Constructing 3 Enjoyable AI Functions with ControlFlow
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The AI business is quickly advancing in direction of creating options utilizing massive language fashions (LLMs) and maximizing the potential of AI fashions. Corporations are in search of instruments that seamlessly combine AI into present codebases with out the hefty prices related to hiring professionals and buying sources. That is the place Controlflow comes into play. With ControlFlow, you possibly can develop complicated AI purposes utilizing only a few traces of code.
On this tutorial, we’ll discover ControlFlow and use it to construct three thrilling AI purposes. The initiatives vary from a easy textual content classifier to complicated AI with a number of brokers, duties, and stream.
What’s ControlFlow?
ControlFlow is a Python framework that gives a structured method for outlining LLM workflows. It consists of three foremost parts for creating AI purposes:
- Duties: These are the basic constructing blocks of AI workflows. They outline discrete, well-defined goals that should be completed by a number of AI brokers.
- Brokers: These are the clever, autonomous entities powering your AI workflows. You’ll be able to outline a mannequin, present it with customized directions, and add varied instruments to create the brokers.
- Flows: Flows are liable for working a number of AI workflows in a specified order. They provide a structured approach to handle duties, brokers, instruments, and shared contexts.
Through the use of ControlFlow, you possibly can seamlessly combine AI capabilities into your Python purposes, achieve extra management over AI workflows, and generate structured outputs somewhat than simply textual content. It lets you construct complicated workflows with ease and is extremely user-friendly. The most effective a part of utilizing ControlFlow is that it lets you observe the AI mannequin’s decision-making course of at each activity.
In easy phrases, ControlFlow has two vital makes use of: it orchestrates your LLM workflows and helps you generate structured outputs, providing you with extra management over AI.
Establishing ControlFlow
We will merely set up ControlFlow by typing the next command within the terminal. It would routinely set up all of the dependencies.
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$ pip set up controlflow |
Generate the OpenAI API key and set it as an atmosphere variable.
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$ export OPENAI_API_KEY=“your-api-key” |
Earlier than utilizing ControlFlow, guarantee it’s correctly put in. Sort the next command within the terminal to view all Python package deal variations related to ControlFlow.
Output:
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ControlFlow model: 0.9.4 Prefect model: 3.0.2 LangChain Core model: 0.2.40 Python model: 3.10.12 Platform: Linux–6.1.85+–x86_64–<b>with</b>–glibc2.35 Path: /usr/native/lib/python3.10 |
Creating an agent and working the duty is kind of easy in ControlFlow. On this instance, we have now created the Horror storytelling agent by offering it with customized directions. Then, we’ll use it to run a easy activity by offering it with a immediate. Finally, we’re producing a brief story.
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import controlflow as cf
teller = cf.Agent(title=“Horror Storyteller”, mannequin=“openai/gpt-4o”, directions=“You might be an older man telling horror tales to youngsters.”)
story = cf.run(“Write a brief story.”, brokers=[teller]) |
Right here is the outcome.
In case you encounter a RuntimeError or runtime situation whereas working the next code in Colab, please rerun the cell as soon as once more.
1. Tweet Classification
Tweet Classifier is a well-liked small challenge for college students, and it often takes them months to construct a correct textual content classifier. Through the use of ControlFlow, we are able to create a correct tweet classifier utilizing just a few traces of code.
- Create a listing of 4 quick tweets.
- Setup an agent with customized directions utilizing the GPT-4-mini mannequin.
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import controlflow as cf
tweets = [ “Negativity spreads too easily here. #sigh”, “Sometimes venting is necessary. #HateTherapy”, “Love fills the air today! 💖 #Blessed”, “Thankful for all my Twitter friends! 🌟” ] # Create a specialised agent classifier = cf.Agent( title=“Tweet Classifier”, mannequin=“openai/gpt-4o-mini”, directions=“You might be an professional at rapidly classifying tweets.”, ) |
- Create a activity to categorise tweets as “hate” or “love”, with a immediate, outcome sort, brokers, and context. We are going to present a listing of tweets as context.
- Run the duty and show the outcomes.
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from typing import Literal
# Arrange a ControlFlow activity to categorise tweets classifications = cf.run( ‘Classify the tweets’, result_type=record[Literal[‘hate’, ‘love’]], brokers=[classifier], context=dict(tweets=tweets), )
print(classifications) |
ControlFlow duties have categorised tweets and generated a listing of labels.
Let’s show them in a correct manner utilizing the colour code (crimson for hate and inexperienced for love).
