mirror of
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docs: anthropic partner package docs (#18109)
This commit is contained in:
Erick Friis
GitHub
parent
b1d9ce541d
commit
f5cf6975ba
@ -17,40 +17,44 @@
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"source": [
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"source": [
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"# ChatAnthropic\n",
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"# ChatAnthropic\n",
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"\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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"This notebook covers how to get started with Anthropic chat models.\n",
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"\n",
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"## Setup\n",
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"\n",
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"For setup instructions, please see the Installation and Environment Setup sections of the [Anthropic Platform page](/docs/integrations/platforms/anthropic.mdx)."
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]
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]
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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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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"id": "91be2e12",
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"metadata": {
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"metadata": {},
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:00.590587Z",
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"start_time": "2024-01-19T11:25:00.127293Z"
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},
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"tags": []
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"from langchain_community.chat_models import ChatAnthropic\n",
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"%pip install -qU langchain-anthropic"
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"from langchain_core.prompts import ChatPromptTemplate"
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]
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},
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{
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"cell_type": "markdown",
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"id": "584ed5ec",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"\n",
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"We'll need to get a [Anthropic](https://console.anthropic.com/settings/keys) and set the `ANTHROPIC_API_KEY` environment variable:"
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]
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]
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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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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"id": "01578ae3",
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"metadata": {
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"metadata": {},
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:04.349676Z",
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"start_time": "2024-01-19T11:25:03.964930Z"
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},
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"tags": []
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},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")"
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
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]
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]
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},
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},
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{
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{
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@ -82,7 +86,9 @@
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"text/plain": "AIMessage(content=' 저는 파이썬을 좋아합니다.')"
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"text/plain": [
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"AIMessage(content=' 저는 파이썬을 좋아합니다.')"
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]
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},
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},
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"execution_count": 3,
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"execution_count": 3,
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"metadata": {},
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"metadata": {},
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@ -90,6 +96,11 @@
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}
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}
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],
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],
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"source": [
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"source": [
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"from langchain_anthropic import ChatAnthropic\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")\n",
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"\n",
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"system = (\n",
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"system = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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")\n",
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@ -128,7 +139,9 @@
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"text/plain": "AIMessage(content=\" Why don't bears like fast food? Because they can't catch it!\")"
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"text/plain": [
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"AIMessage(content=\" Why don't bears like fast food? Because they can't catch it!\")"
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]
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},
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},
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"execution_count": 4,
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"execution_count": 4,
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"metadata": {},
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"metadata": {},
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@ -189,154 +202,6 @@
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"for chunk in chain.stream({}):\n",
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"for chunk in chain.stream({}):\n",
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" print(chunk.content, end=\"\", flush=True)"
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" print(chunk.content, end=\"\", flush=True)"
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]
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]
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},
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{
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"cell_type": "markdown",
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"id": "3737fc8d",
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"metadata": {},
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"source": [
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"# ChatAnthropicMessages\n",
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"\n",
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"LangChain also offers the beta Anthropic Messages endpoint through the new `langchain-anthropic` package."
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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": "c253883f",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-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": 6,
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"id": "07c47c2a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:25.288133Z",
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"start_time": "2024-01-19T11:25:24.438968Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
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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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"from langchain_anthropic import ChatAnthropicMessages\n",
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"\n",
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"chat = ChatAnthropicMessages(model_name=\"claude-instant-1.2\")\n",
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"system = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"\n",
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"chain = prompt | chat\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"Korean\",\n",
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" \"text\": \"I love Python\",\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19e53d75935143fd",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"ChatAnthropicMessages also requires the anthropic_api_key argument, or the ANTHROPIC_API_KEY environment variable must be set. \n",
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"\n",
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"ChatAnthropicMessages 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": 7,
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"id": "e20a139d30e3d333",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:26.012325Z",
