langchain/docs/docs/integrations/tools/riza.ipynb

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   "source": [
    "# Riza Code Interpreter\n",
    "\n",
    "> The Riza Code Interpreter is a WASM-based isolated environment for running Python or JavaScript generated by AI agents.\n",
    "\n",
    "In this notebook we'll create an example of an agent that uses Python to solve a problem that an LLM can't solve on its own:\n",
    "counting the number of 'r's in the word \"strawberry.\"\n",
    "\n",
    "Before you get started grab an API key from the [Riza dashboard](https://dashboard.riza.io). For more guides and a full API reference\n",
    "head over to the [Riza Code Interpreter API documentation](https://docs.riza.io)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894aa87a",
   "metadata": {},
   "source": [
    "Make sure you have the necessary dependencies installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8265cf7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet langchain-community rizaio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e085eb51",
   "metadata": {},
   "source": [
    "Set up your API keys as an environment variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45ba8936",
   "metadata": {},
   "outputs": [],
   "source": [
    "%env ANTHROPIC_API_KEY=<your_anthropic_api_key_here>\n",
    "%env RIZA_API_KEY=<your_riza_api_key_here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efe26fd9-6e33-4f5f-b49b-ea74fa6c4915",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.tools.riza.command import ExecPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd5b952e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.prompts import ChatPromptTemplate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd0b610",
   "metadata": {},
   "source": [
    "Initialize the `ExecPython` tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32f1543f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tools = [ExecPython()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f952d5",
   "metadata": {},
   "source": [
    "Initialize an agent using Anthropic's Claude Haiku model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "71831ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatAnthropic(model=\"claude-3-haiku-20240307\", temperature=0)\n",
    "\n",
    "prompt_template = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\n",
    "            \"system\",\n",
    "            \"You are a helpful assistant. Make sure to use a tool if you need to solve a problem.\",\n",
    "        ),\n",
    "        (\"human\", \"{input}\"),\n",
    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "agent = create_tool_calling_agent(llm, tools, prompt_template)\n",
    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "36b24036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `riza_exec_python` with `{'code': 'word = \"strawberry\"\\nprint(word.count(\"r\"))'}`\n",
      "responded: [{'id': 'toolu_01JwPLAAqqCNCjVuEnK8Fgut', 'input': {}, 'name': 'riza_exec_python', 'type': 'tool_use', 'index': 0, 'partial_json': '{\"code\": \"word = \\\\\"strawberry\\\\\"\\\\nprint(word.count(\\\\\"r\\\\\"))\"}'}]\n",
      "\n",
      "\u001b[0m\u001b[36;1m\u001b[1;3m3\n",
      "\u001b[0m\u001b[32;1m\u001b[1;3m[{'text': '\\n\\nThe word \"strawberry\" contains 3 \"r\" characters.', 'type': 'text', 'index': 0}]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "The word \"strawberry\" contains 3 \"r\" characters.\n"
     ]
    }
   ],
   "source": [
    "# Ask a tough question\n",
    "result = agent_executor.invoke({\"input\": \"how many rs are in strawberry?\"})\n",
    "print(result[\"output\"][0][\"text\"])"
   ]
  }
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