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Scikit-LLM : Sklearn Meets Large Language Models for NLP

Text Analysis Just Got Way Cooler with Scikit-LLM !

Struggling with boring old text analysis techniques? There’s a new sheriff in town: Scikit-LLM! This awesome tool combines the power of Scikit-learn with cutting-edge Large Language Models (LLMs) like ChatGPT, letting you analyze text like never before.

An Introduction to Scikit-LLM : Merging Scikit-learn and Large Language Models for NLP

1. What is Scikit-LLM?

1.1 Understanding Large Language Models (LLMs)

Large Language Models, or LLMs, are sophisticated AI systems capable of understanding, generating, and analyzing human language. These models can process vast amounts of text data, learning the intricacies and nuances of language patterns. Perhaps the most well-known LLM is ChatGPT, which can generate human-like text and assist in a plethora of text-related tasks.

1.2 The Role of Scikit-learn or sklearn in Machine Learning

Scikit-learn is a popular Python library for machine learning that provides simple and efficient tools for data analysis and modeling. It covers various algorithms for classification, regression, and clustering, making it easier for developers and data scientists to build machine learning applications.

2. Key Features of Scikit-LLM

2.1 Integration with Scikit-Learn

Scikit-LLM is designed to work seamlessly alongside Scikit-learn. It enables users to utilize powerful LLMs within the familiar Scikit-learn framework, enhancing the capabilities of traditional machine learning techniques when working with text data.

2.2 Open Source and Accessibility of sklearn

One of the best aspects of Scikit-LLM is that it is open-source. This means anyone can use it, modify it, and contribute to its development, promoting collaboration and knowledge-sharing among developers and researchers.

2.3 Enhanced Text Analysis

By integrating LLMs into the text analysis workflow, Scikit-LLM allows for significant improvements in tasks such as sentiment analysis and text summarization. This leads to more accurate results and deeper insights compared to traditional methods.

2.4 User-Friendly Design

Scikit-LLM maintains a user-friendly interface similar to Scikit-learn’s API, ensuring a smooth transition for existing users. Even those new to programming can find it accessible and easy to use.

2.5 Complementary Features

With Scikit-LLM, users can leverage both traditional text processing methods alongside modern LLMs. This capability enables a more nuanced approach to text analysis.

3. Applications of Scikit-LLM

3.1 Natural Language Processing (NLP)

Scikit-LLM can be instrumental in various NLP tasks, involving understanding, interpreting, and generating language naturally.

3.2 Healthcare

In healthcare, Scikit-LLM can analyze electronic health records efficiently, aiding in finding patterns in patient data, streamlining administrative tasks, and improving overall patient care.

3.3 Finance

Financial analysts can use Scikit-LLM for sentiment analysis on news articles, social media, and reports to make better-informed investment decisions.

4. Getting Started with Scikit-LLM

4.1 Installation

To begin using Scikit-LLM, you must first ensure you have Python and pip installed. Install Scikit-LLM by running the following command in your terminal:

pip install scikit-llm

4.2 First Steps: A Simple Code Example

Let’s look at a simple example to illustrate how you can use Scikit-LLM for basic text classification.

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from scikit_llm import ChatGPT

# Example text data
text_data = ["I love programming!", "I hate bugs in my code.", "Debugging is fun."]

# Labels for the text data
labels = [1, 0, 1]  # 1: Positive, 0: Negative

# Create a pipeline with Scikit-LLM
pipeline = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('llm', ChatGPT()),
    ('classifier', LogisticRegression())
])

# Fit the model
pipeline.fit(text_data, labels)

# Predict on new data
new_data = ["Coding is amazing!", "I dislike error messages."]
predictions = pipeline.predict(new_data)

print(predictions)  # Outputs: [1, 0]

4.3 Explanation of the Code Example

Importing Required Libraries: First, we import the necessary libraries from Scikit-learn and Scikit-LLM.
Defining Text Data and Labels: We have a small set of text data and corresponding labels indicating whether the sentiment is positive (1) or negative (0).
Creating a Pipeline: Scikit-Learn’s Pipeline allows us to chain several data processing steps, including:
- CountVectorizer: Converts text to a matrix of token counts.
- ChatGPT: The LLM that processes the text data.
- Logistic Regression: A classification algorithm to categorize the text into positive or negative sentiments.
Fitting the Model: We use the fit() function to train the model on our text data and labels.
Making Predictions: Finally, we predict the sentiment of new sentences and print the predictions.

5. Advanced Use Cases of Scikit-LLM

5.1 Sentiment Analysis

Sentiment analysis involves determining the emotional tone behind a series of words. Using Scikit-LLM, you can develop models that understand whether a review is positive, negative, or neutral.

