langchain/libs/partners/azure-dynamic-sessions/docs/provider.ipynb

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    "# Azure Container Apps dynamic sessions\n",
    "\n",
    "Azure Container Apps dynamic sessions provides a secure and scalable way to run a Python code interpreter in Hyper-V isolated sandboxes. This allows your agents to run potentially untrusted code in a secure environment. The code interpreter environment includes many popular Python packages, such as NumPy, pandas, and scikit-learn.\n",
    "\n",
    "## Pre-requisites\n",
    "\n",
    "By default, the `SessionsPythonREPLTool` tool uses `DefaultAzureCredential` to authenticate with Azure. Locally, it'll use your credentials from the Azure CLI or VS Code. Install the Azure CLI and log in with `az login` to authenticate.\n",
    "\n",
    "## Using the tool\n",
    "\n",
    "Set variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import dotenv\n",
    "dotenv.load_dotenv()\n",
    "\n",
    "POOL_MANAGEMENT_ENDPOINT = os.getenv(\"POOL_MANAGEMENT_ENDPOINT\")\n",
    "AZURE_OPENAI_ENDPOINT = os.getenv(\"AZURE_OPENAI_ENDPOINT\")"
   ]
  },
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       "'{\\n  \"result\": 42,\\n  \"stdout\": \"\",\\n  \"stderr\": \"\"\\n}'"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from langchain_azure_dynamic_sessions import SessionsPythonREPLTool\n",
    "\n",
    "\n",
    "tool = SessionsPythonREPLTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)\n",
    "tool.run(\"6 * 7\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full agent example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI need to calculate the compound interest on the initial amount over 6 years.\n",
      "Action: Python_REPL\n",
      "Action Input: \n",
      "```python\n",
      "initial_amount = 500\n",
      "interest_rate = 0.05\n",
      "time_period = 6\n",
      "final_amount = initial_amount * (1 + interest_rate)**time_period\n",
      "final_amount\n",
      "```\u001b[0m\u001b[36;1m\u001b[1;3m{\n",
      "  \"result\": 670.0478203125002,\n",
      "  \"stdout\": \"\",\n",
      "  \"stderr\": \"\"\n",
      "}\u001b[0m\u001b[32;1m\u001b[1;3mThe final amount after 6 years will be $670.05\n",
      "Final Answer: $670.05\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input': 'If I put $500 in a bank account with a 5% interest rate, how much money will I have in the account after 6 years?',\n",
       " 'output': '$670.05'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from langchain_azure_dynamic_sessions import SessionsPythonREPLTool\n",
    "from langchain_openai import AzureChatOpenAI\n",
    "from langchain import agents, hub\n",
    "\n",
    "\n",
    "credential = DefaultAzureCredential()\n",
    "os.environ[\"OPENAI_API_TYPE\"] = \"azure_ad\"\n",
    "os.environ[\"OPENAI_API_KEY\"] = credential.get_token(\"https://cognitiveservices.azure.com/.default\").token\n",
    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = AZURE_OPENAI_ENDPOINT\n",
    "\n",
    "llm = AzureChatOpenAI(\n",
    "    azure_deployment=\"gpt-35-turbo\",\n",
    "    openai_api_version=\"2023-09-15-preview\",\n",
    "    streaming=True,\n",
    "    temperature=0,\n",
    ")\n",
    "\n",
    "repl = SessionsPythonREPLTool(\n",
    "    pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT,\n",
    ")\n",
    "\n",
    "tools = [repl]\n",
    "react_agent = agents.create_react_agent(\n",
    "    llm=llm,\n",
    "    tools=tools,\n",
    "    prompt=hub.pull(\"hwchase17/react\"),\n",
    ")\n",
    "\n",
    "react_agent_executor = agents.AgentExecutor(agent=react_agent, tools=tools, verbose=True, handle_parsing_errors=True)\n",
    "\n",
    "react_agent_executor.invoke({\"input\": \"If I put $500 in a bank account with a 5% interest rate, how much money will I have in the account after 6 years?\"})"
   ]
  }
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