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 "cells": [
  {
   "cell_type": "raw",
   "id": "afaf8039",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: Anthropic\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49f1e0d",
   "metadata": {},
   "source": [
    "# ChatAnthropic\n",
    "\n",
    "This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
    "\n",
    "Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Anthropic docs](https://docs.anthropic.com/en/docs/models-overview).\n",
    "\n",
    "\n",
    ":::info AWS Bedrock and Google VertexAI\n",
    "\n",
    "Note that certain Anthropic models can also be accessed via AWS Bedrock and Google VertexAI. See the [ChatBedrock](/docs/integrations/chat/bedrock/) and [ChatVertexAI](/docs/integrations/chat/google_vertex_ai_palm/) integrations to use Anthropic models via these services.\n",
    "\n",
    ":::\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
    "\n",
    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/anthropic) | Package downloads | Package latest |\n",
    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
    "| [ChatAnthropic](https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html) | [langchain-anthropic](https://api.python.langchain.com/en/latest/anthropic_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-anthropic?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-anthropic?style=flat-square&label=%20) |\n",
    "\n",
    "### Model features\n",
    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
    "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
    "\n",
    "## Setup\n",
    "\n",
    "To access Anthropic models you'll need to create an Anthropic account, get an API key, and install the `langchain-anthropic` integration package.\n",
    "\n",
    "### Credentials\n",
    "\n",
    "Head to https://console.anthropic.com/ to sign up for Anthropic and generate an API key. Once you've done this set the ANTHROPIC_API_KEY environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"anthropic_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
   "metadata": {},
   "source": [
    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
   "metadata": {},
   "source": [
    "### Installation\n",
    "\n",
    "The LangChain Anthropic integration lives in the `langchain-anthropic` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -qU langchain-anthropic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
   "metadata": {},
   "source": [
    "## Instantiation\n",
    "\n",
    "Now we can instantiate our model object and generate chat completions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_anthropic import ChatAnthropic\n",
    "\n",
    "llm = ChatAnthropic(\n",
    "    model=\"claude-3-sonnet-20240229\",\n",
    "    temperature=0,\n",
    "    max_tokens=1024,\n",
    "    timeout=None,\n",
    "    max_retries=2,\n",
    "    # other params...\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4f3e15",
   "metadata": {},
   "source": [
    "## Invocation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62e0dbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'id': 'msg_013qztabaFADNnKsHR1rdrju', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 21}}, id='run-a22ab30c-7e09-48f5-bc27-a08a9d8f7fa1-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [\n",
    "    (\n",
    "        \"system\",\n",
    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
    "    ),\n",
    "    (\"human\", \"I love programming.\"),\n",
    "]\n",
    "ai_msg = llm.invoke(messages)\n",
    "ai_msg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voici la traduction en français :\n",
      "\n",
      "J'aime la programmation.\n"
     ]
    }
   ],
   "source": [
    "print(ai_msg.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
   "metadata": {},
   "source": [
    "## Chaining\n",
    "\n",
    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='Ich liebe Programmieren.', response_metadata={'id': 'msg_01FWrA8w9HbjqYPTQ7VryUnp', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 23, 'output_tokens': 11}}, id='run-b749bf20-b46d-4d62-ac73-f59adab6dd7e-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\n",
    "            \"system\",\n",
    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
    "        ),\n",
    "        (\"human\", \"{input}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "chain = prompt | llm\n",
    "chain.invoke(\n",
    "    {\n",
    "        \"input_language\": \"English\",\n",
    "        \"output_language\": \"German\",\n",
    "        \"input\": \"I love programming.\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
   "metadata": {},
   "source": [
    "## Content blocks\n",
    "\n",
    "One key difference to note between Anthropic models and most others is that the contents of a single Anthropic AI message can either be a single string or a **list of content blocks**. For example when an Anthropic model invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized `AIMessage.tool_calls`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a374a24-2534-4e6f-825b-30fab7bbe0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': \"Okay, let's use the GetWeather tool to check the current temperatures in Los Angeles and New York City.\",\n",
       "  'type': 'text'},\n",
       " {'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC',\n",
       "  'input': {'location': 'Los Angeles, CA'},\n",
       "  'name': 'GetWeather',\n",
       "  'type': 'tool_use'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "\n",
    "\n",
    "class GetWeather(BaseModel):\n",
    "    \"\"\"Get the current weather in a given location\"\"\"\n",
    "\n",
    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
    "\n",
    "\n",
    "llm_with_tools = llm.bind_tools([GetWeather])\n",
    "ai_msg = llm_with_tools.invoke(\"Which city is hotter today: LA or NY?\")\n",
    "ai_msg.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b4a1ead-952c-489f-a8d4-355d3fb55f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'GetWeather',\n",
       "  'args': {'location': 'Los Angeles, CA'},\n",
       "  'id': 'toolu_01Tnp5tL7LJZaVyQXKEjbqcC'}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ai_msg.tool_calls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
   "metadata": {},
   "source": [
    "## API reference\n",
    "\n",
    "For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html"
   ]
  }
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