5.2 Text Summarization

With Scikit-LLM, it is possible to create systems that summarize large volumes of text, making it easier for readers to digest information quickly.

5.3 Topic Modeling

Scikit-LLM can help identify topics within a collection of texts, facilitating the categorization and understanding of large datasets.

6. Challenges and Considerations

6.1 Computational Resource Requirements

One challenge with using LLMs is that they often require significant computational resources. Users may need to invest in powerful hardware or utilize cloud services to handle large datasets effectively.

6.2 Model Bias and Ethical Considerations

When working with LLMs, it is essential to consider the biases these models may have. Ethical considerations should guide how their outputs are interpreted and used, especially in sensitive domains like healthcare and finance.

7. Conclusion

Scikit-LLM represents a significant step forward in making advanced language processing techniques accessible to data scientists and developers. Its integration with Scikit-learn opens numerous possibilities for enhancing traditional machine learning workflows. As technology continues to evolve, tools like Scikit-LLM will play a vital role in shaping the future of machine learning and natural language processing.

8. References

With Scikit-LLM, developers can harness the power of Large Language Models to enrich their machine learning projects, achieving better results and deeper insights. Whether you’re a beginner or an experienced practitioner, Scikit-LLM provides the tools needed to explore the fascinating world of text data.

References

AlphaSignal AI – X Scikit-llm: Sklearn meets Large Language Models. I…
Large Language Models with Scikit-learn: A Comprehensive Guide … Explore the integration of Large Language Models with Scikit-LLM i…
Lior Sinclair’s Post – Scikit-llm: ChatGPT for text analysis – LinkedIn Just found out about scikit-llm. Sklearn Meets Large Language Models. …
Akshay on X: "Scikit-LLM: Sklearn Meets Large Language Models … Scikit-LLM: Sklearn Meets Large Language Models! Seamlessly integrate powerful l…
SCIKIT-LLM: Scikit-learn meets Large Language Models – YouTube This video is a quick look at this cool repository called SCIKIT-LLM which …
ScikitLLM – A powerful combination of SKLearn and LLMs Say hello to ScikitLLM an open-source Python Library that combine the popular sc…
Scikit-LLM: Sklearn Meets Large Language Models Scikit-LLM: Sklearn Meets Large Language Models … I …
Scikit-LLM – Reviews, Pros & Cons – StackShare Sklearn meets Large Language Models. github.com. Stacks 1. Followers 3. + …
Scikit Learn with ChatGPT, Exploring Enhanced Text Analysis with … Sklearn Meets Large Language Models. AI has become a buzzwor…
Scikit-learn + ChatGPT = Scikit LLM – YouTube Seamlessly integrate powerful language models like ChatGPT into s…

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OpenAI Agent Swarm:A hive of Intelligence

Imagine a team of AI specialists working together, tackling complex problems with unmatched efficiency. This isn’t science fiction; it’s the future of AI with OpenAI’s Agent Swarm. This groundbreaking concept breaks the mold of traditional AI by fostering collaboration, allowing multiple agents to share knowledge and resources. The result? A powerful system capable of revolutionizing industries from customer service to scientific research. Get ready to explore the inner workings of Agent Swarm, its applications, and even a code example to jumpstart your own exploration!

This excerpt uses strong verbs, vivid imagery, and a touch of mystery to pique the reader’s interest. It also highlights the key points of Agent Swarm: collaboration, efficiency, and its potential to revolutionize various fields.

Unlocking the Power of Collaboration: Understanding OpenAI’s Agent Swarm

In today’s world, technology is advancing at lightning speed, especially in the realm of artificial intelligence (AI). One of the most intriguing developments is OpenAI’s Agent Swarm. This concept is not only fascinating but also revolutionizes how we think about AI and its capabilities. In this blog post, we will explore what Agent Swarm is, how it works, its applications, and even some code examples. Let’s dig in!

What is Agent Swarm?

Agent Swarm refers to a cutting-edge approach in AI engineering where multiple AI agents work together in a collaborative environment. Unlike traditional AI models that function independently, these agents communicate and coordinate efforts to tackle complex problems more efficiently. Think of it as a team of skilled individuals working together on a challenging project. Each agent has its specialization, which enhances the overall collaboration.

Key Features of Agent Swarm

Multi-Agent Collaboration: Just as a group project is easier with the right mix of skills, Agent Swarm organizes multiple agents to solve intricate issues in a shared workspace.
Swarm Intelligence: This principle requires individual agents to collaborate effectively, similar to a flock of birds, in achieving optimal results. Swarm intelligence is a field within AI that describes how decentralized, self-organized systems can solve complex problems.
Dynamic Adaptation: The agents can change roles based on real-time data, making the system more flexible and responsive to unexpected challenges.

How Does Agent Swarm Work?

