community[minor]: Add Riza Python/JS code execution tool (#23995)

- **Description:** Add Riza Python/JS code execution tool
- **Issue:** N/A
- **Dependencies:** an optional dependency on the `rizaio` pypi package
- **Twitter handle:** [@rizaio](https://x.com/rizaio)

[Riza](https://riza.io) is a safe code execution environment for
agent-generated Python and JavaScript that's easy to integrate into
langchain apps. This PR adds two new tool classes to the community
package.

docs/docs/integrations/tools/riza.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7d143c73",
+   "metadata": {},
+   "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\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}