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"start_time": "2024-01-19T11:25:25.288358Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
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},
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"execution_count": 7,
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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 chain.ainvoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"Korean\",\n",
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" \"text\": \"I love Python\",\n",
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" }\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": 8,
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"id": "6f34f1073d7e7120",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:28.323455Z",
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"start_time": "2024-01-19T11:25:26.012040Z"
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},
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"collapsed": false
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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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"Here are some of the most famous tourist attractions in Japan:\n",
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"\n",
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"- Tokyo Tower - A communication and observation tower in Tokyo modeled after the Eiffel Tower. It offers stunning views of the city.\n",
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"\n",
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"- Mount Fuji - Japan's highest and most famous mountain. It's a iconic symbol of Japan and a UNESCO World Heritage Site. \n",
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"\n",
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"- Itsukushima Shrine (Miyajima) - A shrine located on an island in Hiroshima prefecture, known for its \"floating\" torii gate that seems to float on water during high tide.\n",
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"\n",
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"- Himeji Castle - A UNESCO World Heritage Site famous for having withstood numerous battles without destruction to its intricate white walls and sloping, triangular roofs. \n",
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"\n",
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"- Kawaguchiko Station - Near Mount Fuji, this area is known for its scenic Fuji Five Lakes region. \n",
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"\n",
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"- Hiroshima Peace Memorial Park and Museum - Commemorates the world's first atomic bombing in Hiroshima on August 6, 1945. \n",
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"\n",
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"- Arashiyama Bamboo Grove - A renowned bamboo forest located in Kyoto that draws many visitors.\n",
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"\n",
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"- Kegon Falls - One of Japan's largest waterfalls"
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]
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}
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],
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"source": [
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [(\"human\", \"Give me a list of famous tourist attractions in Japan\")]\n",
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")\n",
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"chain = prompt | chat\n",
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"for chunk in chain.stream({}):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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143
docs/docs/integrations/llms/anthropic.ipynb
Normal file
143
docs/docs/integrations/llms/anthropic.ipynb
Normal file
@ -0,0 +1,143 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "602a52a4",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Anthropic\n",
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"---"
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]
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},
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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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"# AnthropicLLM\n",
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"\n",
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"This example goes over how to use LangChain to interact with `Anthropic` models.\n",
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"\n",
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"## Installation"
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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": "59c710c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-anthropic"
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]
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},
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{
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"cell_type": "markdown",
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"id": "560a2f9254963fd7",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Environment Setup\n",
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"\n",
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"We'll need to get a [Anthropic](https://console.anthropic.com/settings/keys) and set the `ANTHROPIC_API_KEY` environment variable:"
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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": "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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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1891df96eb076e1a",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Usage"
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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": "98f70927a87e4745",
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"metadata": {
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"collapsed": false
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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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"'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'"
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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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"from langchain_anthropic import AnthropicLLM\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"\n",
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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.from_template(template)\n",
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"\n",
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"model = AnthropicLLM(model=\"claude-2.1\")\n",
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"\n",
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"chain = prompt | model\n",
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"\n",
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"chain.invoke({\"question\": \"What is LangChain?\"})"
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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": "a52f765c",
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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 3.11.1 64-bit",
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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",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.4"
|
||||||
|
},
|
||||||
|
"vscode": {
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -5,169 +5,39 @@ All functionality related to Anthropic models.
|
|||||||
[Anthropic](https://www.anthropic.com/) is an AI safety and research company, and is the creator of Claude.
|
[Anthropic](https://www.anthropic.com/) is an AI safety and research company, and is the creator of Claude.
|
||||||
This page covers all integrations between Anthropic models and LangChain.
|
This page covers all integrations between Anthropic models and LangChain.
|
||||||
|
|
||||||
## Prompting Overview
|
## Installation
|
||||||
|
|
||||||
Claude is chat-based model, meaning it is trained on conversation data.
|
To use Anthropic models, you will need to install the `langchain-anthropic` package.
|
||||||
However, it is a text based API, meaning it takes in single string.
|
You can do this with the following command:
|
||||||
It expects this string to be in a particular format.
|
|
||||||
This means that it is up the user to ensure that is the case.
|
|
||||||
LangChain provides several utilities and helper functions to make sure prompts that you write -
|
|
||||||
whether formatted as a string or as a list of messages - end up formatted correctly.
|
|
||||||
|
|
||||||
Specifically, Claude is trained to fill in text for the Assistant role as part of an ongoing dialogue
|
```
|
||||||
between a human user (`Human:`) and an AI assistant (`Assistant:`). Prompts sent via the API must contain
|
pip install langchain-anthropic
|
||||||
`\n\nHuman:` and `\n\nAssistant:` as the signals of who's speaking.
|
```
|
||||||
The final turn must always be `\n\nAssistant:` - the input string cannot have `\n\nHuman:` as the final role.
|
|
||||||
|
|
||||||
Because Claude is chat-based but accepts a string as input, it can be treated as either a LangChain `ChatModel` or `LLM`.