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# ANSI escape code for green text GREEN = ‘ 33[92m’ RED = ‘ 33[91m’ RESET = ‘ 33[0m’
# Print tweets alongside their classifications with classification in green for twt, cls in zip(tweets, classifications): if cls == ‘hate’: print(f“Tweet: {twt} | Classification: {RED}{cls.upper()}{RESET}n”) else: print(f“Tweet: {twt} | Classification: {GREEN}{cls.upper()}{RESET}n”) |
Two tweets are labeled “hate,” and two tweets are labeled “love.” This is quite accurate and gives output in a structured format that we can integrate into our existing applications.
2. Book Recommender
Two years ago, I built a Goodreads application on Deepnote, which sparked my interest in creating a book recommendation application.
In this project, we will define a data model for book recommendations using the Pydantic model. This model will ensure that each recommendation includes a title, author, publication year, and genre with the correct field types.
The recommend_books function will use this Pydantic model to define the result type and generate a list of book recommendations based on a specified genre and number of books.
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import controlflow as cf from pydantic import BaseModel, Area
class BookRecommendation(BaseModel): title: str = Area(description=‘The title of the beneficial e book’) writer: str = Area(description=‘The writer of the e book’) year_published: int = Area(description=‘The 12 months the e book was revealed’) style: str = Area(description=‘The style of the e book’)
def recommend_books(style: str, depend: int) -> record[BookRecommendation]: return cf.run( f“Suggest {depend} books within the {style} style with their particulars”, result_type=record[BookRecommendation], context={“style”: style, “depend”: depend} ) |
We are going to now generate 5 Science fiction books.
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recommended_books = recommend_books(style=“Science Fiction”, depend=5) print(recommended_books) |
The agent first generated the beneficial books in textual content type, transformed them into JSON format, and at last remodeled them into the Pydentic mannequin sort, which comprises all the info with the right knowledge sort.
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[ BookRecommendation(title=‘Dune’, author=‘Frank Herbert’, year_published=1965, genre=‘Science Fiction’), BookRecommendation( title=‘Neuromancer’, author=‘William Gibson’, year_published=1984, genre=‘Science Fiction’ ), BookRecommendation( title=‘The Left Hand of Darkness’, author=‘Ursula K. Le Guin’, year_published=1969, genre=‘Science Fiction’ ), BookRecommendation(title=‘Foundation’, author=‘Isaac Asimov’, year_published=1951, genre=‘Science Fiction’), BookRecommendation( title=‘Snow Crash’, author=‘Neal Stephenson’, year_published=1992, genre=‘Science Fiction’ ) ] |
To transform the output into the JSON format, we solely have to make use of the ‘.model_dump_json(indent=2)` perform.
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for e book in recommended_books: print(e book.model_dump_json(indent=2)) |
We now have an output in JSON format that may be simply built-in into an online utility or any code base.
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{ “title”: “Dune”, “writer”: “Frank Herbert”, “year_published”: 1965, “style”: “Science Fiction” }
{ “title”: “Neuromancer”, “writer”: “William Gibson”, “year_published”: 1984, “style”: “Science Fiction” }
{ “title”: “The Left Hand of Darkness”, “writer”: “Ursula Okay. Le Guin”, “year_published”: 1969, “style”: “Science Fiction” }
{ “title”: “Basis”, “writer”: “Isaac Asimov”, “year_published”: 1951, “style”: “Science Fiction” }
{ “title”: “Snow Crash”, “writer”: “Neal Stephenson”, “year_published”: 1992, “style”: “Science Fiction” } |
3. Journey Agent
On this challenge, we’re going to join two duties. One activity will generate the vacation spot primarily based on consumer preferences, after which use this vacation spot to create an in depth itinerary. To create this challenge, we’ll use a number of brokers, a number of duties, and mix them utilizing a stream.
We are going to outline two mannequin lessons for journey preferences and journey itinerary. One will probably be used for consumer enter, and the opposite will probably be used as a construction for output.
Additionally, we’ll create a stream perform that comprises two duties: one for producing the vacation spot and one other for planning the journey primarily based on the consumer’s preferences.
The method will contain producing the locations after which utilizing the vacation spot to plan a visit for “n” days.
