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

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Harrison Chase 2023-04-25 16:11:14 -07:00 committed by GitHub
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179
docs/modules/models/chat/integrations/anthropic.ipynb Normal file
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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."""
import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, Extra, root_validator
@ -123,6 +124,15 @@ class Anthropic(LLM, _AnthropicCommon):
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:
"""Configuration for this pydantic object."""