mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
270 lines
8.8 KiB
Python
270 lines
8.8 KiB
Python
import sys
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from pathlib import Path
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from langchain_community import llms
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from langchain_core.language_models.llms import LLM, BaseLLM
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LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat")
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LLM_FEAT_TABLE_CORRECTION = {
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"TextGen": {"_astream": False, "_agenerate": False},
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"Ollama": {
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"_stream": False,
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},
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"PromptLayerOpenAI": {"batch_generate": False, "batch_agenerate": False},
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}
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CHAT_MODEL_IGNORE = ("FakeListChatModel", "HumanInputChatModel")
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CHAT_MODEL_FEAT_TABLE = {
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"ChatAnthropic": {
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"tool_calling": True,
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"structured_output": True,
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"multimodal": True,
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"package": "langchain-anthropic",
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"link": "/docs/integrations/chat/anthropic/",
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},
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"ChatMistralAI": {
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"tool_calling": True,
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"structured_output": True,
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"json_model": True,
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"package": "langchain-mistralai",
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"link": "/docs/integrations/chat/mistralai/",
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},
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"ChatFireworks": {
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"tool_calling": True,
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"structured_output": True,
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"json_mode": True,
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"package": "langchain-fireworks",
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"link": "/docs/integrations/chat/fireworks/",
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},
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"AzureChatOpenAI": {
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"tool_calling": True,
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"structured_output": True,
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"json_mode": True,
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"multimodal": True,
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"package": "langchain-openai",
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"link": "/docs/integrations/chat/azure_chat_openai/",
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},
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"ChatOpenAI": {
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"tool_calling": True,
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"structured_output": True,
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"json_mode": True,
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"multimodal": True,
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"package": "langchain-openai",
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"link": "/docs/integrations/chat/openai/",
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},
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"ChatTogether": {
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"tool_calling": True,
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"structured_output": True,
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"json_mode": True,
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"package": "langchain-together",
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"link": "/docs/integrations/chat/together/",
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},
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"ChatVertexAI": {
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"tool_calling": True,
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"structured_output": True,
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"multimodal": True,
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"package": "langchain-google-vertexai",
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"link": "/docs/integrations/chat/google_vertex_ai_palm/",
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},
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"ChatGoogleGenerativeAI": {
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"tool_calling": True,
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"multimodal": True,
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"package": "langchain-google-genai",
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"link": "/docs/integrations/chat/google_generative_ai/",
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},
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"ChatGroq": {
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"tool_calling": True,
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"structured_output": True,
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"json_mode": True,
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"package": "langchain-groq",
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"link": "/docs/integrations/chat/groq/",
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},
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"ChatCohere": {
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"tool_calling": True,
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"structured_output": True,
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"package": "langchain-cohere",
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"link": "/docs/integrations/chat/cohere/",
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},
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"ChatBedrock": {
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"tool_calling": True,
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"package": "langchain-aws",
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"link": "/docs/integrations/chat/bedrock/",
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},
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"ChatHuggingFace": {
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"tool_calling": True,
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"local": True,
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"package": "langchain-huggingface",
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"link": "/docs/integrations/chat/huggingface/",
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},
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"ChatOllama": {
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"local": True,
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"package": "langchain-community",
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"link": "/docs/integrations/chat/ollama/",
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},
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"vLLM Chat (via ChatOpenAI)": {
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"local": True,
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"package": "langchain-community",
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"link": "/docs/integrations/chat/vllm/",
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},
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"ChatEdenAI": {
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"tool_calling": True,
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"structured_output": True,
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"package": "langchain-community",
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"link": "/docs/integrations/chat/edenai/",
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},
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}
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LLM_TEMPLATE = """\
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---
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sidebar_position: 1
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sidebar_class_name: hidden
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keywords: [compatibility]
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custom_edit_url:
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---
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# LLMs
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## Features (natively supported)
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All LLMs implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all LLMs basic support for async, streaming and batch, which by default is implemented as below:
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- *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the LLM is being executed, by moving this call to a background thread.
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- *Streaming* support defaults to returning an `Iterator` (or `AsyncIterator` in the case of async streaming) of a single value, the final result returned by the underlying LLM provider. This obviously doesn't give you token-by-token streaming, which requires native support from the LLM provider, but ensures your code that expects an iterator of tokens can work for any of our LLM integrations.
