mirror of
https://github.com/hwchase17/langchain.git
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06f47678ae
**Description:** **TextEmbed** is a high-performance embedding inference server designed to provide a high-throughput, low-latency solution for serving embeddings. It supports various sentence-transformer models and includes the ability to deploy image and text embedding models. TextEmbed offers flexibility and scalability for diverse applications. - **PyPI Package:** [TextEmbed on PyPI](https://pypi.org/project/textembed/) - **Docker Image:** [TextEmbed on Docker Hub](https://hub.docker.com/r/kevaldekivadiya/textembed) - **GitHub Repository:** [TextEmbed on GitHub](https://github.com/kevaldekivadiya2415/textembed) **PR Description** This PR adds functionality for embedding documents and queries using the `TextEmbedEmbeddings` class. The implementation allows for both synchronous and asynchronous embedding requests to a TextEmbed API endpoint. The class handles batching and permuting of input texts to optimize the embedding process. **Example Usage:** ```python from langchain_community.embeddings import TextEmbedEmbeddings # Initialise the embeddings class embeddings = TextEmbedEmbeddings(model="your-model-id", api_key="your-api-key", api_url="your_api_url") # Define a list of documents documents = [ "Data science involves extracting insights from data.", "Artificial intelligence is transforming various industries.", "Cloud computing provides scalable computing resources over the internet.", "Big data analytics helps in understanding large datasets.", "India has a diverse cultural heritage." ] # Define a query query = "What is the cultural heritage of India?" # Embed all documents document_embeddings = embeddings.embed_documents(documents) # Embed the query query_embedding = embeddings.embed_query(query) # Print embeddings for each document for i, embedding in enumerate(document_embeddings): print(f"Document {i+1} Embedding:", embedding) # Print the query embedding print("Query Embedding:", query_embedding) --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
438 lines
16 KiB
Python
438 lines
16 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 TYPE_CHECKING, Any
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if TYPE_CHECKING:
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from langchain_community.embeddings.aleph_alpha import (
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AlephAlphaAsymmetricSemanticEmbedding,
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AlephAlphaSymmetricSemanticEmbedding,
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)
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from langchain_community.embeddings.anyscale import (
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AnyscaleEmbeddings,
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)
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from langchain_community.embeddings.ascend import (
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AscendEmbeddings,
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)
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from langchain_community.embeddings.awa import (
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AwaEmbeddings,
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)
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from langchain_community.embeddings.azure_openai import (
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AzureOpenAIEmbeddings,
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)
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from langchain_community.embeddings.baichuan import (
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BaichuanTextEmbeddings,
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)
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from langchain_community.embeddings.baidu_qianfan_endpoint import (
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QianfanEmbeddingsEndpoint,
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)
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from langchain_community.embeddings.bedrock import (
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BedrockEmbeddings,
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)
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from langchain_community.embeddings.bookend import (
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BookendEmbeddings,
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)
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from langchain_community.embeddings.clarifai import (
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ClarifaiEmbeddings,
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)
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from langchain_community.embeddings.clova import (
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ClovaEmbeddings,
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)
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from langchain_community.embeddings.cohere import (
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CohereEmbeddings,
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)
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from langchain_community.embeddings.dashscope import (
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DashScopeEmbeddings,
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)
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from langchain_community.embeddings.databricks import (
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DatabricksEmbeddings,
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)
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from langchain_community.embeddings.deepinfra import (
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DeepInfraEmbeddings,
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)
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from langchain_community.embeddings.edenai import (
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EdenAiEmbeddings,
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)
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from langchain_community.embeddings.elasticsearch import (
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ElasticsearchEmbeddings,
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)
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from langchain_community.embeddings.embaas import (
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EmbaasEmbeddings,
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)
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from langchain_community.embeddings.ernie import (
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ErnieEmbeddings,
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)
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from langchain_community.embeddings.fake import (
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DeterministicFakeEmbedding,
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FakeEmbeddings,
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)
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from langchain_community.embeddings.fastembed import (
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FastEmbedEmbeddings,
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)
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from langchain_community.embeddings.gigachat import (
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GigaChatEmbeddings,
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)
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from langchain_community.embeddings.google_palm import (
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GooglePalmEmbeddings,
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)
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from langchain_community.embeddings.gpt4all import (
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GPT4AllEmbeddings,
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)
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from langchain_community.embeddings.gradient_ai import (
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GradientEmbeddings,
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)
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from langchain_community.embeddings.huggingface import (
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HuggingFaceBgeEmbeddings,
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HuggingFaceEmbeddings,
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HuggingFaceInferenceAPIEmbeddings,
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HuggingFaceInstructEmbeddings,
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)
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from langchain_community.embeddings.huggingface_hub import (
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HuggingFaceHubEmbeddings,
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)
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from langchain_community.embeddings.infinity import (
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InfinityEmbeddings,
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)
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from langchain_community.embeddings.infinity_local import (
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InfinityEmbeddingsLocal,
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)
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from langchain_community.embeddings.ipex_llm import IpexLLMBgeEmbeddings
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from langchain_community.embeddings.itrex import (
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QuantizedBgeEmbeddings,
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)
