From 926d3fb9acb154a3aee1f8fcdbf6e84a0dc1643f Mon Sep 17 00:00:00 2001 From: Patrick Marlow Date: Tue, 9 Jul 2024 22:52:31 -0600 Subject: [PATCH] feat: add sample genai_agent notebook and tool --- data/get_weather_tool.py | 54 ++ .../genai_agents_101.ipynb | 805 ++++++++++++++++++ 2 files changed, 859 insertions(+) create mode 100644 data/get_weather_tool.py create mode 100644 examples/bot_building_series/genai_agents_101.ipynb diff --git a/data/get_weather_tool.py b/data/get_weather_tool.py new file mode 100644 index 00000000..9d2a41b2 --- /dev/null +++ b/data/get_weather_tool.py @@ -0,0 +1,54 @@ +import logging +import requests +from google.auth import default as google_default_auth +from google.auth.transport.requests import Request as GoogleRequest +from flask import Flask, request +from firebase_admin import initialize_app +from firebase_functions import https_fn + + +initialize_app() +app = Flask(__name__) + +# Set up logging +logging.basicConfig(level=logging.INFO) + +# Google Auth +creds, project = google_default_auth() +auth_req = GoogleRequest() + +def get_request_params(param, default=None): + request_json = request.get_json(silent=True) + request_args = request.args + + return (request_json or {}).get(param, default) or request_args.get(param, default) + +@app.route("/get_weather_grid", methods=["GET", "POST"]) +def get_weather_grid(): + lat = get_request_params("latitude") + long = get_request_params("longitude") + + url = f"https://api.weather.gov/points/{lat},{long}" + response = requests.get(url) + logging.info(f"FIRST RESPONSE: {response}") + if response.status_code == 200: + data = response.json() + logging.info(f"FIRST RESPONSE DATA: {data}") + + response = requests.get(data["properties"]["forecast"]) + logging.info(f"SECOND RESPONSE: {response}") + if response.status_code == 200: + data = response.json() + logging.info(f"SECOND RESPONSE DATA: {data}") + + # just take the weather from the current period + period = data["properties"]["periods"][0] + return {"temperature": period["temperature"], "temperatureUnit": period["temperatureUnit"]} + + return {} + +@https_fn.on_request() +def main(req: https_fn.Request) -> https_fn.Response: + creds.refresh(auth_req) + with app.request_context(req.environ): + return app.full_dispatch_request() diff --git a/examples/bot_building_series/genai_agents_101.ipynb b/examples/bot_building_series/genai_agents_101.ipynb new file mode 100644 index 00000000..842ae958 --- /dev/null +++ b/examples/bot_building_series/genai_agents_101.ipynb @@ -0,0 +1,805 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2024 Google LLC\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "In this notebook, we will show you use the Agent Builder SDK to build a Generative AI Agent + Tools.\n", + "\n", + "## Prerequisites\n", + "- Ensure you have a GCP Service Account key with the Dialogflow API Admin privileges assigned to it.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \"Google
Run in Colab\n", + " \n", + " 		\n", + " \n", + " \"GitHub
View on GitHub\n", + " \n", + " 		\n", + " \n", + " \"Vertex
Open in Vertex AI Workbench\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#If you haven't already, make sure you install the `dfcx-scrapi` library\n", + "!pip install dfcx-scrapi\n", + "\n", + "import sys\n", + "\n", + "# Colab Auth needed to call Client Endpoints (i.e. vertexai)\n", + "if \"google.colab\" in sys.modules:\n", + " # Authenticate user to Google Cloud\n", + " from google.colab import auth as google_auth\n", + " google_auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build a New Agent Application\n", + "The first thing we will do is create a blank Agent Application.
