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add model feat table (#10921)

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Bagatur 2023-09-22 01:10:27 -07:00 committed by GitHub
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8 changed files with 278 additions and 30 deletions
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2
.github/workflows/doc_lint.yml vendored
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@ -19,4 +19,4 @@ jobs:
run: |
# We should not encourage imports directly from main init file
# Expect for hub
git grep 'from langchain import' docs | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
git grep 'from langchain import' docs/{extras,docs_skeleton,snippets} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0

145
docs/_scripts/model_feat_table.py Normal file
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@ -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])
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])
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)

1
docs/api_reference/create_api_rst.py
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@ -1,7 +1,6 @@
"""Script for auto-generating api_reference.rst."""
import importlib
import inspect
import os
import typing
from pathlib import Path
from typing import TypedDict, Sequence, List, Dict, Literal, Union, Optional

4
docs/docs_skeleton/sidebars.js
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@ -99,8 +99,8 @@ module.exports = {
label: "Components",
collapsible: false,
items: [
{ type: "category", label: "LLMs", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/llms" }], link: {type: "generated-index", slug: "integrations/llms" }},
{ type: "category", label: "Chat models", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/chat" }], link: {type: "generated-index", slug: "integrations/chat" }},
{ type: "category", label: "LLMs", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/llms" }], link: { type: 'doc', id: "integrations/llms/index"}},
{ type: "category", label: "Chat models", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/chat" }], link: { type: 'doc', id: "integrations/chat/index"}},
{ type: "category", label: "Document loaders", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/document_loaders" }], link: {type: "generated-index", slug: "integrations/document_loaders" }},
{ type: "category", label: "Document transformers", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/document_transformers" }], link: {type: "generated-index", slug: "integrations/document_transformers" }},
{ type: "category", label: "Text embedding models", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/text_embedding" }], link: {type: "generated-index", slug: "integrations/text_embedding" }},

36
docs/extras/integrations/chat/index.mdx Normal file
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@ -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 />

91
docs/extras/integrations/llms/index.mdx Normal file
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@ -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 />

25
libs/langchain/langchain/chat_models/bedrock.py
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@ -1,7 +1,6 @@
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from typing import Any, Dict, Iterator, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.anthropic import convert_messages_to_prompt_anthropic
@ -59,17 +58,6 @@ class BedrockChat(BaseChatModel, BedrockBase):
delta = chunk.text
yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
raise NotImplementedError(
"""Bedrock doesn't support async requests at the moment."""
)
def _generate(
self,
messages: List[BaseMessage],
@ -98,14 +86,3 @@ class BedrockChat(BaseChatModel, BedrockBase):
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
raise NotImplementedError(
"""Bedrock doesn't support async stream requests at the moment."""
)