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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

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

Use Cases for AI Pipelines

  1. Batch Processing: When handling large amounts of data that need consistent processing.
  2. Hierarchical Workflows: For tasks like data cleaning followed by enrichment and final output generation.
  3. 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:

  1. Import Libraries: Import the CrewAI library to access its features.
  2. Initialize Environment: Create a Crew object linked to your API Key.
  3. Create an Agent: We define an AI Agent called "ResearchAssistant" that utilizes the GPT-3 model.
  4. Function: The generate_response function takes a user’s question and returns the AI’s reply.
  5. 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

  1. Understand Requirements: Clearly outline the goals of your application to guide architectural decisions.
  2. Iterative Development: Start with a minimal viable product that addresses core functionalities, expanding complexity over time.
  3. Monitoring and Observability: Implement tools to monitor performance and make necessary adjustments post-deployment.
  4. 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

  1. Large Language Models for Code Generation | FabricHQ AI Pipelines: A Practical Guide to Coding Your LLM…
  2. Using Generative AI to Automatically Create a Video Talk from an … AI Pipelines: A Practical Guide to Coding Your LLM … create apps that dem…
  3. Data Labeling — How to Select a Data Labeling Company? | by … AI Pipelines: A Practical Guide to Coding Your LLM App…
  4. SonarQube With OpenAI Codex – Better Programming AI Pipelines: A Practical Guide to Coding Your LLM Application … create apps…
  5. Best AI Prompts for Brainboard AI | by Mike Tyson of the Cloud (MToC) … Guide to Coding Your LLM Application. We use CrewA…
  6. 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…
  7. Towards Data Science on LinkedIn: AI Agents vs. AI Pipelines Not sure how to choose the right architecture for your LLM application? Al…
  8. Inside Ferret-UI: Apple’s Multimodal LLM for Mobile … – Towards AI … Application. We use CrewAI to create apps that demonstra…
  9. 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…
  10. Build Your Own Autonomous Agents using OpenAGI – AI Planet Imagine AI agents as your digital sidekicks, tirelessly working t…

Citations

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


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Learning DSPy:Optimizing Question Answering of Local LLMs

Revolutionize AI!
Master question-answering with Mistral NeMo, a powerful LLM, alongside Ollama and DSPy. This post explores optimizing ReAct agents for complex tasks using Mistral NeMo’s capabilities and DSPy’s optimization tools. Unlock the Potential of Local LLMs: Craft intelligent AI systems that understand human needs. Leverage Mistral NeMo for its reasoning and context window to tackle intricate queries. Embrace the Future of AI Development: Start building optimized agents today! Follow our guide and code examples to harness the power of Mistral NeMo, Ollama, and DSPy.

Learning DSPy with Ollama and Mistral-NeMo

In the realm of artificial intelligence, the ability to process and understand human language is paramount. One of the most promising advancements in this area is the emergence of large language models like Mistral NeMo, which excel at complex tasks such as question answering. This blog post will explore how to optimize the performance of a ReAct agent using Mistral NeMo in conjunction with Ollama and DSPy. For further insights into the evolving landscape of AI and the significance of frameworks like DSPy, check out our previous blog discussing the future of prompt engineering here.

What is Mistral NeMo?

Mistral NeMo is a state-of-the-art language model developed in partnership with NVIDIA. With 12 billion parameters, it offers impressive capabilities in reasoning, world knowledge, and coding accuracy. One of its standout features is its large context window, which can handle up to 128,000 tokens of text—this allows it to process and understand long passages, making it particularly useful for complex queries and dialogues (NVIDIA).

Key Features of Mistral NeMo

  1. Large Context Window: This allows Mistral NeMo to analyze and respond to extensive texts, accommodating intricate questions and discussions.
  2. State-of-the-Art Performance: The model excels in reasoning tasks, providing accurate and relevant answers.
  3. Collaboration with NVIDIA: By leveraging NVIDIA’s advanced technology, Mistral NeMo incorporates optimizations that enhance its performance.

