mirror of
https://github.com/hwchase17/langchain
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anthropic docs: deprecated LLM, add chat model (#3549)
This commit is contained in:
Harrison Chase
GitHub
parent
628e93a9a0
commit
52d95ec47d
179
docs/modules/models/chat/integrations/anthropic.ipynb
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179
docs/modules/models/chat/integrations/anthropic.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# Anthropic\n",
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"\n",
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"This notebook covers how to get started with Anthropic chat models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatAnthropic\n",
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"from langchain.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" AIMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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" SystemMessage\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatAnthropic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\" J'aime programmer. \", additional_kwargs={})"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
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"]\n",
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"chat(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
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"metadata": {},
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"source": [
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"## `ChatAnthropic` also supports async and streaming functionality:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks.base import CallbackManager\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}))]], llm_output={})"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chat.agenerate([messages])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" J'adore programmer."
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]
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},
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\" J'adore programmer.\", additional_kwargs={})"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n",
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"chat(messages)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df45f59f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,146 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# Anthropic\n",
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"\n",
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"[Anthropic](https://console.anthropic.com/docs) is creator of the `Claude` LLM.\n",
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"\n",
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"This example goes over how to use LangChain to interact with Anthropic models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e55c0f2e-63e1-4e83-ac44-ffcc1dfeacc8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Install the package\n",
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"!pip install anthropic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cec62d45-afa2-422a-95ef-57f8ab41a6f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get a new token: https://www.anthropic.com/earlyaccess\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"ANTHROPIC_API_KEY = getpass()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6fb585dd",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms import Anthropic\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "035dea0f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f3458d9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm = Anthropic(anthropic_api_key=ANTHROPIC_API_KEY)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a641dbd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9f844993",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\" 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.\""
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4797d719",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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"""Wrapper around Anthropic APIs."""
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"""Wrapper around Anthropic APIs."""
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import re
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import re
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import warnings
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from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
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from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
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from pydantic import BaseModel, Extra, root_validator
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from pydantic import BaseModel, Extra, root_validator
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@ -123,6 +124,15 @@ class Anthropic(LLM, _AnthropicCommon):
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response = model(prompt)
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response = model(prompt)
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"""
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"""
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@root_validator()
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def raise_warning(cls, values: Dict) -> Dict:
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"""Raise warning that this class is deprecated."""
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warnings.warn(
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"This Anthropic LLM is deprecated. "
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"Please use `from langchain.chat_models import ChatAnthropic` instead"
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)
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return values
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class Config:
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class Config:
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"""Configuration for this pydantic object."""
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"""Configuration for this pydantic object."""
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