\n", + "This is the fundamental building block for chaining together Agents, Tools, and adding few-shot examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PROJECT_ID = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dfcx_scrapi.core.agents import Agents\n", + "\n", + "a = Agents()\n", + "agent = a.create_agent(\n", + " project_id=PROJECT_ID,\n", + " display_name=\"SCRAPI AGENTS\",\n", + " gcp_region=\"us-east1\",\n", + " playbook_agent=True\n", + ")\n", + "\n", + "panel = \"(playbooks/00000000-0000-0000-0000-000000000000/basics//right-panel:simulator)\"\n", + "print(f\"AGENT LINK: https://vertexaiconversation.cloud.google.com/{agent.name}/{panel}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test Your Agent\n", + "When your Agent application is first created, there is a \"Default Generative Agent\" created, which has no `goal` or `instructions`.
\n", + "However, you can still interact with it like a generic LLM model.\n", + "\n", + "One of the great aspects of Vertex Agent Builder is that it automatically handles Session Management for you!
\n", + "All Agent Applications are ***immediately*** Production scalable and ready, because they are backed by production grade, scalable Google infrastructure.
\n", + "\n", + "Want to open you app to 100,000 users immediately?
\n", + "We've got you covered! And no extra work on your end." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dfcx_scrapi.core.sessions import Sessions\n", + "\n", + "s = Sessions()\n", + "\n", + "session_id = s.build_session_id(agent.name)\n", + "session_id" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + " USER QUERY: hey, how are you?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: I'm doing well, thank you! How can I help you today?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res = s.detect_intent(agent.name, session_id, \"hey, how are you?\")\n", + "s.parse_result(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + " USER QUERY: what kind of models do you have?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: We have a variety of models to choose from. What type of vehicle are you interested in?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res = s.detect_intent(agent.name, session_id, \"what kind of models do you have?\")\n", + "s.parse_result(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Update the Default Playbook\n", + "Our Agent isn't very useful at this stage, so let's update the `goal` and `instructions` to do something more worthwhile!
\n", + "\n", + "We'll first fetch the handy `playbooks_map` so we can easily refernce IDs which are required by the API endpoints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dfcx_scrapi.core.playbooks import Playbooks\n", + "\n", + "p = Playbooks(agent.name)\n", + "\n", + "playbooks_map = p.get_playbooks_map(agent.name, reverse=True)\n", + "playbooks_map" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GOAL: Default goal\n", + "INSTRUCTIONS: \n" + ] + } + ], + "source": [ + "# You see here that there are is no `goal` or `instruction` sets yet.\n", + "playbook = p.get_playbook(playbooks_map[\"Default Generative Agent\"])\n", + "print(f\"GOAL: {playbook.goal}\")\n", + "print(f\"INSTRUCTIONS: {playbook.instruction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GOAL: You are a friendly Tesla service center agent.\n", + "Your job is to help users book appointments and answer any questions they have.\n", + "INSTRUCTIONS: steps {\n", + " text: \"Greet the user.\"\n", + "}\n", + "steps {\n", + " text: \"Answer any questions the have to the best of your ability.\"\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# We'll provide a simple `goal` and `instruction` set to get started.\n", + "\n", + "playbook = p.update_playbook(\n", + " playbooks_map[\"Default Generative Agent\"],\n", + " goal=\"You are a friendly Tesla service center agent.\\nYour job is to help users book appointments and answer any questions they have.\",\n", + " instructions=[\"Greet the user.\", \"Answer any questions the have to the best of your ability.\"]\n", + " )\n", + "\n", + "print(f\"GOAL: {playbook.goal}\")\n", + "print(f\"INSTRUCTIONS: {playbook.instruction}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Agent Again\n", + "Now that we've updated the `goal` and `instruction` set, let's see how our Agent application reacts." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + " USER QUERY: what kind of models do you have?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: We have a variety of models to choose from, including the Model S, Model 3, Model X, and Model Y. Which one are you interested in?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from dfcx_scrapi.core.sessions import Sessions\n", + "\n", + "s = Sessions()\n", + "\n", + "session_id = s.build_session_id(agent.name)\n", + "res = s.detect_intent(agent.name, session_id, \"what kind of models do you have?\")\n", + "s.parse_result(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Now we're getting somewhere!
