langchain/libs/community/langchain_community/embeddings/__init__.py

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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
"""**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
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
import logging
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from langchain_community.embeddings.aleph_alpha import (
AlephAlphaAsymmetricSemanticEmbedding,
AlephAlphaSymmetricSemanticEmbedding,
)
from langchain_community.embeddings.anyscale import (
AnyscaleEmbeddings,
)
from langchain_community.embeddings.awa import (
AwaEmbeddings,
)
from langchain_community.embeddings.azure_openai import (
AzureOpenAIEmbeddings,
)
from langchain_community.embeddings.baichuan import (
BaichuanTextEmbeddings,
)
from langchain_community.embeddings.baidu_qianfan_endpoint import (
QianfanEmbeddingsEndpoint,
)
from langchain_community.embeddings.bedrock import (
BedrockEmbeddings,
)
from langchain_community.embeddings.bookend import (
BookendEmbeddings,
)
from langchain_community.embeddings.clarifai import (
ClarifaiEmbeddings,
)
from langchain_community.embeddings.clova import (
ClovaEmbeddings,
)
from langchain_community.embeddings.cohere import (
CohereEmbeddings,
)
from langchain_community.embeddings.dashscope import (
DashScopeEmbeddings,
)
from langchain_community.embeddings.databricks import (
DatabricksEmbeddings,
)
from langchain_community.embeddings.deepinfra import (
DeepInfraEmbeddings,
)
from langchain_community.embeddings.edenai import (
EdenAiEmbeddings,
)
from langchain_community.embeddings.elasticsearch import (
ElasticsearchEmbeddings,
)
from langchain_community.embeddings.embaas import (
EmbaasEmbeddings,
)
from langchain_community.embeddings.ernie import (
ErnieEmbeddings,
)
from langchain_community.embeddings.fake import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
from langchain_community.embeddings.fastembed import (
FastEmbedEmbeddings,
)
from langchain_community.embeddings.gigachat import (
GigaChatEmbeddings,
)
from langchain_community.embeddings.google_palm import (
GooglePalmEmbeddings,
)
from langchain_community.embeddings.gpt4all import (
GPT4AllEmbeddings,
)
from langchain_community.embeddings.gradient_ai import (
GradientEmbeddings,
)
from langchain_community.embeddings.huggingface import (
HuggingFaceBgeEmbeddings,
HuggingFaceEmbeddings,
HuggingFaceInferenceAPIEmbeddings,
HuggingFaceInstructEmbeddings,
)
from langchain_community.embeddings.huggingface_hub import (
HuggingFaceHubEmbeddings,
)
from langchain_community.embeddings.infinity import (
InfinityEmbeddings,
)
from langchain_community.embeddings.infinity_local import (
InfinityEmbeddingsLocal,
)
from langchain_community.embeddings.itrex import (
QuantizedBgeEmbeddings,
)
from langchain_community.embeddings.javelin_ai_gateway import (
JavelinAIGatewayEmbeddings,
)
from langchain_community.embeddings.jina import (
JinaEmbeddings,
)
from langchain_community.embeddings.johnsnowlabs import (
JohnSnowLabsEmbeddings,
)
from langchain_community.embeddings.laser import (
LaserEmbeddings,
)
from langchain_community.embeddings.llamacpp import (
LlamaCppEmbeddings,
)
from langchain_community.embeddings.llamafile import (
LlamafileEmbeddings,
)
from langchain_community.embeddings.llm_rails import (
LLMRailsEmbeddings,
)
from langchain_community.embeddings.localai import (
LocalAIEmbeddings,
)
from langchain_community.embeddings.minimax import (
MiniMaxEmbeddings,
)
from langchain_community.embeddings.mlflow import (
MlflowCohereEmbeddings,
MlflowEmbeddings,
)
from langchain_community.embeddings.mlflow_gateway import (
MlflowAIGatewayEmbeddings,
)
from langchain_community.embeddings.modelscope_hub import (
ModelScopeEmbeddings,
)
from langchain_community.embeddings.mosaicml import (
MosaicMLInstructorEmbeddings,
)
from langchain_community.embeddings.nemo import (
NeMoEmbeddings,
)
from langchain_community.embeddings.nlpcloud import (
NLPCloudEmbeddings,
)
from langchain_community.embeddings.oci_generative_ai import (
OCIGenAIEmbeddings,
)