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import controlflow as cf from pydantic import BaseModel from typing import Checklist
# Create specialised brokers destination_recommender = cf.Agent(title=“DestinationRecommender”, mannequin=“openai/gpt-4o-mini”) itinerary_planner = cf.Agent(title=“ItineraryPlanner”, mannequin=“openai/gpt-4o”)
# Outline our knowledge fashions class TravelPreferences(BaseModel): preferred_activities: Checklist[str] funds: str # e.g., “low”, “medium”, “excessive” travel_duration: int # in days preferred_region: str # e.g., “Asia”, “Europe”, “South America”
class TravelItinerary(BaseModel): vacation spot: str daily_schedule: Checklist[str]
@cf.stream def create_travel_itinerary(preferences: TravelPreferences) -> TravelItinerary: # Suggest a single vacation spot inside the popular area vacation spot = cf.run( “Counsel a journey vacation spot primarily based on consumer choice.”, brokers=[destination_recommender], result_type=str, context={ “preferred_activities”: preferences.preferred_activities, “funds”: preferences.funds, “travel_duration”: preferences.travel_duration, “preferred_region”: preferences.most popular_area } )
# Plan every day schedule utilizing the vacation spot daily_schedule = cf.run( “Create a every day schedule for the journey on the chosen vacation spot”, brokers=[itinerary_planner], result_type=Checklist[str], context={ “vacation spot”: vacation spot, “travel_duration”: preferences.travel_duration, “preferred_activities”: preferences.most popular_actions } )
return TravelItinerary(vacation spot=vacation spot, daily_schedule=daily_schedule) |
When you find yourself used to coding ControFlow workflows, you’ll know that it will get fairly straightforward to create a brand new utility and challenge with only a few traces of code.
We are going to create consumer preferences by offering the popular exercise, funds, journey period, and most popular area. Then, we’ll use these consumer preferences to run the stream.
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preferences = TravelPreferences( preferred_activities=[“beach”, “local cuisine”], funds=“medium”, travel_duration=7, preferred_region=“Europe” )
# Create a customized journey itinerary itinerary = create_travel_itinerary(preferences) |
We are going to now print out the vacation spot and the every day itinerary.
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print(“Beneficial Vacation spot:”) print(f“- {itinerary.vacation spot}”)
print(“nnDaily Schedule:”) for schedule in itinerary.daily_schedule: print(f“{schedule}”) |
Our vacation spot is the Amalfi Coast in Italy, and we have now a very good journey plan.
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Beneficial Vacation spot:
– Primarily based on your preferences for seashore actions and native delicacies, alongside with a medium funds and a journey period of 7 days in Europe, I advocate visiting the Amalfi Coast in Italy. This gorgeous vacation spot gives stunning seashores, scrumptious native dishes, and picturesque cities such as Positano and Ravello, excellent for a memorable trip.
Day by day Schedule:
Day 1: Arrival and exploration of Positano – Arrive in Positano and examine into your lodging – Spend the afternoon enjoyable on the Spiaggia Grande seashore – Night: Get pleasure from dinner at a native restaurant, attempting dishes like contemporary seafood and pasta
Day 2: Seaside day and native delicacies in Positano – Morning: Go to Fornillo Seaside, a quieter different to Spiaggia Grande – Afternoon: Take a cooking class to be taught how to make conventional Italian dishes – Night: Stroll by the city and get pleasure from gelato from a native gelateria
Day 3: Day journey to Amalfi and Ravello – Morning: Take a boat or bus to Amalfi – Go to the Amalfi Cathedral and discover the city – Afternoon: Proceed to Ravello and go to Villa Rufolo and its gardens – Night: Return to Positano and dine at a native trattoria
Day 4: Capri Island tour – Morning: Take a ferry to Capri – Discover the Blue Grotto and take a chairlift to Monte Solaro – Afternoon: Wander round the city of Capri and have lunch – Night: Return to Positano and chill out at your lodging
Day 5: Seaside and leisure day in Positano – Morning: Chill out at Arienzo Seaside, identified for its clear waters – Afternoon: Get pleasure from a leisurely lunch at a beachside café – Night: Take a sundown boat tour alongside the coast
Day 6: Discover Sorrento – Morning: Journey to Sorrento by bus or ferry – Go to the historic heart, together with Piazza Tasso and the Marina Grande – Afternoon: Get pleasure from a meal at a native restaurant and pattern limoncello – Night: Return to Positano
Day 7: Departure – Morning: Spend your final hours in Positano purchasing for souvenirs – Afternoon: Depart for the airport or onward vacation spot |
We will simply convert this challenge right into a product and construct a web site that gives customers with journey suggestions.
In case you are going through points working the coder, please try the ControlFlow Tutorial Colab pocket book.
Closing Ideas
The ControlFlow continues to be new, and also you may encounter points when working a few of the examples obtainable within the documentation. The excellent news is that there’s a devoted crew working to resolve these points in actual time, guaranteeing a easy onboarding course of. All it’s a must to do is create a difficulty on the GitHub repository at Points · PrefectHQ/ControlFlow. I confronted some points whereas working it on Colab, however they had been resolved inside two hours, due to Jeremiah Lowin.
I imagine we must be working in direction of constructing AI options that present enterprise worth, as a substitute of solely specializing in bettering mannequin efficiency by just a few proportion factors. To appreciate the total potential of AI, we want instruments like ControlFlow, LangChain, and different AI frameworks that may assist us construct complicated AI purposes with only a few traces of code.
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