embeddings.ollama",
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"OpenAIEmbeddings": "langchain_community.embeddings.openai",
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"QianfanEmbeddingsEndpoint": "langchain_community.embeddings.baidu_qianfan_endpoint", # noqa: E501
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"QuantizedBiEncoderEmbeddings": "langchain_community.embeddings.optimum_intel",
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"SagemakerEndpointEmbeddings": "langchain_community.embeddings.sagemaker_endpoint",
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"SelfHostedEmbeddings": "langchain_community.embeddings.self_hosted",
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"SelfHostedHuggingFaceEmbeddings": "langchain_community.embeddings.self_hosted_hugging_face", # noqa: E501
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"SelfHostedHuggingFaceInstructEmbeddings": "langchain_community.embeddings.self_hosted_hugging_face", # noqa: E501
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"SentenceTransformerEmbeddings": "langchain_community.embeddings.sentence_transformer", # noqa: E501
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"SpacyEmbeddings": "langchain_community.embeddings.spacy_embeddings",
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"SparkLLMTextEmbeddings": "langchain_community.embeddings.sparkllm",
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"TensorflowHubEmbeddings": "langchain_community.embeddings.tensorflow_hub",
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"VertexAIEmbeddings": "langchain_community.embeddings.vertexai",
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"VolcanoEmbeddings": "langchain_community.embeddings.volcengine",
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"VoyageEmbeddings": "langchain_community.embeddings.voyageai",
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"XinferenceEmbeddings": "langchain_community.embeddings.xinference",
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}
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def __getattr__(name: str) -> Any:
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if name in _module_lookup:
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module = importlib.import_module(_module_lookup[name])
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return getattr(module, name)
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raise AttributeError(f"module {__name__} has no attribute {name}")
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__all__ = list(_module_lookup.keys())
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logger = logging.getLogger(__name__)
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# TODO: this is in here to maintain backwards compatibility
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class HypotheticalDocumentEmbedder:
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def __init__(self, *args: Any, **kwargs: Any):
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logger.warning(
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"Using a deprecated class. Please use "
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"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
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)
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
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return H(*args, **kwargs) # type: ignore
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@classmethod
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def from_llm(cls, *args: Any, **kwargs: Any) -> Any:
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logger.warning(
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"Using a deprecated class. Please use "
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"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
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)
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
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return H.from_llm(*args, **kwargs)
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