Challenges in Optimization

While Mistral NeMo is a powerful tool, there are challenges when it comes to optimizing and fine-tuning ReAct agents. One significant issue is that the current documentation does not provide clear guidelines on implementing few-shot learning techniques effectively. This can affect the adaptability and overall performance of the agent in real-world applications (Hugging Face).

What is a ReAct Agent?

Before diving deeper, let’s clarify what a ReAct agent is. ReAct, short for "Reasoning and Acting," refers to AI systems designed to interact with users by answering questions and performing tasks based on user input. These agents can be applied in various fields, from customer service to educational tools (OpenAI).

Integrating DSPy for Optimization

To overcome the challenges mentioned above, we can use DSPy, a framework specifically designed to optimize ReAct agents. Here are some of the key functionalities DSPy offers:

  • Simulating Traces: This feature allows developers to inspect data and simulate traces through the program, helping to generate both good and bad examples.
  • Refining Instructions: DSPy can propose or refine instructions based on performance feedback, making it easier to improve the agent’s effectiveness.

Setting Up a ReAct Agent with Mistral NeMo and DSPy

Now that we have a good understanding of Mistral NeMo and DSPy, let’s look at how to set up a simple ReAct agent using these technologies. Below, you’ll find a code example that illustrates how to initialize the Mistral NeMo model through Ollama and optimize it using DSPy.

Code Example

Here’s a sample code that Uses a dataset called HotPotQA and ColBertV2 a Dataset Retrieval model to test and optimise a ReAct Agent that is using mistral-nemo-latest as the llm

Step-by-Step Breakdown of the Code

1. Importing Libraries configuring Datasets:

First We will import DSpy libraries evaluate,datasets,teleprompt.
The first one is used to check the performance of a dspy agent.
The second one is used to load inbuilt datasets to evaluate the performance of the LLms
The third one is used as an optimisation framework for training and tuning the prompts that are provided to the LLMs



import dspy
from dspy.evaluate import Evaluate
from dspy.datasets.hotpotqa import HotPotQA
from dspy.teleprompt import BootstrapFewShotWithRandomSearch

ollama=dspy.OllamaLocal(model='mistral-nemo:latest')
colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.configure(lm=ollama, rm=colbert)

2. Loading some data:

We will now load the Data and segment to into training data, testing data and development data



dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=300, test_size=0)
trainset = [x.with_inputs('question') for x in dataset.train[0:150]]
valset = [x.with_inputs('question') for x in dataset.train[150:200]]
devset = [x.with_inputs('question') for x in dataset.dev]

# show an example datapoint; it's just a question-answer pair
trainset[23]

3. Creating a ReAct Agent:

First we will make a default (Dumb 😂) ReAct agent


agent = dspy.ReAct("question -> answer", tools=[dspy.Retrieve(k=1)])

4. Evaluting the agent:

Set up an evaluator on the first 300 examples of the devset.


config = dict(num_threads=8, display_progress=True, display_table=25)
evaluate = Evaluate(devset=devset, metric=dspy.evaluate.answer_exact_match, **config)

evaluate(agent)

5. Optimizing the ReAct Agent:

Now we will (try to) put some brains into the dumb agent by training it


config = dict(max_bootstrapped_demos=2, max_labeled_demos=0, num_candidate_programs=5, num_threads=8)
tp = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match, **config)
optimized_react = tp.compile(agent, trainset=trainset, valset=valset)

6. Testing the Agent:

Now we will check if the agents have become smart (enough)


evaluate(optimized_react)

Conclusion

Integrating MistralNeMo with Ollama and DSPy presents a powerful framework for developing and optimizing question-answering ReAct agents. By leveraging the model’s extensive capabilities, including its large context window tool calling capabilities and advanced reasoning skills, developers can create AI agents that efficiently handle complex queries with high accuracy in a local setting.

However, it’s essential to address the gaps in current documentation regarding optimization techniques for Local and opensource models and agents. By understanding these challenges and utilizing tools like DSPy, developers can significantly enhance the performance of their AI projects.

As AI continues to evolve, the integration of locally running models like Mistral NeMo will play a crucial role in creating intelligent systems capable of understanding and responding to human needs. With the right tools and strategies, developers can harness the full potential of these technologies, ultimately leading to more sophisticated and effective AI applications.