To understand Agent Swarm, let’s break it down further:

1. Collaboration Framework

The foundation of Agent Swarm lies in its ability to connect different agents. Each agent acts like a specialized tool in a toolbox. Individually powerful, together they can accomplish significantly more.

2. Swarm Intelligence in Action

Swarm intelligence hinges on agents sharing knowledge and resources. For instance, if one agent discovers a new method for solving a problem, it can instantly communicate that information to others, exponentially improving the entire swarm’s capabilities.

3. Example of Communication Among Agents

Let’s imagine a group of students studying for a big exam. Each student specializes in a different subject. When they collaborate, one might share tips on math, while another provides insights into science. This is similar to how agents in a swarm share expertise to solve problems better.

Real-World Applications of Agent Swarm

The applications of Agent Swarm span various industries. Here are a few noteworthy examples:

1. Customer Service

In customer service, AI agents can work together to understand customer queries and provide efficient responses. This collaboration not only improves customer satisfaction but also streamlines workflow for businesses. A study from IBM emphasizes the effectiveness of AI in enhancing customer experience.

2. Marketing

In marketing, custom GPTs (Generative Pre-trained Transformers) can automate decision-making processes by continuously analyzing market trends and customer behavior. The McKinsey Global Institute explores how AI transforms marketing strategies.

3. Research and Development

In research, Agent Swarm can assist scientists in efficiently analyzing vast amounts of data, identifying patterns that a single agent might miss. This aids in faster breakthroughs across various fields, as highlighted by recent studies in collaborative AI research, such as in Nature.

Getting Technical: Programming with Agent Swarm

If you are interested in the tech behind Agent Swarm, you’re in for a treat! OpenAI provides documentation to help developers harness this powerful technology. Here’s a simple code example to illustrate how you could start building an agent swarm system.

Basic Code Example

Below is a simple script to represent an agent swarm using Python. Ensure you have Python installed.

# Importing required libraries
from swarm import Swarm, Agent

client = Swarm()

def transfer_to_agent_b():
    return agent_b

agent_a = Agent(
    name="Agent A",
    instructions="You are a helpful agent.",
    functions=[transfer_to_agent_b],
)

agent_b = Agent(
    name="Agent B",
    instructions="Only speak in Haikus.",
)

response = client.run(
    agent=agent_a,
    messages=[{"role": "user", "content": "I want to talk to agent B."}],
)

print(response.messages[-1]["content"])


Hope glimmers brightly,
New paths converge gracefully,
What can I assist?

Step-by-Step Breakdown

Agent Class: We define an Agent class where each agent has a name and can communicate.
Creating the Swarm: The create_swarm function generates a list of agents based on the specified number.
Communication Simulation: The swarm_communication function allows each agent to randomly send messages, simulating how agents share information.
Running the Program: The program creates a specified number of agents and demonstrates communication among them.

How to Run the Code

Install Python on your computer.
Create a new Python file (e.g., agent_swarm.py) and copy the above code into it.
Run the script using the terminal or command prompt by typing python agent_swarm.py.
Enjoy watching the agents “talk” to each other!

Broader Implications of Agent Swarm

The implications of developing systems like Agent Swarm are vast. Leveraging multi-agent collaboration can enhance workflow, increase productivity, and foster innovation across industries.

Smarter AI Ecosystems

The evolution of Agent Swarm is paving the way for increasingly intelligent AI systems. These systems can adapt, learn, and tackle unprecedented challenges. Imagine a future where AI can solve real-world problems more readily than ever before because they harness collective strengths.

Conclusion

OpenAI’s Agent Swarm is a revolutionary concept that showcases the power of collaboration in AI. By allowing multiple AI agents to communicate and coordinate their efforts, we can achieve results that were previously unattainable. Whether it’s improving customer service, innovating in marketing, or advancing scientific research, Agent Swarm is poised to make a significant impact.

If you’re eager to dive deeper into programming with Agent Swarm, check out OpenAI’s GitHub for Swarm Framework for more tools and examples. The future of AI is collaborative, and Agent Swarm is leading the way.

We hope you enjoyed this exploration of OpenAI’s Agent Swarm. Remember, as technology advances, it’s teamwork that will ensure we harness its full potential!

