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Basic agent ops example #58

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Dec 16, 2023
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Basic agent ops example #58

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basic agent ops example
bboynton97 Dec 15, 2023
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better key demo
bboynton97 Dec 15, 2023
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298 changes: 298 additions & 0 deletions examples/openai-gpt.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Monitoring \n",
"This is an example of how to use the AgentOps library for basic Agent monitoring with OpenAI's GPT"
],
"metadata": {
"collapsed": false
},
"id": "212799f5255f3513"
},
{
"cell_type": "markdown",
"source": [
"_At this time, make sure to install openai==0.28.1_"
],
"metadata": {
"collapsed": false
},
"id": "6d717e4c8bf0de9c"
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [],
"source": [
"import openai\n",
"import agentops\n",
"from dotenv import load_dotenv\n",
"import os\n",
"from openai import ChatCompletion"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:47.472626Z",
"start_time": "2023-12-15T21:33:47.448868Z"
}
},
"id": "c51dcbda80eb8c53"
},
{
"cell_type": "markdown",
"source": [
"Next, we'll grab our two API keys. You can use dotenv like below or however else you like to load environment variables"
],
"metadata": {
"collapsed": false
},
"id": "33bb35b82e9a8f71"
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [],
"source": [
"load_dotenv()\n",
"OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') or \"<your_openai_key>\"\n",
"AGENTOPS_API_KEY = os.getenv('AGENTOPS_API_KEY') or \"<your_agentops_key>\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:47.473321Z",
"start_time": "2023-12-15T21:33:47.460144Z"
}
},
"id": "cbc5d13679e269dc"
},
{
"cell_type": "markdown",
"source": [
"The AgentOps library is designed to be a plug-and-play replacement for the OpenAI Client, maximizing use with minimal install effort."
],
"metadata": {
"collapsed": false
},
"id": "82fdff21ff00ef37"
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [],
"source": [
"ao_client = agentops.Client(AGENTOPS_API_KEY)\n",
"openai.api_key = OPENAI_API_KEY"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:48.179508Z",
"start_time": "2023-12-15T21:33:47.463468Z"
}
},
"id": "fe8116d5969f1d23"
},
{
"cell_type": "markdown",
"source": [
"Now just use OpenAI as you would normally!"
],
"metadata": {
"collapsed": false
},
"id": "3c20bbfa91b3419c"
},
{
"cell_type": "markdown",
"source": [
"## Single Session with ChatCompletion"
],
"metadata": {
"collapsed": false
},
"id": "b42f5685ac4af5c2"
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"data": {
"text/plain": "<OpenAIObject chat.completion id=chatcmpl-8WA6WNna1UiKr7dg1F1fGtAuTRZeB at 0x116b48860> JSON: {\n \"id\": \"chatcmpl-8WA6WNna1UiKr7dg1F1fGtAuTRZeB\",\n \"object\": \"chat.completion\",\n \"created\": 1702676028,\n \"model\": \"gpt-3.5-turbo-0613\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Hello! How can I assist you today?\"\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 8,\n \"completion_tokens\": 9,\n \"total_tokens\": 17\n },\n \"system_fingerprint\": null\n}"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message = {\"role\": \"user\", \"content\": \"Hello\"},\n",
"ChatCompletion.create(\n",
" model='gpt-3.5-turbo', messages=message, temperature=0.5)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:49.022014Z",
"start_time": "2023-12-15T21:33:48.181090Z"
}
},
"id": "9cd47d3fa1e252e1"
},
{
"cell_type": "markdown",
"source": [
"Make sure to end your session with a `Result` (Success|Fail|Indeterminate) for better tracking"
],
"metadata": {
"collapsed": false
},
"id": "bf75276ad9fbb3f4"
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [],
"source": [
"ao_client.end_session('Success')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:50.619934Z",
"start_time": "2023-12-15T21:33:49.021089Z"
}
},
"id": "f59fe80a7e00e6e8"
},
{
"cell_type": "markdown",
"source": [
"Now if you check the AgentOps dashboard, you should see information related to this run!"
],
"metadata": {
"collapsed": false
},
"id": "318a7186c1be2d59"
},
{
"cell_type": "markdown",
"source": [
"# Events\n",
"Additionally, you can track custom events via AgentOps.\n",
"Lets record an event when an LLM says \"Hello\""
],
"metadata": {
"collapsed": false
},
"id": "ccf998561cb9a834"
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [],
"source": [
"# Create new session\n",
"ao_client.start_session()\n",
"\n",
"# Optionally, we can add tags to the session\n",
"# ao_client.start_session(['Hello Tracker'])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:50.914863Z",
"start_time": "2023-12-15T21:33:50.620442Z"
}
},
"id": "f5a1a63ff4ecf127"
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [],
"source": [
"message = {\"role\": \"user\", \"content\": \"Hello\"},\n",
"response = ChatCompletion.create(\n",
" model='gpt-3.5-turbo', messages=message, temperature=0.5)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:33:51.321518Z",
"start_time": "2023-12-15T21:33:50.915612Z"
}
},
"id": "7daafba014c1a4d8"
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📝 Recording Event\n"
]
}
],
"source": [
"from agentops import Event\n",
"\n",
"if \"hello\" in str(response.choices[0].message.content).lower():\n",
" print('📝 Recording Event')\n",
" ao_client.record(Event(event_type=\"said_hello\"))"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:34:24.301681Z",
"start_time": "2023-12-15T21:34:24.297258Z"
}
},
"id": "b45754a57148eed1"
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [],
"source": [
"ao_client.end_session('Success')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-12-15T21:34:26.439101Z",
"start_time": "2023-12-15T21:34:26.436252Z"
}
},
"id": "4ca2b49fc06adddb"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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},
"nbformat": 4,
"nbformat_minor": 5
}