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from langchain_community.embeddings.javelin_ai_gateway import (
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JavelinAIGatewayEmbeddings,
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)
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from langchain_community.embeddings.jina import (
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JinaEmbeddings,
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)
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from langchain_community.embeddings.johnsnowlabs import (
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JohnSnowLabsEmbeddings,
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)
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from langchain_community.embeddings.laser import (
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LaserEmbeddings,
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)
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from langchain_community.embeddings.llamacpp import (
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LlamaCppEmbeddings,
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)
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from langchain_community.embeddings.llamafile import (
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LlamafileEmbeddings,
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)
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from langchain_community.embeddings.llm_rails import (
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LLMRailsEmbeddings,
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)
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from langchain_community.embeddings.localai import (
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LocalAIEmbeddings,
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)
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from langchain_community.embeddings.minimax import (
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MiniMaxEmbeddings,
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)
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from langchain_community.embeddings.mlflow import (
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MlflowCohereEmbeddings,
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MlflowEmbeddings,
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)
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from langchain_community.embeddings.mlflow_gateway import (
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MlflowAIGatewayEmbeddings,
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)
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from langchain_community.embeddings.modelscope_hub import (
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ModelScopeEmbeddings,
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)
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from langchain_community.embeddings.mosaicml import (
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MosaicMLInstructorEmbeddings,
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)
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from langchain_community.embeddings.nemo import (
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NeMoEmbeddings,
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)
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from langchain_community.embeddings.nlpcloud import (
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NLPCloudEmbeddings,
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)
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from langchain_community.embeddings.oci_generative_ai import (
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OCIGenAIEmbeddings,
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)
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from langchain_community.embeddings.octoai_embeddings import (
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OctoAIEmbeddings,
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)
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from langchain_community.embeddings.ollama import (
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OllamaEmbeddings,
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)
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from langchain_community.embeddings.openai import (
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OpenAIEmbeddings,
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)
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from langchain_community.embeddings.openvino import (
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OpenVINOBgeEmbeddings,
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OpenVINOEmbeddings,
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)
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from langchain_community.embeddings.optimum_intel import (
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QuantizedBiEncoderEmbeddings,
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)
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from langchain_community.embeddings.oracleai import (
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OracleEmbeddings,
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)
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from langchain_community.embeddings.ovhcloud import (
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OVHCloudEmbeddings,
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)
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from langchain_community.embeddings.premai import (
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PremAIEmbeddings,
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)
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from langchain_community.embeddings.sagemaker_endpoint import (
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SagemakerEndpointEmbeddings,
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)
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from langchain_community.embeddings.sambanova import (
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SambaStudioEmbeddings,
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)
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from langchain_community.embeddings.self_hosted import (
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SelfHostedEmbeddings,
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)
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from langchain_community.embeddings.self_hosted_hugging_face import (
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SelfHostedHuggingFaceEmbeddings,
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SelfHostedHuggingFaceInstructEmbeddings,
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)
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from langchain_community.embeddings.sentence_transformer import (
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SentenceTransformerEmbeddings,
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)
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from langchain_community.embeddings.solar import (
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SolarEmbeddings,
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)
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from langchain_community.embeddings.spacy_embeddings import (
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SpacyEmbeddings,
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)
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from langchain_community.embeddings.sparkllm import (
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SparkLLMTextEmbeddings,
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)
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from langchain_community.embeddings.tensorflow_hub import (
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TensorflowHubEmbeddings,
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)
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from langchain_community.embeddings.textembed import (
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TextEmbedEmbeddings,
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)
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from langchain_community.embeddings.titan_takeoff import (
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TitanTakeoffEmbed,
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)
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from langchain_community.embeddings.vertexai import (
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VertexAIEmbeddings,
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)
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from langchain_community.embeddings.volcengine import (
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VolcanoEmbeddings,
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)
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from langchain_community.embeddings.voyageai import (
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VoyageEmbeddings,
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)
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from langchain_community.embeddings.xinference import (
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XinferenceEmbeddings,
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)
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from langchain_community.embeddings.yandex import (
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YandexGPTEmbeddings,
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)
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from langchain_community.embeddings.zhipuai import (
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ZhipuAIEmbeddings,
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)