|
## Environment Setup
|
||||||
This means there are two wrappers in LangChain - `ChatAnthropic` and `Anthropic`.
|
|
||||||
It is generally recommended to use the `ChatAnthropic` wrapper, and format your prompts as `ChatMessage`s (we will show examples of this below).
|
|
||||||
This is because it keeps your prompt in a general format that you can easily then also use with other models (should you want to).
|
|
||||||
However, if you want more fine-grained control over the prompt, you can use the `Anthropic` wrapper - we will show and example of this as well.
|
|
||||||
The `Anthropic` wrapper however is deprecated, as all functionality can be achieved in a more generic way using `ChatAnthropic`.
|
|
||||||
|
|
||||||
## Prompting Best Practices
|
|
||||||
|
|
||||||
Anthropic models have several prompting best practices compared to OpenAI models.
|
|
||||||
|
|
||||||
**No System Messages**
|
|
||||||
|
|
||||||
Anthropic models are not trained on the concept of a "system message".
|
|
||||||
We have worked with the Anthropic team to handle them somewhat appropriately (a Human message with an `admin` tag)
|
|
||||||
but this is largely a hack and it is recommended that you do not use system messages.
|
|
||||||
|
|
||||||
**AI Messages Can Continue**
|
|
||||||
|
|
||||||
A completion from Claude is a continuation of the last text in the string which allows you further control over Claude's output.
|
|
||||||
For example, putting words in Claude's mouth in a prompt like this:
|
|
||||||
|
|
||||||
`\n\nHuman: Tell me a joke about bears\n\nAssistant: What do you call a bear with no teeth?`
|
|
||||||
|
|
||||||
This will return a completion like this `A gummy bear!` instead of a whole new assistant message with a different random bear joke.
|
|
||||||
|
|
||||||
|
To use Anthropic models, you will need to set the `ANTHROPIC_API_KEY` environment variable.
|
||||||
|
You can get an Anthropic API key [here](https://console.anthropic.com/settings/keys)
|
||||||
|
|
||||||
## `ChatAnthropic`
|
## `ChatAnthropic`
|
||||||
|
|
||||||
`ChatAnthropic` is a subclass of LangChain's `ChatModel`, meaning it works best with `ChatPromptTemplate`.
|
`ChatAnthropic` is a subclass of LangChain's `ChatModel`.
|
||||||
You can import this wrapper with the following code:
|
You can import this wrapper with the following code:
|
||||||
|
|
||||||
```
|
```
|
||||||
from langchain_community.chat_models import ChatAnthropic
|
from langchain_anthropic import ChatAnthropic
|
||||||
model = ChatAnthropic()
|
model = ChatAnthropic(model='claude-2.1')
|
||||||
```
|
```
|
||||||
|
|
||||||
When working with ChatModels, it is preferred that you design your prompts as `ChatPromptTemplate`s.
|
Read more in the [ChatAnthropic documentation](/docs/integrations/chat/anthropic).
|
||||||
Here is an example below of doing that:
|
|
||||||
|
|
||||||
|
## `AnthropicLLM`
|
||||||
|
|
||||||
|
`AnthropicLLM` is a subclass of LangChain's `LLM`. It is a wrapper around Anthropic's
|
||||||
|
text-based completion endpoints.
|
||||||
|
|
||||||
|
```python
|
||||||
|
from langchain_anthropic import AnthropicLLM
|
||||||
|
|
||||||
|
model = AnthropicLLM(model='claude-2.1')
|
||||||
```
|
```
|
||||||
from langchain_core.prompts import ChatPromptTemplate
|
|
||||||
|
|
||||||
prompt = ChatPromptTemplate.from_messages([
|
|
||||||
("system", "You are a helpful chatbot"),
|
|
||||||
("human", "Tell me a joke about {topic}"),
|
|
||||||
])
|
|
||||||
```
|
|
||||||
|
|
||||||
You can then use this in a chain as follows:
|
|
||||||
|
|
||||||
```
|
|
||||||
chain = prompt | model
|
|
||||||
chain.invoke({"topic": "bears"})
|
|
||||||
```
|
|
||||||
|
|
||||||
How is the prompt actually being formatted under the hood? We can see that by running the following code
|
|
||||||
|
|
||||||
```
|
|
||||||
prompt_value = prompt.format_prompt(topic="bears")
|
|
||||||
model.convert_prompt(prompt_value)
|
|
||||||
```
|
|
||||||
|
|
||||||
This produces the following formatted string:
|
|
||||||
|
|
||||||
```
|
|
||||||
'\n\nYou are a helpful chatbot\n\nHuman: Tell me a joke about bears\n\nAssistant:'
|
|
||||||
```
|
|
||||||
|
|
||||||
We can see that under the hood LangChain is not appending any prefix/suffix to `SystemMessage`'s. This is because Anthropic has no concept of `SystemMessage`.
