langchain/docs/tracing/agent_with_tracing.ipynb

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"cells": [
{
"cell_type": "markdown",
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"metadata": {},
"source": [
"# Tracing Walkthrough"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "17c04cc6-c93d-4b6c-a033-e897577f4ed1",
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"import os\n",
"os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\"\n",
"\n",
"## Uncomment this if using hosted setup.\n",
"\n",
"# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://langchain-api-gateway-57eoxz8z.uc.gateway.dev\" \n",
"\n",
"## Uncomment this if you want traces to be recorded to \"my_session\" instead of default.\n",
"\n",
"# os.environ[\"LANGCHAIN_SESSION\"] = \"my_session\" \n",
"\n",
"## Better to set this environment variable in the terminal\n",
"## Uncomment this if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = \"my_api_key\" \n",
"\n",
"import langchain\n",
"from langchain.agents import Tool, initialize_agent, load_tools\n",
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"from langchain.agents import AgentType\n",
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"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"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": []
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"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\n",
"\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"
]
},
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"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?\")"
]
},
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{
"cell_type": "code",
"execution_count": 4,
"id": "4829eb1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mQuestion: What is 2 raised to .123243 power?\n",
"Thought: I need a calculator to solve this problem.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"Observation: calculator is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made a mistake, I need to use the correct tool for this question.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: calculator is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made a mistake, the tool name is actually \"calc\" instead of \"calculator\".\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"calc\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: calc is not a valid tool, try another one.\n",
"\u001b[32;1m\u001b[1;3mI made another mistake, the tool name is actually \"Calculator\" instead of \"calc\".\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"2^0.123243\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe final answer is 1.0891804557407723.\n",
"Final Answer: 1.0891804557407723\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
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"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",
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")\n",
"\n",
"agent.run(\"What is 2 raised to .123243 power?\")"
]
},
{
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