Spaces:
Running
Running
File size: 8,912 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Aim\n",
"\n",
"Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. \n",
"\n",
"With Aim, you can easily debug and examine an individual execution:\n",
"\n",
"\n",
"\n",
"Additionally, you have the option to compare multiple executions side by side:\n",
"\n",
"\n",
"\n",
"Aim is fully open source, [learn more](https://github.com/aimhubio/aim) about Aim on GitHub.\n",
"\n",
"Let's move forward and see how to enable and configure Aim callback."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Tracking LangChain Executions with Aim</h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we will explore three usage scenarios. To start off, we will install the necessary packages and import certain modules. Subsequently, we will configure two environment variables that can be established either within the Python script or through the terminal."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mf88kuCJhbVu"
},
"outputs": [],
"source": [
"!pip install aim\n",
"!pip install langchain\n",
"!pip install openai\n",
"!pip install google-search-results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g4eTuajwfl6L"
},
"outputs": [],
"source": [
"import os\n",
"from datetime import datetime\n",
"\n",
"from langchain.llms import OpenAI\n",
"from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our examples use a GPT model as the LLM, and OpenAI offers an API for this purpose. You can obtain the key from the following link: https://platform.openai.com/account/api-keys .\n",
"\n",
"We will use the SerpApi to retrieve search results from Google. To acquire the SerpApi key, please go to https://serpapi.com/manage-api-key ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T1bSmKd6V2If"
},
"outputs": [],
"source": [
"os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
"os.environ[\"SERPAPI_API_KEY\"] = \"...\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QenUYuBZjIzc"
},
"source": [
"The event methods of `AimCallbackHandler` accept the LangChain module or agent as input and log at least the prompts and generated results, as well as the serialized version of the LangChain module, to the designated Aim run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KAz8weWuUeXF"
},
"outputs": [],
"source": [
"session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
"aim_callback = AimCallbackHandler(\n",
" repo=\".\",\n",
" experiment_name=\"scenario 1: OpenAI LLM\",\n",
")\n",
"\n",
"callbacks = [StdOutCallbackHandler(), aim_callback]\n",
"llm = OpenAI(temperature=0, callbacks=callbacks)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b8WfByB4fl6N"
},
"source": [
"The `flush_tracker` function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Scenario 1</h3> In the first scenario, we will use OpenAI LLM."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o_VmneyIUyx8"
},
"outputs": [],
"source": [
"# scenario 1 - LLM\n",
"llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
"aim_callback.flush_tracker(\n",
" langchain_asset=llm,\n",
" experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Scenario 2</h3> Scenario two involves chaining with multiple SubChains across multiple generations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "trxslyb1U28Y"
},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uauQk10SUzF6"
},
"outputs": [],
"source": [
"# scenario 2 - Chain\n",
"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
"Title: {title}\n",
"Playwright: This is a synopsis for the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
"\n",
"test_prompts = [\n",
" {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
" {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
" {\"title\": \"the best in class mlops tooling\"},\n",
"]\n",
"synopsis_chain.apply(test_prompts)\n",
"aim_callback.flush_tracker(\n",
" langchain_asset=synopsis_chain, experiment_name=\"scenario 3: Agent with Tools\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Scenario 3</h3> The third scenario involves an agent with tools."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_jN73xcPVEpI"
},
"outputs": [],
"source": [
"from langchain.agents import initialize_agent, load_tools\n",
"from langchain.agents import AgentType"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Gpq4rk6VT9cu",
"outputId": "68ae261e-d0a2-4229-83c4-762562263b66"
},
"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 find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
"Action: Search\n",
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
"Action: Search\n",
"Action Input: \"Camila Morrone age\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
"Action: Calculator\n",
"Action Input: 25^0.43\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
],
"source": [
"# scenario 3 - Agent with Tools\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
"agent = initialize_agent(\n",
" tools,\n",
" llm,\n",
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
" callbacks=callbacks,\n",
")\n",
"agent.run(\n",
" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
")\n",
"aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|