|
|
||||||
Anthropic requires all prompts to end with assistant messages. This means if the last message is not an assistant message, the suffix `Assistant:` will automatically be inserted.
|
|
||||||
|
|
||||||
If you decide instead to use a normal PromptTemplate (one that just works on a single string) let's take a look at
|
|
||||||
what happens:
|
|
||||||
|
|
||||||
```
|
|
||||||
from langchain.prompts import PromptTemplate
|
|
||||||
|
|
||||||
prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
|
|
||||||
prompt_value = prompt.format_prompt(topic="bears")
|
|
||||||
model.convert_prompt(prompt_value)
|
|
||||||
```
|
|
||||||
|
|
||||||
This produces the following formatted string:
|
|
||||||
|
|
||||||
```
|
|
||||||
'\n\nHuman: Tell me a joke about bears\n\nAssistant:'
|
|
||||||
```
|
|
||||||
|
|
||||||
We can see that it automatically adds the Human and Assistant tags.
|
|
||||||
What is happening under the hood?
|
|
||||||
First: the string gets converted to a single human message. This happens generically (because we are using a subclass of `ChatModel`).
|
|
||||||
Then, similarly to the above example, an empty Assistant message is getting appended.
|
|
||||||
This is Anthropic specific.
|
|
||||||
|
|
||||||
## [Deprecated] `Anthropic`
|
|
||||||
|
|
||||||
This `Anthropic` wrapper is subclassed from `LLM`.
|
|
||||||
We can import it with:
|
|
||||||
|
|
||||||
```
|
|
||||||
from langchain_community.llms import Anthropic
|
|
||||||
model = Anthropic()
|
|
||||||
```
|
|
||||||
|
|
||||||
This model class is designed to work with normal PromptTemplates. An example of that is below:
|
|
||||||
|
|
||||||
```
|
|
||||||
prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
|
|
||||||
chain = prompt | model
|
|
||||||
chain.invoke({"topic": "bears"})
|
|
||||||
```
|
|
||||||
|
|
||||||
Let's see what is going on with the prompt templating under the hood!
|
|
||||||
|
|
||||||
```
|
|
||||||
prompt_value = prompt.format_prompt(topic="bears")
|
|
||||||
model.convert_prompt(prompt_value)
|
|
||||||
```
|
|
||||||
|
|
||||||
This outputs the following
|
|
||||||
|
|
||||||
```
|
|
||||||
'\n\nHuman: Tell me a joke about bears\n\nAssistant: Sure, here you go:\n'
|
|
||||||
```
|
|
||||||
|
|
||||||
Notice that it adds the Human tag at the start of the string, and then finishes it with `\n\nAssistant: Sure, here you go:`.
|
|
||||||
The extra `Sure, here you go` was added on purpose by the Anthropic team.
|
|
||||||
|
|
||||||
What happens if we have those symbols in the prompt directly?
|
|
||||||
|
|
||||||
```
|
|
||||||
prompt = PromptTemplate.from_template("Human: Tell me a joke about {topic}")
|
|
||||||
prompt_value = prompt.format_prompt(topic="bears")
|
|
||||||
model.convert_prompt(prompt_value)
|
|
||||||
```
|
|
||||||
|
|
||||||
This outputs:
|
|
||||||
|
|
||||||
```
|
|
||||||
'\n\nHuman: Tell me a joke about bears'
|
|
||||||
```
|
|
||||||
|
|
||||||
We can see that we detect that the user is trying to use the special tokens, and so we don't do any formatting.
|
|
||||||
|
|
||||||
## `ChatAnthropicMessages` (Beta)
|
|
||||||
|
|
||||||
`ChatAnthropicMessages` uses the beta release of Anthropic's new Messages API.
|
|
||||||
|
|
||||||
You can use it from the `langchain-anthropic` package, which you can install with `pip install langchain-anthropic`.
|
|
||||||
|
|
||||||
For more information, see the [ChatAnthropicMessages docs](../chat/anthropic#chatanthropicmessages)
|
|
||||||
Loading…
Reference in New Issue
Block a user