\n", + "When we update our Agent application's `goal` and `instruction` sets, we are able to steer the Agent towards doing the task we design for it.
\n", + "Notice the difference in responses when we ask the intentionally ambiguous question about \"models\".
\n", + "\n", + "We can continue to take this further by creating Tools for our Agent to use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create Tools\n", + "Tools allow your Agent to interact with the outside world.
\n", + "There are 3 primary tool types that you can use:\n", + "- OpenAPI Spec\n", + "- Functions\n", + "- Data Stores\n", + "\n", + "In this next section, we'll create an 2 `OpenAPI Spec` tools that can do the following:\n", + "- Search for points of interest based on the [Google Places API](https://developers.google.com/maps/documentation/places/web-service/overview)\n", + "- Find the weather using the [National Weather Service Web API](https://www.weather.gov/documentation/services-web-api)\n", + "\n", + "** NOTE: ** We are using a custom Cloud Run endpoint to host our code here. You will need to define your OpenAPI spec with _your_ endpoint and _your_ endpoint specs based on how it is deployed. You WILL NOT have access to our endpoints!
\n", + "\n", + "For more information, see [Vertex Agents Tools](https://cloud.google.com/dialogflow/vertex/docs/concept/tools)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy Cloud Run Endpoints\n", + "Using the following code, you can deploy a Cloud Run endpoint to call the National Weather Service API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%mkdir get_weather\n", + "%cd get_weather" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing requirements.txt\n" + ] + } + ], + "source": [ + "%%writefile requirements.txt\n", + "functions-framework==3.*\n", + "firebase-admin==6.2.*\n", + "firebase-functions\n", + "requests==2.*" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zsh:1: command not found: wget\n" + ] + } + ], + "source": [ + "!wget https://raw.githubusercontent.com/GoogleCloudPlatform/dfcx-scrapi/main/data/get_weather_tool.py\n", + "!mv get_weather_tool.py main.py\n", + "!gcloud config set project $PROJECT_ID\n", + "!gcloud functions deploy get_weather --region \"us-central1\" --runtime python311 --trigger-http --entry-point main --source main.zip --no-allow-unauthenticated --gen2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define OpenAPI Specs\n", + "Before proceeding, make sure you replace the URL in the `get_weather_spec` with the CLOUD RUN ENDPOINT that you just deployed!
\n", + "Be sure to use the CLOUD RUN ENDPOINT, NOT the Cloud Function endpoint!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "get_weather_spec = \"\"\"\n", + "openapi: 3.0.0\n", + "info:\n", + " title: get_weather\n", + " version: 1.0.0\n", + "\n", + "servers:\n", + " - url: YOUR_ENDPOINT_HERE\n", + "\n", + "paths:\n", + " /get_weather_grid:\n", + " get:\n", + " summary: Returns the current grid information for a city and state\n", + " operationId: get_weather_grid\n", + " parameters:\n", + " - name: latitude\n", + " in: query\n", + " required: true\n", + " schema:\n", + " type: string\n", + " - name: longitude\n", + " in: query\n", + " required: true\n", + " schema:\n", + " type: string\n", + " responses:\n", + " '200':\n", + " description: OK\n", + " content:\n", + " application/json:\n", + " schema:\n", + " type: object\n", + " properties:\n", + " data:\n", + " type: string\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Call Create Tools\n", + "For creating the `OpenAPI Spec` tools, we'll use the `build_open_api_tool` helper method to create the proper Tool object, then pass it to the `create_tool` method and capture the result for each tool." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from dfcx_scrapi.core.tools import Tools\n", + "\n", + "t = Tools()\n", + "\n", + "# Tool 1 - get_weather\n", + "weather_tool = t.build_open_api_tool(\n", + " \"get_weather\",\n", + " spec=get_weather_spec,\n", + " description=\"Get the current weather for the provided city.\"\n", + " )\n", + "tool1 = t.create_tool(agent.name, weather_tool)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create Weather Agent\n", + "Now that we have 2 new tools to work with, let's apply them to a new Agent in our application." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dfcx_scrapi.core.playbooks import Playbooks\n", + "\n", + "p = Playbooks(agent_id=agent.name)\n", + "\n", + "instructions = [\n", + " \"Use the ${TOOL:get_weather} to get the current city/state weather grid information.\",\n", + " \"- If the user only provides the city you can assume the state unless otherwise specified.\"]\n", + "\n", + "# New Playbook\n", + "weather_agent = p.create_playbook(\n", + " agent.name,\n", + " display_name=\"Weather Agent\",\n", + " referenced_tools=[tool1.name],\n", + " goal=\"You are a senior weather advisor at a network broadcast station. Your job is to predict the weather!\",\n", + " instructions=instructions\n", + ")\n", + "\n", + "playbooks_map = p.get_playbooks_map(agent.name, reverse=True)\n", + "playbooks_map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chain Agents\n", + "In order for our Agents to work together properly, we need to chain them together and update the instruction set.\n", + "\n", + "Remember, our current architecture looks like this:\n", + "- Default Generative Agent\n", + "- Weather Agent\n", + "\n", + "These are mutually exclusive Agents that cannot interact yet.
