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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| import os | ||
| from pathlib import Path | ||
|
|
||
| from langchain import chat_models, llms | ||
| from langchain.chat_models.base import BaseChatModel, SimpleChatModel | ||
| from langchain.llms.base import BaseLLM, LLM | ||
|
|
||
| INTEGRATIONS_DIR = ( | ||
| Path(os.path.abspath(__file__)).parents[1] / "extras" / "integrations" | ||
| ) | ||
| LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat") | ||
| LLM_FEAT_TABLE_CORRECTION = { | ||
| "TextGen": {"_astream": False, "_agenerate": False}, | ||
| "Ollama": { | ||
| "_stream": False, | ||
| }, | ||
| "PromptLayerOpenAI": {"batch_generate": False, "batch_agenerate": False}, | ||
| } | ||
| CHAT_MODEL_IGNORE = ("FakeListChatModel", "HumanInputChatModel") | ||
| CHAT_MODEL_FEAT_TABLE_CORRECTION = { | ||
| "ChatMLflowAIGateway": {"_agenerate": False}, | ||
| "PromptLayerChatOpenAI": {"_stream": False, "_astream": False}, | ||
| "ChatKonko": {"_astream": False, "_agenerate": False}, | ||
| } | ||
|
|
||
| LLM_TEMPLATE = """\ | ||
| --- | ||
| sidebar_position: 0 | ||
| sidebar_class_name: hidden | ||
| --- | ||
| # LLMs | ||
| import DocCardList from "@theme/DocCardList"; | ||
| ## Features (natively supported) | ||
| All `LLM`s implement the LCEL `Runnable` interface, meaning they all expose functioning `invoke`, `ainvoke`, `batch`, `abatch`, `stream`, and `astream` methods. | ||
| *That is, they all have functioning sync, async, streaming, and batch generation methods.* | ||
| This table highlights specifically those integrations that **natively support** batching, streaming, and asynchronous generation (meaning these features are built into the 3rd-party integration). | ||
| {table} | ||
| <DocCardList /> | ||
| """ | ||
|
|
||
| CHAT_MODEL_TEMPLATE = """\ | ||
| --- | ||
| sidebar_position: 1 | ||
| sidebar_class_name: hidden | ||
| --- | ||
| # Chat models | ||
| import DocCardList from "@theme/DocCardList"; | ||
| ## Features (natively supported) | ||
| All `ChatModel`s implement the LCEL `Runnable` interface, meaning they all expose functioning `invoke`, `ainvoke`, `stream`, and `astream` (and `batch`, `abatch`) methods. | ||
| *That is, they all have functioning sync, async and streaming generation methods.* | ||
| This table highlights specifically those integrations that **natively support** streaming and asynchronous generation (meaning these features are built into the 3rd-party integration). | ||
| {table} | ||
| <DocCardList /> | ||
| """ | ||
|
|
||
|
|
||
| def get_llm_table(): | ||
| llm_feat_table = {} | ||
| for cm in llms.__all__: | ||
| llm_feat_table[cm] = {} | ||
| cls = getattr(llms, cm) | ||
| if issubclass(cls, LLM): | ||
| for feat in ("_stream", "_astream", ("_acall", "_agenerate")): | ||
| if isinstance(feat, tuple): | ||
| feat, name = feat | ||
| else: | ||
| feat, name = feat, feat | ||
| llm_feat_table[cm][name] = getattr(cls, feat) != getattr(LLM, feat) | ||
| else: | ||
| for feat in [ | ||
| "_stream", | ||
| "_astream", | ||
| ("_generate", "batch_generate"), | ||
| "_agenerate", | ||
| ("_agenerate", "batch_agenerate"), | ||
| ]: | ||
| if isinstance(feat, tuple): | ||
| feat, name = feat | ||
| else: | ||
| feat, name = feat, feat | ||
| llm_feat_table[cm][name] = getattr(cls, feat) != getattr(BaseLLM, feat) | ||