By following the guidance provided in this blog post, you can start creating your own optimized question-answering agents using Mistral NeMo, Ollama, and DSPy. Happy coding!

References

  1. Creating ReAct AI Agents with Mistral-7B/Mixtral and Ollama using … Creating ReAct AI Agents with Mistral-7B/Mixtral a…
  2. Mistral NeMo – Hacker News Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, …

  3. Lack of Guidance on Optimizing/Finetuning ReAct Agent with Few … The current ReAct documentation lacks clear instructions on optimizing or fine…

  4. Introducing Mistral NeMo – Medium Mistral NeMo is an advanced 12 billion parameter model developed in co…

  5. Optimizing Multi-Agent Systems with Mistral Large, Nemo … – Zilliz Agents can handle complex tasks with minimal human intervention. Learn how to bu…

  6. mistral-nemo – Ollama Mistral NeMo is a 12B model built in collaboration with NVIDIA. Mistra…
  7. Mistral NeMo : THIS IS THE BEST LLM Right Now! (Fully … – YouTube … performance loss. Multilingual Support: The new Tekken t…

  8. dspy/README.md at main · stanfordnlp/dspy – GitHub Current DSPy optimizers can inspect your data, simulate traces …

  9. Is Prompt Engineering Dead? DSPy Says Yes! AI&U


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## Declaration:

### The whole blog itself is written using Ollama, CrewAi and DSpy

👀

Ollama Enhances Tool Use for LLMs

Ollama’s Game Changer: LLMs Get Superpowers!

New update lets language models use external tools! This unlocks a world of possibilities for AI development – imagine data analysis, web scraping, and more, all powered by AI. Dive in and see the future of AI!

Ollama brings Tool calling support to LLMs in the latest Update

Artificial intelligence is changing fast. Making language models better can change how we interact with technology. Ollama’s newest update adds big improvements to tool use. Now, large language models (LLMs) can handle more tasks, and they can do it more efficiently. This post will look at the key features of this update and how they might impact AI development and different industries.

The Game-Changing Tool Support Feature in Ollama

The most exciting part of Ollama’s update is the tool support feature. This new feature lets models use external tools. This process is called "tool calling." Developers can list tools in the Ollama API, and the models will use these tools to complete tasks.

This feature changes how we interact with LLMs. It goes from a simple Q&A format to a more dynamic, task-focused approach. Instead of just answering questions, models can now perform tasks like data analysis, web scraping, or even connecting with third-party APIs. This makes the models more interactive and opens up new possibilities for developers.

For more on tool calling, check out the official Ollama documentation.

Compatibility with Popular Ollama Models

One of the best things about this update is its compatibility with well-known models, like the new Llama 3.1. Users can pick the model that works best for their task, making the platform more useful.

For developers, this means they can use different models for different projects. Some models might be better at understanding language, while others might be better at creating content or analyzing data. This choice allows developers to build more efficient and tailored applications.

To learn more about Llama 3.1 and its features, visit Hugging Face.

Sandboxing for Security and Stability

With new tech comes concerns about security and stability. The Ollama team has thought about this by adding a sandboxed environment for tool operations. This means tools run in a safe, controlled space. It reduces the chance of unwanted problems or security issues when using external resources.

Sandboxing makes sure developers can add tools to their apps without worrying about harming system stability or security. This focus on safety helps build trust, especially when data privacy and security are so important today. For more on sandboxing, see OWASP’s guidelines.

Promoting Modularity and Management

The tool support feature not only adds functionality but also promotes modularity and management. Users can manage and update each tool separately. This makes it easier to add new tools and features to existing apps. This modular approach helps developers move faster and make improvements more quickly.

For example, if a developer wants to add a new data visualization tool or replace an old analytics tool, they can do it without changing the whole app. This flexibility is valuable in the fast-moving world of AI development.

Expanding Practical Applications

Ollama’s tool support feature has many uses. The ability to call tools makes it possible to handle simple tasks and more complex operations that involve multiple tools. This greatly enhances what developers and researchers can do with AI.

