2023-12-11 21:53:30 +00:00
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from langchain_core.language_models.llms import BaseLLM
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from langchain_community import llms
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EXPECT_ALL = [
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"AI21",
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"AlephAlpha",
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"AmazonAPIGateway",
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"Anthropic",
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"Anyscale",
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2023-12-20 06:16:57 +00:00
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"Aphrodite",
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2023-12-11 21:53:30 +00:00
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"Arcee",
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"Aviary",
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"AzureMLOnlineEndpoint",
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"AzureOpenAI",
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"Banana",
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"Baseten",
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"Beam",
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"Bedrock",
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"CTransformers",
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"CTranslate2",
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"CerebriumAI",
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"ChatGLM",
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"Clarifai",
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"Cohere",
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"Databricks",
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"DeepInfra",
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"DeepSparse",
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"EdenAI",
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"FakeListLLM",
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"Fireworks",
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"ForefrontAI",
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"GigaChat",
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"GPT4All",
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"GooglePalm",
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"GooseAI",
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"GradientLLM",
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"HuggingFaceEndpoint",
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"HuggingFaceHub",
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"HuggingFacePipeline",
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"HuggingFaceTextGenInference",
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"HumanInputLLM",
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"KoboldApiLLM",
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2024-01-24 02:22:32 +00:00
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"Konko",
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2023-12-11 21:53:30 +00:00
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"LlamaCpp",
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"TextGen",
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"ManifestWrapper",
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"Minimax",
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"MlflowAIGateway",
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"Modal",
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"MosaicML",
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"Nebula",
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2023-12-20 19:52:20 +00:00
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"OCIModelDeploymentTGI",
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"OCIModelDeploymentVLLM",
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community[minor]: Add OCI Generative AI integration (#16548)
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** Adding Oracle Cloud Infrastructure Generative AI
integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully
managed service that provides a set of state-of-the-art, customizable
large language models (LLMs) that cover a wide range of use cases, and
which is available through a single API. Using the OCI Generative AI
service you can access ready-to-use pretrained models, or create and
host your own fine-tuned custom models based on your own data on
dedicated AI clusters.
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
- **Issue:** None,
- **Dependencies:** OCI Python SDK,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
Passed
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
we provide unit tests. However, we cannot provide integration tests due
to Oracle policies that prohibit public sharing of api keys.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-01-25 02:23:50 +00:00
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"OCIGenAI",
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2023-12-11 21:53:30 +00:00
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"NIBittensorLLM",
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"NLPCloud",
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"Ollama",
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"OpenAI",
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"OpenAIChat",
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"OpenLLM",
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"OpenLM",
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"PaiEasEndpoint",
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"Petals",
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"PipelineAI",
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"Predibase",
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"PredictionGuard",
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"PromptLayerOpenAI",
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"PromptLayerOpenAIChat",
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"OpaquePrompts",
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"RWKV",
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"Replicate",
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"SagemakerEndpoint",
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"SelfHostedHuggingFaceLLM",
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"SelfHostedPipeline",
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"StochasticAI",
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"TitanTakeoff",
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"TitanTakeoffPro",
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"Tongyi",
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"VertexAI",
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"VertexAIModelGarden",
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"VLLM",
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"VLLMOpenAI",
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"Writer",
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"OctoAIEndpoint",
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"Xinference",
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"JavelinAIGateway",
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"QianfanLLMEndpoint",
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"YandexGPT",
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"VolcEngineMaasLLM",
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"WatsonxLLM",
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]
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def test_all_imports() -> None:
|
|
|
|
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"""Simple test to make sure all things can be imported."""
|
|
|
|
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for cls in llms.__all__:
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assert issubclass(getattr(llms, cls), BaseLLM)
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assert set(llms.__all__) == set(EXPECT_ALL)
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