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dee72c46c1
In collaboration with @rlouf I build an [outlines](https://dottxt-ai.github.io/outlines/latest/) integration for langchain! I think this is really useful for doing any type of structured output locally. [Dottxt](https://dottxt.co) spend alot of work optimising this process at a lower level ([outlines-core](https://pypi.org/project/outlines-core/0.1.14/) written in rust) so I think this is a better alternative over all current approaches in langchain to do structured output. It also implements the `.with_structured_output` method so it should be a drop in replacement for a lot of applications. The integration includes: - **Outlines LLM class** - **ChatOutlines class** - **Tutorial Cookbooks** - **Documentation Page** - **Validation and error messages** - **Exposes Outlines Structured output features** - **Support for multiple backends** - **Integration and Unit Tests** Dependencies: `outlines` + additional (depending on backend used) I am not sure if the unit-tests comply with all requirements, if not I suggest to just remove them since I don't see a useful way to do it differently. ### Quick overview: Chat Models: <img width="698" alt="image" src="https://github.com/user-attachments/assets/05a499b9-858c-4397-a9ff-165c2b3e7acc"> Structured Output: <img width="955" alt="image" src="https://github.com/user-attachments/assets/b9fcac11-d3e5-4698-b1ae-8c4cb3d54c45"> --------- Co-authored-by: Vadym Barda <vadym@langchain.dev>
116 lines
2.2 KiB
Python
116 lines
2.2 KiB
Python
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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"Aphrodite",
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"Arcee",
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"Aviary",
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"AzureMLOnlineEndpoint",
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"AzureOpenAI",
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"BaichuanLLM",
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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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"Friendli",
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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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"IpexLLM",
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"KoboldApiLLM",
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"Konko",
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"LlamaCpp",
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"Llamafile",
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"TextGen",
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"ManifestWrapper",
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"Minimax",
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"Mlflow",
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"MlflowAIGateway",
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"MLXPipeline",
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"Modal",
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"MosaicML",
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"Nebula",
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"OCIModelDeploymentLLM",
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"OCIModelDeploymentTGI",
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"OCIModelDeploymentVLLM",
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"OCIGenAI",
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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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"Outlines",
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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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"SambaNovaCloud",
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"SambaStudio",
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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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"Together",
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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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"WeightOnlyQuantPipeline",
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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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"Yuan2",
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"YiLLM",
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"You",
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"VolcEngineMaasLLM",
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"WatsonxLLM",
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"SparkLLM",
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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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