Imagine a researcher working with large datasets. With the new tool support, they can use a language model to gain insights, a data visualization tool to create graphs, and a statistical analysis tool—all in one workflow. This saves time and makes the analysis process richer, as different tools can provide unique insights.

Industries like healthcare, finance, and education can benefit a lot from these improvements. In healthcare, LLMs could help analyze patient data and connect with external databases for real-time information. In finance, they could help predict market trends and assess risk with the help of analytical tools. For industry-specific AI applications, check out McKinsey’s insights.

Learning Resources and Community Engagement

Learning how to use these new features is crucial. Ollama provides plenty of resources, including tutorials and documentation, to help users implement tool calling in their apps. These resources include examples of API calls and tips for managing tools.

This update has also sparked discussions in the AI community. Platforms like Reddit and Hacker News are now buzzing with users sharing insights, experiences, and creative ways to use the new tool capabilities. This community engagement helps users learn faster as they can benefit from shared knowledge.

YouTube video player

##### **Example from Fahd Mirza**

YouTube video player

##### **Example from LangChain**

YouTube video player

##### **Example from Mervin Praison**

## Conclusion: The Future of AI Development with Ollama

In conclusion, Ollama’s latest update on tool use is a big step forward in improving language models. By making it possible for developers to create more dynamic and responsive apps, this update makes Ollama a powerful tool for AI research and development.

With model compatibility, security through sandboxing, modular management, and a wide range of practical uses, developers now have the resources to push the limits of what’s possible with AI. As the community explores these features, we can expect to see innovative solutions across different sectors. This will enhance how we interact with technology and improve our daily lives.

With Ollama leading the way in tool integration for language models, the future of AI development looks bright. We are just starting to see what these advancements can do. As developers use tool calling, we can expect a new era of creativity and efficiency in AI applications. Whether you’re an experienced developer or just starting out in AI, now is the perfect time to explore what Ollama’s update has to offer.

## *References*
1. Tool support · Ollama Blog [To enable tool calling, provide a list of available tools via the tool…](https://ollama.com/blog/tool-support)
2. Ollama’s Latest Update: Tool Use – AI Advances [Ollama’s Latest Update: Tool Use. Everything you need to know abo…](https://ai.gopubby.com/ollamas-latest-update-tool-use-7b809e15be5c)
3. Releases · ollama/ollama – GitHub [Ollama now supports tool calling with po…](https://github.com/ollama/ollama/releases)
4. Tool support now in Ollama! : r/LocalLLaMA – Reddit [Tool calling is now supported using their OpenAI compatible API. Com…](https://www.reddit.com/r/LocalLLaMA/comments/1ecdh1c/tool_support_now_in_ollama/)
5. Ollama now supports tool calling with popular models in local LLM [The first I think of when anyone mentions agent-like “tool use” i…](https://news.ycombinator.com/item?id=41291425)
6. ollama/docs/faq.md at main – GitHub [Updates can also be installed by downloading …](https://github.com/ollama/ollama/blob/main/docs/faq.md)
7. Ollama Tool Call: EASILY Add AI to ANY Application, Here is how [Welcome to our latest tutorial on Ollama Tool Calling! In this vi…](https://www.youtube.com/watch?v=0THuClFvfic)
8. Ollama [Get up and running with large language m…](https://ollama.com/)
9. Mastering Tool Calling in Ollama – Medium [Using Tools in Ollama API Calls. To use tools in…](https://medium.com/@conneyk8/mastering-tool-usage-in-ollama-2efdddf79f2e)
10. Spring AI with Ollama Tool Support [Earlier this week, Ollama introduced an excit…](https://spring.io/blog/2024/07/26/spring-ai-with-ollama-tool-support)

—-

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Ollama: how to set up a local AI server in your PC:

In the world of artificial intelligence, the ability to run AI language models locally is a significant advancement. It ensures privacy and security by keeping data within your own infrastructure. One of the tools that make this possible is Ollama. In this guide, we will walk you through the detailed process of setting up a local AI server with Ollama. This step-by-step guide is designed to be informative and engaging, ensuring that you can successfully set up your local AI server, regardless of your technical background.