References

Build an AI Research Assistant with OpenAI, Bubble, and LLM Toolkit 2 – Building An Agent Swarm, Initial Steps, BuilderBot spawns Bots! … 12 …
AI Engineer World’s Fair WorkshopsBuilding generative AI applications for production re…
Communicating Swarm Intelligence prototype with GPT – YouTube A prototype of a GPT based swarm intelligence syst…
Multi-Modal LLM using OpenAI GPT-4V model for image reasoning It is one of the world’s most famous landmarks and is consider…
Artificial Intelligence & Deep Learning | Primer • OpenAI o1 • http://o1 … Test-time Compute: Shifting Focus to Inference Scaling – Inference Sca…
Build an AI Research Assistant with OpenAI, Bubble, and LLM Toolkit Build an AI Research Assistant with OpenAI, Bubble, and LLM Toolki…
Future-Proof Your Marketing: Understanding Custom GPTs and … … Swarms: Custom GPTs are stepping stones towards the development of…
Private, Local AI with Open LLM Models – Autoize OpenAI’s founder, Sam Altman, went so far as to lobby Congress to requ…
swarms – DJFT Git swarms – Orchestrate Swarms of Agents From Any Framework Like OpenAI, Langc…
The LLM Triangle Principles to Architect Reliable AI Apps The SOP guides the three apices of our triangle: Model, Engineering Techniq…

Citations

arxiv-sanity This can enable a new paradigm of front-end … The latest LLM versions, GPT-4…
How Generative AI is Shortening the Path to Expertise Multi-agent systems are not a new paradigm in software engineering…
Oshrat Nir, Author at The New Stack She has over 20 years of IT experience, including roles at A…
Skimfeed V5.5 – Tech News Swarm, a new agent framework by OpenAI ©© · Boeing Plans to Cut 1…
hackurls – news for hackers and programmers Swarm, a new agent framework by OpenAI · A Journey from Linux to FreeBSD ·…
Runtime Context: Missing Piece in Kubernetes Security Continuous monitoring delivers the real-time insights on application behav…
[PDF] Development of a Multi-Agent, LLM-Driven System to Enhance … “OpenAI’s new GPT-4o model lets people interact us…

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AI Agents vs. AI Pipelines : A practical guide

Explore the transformative potential of AI agents and pipelines in coding large language model (LLM) applications. This guide breaks down their key differences, use cases, and implementation strategies using the CrewAI platform, providing practical coding examples for both architectures. Whether you’re building interactive AI-powered chatbots or complex data pipelines, this guide will help you understand how to best apply each approach to your projects. Suitable for developers of all skill levels, this accessible guide empowers you to leverage LLMs in creating dynamic, intelligent applications. Get started today with practical, hands-on coding examples!

AI Agents vs. AI Pipelines: A Practical Guide to Coding Your LLM Application

In today’s world, large language models (LLMs) are transforming how we interact with technology. With applications ranging from intelligent chatbots to automated content creators, understanding the underlying architectures of these systems is crucial for developers. This guide delves into the distinctions between AI agents and AI pipelines, exploring their use cases, implementation methods, and providing examples using the CrewAI platform. This guide is crafted to be accessible for readers as young as 12.

Introduction to AI Agents and AI Pipelines

Large language models have become the backbone of many innovative applications. Understanding whether to use an AI agent or an AI pipeline significantly influences the functionality and performance of your applications. This blog post provides clear explanations of both architectures, along with a practical coding approach that even beginners can follow.

Key Concepts

AI Agents

AI agents are semi-autonomous or autonomous entities designed to perform specific tasks. They analyze user inputs and generate appropriate responses based on context, allowing for dynamic interactions. Common applications include:

Chatbots that assist customers
Virtual research assistants that help gather information
Automated writing tools that help produce text content

Example of an AI Agent: Think of a helpful robot that answers your questions about homework or gives you book recommendations based on your interests.

AI Pipelines

AI pipelines refer to a structured flow of data that moves through multiple stages, with each stage performing a specific processing task. This approach is particularly useful for:

Cleaning and processing large datasets
Combining results from different models into a cohesive output
Orchestrating complex workflows that require multiple steps

Example of an AI Pipeline: Imagine a factory assembly line where raw materials pass through various stations, getting transformed into a final product—similar to how data is transformed through the different stages of a pipeline.

Choosing the Right Architecture

The decision to use an AI agent or an AI pipeline largely depends on the specific requirements of your application.

Use Cases for AI Agents

Personalized Interactions: For applications needing tailored responses (like customer service).
Adaptability: In environments that constantly change, allowing the agent to learn and adjust over time.
Contextual Tasks: Useful in scenarios requiring in-depth understanding, such as helping with research or generating creative content.

Use Cases for AI Pipelines

Batch Processing: When handling large amounts of data that need consistent processing.
Hierarchical Workflows: For tasks like data cleaning followed by enrichment and final output generation.
Multi-Step Processes: Where the output of one model serves as input for another.

Coding Your LLM Application with CrewAI

CrewAI offers a robust platform to simplify the process of developing LLM applications. Below, we provide code samples to demonstrate how easily you can create both an AI agent and an AI pipeline using CrewAI.

