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anthropic docs: deprecated LLM, add chat model (#3549)

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Harrison Chase 1 year ago committed by GitHub
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179
docs/modules/models/chat/integrations/anthropic.ipynb
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@ -0,0 +1,179 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bf733a38-db84-4363-89e2-de6735c37230",
"metadata": {},
"source": [
"# Anthropic\n",
"\n",
"This notebook covers how to get started with Anthropic chat models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatAnthropic\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import (\n",
" AIMessage,\n",
" HumanMessage,\n",
" SystemMessage\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatAnthropic()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" J'aime programmer. \", additional_kwargs={})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "markdown",
"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
"metadata": {},
"source": [
"## `ChatAnthropic` also supports async and streaming functionality:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.callbacks.base import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}))]], llm_output={})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await chat.agenerate([messages])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" J'adore programmer."
]
},
{
"data": {
"text/plain": [
"AIMessage(content=\" J'adore programmer.\", additional_kwargs={})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n",
"chat(messages)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df45f59f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

146
docs/modules/models/llms/integrations/anthropic_example.ipynb
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@ -1,146 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "9597802c",
"metadata": {},
"source": [
"# Anthropic\n",
"\n",
"[Anthropic](https://console.anthropic.com/docs) is creator of the `Claude` LLM.\n",
"\n",
"This example goes over how to use LangChain to interact with Anthropic models."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e55c0f2e-63e1-4e83-ac44-ffcc1dfeacc8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Install the package\n",
"!pip install anthropic"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cec62d45-afa2-422a-95ef-57f8ab41a6f9",
"metadata": {},
"outputs": [],
"source": [
"# get a new token: https://www.anthropic.com/earlyaccess\n",
"\n",
"from getpass import getpass\n",
"\n",
"ANTHROPIC_API_KEY = getpass()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6fb585dd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import Anthropic\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "035dea0f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f3458d9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"llm = Anthropic(anthropic_api_key=ANTHROPIC_API_KEY)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a641dbd9",
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9f844993",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" Step 1: Justin Beiber was born on March 1, 1994\\nStep 2: The NFL season ends with the Super Bowl in January/February\\nStep 3: Therefore, the Super Bowl that occurred closest to Justin Beiber's birth would be Super Bowl XXIX in 1995\\nStep 4: The San Francisco 49ers won Super Bowl XXIX in 1995\\n\\nTherefore, the answer is the San Francisco 49ers won the Super Bowl in the year Justin Beiber was born.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4797d719",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

10
langchain/llms/anthropic.py
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@ -1,5 +1,6 @@
"""Wrapper around Anthropic APIs.""" """Wrapper around Anthropic APIs."""
import re import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, Extra, root_validator from pydantic import BaseModel, Extra, root_validator
@ -123,6 +124,15 @@ class Anthropic(LLM, _AnthropicCommon):
response = model(prompt) response = model(prompt)
""" """
@root_validator()
def raise_warning(cls, values: Dict) -> Dict:
"""Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
)
return values
class Config: class Config:
"""Configuration for this pydantic object.""" """Configuration for this pydantic object."""

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