{
 "cells": [
  {
   "cell_type": "raw",
   "id": "53fbf15f",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: Cohere\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
    "# Cohere\n",
    "\n",
    "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
    "\n",
    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "The integration lives in the `langchain-cohere` package. We can install these with:\n",
    "\n",
    "```bash\n",
    "pip install -U langchain-cohere\n",
    "```\n",
    "\n",
    "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf690fbb",
   "metadata": {},
   "source": [
    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f11de02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
   "metadata": {},
   "source": [
    "## Usage\n",
    "\n",
    "ChatCohere supports all [ChatModel](/docs/modules/model_io/chat/) functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_cohere import ChatCohere\n",
    "from langchain_core.messages import HumanMessage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chat = ChatCohere(model=\"command\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
    "chat.invoke(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "await chat.ainvoke(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 && 5"
     ]
    }
   ],
   "source": [
    "for chunk in chat.stream(messages):\n",
    "    print(chunk.content, end=\"\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "064288e4-f184-4496-9427-bcf148fa055e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat.batch([messages])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c56460",
   "metadata": {},
   "source": [
    "## Chaining\n",
    "\n",
    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0851b103",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
    "chain = prompt | chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae950c0f-1691-47f1-b609-273033cae707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"topic\": \"bears\"})"
   ]
  }
 ],
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