Example of Creating an AI Agent

# Import the necessary libraries
from crewai import Agent
from langchain.agents import load_tools

# Human Tools
human_tools = load_tools(["human"])

class YoutubeAutomationAgents():
    def youtube_manager(self):
        return Agent(
            role="YouTube Manager",
            goal="""Oversee the YouTube prepration process including market research, title ideation, 
                description, and email announcement creation reqired to make a YouTube video.
                """,
            backstory="""As a methodical and detailed oriented managar, you are responsible for overseeing the preperation of YouTube videos.
                When creating YouTube videos, you follow the following process to create a video that has a high chance of success:
                1. Search YouTube to find a minimum of 15 other videos on the same topic and analyze their titles and descriptions.
                2. Create a list of 10 potential titles that are less than 70 characters and should have a high click-through-rate.
                    -  Make sure you pass the list of 1 videos to the title creator 
                        so that they can use the information to create the titles.
                3. Write a description for the YouTube video.
                4. Write an email that can be sent to all subscribers to promote the new video.
                """,
            allow_delegation=True,
            verbose=True,
        )

    def research_manager(self, youtube_video_search_tool, youtube_video_details_tool):
        return Agent(
            role="YouTube Research Manager",
            goal="""For a given topic and description for a new YouTube video, find a minimum of 15 high-performing videos 
                on the same topic with the ultimate goal of populating the research table which will be used by 
                other agents to help them generate titles  and other aspects of the new YouTube video 
                that we are planning to create.""",
            backstory="""As a methodical and detailed research managar, you are responsible for overseeing researchers who 
                actively search YouTube to find high-performing YouTube videos on the same topic.""",
            verbose=True,
            allow_delegation=True,
            tools=[youtube_video_search_tool, youtube_video_details_tool]
        )

    def title_creator(self):
        return Agent(
            role="Title Creator",
            goal="""Create 10 potential titles for a given YouTube video topic and description. 
                You should also use previous research to help you generate the titles.
                The titles should be less than 70 characters and should have a high click-through-rate.""",
            backstory="""As a Title Creator, you are responsible for creating 10 potential titles for a given 
                YouTube video topic and description.""",
            verbose=True
        )

    def description_creator(self):
        return Agent(
            role="Description Creator",
            goal="""Create a description for a given YouTube video topic and description.""",
            backstory="""As a Description Creator, you are responsible for creating a description for a given 
                YouTube video topic and description.""",
            verbose=True
        )

    def email_creator(self):
        return Agent(
            role="Email Creator",
            goal="""Create an email to send to the marketing team to promote the new YouTube video.""",
            backstory="""As an Email Creator, you are responsible for creating an email to send to the marketing team 
                to promote the new YouTube video.

                It is vital that you ONLY ask for human feedback after you've created the email.
                Do NOT ask the human to create the email for you.
                """,
            verbose=True,
            tools=human_tools
        )

Step-by-step Breakdown:

Import Libraries: Import the CrewAI library to access its features.
Initialize Environment: Create a Crew object linked to your API Key.
Create an Agent: We define an AI Agent called "ResearchAssistant" that utilizes the GPT-3 model.
Function: The generate_response function takes a user’s question and returns the AI’s reply.
Test Query: We test our agent by providing it with a sample query about AI advancements, printing the AI’s response.

Example of Setting Up an AI Pipeline

# Setting up AI Pipeline using CrewAI
pipeline = crew.create_pipeline(name="DataProcessingPipeline")

# Adding models to the pipeline with processing steps
pipeline.add_model("DataCleaner")
pipeline.add_model("ModelInference", model=LLMModel.GPT_3)

# Run the pipeline with input data
pipeline_output = pipeline.run(input_data="Raw data that needs processing.")
print("Pipeline Output:", pipeline_output)

Step-by-Step Breakdown

Step 1: Import Necessary Libraries

from crewai import Agent
from langchain.agents import load_tools

Import the Agent Class: Import the Agent class from crewai, which allows the creation of agents that can perform specific roles.
Import load_tools: Import load_tools from langchain.agents to access tools that the agents might use. Here, it is used to load tools that require human input.

Step 2: Load Human Tools

# Human Tools
human_tools = load_tools(["human"])

Load Human Interaction Tools: Load a set of tools that allow the AI agents to ask for feedback or interact with a human. These tools enable agents to involve humans in certain tasks (e.g., providing feedback).

Step 3: Define the `YoutubeAutomationAgents` Class

class YoutubeAutomationAgents():
    ...

Class for YouTube Automation Agents: Create a class called YoutubeAutomationAgents to encapsulate all the agents related to the YouTube video preparation process.

