langchain/libs/partners/cohere/docs/cohere_agent.ipynb

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   "source": [
    "---\n",
    "sidebar_position: 0\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohere Tools\n",
    "\n",
    "The following notebook goes over how to use the Cohere tools agent:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prerequisites for this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# install package\n",
    "!pip install langchain langchain-cohere\n",
    "!pip install wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.agents import AgentExecutor\n",
    "from langchain.retrievers import WikipediaRetriever\n",
    "from langchain.tools.retriever import create_retriever_tool\n",
    "from langchain_cohere import create_cohere_tools_agent\n",
    "from langchain_cohere.chat_models import ChatCohere\n",
    "from langchain_core.prompts import ChatPromptTemplate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the prompt template and cohere model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the prompt\n",
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"Write all output in capital letters. {input}\"\n",
    ")\n",
    "\n",
    "# Create the Cohere chat model\n",
    "chat = ChatCohere(cohere_api_key=\"API_KEY\", model=\"command-r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we use a Wikipedia retrieval tool "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = WikipediaRetriever()\n",
    "retriever_tool = create_retriever_tool(\n",
    "    retriever,\n",
    "    \"wikipedia\",\n",
    "    \"Search for information on Wikipedia\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create the cohere tool agent and call with the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mwikipedia\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input': 'Who founded Cohere?',\n",
       " 'text': 'COHERE WAS FOUNDED BY AIDAN GOMEZ, IVAN ZAPATA, AND ALON GELLA.',\n",
       " 'additional_info': {'documents': [{'answer': '',\n",
       "    'id': 'wikipedia:0:0',\n",
       "    'tool_name': 'wikipedia'}],\n",
       "  'citations': [ChatCitation(start=22, end=63, text='AIDAN GOMEZ, IVAN ZAPATA, AND ALON GELLA.', document_ids=['wikipedia:0:0'])],\n",
       "  'search_results': None,\n",
       "  'search_queries': None,\n",
       "  'is_search_required': None,\n",
       "  'generation_id': '3b7e96be-8aad-4fa0-9ae3-7a38e800c289',\n",
       "  'token_count': {'prompt_tokens': 740,\n",
       "   'response_tokens': 27,\n",
       "   'total_tokens': 767,\n",
       "   'billed_tokens': 48}}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent = create_cohere_tools_agent(\n",
    "    llm=chat,\n",
    "    tools=[retriever_tool],\n",
    "    prompt=prompt,\n",
    ")\n",
    "agent_executor = AgentExecutor(agent=agent, tools=[retriever_tool], verbose=True)\n",
    "agent_executor.invoke({\"input\": \"Who founded Cohere?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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