mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
917 lines
23 KiB
Plaintext
917 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ef3909cf-72ca-4841-85c6-ef4e0eae3aaf",
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"metadata": {},
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"source": [
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"# SageMaker Tracking\n",
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"\n",
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"This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into SageMaker Experiments. Here, we use different scenarios to showcase the capability:\n",
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"* **Scenario 1**: *Single LLM* - A case where a single LLM model is used to generate output based on a given prompt.\n",
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"* **Scenario 2**: *Sequential Chain* - A case where a sequential chain of two LLM models is used.\n",
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"* **Scenario 3**: *Agent with Tools (Chain of Thought)* - A case where multiple tools (search and math) are used in addition to an LLM.\n",
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"\n",
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"[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly and easily build, train and deploy machine learning (ML) models. \n",
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"\n",
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"[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of Amazon SageMaker that lets you organize, track, compare and evaluate ML experiments and model versions.\n",
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"\n",
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"In this notebook, we will create a single experiment to log the prompts from each scenario."
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]
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},
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{
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"cell_type": "markdown",
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"id": "94c22cb4-3b1c-432b-b3be-0235eec79c5c",
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"metadata": {},
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"source": [
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"## Installation and Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2353436d-17fe-4f58-a2f9-c299d56393fd",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install sagemaker\n",
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"!pip install openai\n",
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"!pip install google-search-results"
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]
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},
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{
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"cell_type": "markdown",
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"id": "65dcf62e-7a38-4119-adb9-d9e884e82499",
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"metadata": {
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"tags": []
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},
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"source": [
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"First, setup the required API keys\n",
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"* OpenAI: https://platform.openai.com/account/api-keys (For OpenAI LLM model)\n",
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"* Google SERP API: https://serpapi.com/manage-api-key (For Google Search Tool)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5ec2b898-0cfc-4308-8e86-569cd7b7cf41",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"## Add your API keys below\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<ADD-KEY-HERE>\"\n",
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"os.environ[\"SERPAPI_API_KEY\"] = \"<ADD-KEY-HERE>\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80968ebf-519f-46de-8703-97532ac39e3e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import LLMChain, SimpleSequentialChain\n",
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"from langchain.agents import initialize_agent, load_tools\n",
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"from langchain.agents import Tool\n",
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"from langchain.callbacks import SageMakerCallbackHandler\n",
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"\n",
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"from sagemaker.analytics import ExperimentAnalytics\n",
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"from sagemaker.session import Session\n",
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"from sagemaker.experiments.run import Run"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b67d031f-a01f-4009-ad29-c80ab8ad50ea",
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"metadata": {},
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"source": [
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"## LLM Prompt Tracking"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "da2d70ee-173b-469d-a718-54c33d862844",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#LLM Hyperparameters\n",
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"HPARAMS = {\n",
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" \"temperature\": 0.1,\n",
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" \"model_name\": \"text-davinci-003\",\n",
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"}\n",
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"\n",
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"#Bucket used to save prompt logs (Use `None` is used to save the default bucket or otherwise change it)\n",
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"BUCKET_NAME = None\n",
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"\n",
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"#Experiment name\n",
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"EXPERIMENT_NAME = \"langchain-sagemaker-tracker\"\n",
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"\n",
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"#Create SageMaker Session with the given bucket\n",
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"session = Session(default_bucket=BUCKET_NAME)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7239a39a-08d8-43cb-8922-81abdd5d9ebf",
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"metadata": {},
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"source": [
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"### Scenario 1 - LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "abc00335-50c8-4119-adb8-4c4ab8522e23",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"RUN_NAME = \"run-scenario-1\"\n",
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"PROMPT_TEMPLATE = \"tell me a joke about {topic}\"\n",
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"INPUT_VARIABLES = {\"topic\": \"fish\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a3a3cbe-db85-4255-8d8b-eaafdca8c6e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
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"\n",
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" # Create SageMaker Callback\n",
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" sagemaker_callback = SageMakerCallbackHandler(run)\n",
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"\n",
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" # Define LLM model with callback\n",
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" llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
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"\n",
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" # Create prompt template\n",
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" prompt = PromptTemplate.from_template(template=PROMPT_TEMPLATE)\n",
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"\n",
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" # Create LLM Chain\n",
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" chain = LLMChain(llm=llm, prompt=prompt, callbacks=[sagemaker_callback])\n",
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"\n",
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" # Run chain\n",
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" chain.run(**INPUT_VARIABLES)\n",
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"\n",
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" # Reset the callback\n",
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" sagemaker_callback.flush_tracker()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7dc69934-9f42-40b7-9931-36a3371a38da",