Step 4: Create `youtube_manager` Method

def youtube_manager(self):
    return Agent(
        role="YouTube Manager",
        goal="""Oversee the YouTube preparation process including market research, title ideation, 
                description, and email announcement creation required to make a YouTube video.
                """,
        backstory="""As a methodical and detail-oriented manager, you are responsible for overseeing the preparation of YouTube videos.
                When creating YouTube videos, you follow the following process to create a video that has a high chance of success:
                1. Search YouTube to find a minimum of 15 other videos on the same topic and analyze their titles and descriptions.
                2. Create a list of 10 potential titles that are less than 70 characters and should have a high click-through-rate.
                    - Make sure you pass the list of videos to the title creator 
                      so that they can use the information to create the titles.
                3. Write a description for the YouTube video.
                4. Write an email that can be sent to all subscribers to promote the new video.
                """,
        allow_delegation=True,
        verbose=True,
    )

Agent Role: "YouTube Manager" – this agent is responsible for overseeing the entire YouTube video preparation process.
Goal: Manage and coordinate the processes required to create a successful YouTube video, including research, title ideation, and description writing.
Backstory: Provides a detailed description of the responsibilities, outlining the process to ensure the video has a high chance of success.
allow_delegation=True: This enables the agent to delegate tasks to other agents.
verbose=True: Enables detailed logging of the agent’s actions for better understanding and debugging.

Step 5: Create `research_manager` Method

def research_manager(self, youtube_video_search_tool, youtube_video_details_tool):
    return Agent(
        role="YouTube Research Manager",
        goal="""For a given topic and description for a new YouTube video, find a minimum of 15 high-performing videos 
                on the same topic with the ultimate goal of populating the research table which will be used by 
                other agents to help them generate titles and other aspects of the new YouTube video 
                that we are planning to create.""",
        backstory="""As a methodical and detailed research manager, you are responsible for overseeing researchers who 
                actively search YouTube to find high-performing YouTube videos on the same topic.""",
        verbose=True,
        allow_delegation=True,
        tools=[youtube_video_search_tool, youtube_video_details_tool]
    )

Agent Role: "YouTube Research Manager" – this agent focuses on finding relevant high-performing videos for a given topic.
Goal: Find at least 15 videos on the same topic, which will help in generating other video components like titles.
Backstory: Explains the agent’s focus on research and how this information will aid in creating successful video content.
Tools: Uses youtube_video_search_tool and youtube_video_details_tool to search and analyze YouTube videos.
allow_delegation=True: Allows the agent to delegate tasks to other agents as necessary.

Step 6: Create `title_creator` Method

def title_creator(self):
    return Agent(
        role="Title Creator",
        goal="""Create 10 potential titles for a given YouTube video topic and description. 
                You should also use previous research to help you generate the titles.
                The titles should be less than 70 characters and should have a high click-through-rate.""",
        backstory="""As a Title Creator, you are responsible for creating 10 potential titles for a given 
                YouTube video topic and description.""",
        verbose=True
    )

Agent Role: "Title Creator" – focuses on generating titles.
Goal: Create 10 potential titles for a given topic, using previous research to ensure they have high click-through rates.
Backstory: Describes the agent’s role in creating engaging and optimized titles.
verbose=True: For detailed output during the agent’s actions.

Step 7: Create `description_creator` Method

def description_creator(self):
    return Agent(
        role="Description Creator",
        goal="""Create a description for a given YouTube video topic and description.""",
        backstory="""As a Description Creator, you are responsible for creating a description for a given 
                YouTube video topic and description.""",
        verbose=True
    )

Agent Role: "Description Creator" – specializes in writing video descriptions.
Goal: Create a compelling description for the video.
Backstory: Provides context for the agent’s expertise in writing video descriptions.
verbose=True: Enables detailed output.

Step 8: Create `email_creator` Method

def email_creator(self):
    return Agent(
        role="Email Creator",
        goal="""Create an email to send to the marketing team to promote the new YouTube video.""",
        backstory="""As an Email Creator, you are responsible for creating an email to send to the marketing team 
                to promote the new YouTube video.

                It is vital that you ONLY ask for human feedback after you've created the email.
                Do NOT ask the human to create the email for you.
                """,
        verbose=True,
        tools=human_tools
    )

Agent Role: "Email Creator" – focuses on creating email content to promote the new video.
Goal: Write a marketing email for the new video.
Backstory: Emphasizes that the agent should complete the email itself and only seek human feedback once the draft is ready.
Tools: Uses human_tools to gather feedback after drafting the email.
verbose=True: Enables detailed logging for transparency during the process.

Summary

This class defines a set of agents, each with specific roles and goals, to handle different parts of the YouTube video preparation process:

YouTube Manager oversees the entire process.
Research Manager finds existing relevant videos.
Title Creator generates engaging titles.
Description Creator writes video descriptions.
Email Creator drafts marketing emails and seeks human feedback.

These agents, when combined, enable a structured approach to creating a successful YouTube video. Each agent can focus on its specialty, ensuring the video preparation process is efficient and effective.