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__all__ = [
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"AlephAlphaAsymmetricSemanticEmbedding",
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"AlephAlphaSymmetricSemanticEmbedding",
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"AnyscaleEmbeddings",
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"AscendEmbeddings",
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"AwaEmbeddings",
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"AzureOpenAIEmbeddings",
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"BaichuanTextEmbeddings",
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"BedrockEmbeddings",
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"BookendEmbeddings",
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"ClarifaiEmbeddings",
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"ClovaEmbeddings",
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"CohereEmbeddings",
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"DashScopeEmbeddings",
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"DatabricksEmbeddings",
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"DeepInfraEmbeddings",
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"DeterministicFakeEmbedding",
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"EdenAiEmbeddings",
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"ElasticsearchEmbeddings",
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"EmbaasEmbeddings",
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"ErnieEmbeddings",
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"FakeEmbeddings",
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"FastEmbedEmbeddings",
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"GPT4AllEmbeddings",
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"GigaChatEmbeddings",
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"GooglePalmEmbeddings",
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"GradientEmbeddings",
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"HuggingFaceBgeEmbeddings",
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"HuggingFaceEmbeddings",
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"HuggingFaceHubEmbeddings",
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"HuggingFaceInferenceAPIEmbeddings",
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"HuggingFaceInstructEmbeddings",
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"InfinityEmbeddings",
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"InfinityEmbeddingsLocal",
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"IpexLLMBgeEmbeddings",
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"JavelinAIGatewayEmbeddings",
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"JinaEmbeddings",
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"JohnSnowLabsEmbeddings",
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"LLMRailsEmbeddings",
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"LaserEmbeddings",
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"LlamaCppEmbeddings",
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"LlamafileEmbeddings",
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"LocalAIEmbeddings",
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"MiniMaxEmbeddings",
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"MlflowAIGatewayEmbeddings",
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"MlflowCohereEmbeddings",
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"MlflowEmbeddings",
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"ModelScopeEmbeddings",
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"MosaicMLInstructorEmbeddings",
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"NLPCloudEmbeddings",
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"NeMoEmbeddings",
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"OCIGenAIEmbeddings",
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"OctoAIEmbeddings",
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"OllamaEmbeddings",
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"OpenAIEmbeddings",
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"OpenVINOBgeEmbeddings",
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"OpenVINOEmbeddings",
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"OracleEmbeddings",
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"OVHCloudEmbeddings",
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"PremAIEmbeddings",
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"QianfanEmbeddingsEndpoint",
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"QuantizedBgeEmbeddings",
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"QuantizedBiEncoderEmbeddings",
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"SagemakerEndpointEmbeddings",
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"SambaStudioEmbeddings",
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"SelfHostedEmbeddings",
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"SelfHostedHuggingFaceEmbeddings",
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"SelfHostedHuggingFaceInstructEmbeddings",
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"SentenceTransformerEmbeddings",
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"SolarEmbeddings",
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"SpacyEmbeddings",
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"SparkLLMTextEmbeddings",
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"TensorflowHubEmbeddings",
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"TextEmbedEmbeddings",
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"TitanTakeoffEmbed",
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"VertexAIEmbeddings",
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"VolcanoEmbeddings",
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"VoyageEmbeddings",
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"XinferenceEmbeddings",
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"YandexGPTEmbeddings",
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"ZhipuAIEmbeddings",
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]
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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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"AnyscaleEmbeddings": "langchain_community.embeddings.anyscale",
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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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"ClovaEmbeddings": "langchain_community.embeddings.clova",
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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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"GigaChatEmbeddings": "langchain_community.embeddings.gigachat",
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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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"IpexLLMBgeEmbeddings": "langchain_community.embeddings.ipex_llm",
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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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"OpenVINOEmbeddings": "langchain_community.embeddings.openvino",
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"OpenVINOBgeEmbeddings": "langchain_community.embeddings.openvino",
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"QianfanEmbeddingsEndpoint": "langchain_community.embeddings.baidu_qianfan_endpoint", # noqa: E501
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"QuantizedBgeEmbeddings": "langchain_community.embeddings.itrex",
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"QuantizedBiEncoderEmbeddings": "langchain_community.embeddings.optimum_intel",
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"OracleEmbeddings": "langchain_community.embeddings.oracleai",
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"OVHCloudEmbeddings": "langchain_community.embeddings.ovhcloud",
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"SagemakerEndpointEmbeddings": "langchain_community.embeddings.sagemaker_endpoint",
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"SambaStudioEmbeddings": "langchain_community.embeddings.sambanova",
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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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"SolarEmbeddings": "langchain_community.embeddings.solar",
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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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"TextEmbedEmbeddings": "langchain_community.embeddings.textembed",
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"TitanTakeoffEmbed": "langchain_community.embeddings.titan_takeoff",
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"PremAIEmbeddings": "langchain_community.embeddings.premai",
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"YandexGPTEmbeddings": "langchain_community.embeddings.yandex",
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"AscendEmbeddings": "langchain_community.embeddings.ascend",
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"ZhipuAIEmbeddings": "langchain_community.embeddings.zhipuai",
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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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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(
|
|
"Using a deprecated class. Please use "
|
|
"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
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|
)
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|
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
|
|
|
|
return H.from_llm(*args, **kwargs)
|