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625 lines
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Plaintext
625 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Weights & Biases\n",
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"\n",
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"This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.\n",
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"\n",
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"Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing\n",
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"\n",
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"View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B--VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install wandb\n",
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"!pip install pandas\n",
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"!pip install textstat\n",
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"!pip install spacy\n",
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"!python -m spacy download en_core_web_sm"
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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": 1,
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"metadata": {
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"id": "T1bSmKd6V2If"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"WANDB_API_KEY\"] = \"\"\n",
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"# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
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"# os.environ[\"SERPAPI_API_KEY\"] = \"\""
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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": 2,
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"metadata": {
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"id": "8WAGnTWpUUnD"
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},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
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"from langchain.llms import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```\n",
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"Callback Handler that logs to Weights and Biases.\n",
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"\n",
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"Parameters:\n",
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" job_type (str): The type of job.\n",
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" project (str): The project to log to.\n",
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" entity (str): The entity to log to.\n",
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" tags (list): The tags to log.\n",
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" group (str): The group to log to.\n",
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" name (str): The name of the run.\n",
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" notes (str): The notes to log.\n",
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" visualize (bool): Whether to visualize the run.\n",
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" complexity_metrics (bool): Whether to log complexity metrics.\n",
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" stream_logs (bool): Whether to stream callback actions to W&B\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "cxBFfZR8d9FC"
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},
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"source": [
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"```\n",
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"Default values for WandbCallbackHandler(...)\n",
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"\n",
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"visualize: bool = False,\n",
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"complexity_metrics: bool = False,\n",
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"stream_logs: bool = False,\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"
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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": 3,
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"metadata": {
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"id": "KAz8weWuUeXF"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mharrison-chase\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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]
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},
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{
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"data": {
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"text/html": [
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"Tracking run with wandb version 0.14.0"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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{
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"data": {
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"text/html": [
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"Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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{
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"data": {
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"text/html": [
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" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.\n"
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]
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}
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],
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"source": [
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"\"\"\"Main function.\n",
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"\n",
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"This function is used to try the callback handler.\n",
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"Scenarios:\n",
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"1. OpenAI LLM\n",
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"2. Chain with multiple SubChains on multiple generations\n",
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"3. Agent with Tools\n",
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"\"\"\"\n",
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"session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
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"wandb_callback = WandbCallbackHandler(\n",
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" job_type=\"inference\",\n",
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" project=\"langchain_callback_demo\",\n",
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" group=f\"minimal_{session_group}\",\n",
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" name=\"llm\",\n",
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" tags=[\"test\"],\n",
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")\n",
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"callbacks = [StdOutCallbackHandler(), wandb_callback]\n",
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"llm = OpenAI(temperature=0, callbacks=callbacks)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Q-65jwrDeK6w"
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},
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"source": [
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"\n",
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"\n",
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"```\n",
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"# Defaults for WandbCallbackHandler.flush_tracker(...)\n",
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"\n",
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"reset: bool = True,\n",
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"finish: bool = False,\n",
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"```\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `flush_tracker` function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright."
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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": 4,
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"metadata": {
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"id": "o_VmneyIUyx8"
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" View run <strong style=\"color:#cdcd00\">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"version_minor": 0
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"text/plain": [
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"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016745895149999985, max=1.0…"
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/html": [
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"Tracking run with wandb version 0.14.0"
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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},
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{
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"data": {
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"text/html": [
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"Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# SCENARIO 1 - LLM\n",
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"llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
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"wandb_callback.flush_tracker(llm, name=\"simple_sequential\")"
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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": 5,
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"metadata": {
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"id": "trxslyb1U28Y"
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},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import LLMChain"
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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": 6,
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"metadata": {
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"id": "uauQk10SUzF6"
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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},
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"data": {
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"text/html": [
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" View run <strong style=\"color:#cdcd00\">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)"
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],
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"Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
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],
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>"
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"output_type": "display_data"
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}
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],
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"source": [
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"# SCENARIO 2 - Chain\n",
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"template = \"\"\"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 = PromptTemplate(input_variables=[\"title\"], template=template)\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
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"\n",
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"test_prompts = [\n",
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" {\n",
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" \"title\": \"documentary about good video games that push the boundary of game design\"\n",
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" },\n",
|
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" {\"title\": \"cocaine bear vs heroin wolf\"},\n",
|
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" {\"title\": \"the best in class mlops tooling\"},\n",
|
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"]\n",
|
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"synopsis_chain.apply(test_prompts)\n",
|
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"wandb_callback.flush_tracker(synopsis_chain, name=\"agent\")"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"id": "_jN73xcPVEpI"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.agents import initialize_agent, load_tools\n",
|
|
"from langchain.agents import AgentType"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"id": "Gpq4rk6VT9cu"
|
|
},
|
|
"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;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.\u001b[0m\n",
|
|
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.43 power.\n",
|
|
"Action: Calculator\n",
|
|
"Action Input: 26^0.43\u001b[0m\n",
|
|
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\n",
|
|
"\u001b[0m\n",
|
|
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
|
|
"Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
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"text/html": [
|
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"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
|
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],
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"text/plain": [
|
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
|
{
|
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"data": {
|
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"text/html": [
|
|
" View run <strong style=\"color:#cdcd00\">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)"
|
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],
|
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"text/plain": [
|
|
"<IPython.core.display.HTML object>"
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
|
{
|
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"data": {
|
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"text/html": [
|
|
"Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
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}
|
|
],
|
|
"source": [
|
|
"# SCENARIO 3 - Agent with Tools\n",
|
|
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
|
|
"agent = initialize_agent(\n",
|
|
" tools,\n",
|
|
" llm,\n",
|
|
" agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
|
|
")\n",
|
|
"agent.run(\n",
|
|
" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\",\n",
|
|
" callbacks=callbacks,\n",
|
|
")\n",
|
|
"wandb_callback.flush_tracker(agent, reset=False, finish=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
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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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"colab": {
|
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"provenance": []
|
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},
|
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
|
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
|
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"version": "3.9.1"
|
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}
|
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},
|
|
"nbformat": 4,
|
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"nbformat_minor": 1
|
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}
|