Ollama: Run local AI server in your PC:

Abstract

"In the world of artificial intelligence, the ability to run AI language models locally is a significant advancement. It ensures privacy and security by keeping data within your own infrastructure. One of the tools that make this possible is Ollama. In this guide, we will walk you through the detailed process of setting up a local AI server with Ollama. This step-by-step guide is designed to be informative and engaging, ensuring that you can successfully set up your local AI server, regardless of your technical background."

Introduction

In today\’s digital age, artificial intelligence (AI) has become an integral part of many industries, from healthcare and finance to education and entertainment. One of the key challenges in using AI is ensuring that your data remains secure and private. This is where running AI models locally comes into play. By setting up a local AI server, you can run queries on your private data without sending it to external servers, thus safeguarding your information.

Ollama is a powerful tool that allows you to set up and run AI language models locally. It provides a flexible and user-friendly interface for managing and running AI models. In this guide, we will cover the essential steps to set up a local AI server with Ollama, including downloading and installing the software, setting it up on different operating systems, and integrating it with other tools like Open webui and Python.

1. Downloading Ollama

The first step in setting up your local AI server is to download Ollama. This process is straightforward and can be completed in a few steps:

  1. Visit the Ollama Website:
    • Open your web browser and navigate to the Ollama website. You can search for Ollama in your favorite search engine or type the URL directly into the address bar.
  2. Select Your Operating System:
    • Once you are on the Ollama website, you will need to select your operating system. Ollama supports both Windows and Linux (Ubuntu) operating systems.
  3. Follow the Installation Instructions:
    • After selecting your operating system, follow the installation instructions provided on the website. These instructions will guide you through the download and installation process.

1.1. Downloading Ollama for Windows

If you are a Windows user, you can download Ollama using the following steps:

  1. Download the Installer:
    • Click on the Download button for the Windows version of Ollama. This will download the installer to your computer.
  2. Run the Installer:
    • Once the download is complete, run the installer and follow the on-screen instructions to install Ollama on your Windows PC.

1.2. Downloading Ollama for Linux (Ubuntu)

For Linux users, the process is slightly different:

  1. Download the Installer:
    • Click on the Download button for the Linux (Ubuntu) version of Ollama. This will download the installer to your computer.
  2. Run the Installer:
    • Once the download is complete, run the installer and follow the on-screen instructions to install Ollama on your Linux (Ubuntu) PC.

2. Setting Up Ollama on Windows

Setting up Ollama on Windows involves using the Windows Subsystem for Linux (WSL). This step is not necessary if the window version of Ollama installer works properply.

2.1. Installing WSL

To install WSL, follow these steps:

  1. Enable WSL:
    • Go to Settings on your Windows PC, then navigate to Update & Security > For Developers and enable Developer Mode.
  2. Install WSL:
    • Open the Microsoft Store and search for WSL. Select the Windows Subsystem for Linux app and install it.
  3. Set Up WSL:
    • Once installed, open WSL from the Start menu. Follow the on-screen instructions to set up WSL on your Windows PC.
  4. Watch a tutorial on setting up WSL and WSL2:
    • If you are still in doubt you can watch this video below where network check shows how to set up WSL

2.2. Installing Ollama on WSL

Once WSL is set up, you can install Ollama:

  1. Open WSL:
    • Open WSL from the Start menu.
  2. Install Dependencies:
    • Run the following commands to install the necessary dependencies:
      sudo apt-get update
      sudo apt-get install -y build-essential libssl-dev libffi-dev python-dev python-pip
  3. Install Ollama:
    • Run the following command to install ollama:
      pip install ollama
  4. Configure Ollama:
    • Follow the on-screen instructions to configure Ollama on WSL.

3. Setting Up Ollama on Ubuntu/Linux

For Linux users, setting up Ollama involves a step-by-step guide to install and configure the software using Open webui.

