langchain/libs/community/langchain_community/embeddings/__init__.py
高璟琦 ec7a59c96c
community[minor]: Add solar embedding (#19761)
Solar is a large language model developed by
[Upstage](https://upstage.ai/). It's a powerful and purpose-trained LLM.
You can visit the embedding service provided by Solar within this pr.

You may get **SOLAR_API_KEY** from
https://console.upstage.ai/services/embedding
You can refer to more details about accepted llm integration at
https://python.langchain.com/docs/integrations/llms/solar.
2024-03-29 09:36:05 -07:00

125 lines
6.6 KiB
Python

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