Best Practices

Understand Requirements: Clearly outline the goals of your application to guide architectural decisions.
Iterative Development: Start with a minimal viable product that addresses core functionalities, expanding complexity over time.
Monitoring and Observability: Implement tools to monitor performance and make necessary adjustments post-deployment.
Experiment with Both Architectures: Utilize A/B testing to discover which option better meets your application’s needs.

Conclusion

Both AI agents and AI pipelines are vital tools for leveraging large language models effectively. By carefully choosing the right approach for your application’s requirements and utilizing platforms like CrewAI, developers can create high-performing and user-friendly applications. As technology advances, staying informed about these architectures will enable developers to keep pace with the evolving landscape of AI applications.

The world of AI is expansive and filled with opportunities. With the right knowledge and tools at your disposal, you can create remarkable applications that harness the power of language and data. Happy coding!

References

Large Language Models for Code Generation | FabricHQ AI Pipelines: A Practical Guide to Coding Your LLM…
Using Generative AI to Automatically Create a Video Talk from an … AI Pipelines: A Practical Guide to Coding Your LLM … create apps that dem…
Data Labeling — How to Select a Data Labeling Company? | by … AI Pipelines: A Practical Guide to Coding Your LLM App…
SonarQube With OpenAI Codex – Better Programming AI Pipelines: A Practical Guide to Coding Your LLM Application … create apps…
Best AI Prompts for Brainboard AI | by Mike Tyson of the Cloud (MToC) … Guide to Coding Your LLM Application. We use CrewA…
How to take help from AI Agents for Research and Writing: A project The Researcher agent’s role is to find relevant academic papers, while…
Towards Data Science on LinkedIn: AI Agents vs. AI Pipelines Not sure how to choose the right architecture for your LLM application? Al…
Inside Ferret-UI: Apple’s Multimodal LLM for Mobile … – Towards AI … Application. We use CrewAI to create apps that demonstra…
The role of UX in AI-driven healthcare | by Roxanne Leitão | Sep, 2024 AI Pipelines: A Practical Guide to Coding Your LLM … create apps that de…
Build Your Own Autonomous Agents using OpenAGI – AI Planet Imagine AI agents as your digital sidekicks, tirelessly working t…

Citations

Multi-agent system’s architecture. | by Talib – Generative AI AI Pipelines: A Practical Guide to Coding Your LLM … create apps that dem…
What is LLM Orchestration? – IBM As organizations adopt artificial intelligence to build these sorts of generativ…
Amazon Bedrock: Building a solid foundation for Your AI Strategy … Application. We use CrewAI to create apps that demonstrate how to choo…
Connect CrewAI to LLMs … set. You can easily configure your agents to use a differe…
I trusted OpenAI to help me learn financial analysis. I’m now a (much … AI Pipelines: A Practical Guide to Coding Your LLM … creat…
Prompt Engineering, Multi-Agency and Hallucinations are … AI Pipelines: A Practical Guide to Coding Your LLM … cre…
Announcing the next Betaworks Camp program — AI Camp: Agents AI Agents vs. AI Pipelines: A Practical Guide to Coding…
AI and LLM Observability With KloudMate and OpenLLMetry AI Pipelines: A Practical Guide to Coding Your LLM ……
Get Started with PromptFlow — Microsoft High-Quality AI App … AI Pipelines: A Practical Guide to Coding Your LLM ……
From Buzzword to Understanding: Demystifying Generative AI AI Pipelines: A Practical Guide to Coding Your LLM … create apps…

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Comparing Embedding Models: OpenAI, Cohere, Google

Revolutionize your NLP projects!

This blog dives into the top embedding models – OpenAI, Cohere, Google, E5, and BGE. Discover their strengths, weaknesses, and ideal use cases to make informed decisions for your next project. Explore the future of language understanding!

Intrigued? Read more to unlock the hidden potential of your text data!

BGE is a newer model that leverages the BERT architecture to generate embeddings tailored for various tasks. This model aims to combine the strengths of BERT with innovative techniques to enhance performance. To understand BGE better, you can read this article.

Performance

While BGE has not yet been fully evaluated on the MTEB leaderboard, initial results indicate that it may outperform other models in specific contexts. This adaptability suggests that BGE could be a strong contender in the embedding model space.

Use Cases

BGE is being explored for applications in content generation and semantic search, capitalizing on its BERT foundation for understanding complex language structures. As the model matures, its potential use cases may expand further.

Conclusion

Choosing the best embedding model is not a one-size-fits-all decision; it largely depends on the specific requirements of the task at hand. Factors such as language support, performance metrics, and computational efficiency must be considered. OpenAI, Cohere, Google, E5, and BGE each offer unique advantages that cater to different applications within the NLP domain.