\n", + "We want our architecture to look like this instead:\n", + "- Default Generative Agent -> Weather Agent\n", + "\n", + "In order to do this, let's update the instruction set on our `Default Generative Agent`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p.update_playbook(\n", + " playbooks_map[\"Default Generative Agent\"],\n", + " instructions=[\"If the user needs help with the weather, call ${AGENT: Weather Agent}\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Agent Again\n", + "With our Agents chained together, let's test with a new query about weather in a specific city!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + " USER QUERY: what is the weather like in Austin, Texas?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "TOOL CALL: get_weather_grid -> 30.2672" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "TOOL RESULT: {'temperatureUnit': 'F', 'temperature': 77.0}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: The current temperature in Austin, Texas is 77 degrees Fahrenheit." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from dfcx_scrapi.core.sessions import Sessions\n", + "\n", + "s = Sessions()\n", + "\n", + "session_id = s.build_session_id(agent.name)\n", + "res = s.detect_intent(agent.name, session_id, \"what is the weather like in Austin, Texas?\")\n", + "s.parse_result(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi Turn Session\n", + "Example of a Multi Turn Session with some \"cleaner\" readability for colab testing." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- TURN 1 --- \n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + " USER QUERY: What colors does the model 3 come in?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: The Model 3 comes in 5 colors: black, white, blue, red, and gray." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- TURN 2 --- \n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + " USER QUERY: What's the weather like in Boston right now? I'm thinking about walking to the Tesla dealership." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "TOOL CALL: get_weather_grid -> 42.3601" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "TOOL RESULT: {'temperatureUnit': 'F', 'temperature': 76.0}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "AGENT RESPONSE: The current temperature in Boston is 76 degrees Fahrenheit." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "session_id = s.build_session_id(agent.name)\n", + "\n", + "conversation = [\n", + " \"What colors does the model 3 come in?\",\n", + " \"What's the weather like in Boston right now? I'm thinking about walking to the Tesla dealership.\"\n", + " ]\n", + "\n", + "i = 1\n", + "for utterance in conversation:\n", + " print(f\"\\n--- TURN {i} --- \\n\")\n", + " res = s.detect_intent(agent.name, session_id, utterance)\n", + " s.parse_result(res)\n", + " i+=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GenAI Agents 101 End\n", + "In this notebook we've shown how to do the following:\n", + "- Create a new Agent Application\n", + "- Update the default Agent instructions\n", + "- Create a new Tool\n", + "- Create a new Agent and apply the new Tool\n", + "- Chain together our Agents + Tool to make a simple, cohesive application" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a46639dc4ce59764707f7d29e7ddf4b543fd040f828fe9a3cb5baeb9324df219" + }, + "kernelspec": { + "display_name": "Python 3.8.10 64-bit ('scrapi': conda)", + "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": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}