from langchain_community.embeddings.octoai_embeddings import (
OctoAIEmbeddings,
)
from langchain_community.embeddings.ollama import (
OllamaEmbeddings,
)
from langchain_community.embeddings.openai import (
OpenAIEmbeddings,
)
from langchain_community.embeddings.openvino import (
OpenVINOBgeEmbeddings,
OpenVINOEmbeddings,
)
from langchain_community.embeddings.optimum_intel import (
QuantizedBiEncoderEmbeddings,
)
community[minor]: Oraclevs integration (#21123) Thank you for contributing to LangChain! - Oracle AI Vector Search Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. - Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. This Pull Requests Adds the following functionalities Oracle AI Vector Search : Vector Store Oracle AI Vector Search : Document Loader Oracle AI Vector Search : Document Splitter Oracle AI Vector Search : Summary Oracle AI Vector Search : Oracle Embeddings - We have added unit tests and have our own local unit test suite which verifies all the code is correct. We have made sure to add guides for each of the components and one end to end guide that shows how the entire thing runs. - We have made sure that make format and make lint run clean. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com> Co-authored-by: hroyofc <harichandan.roy@oracle.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-05-04 03:15:35 +00:00
from langchain_community.embeddings.oracleai import (
OracleEmbeddings,
community[minor]: Oraclevs integration (#21123) Thank you for contributing to LangChain! - Oracle AI Vector Search Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. - Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. This Pull Requests Adds the following functionalities Oracle AI Vector Search : Vector Store Oracle AI Vector Search : Document Loader Oracle AI Vector Search : Document Splitter Oracle AI Vector Search : Summary Oracle AI Vector Search : Oracle Embeddings - We have added unit tests and have our own local unit test suite which verifies all the code is correct. We have made sure to add guides for each of the components and one end to end guide that shows how the entire thing runs. - We have made sure that make format and make lint run clean. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com> Co-authored-by: hroyofc <harichandan.roy@oracle.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-05-04 03:15:35 +00:00
)
from langchain_community.embeddings.premai import (
PremAIEmbeddings,
)
from langchain_community.embeddings.sagemaker_endpoint import (
SagemakerEndpointEmbeddings,
)
from langchain_community.embeddings.sambanova import (
SambaStudioEmbeddings,
)
from langchain_community.embeddings.self_hosted import (
SelfHostedEmbeddings,
)
from langchain_community.embeddings.self_hosted_hugging_face import (
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.embeddings.solar import (
SolarEmbeddings,
)
from langchain_community.embeddings.spacy_embeddings import (
SpacyEmbeddings,
)
from langchain_community.embeddings.sparkllm import (
SparkLLMTextEmbeddings,
)
from langchain_community.embeddings.tensorflow_hub import (
TensorflowHubEmbeddings,
)
from langchain_community.embeddings.titan_takeoff import (
TitanTakeoffEmbed,
)
from langchain_community.embeddings.vertexai import (
VertexAIEmbeddings,
)
from langchain_community.embeddings.volcengine import (
VolcanoEmbeddings,
)
from langchain_community.embeddings.voyageai import (
VoyageEmbeddings,
)
from langchain_community.embeddings.xinference import (
XinferenceEmbeddings,
)
from langchain_community.embeddings.yandex import (
YandexGPTEmbeddings,
)
__all__ = [
"AlephAlphaAsymmetricSemanticEmbedding",
"AlephAlphaSymmetricSemanticEmbedding",
"AnyscaleEmbeddings",
"AwaEmbeddings",
"AzureOpenAIEmbeddings",
"BaichuanTextEmbeddings",
"BedrockEmbeddings",
"BookendEmbeddings",
"ClarifaiEmbeddings",
"ClovaEmbeddings",
"CohereEmbeddings",
"DashScopeEmbeddings",
"DatabricksEmbeddings",
"DeepInfraEmbeddings",
"DeterministicFakeEmbedding",
"EdenAiEmbeddings",
"ElasticsearchEmbeddings",
"EmbaasEmbeddings",
"ErnieEmbeddings",
"FakeEmbeddings",
