{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5371a9bb",
   "metadata": {},
   "source": [
    "# Tracing Walkthrough\n",
    "\n",
    "There are two recommended ways to trace your LangChains:\n",
    "\n",
    "1. Setting the `LANGCHAIN_TRACING` environment variable to \"true\".\n",
    "1. Using a context manager with tracing_enabled() to trace a particular block of code.\n",
    "\n",
    "**Note** if the environment variable is set, all code will be traced, regardless of whether or not it's within the context manager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17c04cc6-c93d-4b6c-a033-e897577f4ed1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"LANGCHAIN_TRACING\"] = \"true\"\n",
    "\n",
    "## Uncomment below if using hosted setup.\n",
    "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://langchain-api-gateway-57eoxz8z.uc.gateway.dev\" \n",
    "\n",
    "## Uncomment below if you want traces to be recorded to \"my_session\" instead of \"default\".\n",
    "# os.environ[\"LANGCHAIN_SESSION\"] = \"my_session\"  \n",
    "\n",
    "## Better to set this environment variable in the terminal\n",
    "## Uncomment below if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
    "# os.environ[\"LANGCHAIN_API_KEY\"] = \"my_api_key\"  \n",
    "\n",
    "import langchain\n",
    "from langchain.agents import Tool, initialize_agent, load_tools\n",
    "from langchain.agents import AgentType\n",
    "from langchain.callbacks import tracing_enabled\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.llms import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b62cd48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "tools = load_tools([\"llm-math\"], llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 2^.123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
      "Final Answer: 1.0891804557407723\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.0891804557407723'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent = initialize_agent(\n",
    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
    ")\n",
    "\n",
    "agent.run(\"What is 2 raised to .123243 power?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4829eb1d",
   "metadata": {
    "tags": []
   },
   "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 use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 2 ^ .123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
      "Final Answer: 1.0891804557407723\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.0891804557407723'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agent run with tracing using a chat model\n",
    "agent = initialize_agent(\n",
    "    tools, ChatOpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
    ")\n",
    "\n",
    "agent.run(\"What is 2 raised to .123243 power?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76abfd82",
   "metadata": {
    "tags": []
   },
   "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 use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 2 ^ .123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
      "Final Answer: 1.0891804557407723\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 5 ^ .123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2193914912400514\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
      "Final Answer: 1.2193914912400514\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Both of the agent runs will be traced because the environment variable is set\n",
    "agent.run(\"What is 2 raised to .123243 power?\")\n",
    "with tracing_enabled() as session:\n",
    "    agent.run(\"What is 5 raised to .123243 power?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe833c33-033f-4806-be0c-cc3d147db13d",
   "metadata": {
    "tags": []
   },
   "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 use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 5 ^ .123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2193914912400514\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
      "Final Answer: 1.2193914912400514\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 2 ^ .123243\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI now know the answer to the question. \n",
      "Final Answer: 1.0891804557407723\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.0891804557407723'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we unset the environment variable and use a context manager.\n",
    "if \"LANGCHAIN_TRACING\" in os.environ:\n",
    "    del os.environ[\"LANGCHAIN_TRACING\"]\n",
    "\n",
    "# here, we are writing traces to \"my_test_session\"\n",
    "with tracing_enabled(\"my_session\") as session:\n",
    "    assert session\n",
    "    agent.run(\"What is 5 raised to .123243 power?\")  # this should be traced\n",
    "\n",
    "agent.run(\"What is 2 raised to .123243 power?\")  # this should not be traced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b34105a4-be8e-46e4-8abe-01adba3ba727",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\n",
      "\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 3^0.123\u001b[0m\u001b[32;1m\u001b[1;3mI need to use a calculator to solve this.\n",
      "Action: Calculator\n",
      "Action Input: 2^0.123\u001b[0m\u001b[32;1m\u001b[1;3mAny number raised to the power of 0 is 1, but I'm not sure about a decimal power.\n",
      "Action: Calculator\n",
      "Action Input: 1^.123\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.1446847956963533\u001b[0m\n",
      "Thought:\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0889970153361064\u001b[0m\n",
      "Thought:\n",
      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0\u001b[0m\n",
      "Thought:\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.0'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The context manager is concurrency safe:\n",
    "import asyncio \n",
    "if \"LANGCHAIN_TRACING\" in os.environ:\n",
    "    del os.environ[\"LANGCHAIN_TRACING\"]\n",
    "    \n",
    "questions = [f\"What is {i} raised to .123 power?\" for i in range(1,4)]\n",
    "\n",
    "# start a background task\n",
    "task = asyncio.create_task(agent.arun(questions[0]))  # this should not be traced\n",
    "with tracing_enabled() as session:\n",
    "    assert session\n",
    "    tasks = [agent.arun(q) for q in questions[1:3]]  # these should be traced\n",
    "    await asyncio.gather(*tasks)\n",
    "\n",
    "await task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e46c85b-2ac0-4661-abed-9c2bf3036820",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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