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"metadata": {},
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"source": [
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"### Scenario 2 - Sequential Chain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50b75ef9-9825-4ccc-8414-4cd7525a1b68",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"RUN_NAME = \"run-scenario-2\"\n",
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"\n",
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"PROMPT_TEMPLATE_1 = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
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"Title: {title}\n",
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"Playwright: This is a synopsis for the above play:\"\"\"\n",
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"PROMPT_TEMPLATE_2 = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
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"Play Synopsis: {synopsis}\n",
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"Review from a New York Times play critic of the above play:\"\"\"\n",
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"\n",
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"INPUT_VARIABLES = {\n",
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" \"input\": \"documentary about good video games that push the boundary of game design\"\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb7fff5f-e89f-40e2-96b4-3641a0b6e9b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
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"\n",
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" # Create SageMaker Callback\n",
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" sagemaker_callback = SageMakerCallbackHandler(run)\n",
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"\n",
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" # Create prompt templates for the chain\n",
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" prompt_template1 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_1)\n",
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" prompt_template2 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_2)\n",
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"\n",
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" # Define LLM model with callback\n",
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" llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
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"\n",
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" # Create chain1\n",
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" chain1 = LLMChain(llm=llm, prompt=prompt_template1, callbacks=[sagemaker_callback])\n",
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"\n",
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" # Create chain2\n",
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" chain2 = LLMChain(llm=llm, prompt=prompt_template2, callbacks=[sagemaker_callback])\n",
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"\n",
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" # Create Sequential chain\n",
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" overall_chain = SimpleSequentialChain(chains=[chain1, chain2], callbacks=[sagemaker_callback])\n",
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"\n",
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" # Run overall sequential chain\n",
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" overall_chain.run(**INPUT_VARIABLES)\n",
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"\n",
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" # Reset the callback\n",
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" sagemaker_callback.flush_tracker()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6b82bd0e-c626-4797-bb06-c1983f176315",
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"metadata": {},
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"source": [
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"### Scenario 3 - Agent with Tools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b5066f03-49dc-4868-be8e-d21ce22063fe",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"RUN_NAME = \"run-scenario-3\"\n",
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"PROMPT_TEMPLATE = \"Who is the oldest person alive? And what is their current age raised to the power of 1.51?\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "98385c42-9e44-4b03-b76d-007cb4797864",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
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"\n",
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" # Create SageMaker Callback\n",
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" sagemaker_callback = SageMakerCallbackHandler(run)\n",
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"\n",
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" # Define LLM model with callback\n",
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" llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
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"\n",
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" # Define tools\n",
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" tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=[sagemaker_callback])\n",
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"\n",
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" # Initialize agent with all the tools\n",
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" agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", callbacks=[sagemaker_callback])\n",
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"\n",
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" # Run agent\n",
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" agent.run(input=PROMPT_TEMPLATE)\n",
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"\n",
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" # Reset the callback\n",
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" sagemaker_callback.flush_tracker()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c306a1c9-99f8-476d-96db-347746f5cfe0",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Load Log Data\n",
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"\n",
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"Once the prompts are logged, we can easily load and convert them to Pandas DataFrame as follows."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec7b4af2-e01d-4f6c-9de5-70d2b4acb9e6",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#Load\n",
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"logs = ExperimentAnalytics(experiment_name=EXPERIMENT_NAME)\n",
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"\n",
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"#Convert as pandas dataframe\n",
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"df = logs.dataframe(force_refresh=True)\n",
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"\n",
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"print(df.shape)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29991c75-f9cf-4c36-abfd-903c09fb170d",
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"metadata": {},
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"source": [
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"As can be seen above, there are three runs (rows) in the experiment corresponding to each scenario. Each run logs the prompts and related LLM settings/hyperparameters as json and are saved in s3 bucket. Feel free to load and explore the log data from each json path."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "61a695d6-0aef-4284-9e12-eea8bc143dbd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"availableInstances": [
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{
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"_defaultOrder": 0,
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"_isFastLaunch": true,
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"hideHardwareSpecs": false,
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"memoryGiB": 4,
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"name": "ml.t3.medium",
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"vcpuNum": 2
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"name": "ml.t3.large",
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"name": "ml.t3.xlarge",
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},
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"_defaultOrder": 4,
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},
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},
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},
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