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- *Batch* support defaults to calling the underlying LLM in parallel for each input by making use of a thread pool executor (in the sync batch case) or `asyncio.gather` (in the async batch case). The concurrency can be controlled with the `max_concurrency` key in `RunnableConfig`.
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Each LLM integration can optionally provide native implementations for async, streaming or batch, which, for providers that support it, can be more efficient. The table shows, for each integration, which features have been implemented with native support.
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{table}
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"""
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CHAT_MODEL_TEMPLATE = """\
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---
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sidebar_position: 0
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sidebar_class_name: hidden
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keywords: [compatibility]
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custom_edit_url:
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hide_table_of_contents: true
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---
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# Chat models
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## Advanced features
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The following table shows all the chat models that support one or more advanced features.
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{table}
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"""
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def get_llm_table():
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llm_feat_table = {}
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for cm in llms.__all__:
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llm_feat_table[cm] = {}
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cls = getattr(llms, cm)
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if issubclass(cls, LLM):
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for feat in ("_stream", "_astream", ("_acall", "_agenerate")):
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if isinstance(feat, tuple):
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feat, name = feat
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else:
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feat, name = feat, feat
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llm_feat_table[cm][name] = getattr(cls, feat) != getattr(LLM, feat)
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else:
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for feat in [
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"_stream",
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"_astream",
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("_generate", "batch_generate"),
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"_agenerate",
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("_agenerate", "batch_agenerate"),
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]:
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if isinstance(feat, tuple):
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feat, name = feat
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else:
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feat, name = feat, feat
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llm_feat_table[cm][name] = getattr(cls, feat) != getattr(BaseLLM, feat)
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final_feats = {
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k: v
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for k, v in {**llm_feat_table, **LLM_FEAT_TABLE_CORRECTION}.items()
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if k not in LLM_IGNORE
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}
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header = [
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"model",
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"_agenerate",
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"_stream",
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"_astream",
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"batch_generate",
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"batch_agenerate",
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]
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title = [
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"Model",
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"Invoke",
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"Async invoke",
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"Stream",
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"Async stream",
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"Batch",
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"Async batch",
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]
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rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
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for llm, feats in sorted(final_feats.items()):
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rows += [[llm, "✅"] + ["✅" if feats.get(h) else "❌" for h in header[1:]]]
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return "\n".join(["|".join(row) for row in rows])
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def get_chat_model_table() -> str:
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"""Get the table of chat models."""
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header = [
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"model",
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"tool_calling",
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"structured_output",
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"json_mode",
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"local",
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"multimodal",
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"package",
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]
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title = [
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"Model",
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"[Tool calling](/docs/how_to/tool_calling/)",
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"[Structured output](/docs/how_to/structured_output/)",
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"JSON mode",
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"Local",
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"[Multimodal](/docs/how_to/multimodal_inputs/)",
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"Package",
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]
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rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
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for llm, feats in sorted(CHAT_MODEL_FEAT_TABLE.items()):
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# Fields are in the order of the header
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row = [
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f"[{llm}]({feats['link']})",
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]
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for h in header[1:]:
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value = feats.get(h)
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if h == "package":
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row.append(value or "langchain-community")
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else:
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if value == "partial":
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row.append("🟡")
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elif value is True:
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row.append("✅")
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else:
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row.append("❌")
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rows.append(row)
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return "\n".join(["|".join(row) for row in rows])
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if __name__ == "__main__":
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output_dir = Path(sys.argv[1])
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output_integrations_dir = output_dir / "integrations"
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output_integrations_dir_llms = output_integrations_dir / "llms"
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output_integrations_dir_chat = output_integrations_dir / "chat"
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output_integrations_dir_llms.mkdir(parents=True, exist_ok=True)
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output_integrations_dir_chat.mkdir(parents=True, exist_ok=True)
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llm_page = LLM_TEMPLATE.format(table=get_llm_table())
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with open(output_integrations_dir / "llms" / "index.mdx", "w") as f:
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f.write(llm_page)
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chat_model_page = CHAT_MODEL_TEMPLATE.format(table=get_chat_model_table())
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with open(output_integrations_dir / "chat" / "index.mdx", "w") as f:
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f.write(chat_model_page)
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