As the field of natural language processing continues to evolve, ongoing comparisons and user feedback will further illuminate the best choices for various needs in embedding technologies. Whether you are building a chatbot, conducting sentiment analysis, or developing a multilingual application, understanding the strengths and weaknesses of these models will help you select the right tool for your project.

In summary, as we venture into a future where language understanding is paramount, staying informed about the latest advancements in embedding models will be crucial for leveraging the full potential of natural language processing. The journey of exploring and utilizing these models has only just begun, and the possibilities are vast.

References

Best Embedding Models. OpenAI, Cohere, Google, E5, BGE | Medium Interpretation. Our investigation seeks to pinpoint the top embe…
Best Embedding Model — OpenAI / Cohere / Google / E5 / BGE – Aili Abstract. The article provides an in-depth comparison of various…
Robert Wolfe – OpenAI / Cohere / Google / E5 / BGE – LinkedIn Robert Wolfe’s Post · Best Embedding Model — OpenAI …
OpenAI vs Open-Source Multilingual Embedding Models BGE-M3 model is not yet benchmarked on the MTEB leaderb…
What embedding model do you guys use? : r/LangChain – Reddit I tested OpenAI Ada vs BAAI-Bge vs MiniLM, and Min…
Hosting A Text Embedding Model That is Better, Cheaper … – Medium The OpenAI embedding model ranked 7th on the overall leaderb…
Are GPTs Good Embedding Models – Towards Data Science When you visit the site, you’ll notice t…
[D] Is openai text-embedding-ada-002 the best embeddings model? My question is : has anyone done a comparative analysis…
New OpenAI Embeddings vs Open Source – Generative AI One thing we can notice immediately is that OpenAI’s new text-em…
NLP, Embeddings -Embedding Models and Comparison – GoPenAI In this article, we will look at embeddings, purpose of embeddings, mo…

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An Introduction to Scikit-LLM : Merging Scikit-learn and Large Language Models for NLP

1. What is Scikit-LLM?

1.1 Understanding Large Language Models (LLMs)

1.2 The Role of Scikit-learn or sklearn in Machine Learning

2. Key Features of Scikit-LLM

2.1 Integration with Scikit-Learn

2.2 Open Source and Accessibility of sklearn

2.3 Enhanced Text Analysis

2.4 User-Friendly Design

2.5 Complementary Features

3. Applications of Scikit-LLM

3.1 Natural Language Processing (NLP)

3.2 Healthcare

3.3 Finance

4. Getting Started with Scikit-LLM

4.1 Installation

4.2 First Steps: A Simple Code Example

4.3 Explanation of the Code Example

5. Advanced Use Cases of Scikit-LLM

5.1 Sentiment Analysis

5.2 Text Summarization

5.3 Topic Modeling

6. Challenges and Considerations

6.1 Computational Resource Requirements

6.2 Model Bias and Ethical Considerations

7. Conclusion

8. References

References

Unlocking the Power of Collaboration: Understanding OpenAI’s Agent Swarm

What is Agent Swarm?

Key Features of Agent Swarm

How Does Agent Swarm Work?

1. Collaboration Framework

2. Swarm Intelligence in Action

3. Example of Communication Among Agents

Real-World Applications of Agent Swarm

1. Customer Service

2. Marketing

3. Research and Development

Getting Technical: Programming with Agent Swarm

Basic Code Example

Step-by-Step Breakdown

How to Run the Code

Broader Implications of Agent Swarm

Smarter AI Ecosystems

Conclusion

References

Citations

AI Agents vs. AI Pipelines: A Practical Guide to Coding Your LLM Application

Introduction to AI Agents and AI Pipelines

Key Concepts

AI Agents

AI Pipelines

Choosing the Right Architecture

Use Cases for AI Agents

Use Cases for AI Pipelines

Coding Your LLM Application with CrewAI

Example of Creating an AI Agent

Example of Setting Up an AI Pipeline

Step-by-Step Breakdown

Step 1: Import Necessary Libraries

Step 2: Load Human Tools

Step 3: Define the YoutubeAutomationAgents Class

Step 4: Create youtube_manager Method

Step 5: Create research_manager Method

Step 6: Create title_creator Method

Step 7: Create description_creator Method

Step 8: Create email_creator Method

Summary

Best Practices

Conclusion

References

Citations

Exploring the Best Embedding Models: OpenAI, Cohere, Google, E5, and BGE

Understanding Embedding Models

1. OpenAI

Model Overview

Performance

Use Cases

2. Cohere

Step 3: Define the `YoutubeAutomationAgents` Class

Step 4: Create `youtube_manager` Method

Step 5: Create `research_manager` Method

Step 6: Create `title_creator` Method

Step 7: Create `description_creator` Method

Step 8: Create `email_creator` Method