"FastEmbedEmbeddings",
"GPT4AllEmbeddings",
"GigaChatEmbeddings",
"GooglePalmEmbeddings",
"GradientEmbeddings",
"HuggingFaceBgeEmbeddings",
"HuggingFaceEmbeddings",
"HuggingFaceHubEmbeddings",
"HuggingFaceInferenceAPIEmbeddings",
"HuggingFaceInstructEmbeddings",
"InfinityEmbeddings",
"InfinityEmbeddingsLocal",
"JavelinAIGatewayEmbeddings",
"JinaEmbeddings",
"JohnSnowLabsEmbeddings",
"LLMRailsEmbeddings",
"LaserEmbeddings",
"LlamaCppEmbeddings",
"LlamafileEmbeddings",
"LocalAIEmbeddings",
"MiniMaxEmbeddings",
"MlflowAIGatewayEmbeddings",
"MlflowCohereEmbeddings",
"MlflowEmbeddings",
"ModelScopeEmbeddings",
"MosaicMLInstructorEmbeddings",
"NLPCloudEmbeddings",
"NeMoEmbeddings",
"OCIGenAIEmbeddings",
"OctoAIEmbeddings",
"OllamaEmbeddings",
"OpenAIEmbeddings",
"OpenVINOBgeEmbeddings",
"OpenVINOEmbeddings",
community[minor]: Oraclevs integration (#21123) Thank you for contributing to LangChain! - Oracle AI Vector Search Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. - Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. This Pull Requests Adds the following functionalities Oracle AI Vector Search : Vector Store Oracle AI Vector Search : Document Loader Oracle AI Vector Search : Document Splitter Oracle AI Vector Search : Summary Oracle AI Vector Search : Oracle Embeddings - We have added unit tests and have our own local unit test suite which verifies all the code is correct. We have made sure to add guides for each of the components and one end to end guide that shows how the entire thing runs. - We have made sure that make format and make lint run clean. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com> Co-authored-by: hroyofc <harichandan.roy@oracle.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-05-04 03:15:35 +00:00
"OracleEmbeddings",
"PremAIEmbeddings",
"QianfanEmbeddingsEndpoint",
"QuantizedBgeEmbeddings",
"QuantizedBiEncoderEmbeddings",
"SagemakerEndpointEmbeddings",
"SambaStudioEmbeddings",
"SelfHostedEmbeddings",
"SelfHostedHuggingFaceEmbeddings",
"SelfHostedHuggingFaceInstructEmbeddings",
"SentenceTransformerEmbeddings",
"SolarEmbeddings",
"SpacyEmbeddings",
"SparkLLMTextEmbeddings",
"TensorflowHubEmbeddings",
"TitanTakeoffEmbed",
"VertexAIEmbeddings",
"VolcanoEmbeddings",
"VoyageEmbeddings",
"XinferenceEmbeddings",
"YandexGPTEmbeddings",
]
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
_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",
"ClovaEmbeddings": "langchain_community.embeddings.clova",
"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",
community[minor]: Oraclevs integration (#21123) Thank you for contributing to LangChain! - Oracle AI Vector Search Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. - Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. This Pull Requests Adds the following functionalities Oracle AI Vector Search : Vector Store Oracle AI Vector Search : Document Loader Oracle AI Vector Search : Document Splitter Oracle AI Vector Search : Summary Oracle AI Vector Search : Oracle Embeddings - We have added unit tests and have our own local unit test suite which verifies all the code is correct. We have made sure to add guides for each of the components and one end to end guide that shows how the entire thing runs. - We have made sure that make format and make lint run clean. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com> Co-authored-by: hroyofc <harichandan.roy@oracle.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-05-04 03:15:35 +00:00
"OracleEmbeddings": "langchain_community.embeddings.oracleai",
"SagemakerEndpointEmbeddings": "langchain_community.embeddings.sagemaker_endpoint",
"SambaStudioEmbeddings": "langchain_community.embeddings.sambanova",
"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",
"TitanTakeoffEmbed": "langchain_community.embeddings.titan_takeoff",
"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}")
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
logger = logging.getLogger(__name__)
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
# 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)