mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
8021d2a2ab
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>
83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
from langchain_community.embeddings import __all__, _module_lookup
|
|
|
|
EXPECTED_ALL = [
|
|
"OpenAIEmbeddings",
|
|
"AnyscaleEmbeddings",
|
|
"AzureOpenAIEmbeddings",
|
|
"BaichuanTextEmbeddings",
|
|
"ClarifaiEmbeddings",
|
|
"CohereEmbeddings",
|
|
"DatabricksEmbeddings",
|
|
"ElasticsearchEmbeddings",
|
|
"FastEmbedEmbeddings",
|
|
"HuggingFaceEmbeddings",
|
|
"HuggingFaceInferenceAPIEmbeddings",
|
|
"InfinityEmbeddings",
|
|
"InfinityEmbeddingsLocal",
|
|
"GradientEmbeddings",
|
|
"JinaEmbeddings",
|
|
"LaserEmbeddings",
|
|
"LlamaCppEmbeddings",
|
|
"LlamafileEmbeddings",
|
|
"LLMRailsEmbeddings",
|
|
"HuggingFaceHubEmbeddings",
|
|
"MlflowAIGatewayEmbeddings",
|
|
"MlflowEmbeddings",
|
|
"MlflowCohereEmbeddings",
|
|
"ModelScopeEmbeddings",
|
|
"TensorflowHubEmbeddings",
|
|
"SagemakerEndpointEmbeddings",
|
|
"HuggingFaceInstructEmbeddings",
|
|
"MosaicMLInstructorEmbeddings",
|
|
"SelfHostedEmbeddings",
|
|
"SelfHostedHuggingFaceEmbeddings",
|
|
"SelfHostedHuggingFaceInstructEmbeddings",
|
|
"FakeEmbeddings",
|
|
"DeterministicFakeEmbedding",
|
|
"AlephAlphaAsymmetricSemanticEmbedding",
|
|
"AlephAlphaSymmetricSemanticEmbedding",
|
|
"SentenceTransformerEmbeddings",
|
|
"GooglePalmEmbeddings",
|
|
"MiniMaxEmbeddings",
|
|
"VertexAIEmbeddings",
|
|
"BedrockEmbeddings",
|
|
"DeepInfraEmbeddings",
|
|
"EdenAiEmbeddings",
|
|
"DashScopeEmbeddings",
|
|
"EmbaasEmbeddings",
|
|
"OctoAIEmbeddings",
|
|
"SpacyEmbeddings",
|
|
"NLPCloudEmbeddings",
|
|
"GPT4AllEmbeddings",
|
|
"GigaChatEmbeddings",
|
|
"XinferenceEmbeddings",
|
|
"LocalAIEmbeddings",
|
|
"AwaEmbeddings",
|
|
"HuggingFaceBgeEmbeddings",
|
|
"ErnieEmbeddings",
|
|
"JavelinAIGatewayEmbeddings",
|
|
"OllamaEmbeddings",
|
|
"OracleEmbeddings",
|
|
"QianfanEmbeddingsEndpoint",
|
|
"JohnSnowLabsEmbeddings",
|
|
"VoyageEmbeddings",
|
|
"BookendEmbeddings",
|
|
"VolcanoEmbeddings",
|
|
"OCIGenAIEmbeddings",
|
|
"QuantizedBiEncoderEmbeddings",
|
|
"NeMoEmbeddings",
|
|
"SparkLLMTextEmbeddings",
|
|
"TitanTakeoffEmbed",
|
|
"QuantizedBgeEmbeddings",
|
|
"PremAIEmbeddings",
|
|
"YandexGPTEmbeddings",
|
|
"OpenVINOEmbeddings",
|
|
"OpenVINOBgeEmbeddings",
|
|
"SolarEmbeddings",
|
|
]
|
|
|
|
|
|
def test_all_imports() -> None:
|
|
assert set(__all__) == set(EXPECTED_ALL)
|
|
assert set(__all__) == set(_module_lookup.keys())
|