| final_feats = { | ||
| k: v | ||
| for k, v in {**llm_feat_table, **LLM_FEAT_TABLE_CORRECTION}.items() | ||
| if k not in LLM_IGNORE | ||
| } | ||
|
|
||
| header = [ | ||
| "model", | ||
| "_agenerate", | ||
| "_stream", | ||
| "_astream", | ||
| "batch_generate", | ||
| "batch_agenerate", | ||
| ] | ||
| title = ["Model", "Generate", "Async generate", "Stream", "Async stream", "Batch", "Async batch"] | ||
| rows = [title, [":-"] + [":-:"] * (len(title) - 1)] | ||
| for llm, feats in sorted(final_feats.items()): | ||
| rows += [[llm, "✅"] + ["✅" if feats.get(h) else "❌" for h in header[1:]]] | ||
| return "\n".join(["|".join(row) for row in rows]) | ||
|
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||
|
|
||
| def get_chat_model_table(): | ||
| feat_table = {} | ||
| for cm in chat_models.__all__: | ||
| feat_table[cm] = {} | ||
| cls = getattr(chat_models, cm) | ||
| if issubclass(cls, SimpleChatModel): | ||
| comparison_cls = SimpleChatModel | ||
| else: | ||
| comparison_cls = BaseChatModel | ||
| for feat in ("_stream", "_astream", "_agenerate"): | ||
| feat_table[cm][feat] = getattr(cls, feat) != getattr(comparison_cls, feat) | ||
| final_feats = { | ||
| k: v | ||
| for k, v in {**feat_table, **CHAT_MODEL_FEAT_TABLE_CORRECTION}.items() | ||
| if k not in CHAT_MODEL_IGNORE | ||
| } | ||
| header = ["model", "_agenerate", "_stream", "_astream"] | ||
| title = ["Model", "Generate", "Async generate", "Stream", "Async stream"] | ||
| rows = [title, [":-"] + [":-:"] * (len(title) - 1)] | ||
| for llm, feats in sorted(final_feats.items()): | ||
| rows += [[llm, "✅"] + ["✅" if feats.get(h) else "❌" for h in header[1:]]] | ||
| return "\n".join(["|".join(row) for row in rows]) | ||
|
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||
|
|
||
| if __name__ == "__main__": | ||
| llm_page = LLM_TEMPLATE.format(table=get_llm_table()) | ||
| with open(INTEGRATIONS_DIR / "llms" / "index.mdx", "w") as f: | ||
| f.write(llm_page) | ||
| chat_model_page = CHAT_MODEL_TEMPLATE.format(table=get_chat_model_table()) | ||
| with open(INTEGRATIONS_DIR / "chat" / "index.mdx", "w") as f: | ||
| f.write(chat_model_page) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,36 @@ | ||
| --- | ||
| sidebar_position: 1 | ||
| sidebar_class_name: hidden | ||
| --- | ||
|
|
||
| # Chat models | ||
|
|
||
| import DocCardList from "@theme/DocCardList"; | ||
|
|
||
| ## Features (natively supported) | ||
| All `ChatModel`s implement the LCEL `Runnable` interface, meaning they all expose functioning `invoke`, `ainvoke`, `stream`, and `astream` (and `batch`, `abatch`) methods. | ||
| *That is, they all have functioning sync, async and streaming generation methods.* | ||
|
|
||
| This table highlights specifically those integrations that **natively support** streaming and asynchronous generation (meaning these features are built into the 3rd-party integration). | ||
|
|
||
| Model|Generate|Async generate|Stream|Async stream | ||
| :-|:-:|:-:|:-:|:-: | ||
| AzureChatOpenAI|✅|✅|✅|✅ | ||
| BedrockChat|✅|❌|✅|❌ | ||
| ChatAnthropic|✅|✅|✅|✅ | ||
| ChatAnyscale|✅|✅|✅|✅ | ||
| ChatGooglePalm|✅|✅|❌|❌ | ||
| ChatJavelinAIGateway|✅|✅|❌|❌ | ||
| ChatKonko|✅|❌|❌|❌ | ||
| ChatLiteLLM|✅|✅|✅|✅ | ||
| ChatMLflowAIGateway|✅|❌|❌|❌ | ||
| ChatOllama|✅|❌|✅|❌ | ||
| ChatOpenAI|✅|✅|✅|✅ | ||
