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Code Issues Packages Projects Releases Wiki Activity

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."""
)

4
libs/langchain/langchain/utilities/vertexai.py
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@ -30,8 +30,8 @@ def init_vertexai(
project: The default GCP project to use when making Vertex API calls.
location: The default location to use when making API calls.
credentials: The default custom
credentials to use when making API calls. If not provided credentials
will be ascertained from the environment.
credentials to use when making API calls. If not provided credentials
will be ascertained from the environment.
Raises:
ImportError: If importing vertexai SDK did not succeed.