| ChatVertexAI|✅|❌|✅|❌ | ||
| ErnieBotChat|✅|❌|❌|❌ | ||
| JinaChat|✅|✅|✅|✅ | ||
| MiniMaxChat|✅|✅|❌|❌ | ||
| PromptLayerChatOpenAI|✅|❌|❌|❌ | ||
| QianfanChatEndpoint|✅|✅|✅|✅ | ||
|
|
||
| <DocCardList /> |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| --- | ||
| sidebar_position: 0 | ||
| sidebar_class_name: hidden | ||
| --- | ||
|
|
||
| # LLMs | ||
|
|
||
| import DocCardList from "@theme/DocCardList"; | ||
|
|
||
| ## Features (natively supported) | ||
| All `LLM`s implement the LCEL `Runnable` interface, meaning they all expose functioning `invoke`, `ainvoke`, `batch`, `abatch`, `stream`, and `astream` methods. | ||
| *That is, they all have functioning sync, async, streaming, and batch generation methods.* | ||
|
|
||
| This table highlights specifically those integrations that **natively support** batching, streaming, and asynchronous generation (meaning these features are built into the 3rd-party integration). | ||
|
|
||
| Model|Generate|Async generate|Stream|Async stream|Batch|Async batch | ||
| :-|:-:|:-:|:-:|:-:|:-:|:-: | ||
| AI21|✅|❌|❌|❌|❌|❌ | ||
| AlephAlpha|✅|❌|❌|❌|❌|❌ | ||
| AmazonAPIGateway|✅|❌|❌|❌|❌|❌ | ||
| Anthropic|✅|✅|✅|✅|❌|❌ | ||
| Anyscale|✅|❌|❌|❌|❌|❌ | ||
| Aviary|✅|❌|❌|❌|❌|❌ | ||
| AzureMLOnlineEndpoint|✅|❌|❌|❌|❌|❌ | ||
| AzureOpenAI|✅|✅|✅|✅|✅|✅ | ||
| Banana|✅|❌|❌|❌|❌|❌ | ||
| Baseten|✅|❌|❌|❌|❌|❌ | ||
| Beam|✅|❌|❌|❌|❌|❌ | ||
| Bedrock|✅|❌|✅|❌|❌|❌ | ||
| CTransformers|✅|✅|❌|❌|❌|❌ | ||
| CTranslate2|✅|❌|❌|❌|✅|❌ | ||
| CerebriumAI|✅|❌|❌|❌|❌|❌ | ||
| ChatGLM|✅|❌|❌|❌|❌|❌ | ||
| Clarifai|✅|❌|❌|❌|❌|❌ | ||
| Cohere|✅|✅|❌|❌|❌|❌ | ||
| Databricks|✅|❌|❌|❌|❌|❌ | ||
| DeepInfra|✅|❌|❌|❌|❌|❌ | ||
| DeepSparse|✅|❌|❌|❌|❌|❌ | ||
| EdenAI|✅|✅|❌|❌|❌|❌ | ||
| Fireworks|✅|✅|❌|❌|✅|✅ | ||
| FireworksChat|✅|✅|❌|❌|✅|✅ | ||
| ForefrontAI|✅|❌|❌|❌|❌|❌ | ||
| GPT4All|✅|❌|❌|❌|❌|❌ | ||
| GooglePalm|✅|❌|❌|❌|✅|❌ | ||
| GooseAI|✅|❌|❌|❌|❌|❌ | ||
| GradientLLM|✅|✅|❌|❌|❌|❌ | ||
| HuggingFaceEndpoint|✅|❌|❌|❌|❌|❌ | ||
| HuggingFaceHub|✅|❌|❌|❌|❌|❌ | ||
| HuggingFacePipeline|✅|❌|❌|❌|❌|❌ | ||
| HuggingFaceTextGenInference|✅|✅|✅|✅|❌|❌ | ||
| HumanInputLLM|✅|❌|❌|❌|❌|❌ | ||
| JavelinAIGateway|✅|✅|❌|❌|❌|❌ | ||
| KoboldApiLLM|✅|❌|❌|❌|❌|❌ | ||
| LlamaCpp|✅|❌|✅|❌|❌|❌ | ||
| ManifestWrapper|✅|❌|❌|❌|❌|❌ | ||
| Minimax|✅|❌|❌|❌|❌|❌ | ||
| MlflowAIGateway|✅|❌|❌|❌|❌|❌ | ||
| Modal|✅|❌|❌|❌|❌|❌ | ||
| MosaicML|✅|❌|❌|❌|❌|❌ | ||
| NIBittensorLLM|✅|❌|❌|❌|❌|❌ | ||
| NLPCloud|✅|❌|❌|❌|❌|❌ | ||
| Nebula|✅|❌|❌|❌|❌|❌ | ||
| OctoAIEndpoint|✅|❌|❌|❌|❌|❌ | ||
| Ollama|✅|❌|❌|❌|❌|❌ | ||
| OpaquePrompts|✅|❌|❌|❌|❌|❌ | ||
| OpenAI|✅|✅|✅|✅|✅|✅ | ||
| OpenLLM|✅|✅|❌|❌|❌|❌ | ||
| OpenLM|✅|✅|✅|✅|✅|✅ | ||
| Petals|✅|❌|❌|❌|❌|❌ | ||
| PipelineAI|✅|❌|❌|❌|❌|❌ | ||
| Predibase|✅|❌|❌|❌|❌|❌ | ||
| PredictionGuard|✅|❌|❌|❌|❌|❌ | ||
| PromptLayerOpenAI|✅|❌|❌|❌|❌|❌ | ||
| QianfanLLMEndpoint|✅|✅|✅|✅|❌|❌ | ||
| RWKV|✅|❌|❌|❌|❌|❌ | ||
| Replicate|✅|❌|✅|❌|❌|❌ | ||
| SagemakerEndpoint|✅|❌|❌|❌|❌|❌ | ||
| SelfHostedHuggingFaceLLM|✅|❌|❌|❌|❌|❌ | ||
| SelfHostedPipeline|✅|❌|❌|❌|❌|❌ | ||
| StochasticAI|✅|❌|❌|❌|❌|❌ | ||
| TextGen|✅|❌|❌|❌|❌|❌ | ||
| TitanTakeoff|✅|❌|✅|❌|❌|❌ | ||
| Tongyi|✅|❌|❌|❌|❌|❌ | ||
| VLLM|✅|❌|❌|❌|✅|❌ | ||
| VLLMOpenAI|✅|✅|✅|✅|✅|✅ | ||
| VertexAI|✅|✅|❌|❌|❌|❌ | ||
| VertexAIModelGarden|✅|✅|❌|❌|❌|❌ | ||
| Writer|✅|❌|❌|❌|❌|❌ | ||
| Xinference|✅|❌|❌|❌|❌|❌ | ||
|
|
||
| <DocCardList /> |
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