3.1. Installing Dependencies

First, install the necessary dependencies:

  1. Update Your System:
    • Run the following command to update your system:
      sudo apt-get update
  2. Install Dependencies:
    • Run the following command to install the necessary dependencies: “`
      sudo apt-get install -y build-essential libssl-dev libffi-dev python-dev python-pip

3.2. Installing Ollama

Next, install Ollama:

  1. Install ollama:
    • Run the following command to install ollama:
      pip install ollama
  2. Configure Ollama:
    • Follow the on-screen instructions to configure Ollama on your Linux system.

3.3. Using Open webui

Open webui provides a user-friendly interface for managing and running AI models with Ollama:

  1. Install Open webui:
    • Run the following command to install Open webui:
      pip install open-webui
  2. Configure Open webui:
    • Follow the on-screen instructions to configure Open webui.
  3. Run Open webui:
    • Run the following command to start Open webui:
      open-webui

4. Running AI Models Locally

Once Ollama is installed and configured, you can run AI language models locally. This involves setting up the model and ensuring that the environment is correctly configured to run queries on your private data without security concerns.

4.1. Setting Up the Model

To set up the model, follow these steps:

  1. Download the Model:
    • Download the AI model you want to use from the Ollama repository.
  2. Configure the Model:
    • Follow the on-screen instructions to configure the model.
  3. Run the Model:
    • Run the following command to start the model:
      ollama run 

4.2. Ensuring Privacy and Security

To ensure privacy and security, make sure that your environment is correctly configured:

  1. Check Permissions:
    • Ensure that the necessary permissions are set for the model to run securely.
  2. Use Secure Data:
    • Use secure data sources and ensure that your data is encrypted.

5. Using Open webui

Open webui provides a user-friendly interface for managing and running AI models with Ollama. Here’s how you can use it:

5.1. Accessing Open webui

To access Open webui, follow these steps:

  1. Open a Web Browser:
    • Open a web browser on your computer.
  2. Navigate to Open webui:
    • Navigate to the URL provided by Open webui.

5.2. Managing AI Models

Once you are logged into Open webui, you can manage and run AI models:

  1. Upload Models:
    • Upload the AI models you want to use.
  2. Configure Models:
    • Configure the models as needed.
  3. Run Queries:
    • Run queries on your private data using the models.

6. Python Integration

For developers, Ollama can be integrated with Python. This allows you to run Ollama using Python scripts.

6.1. Installing Ollama for Python

To install Ollama for Python, follow these steps:

  1. Install Ollama:
    • Run the following command to install Ollama:
      pip install ollama
  2. Import ollama:
    • Import Ollama in your Python script:
      import Ollama

6.2. Running Ollama with Python

To run Ollama using Python, follow these steps:

  1. Create a Python Script:
    • Create a Python script to run Ollama.
  2. Run the Script:
    • Run the script using Python:
      python ollama_script.py

7. Running LLMs Locally

Ollama supports running large language models (LLMs) locally. This allows users to run queries on their private data without sending it to external servers, ensuring privacy and security.

7.1. Downloading LLMs

To download LLMs, follow these steps:

  1. Download the Model:
    • go to Ollama click on the models section
  2. Choose the Model:
    • Look for a model within 12B parameters if you don\’t GPU\’s with big VRAMs like 16GB, 24GB. Example RTX 4080 ,RTX 4070Ti Super. AMD GPUs can work if the current driver is stable and supports ROCm or HIP.
    • Most of the consumer grade GPUs have around 12 GB VRAM and atleast 32 GB DDR4 RAM. Hence, going for smaller models like Llama 3.1: 8B, Mistral-Nemo:12B should be ideal for running on you PC / Gaming Laptop.
  3. Run the Model:
    • Run the following command to start the model:
      ollama  run 

7.2. Ensuring Privacy and Security

To ensure privacy and security, make sure that your environment is correctly configured:

  1. Check Permissions:
    • Ensure that the necessary permissions are set for the model to run securely.
  2. Use Secure Data:
    • Use secure data sources and ensure that your data is encrypted.

8. Video Tutorials

For those who prefer a visual walkthrough, there are several video tutorials available on YouTube that provide a step-by-step guide to setting up Ollama and running AI models locally.

8.1. Finding Video Tutorials

To find video tutorials, follow these steps:

  1. Search for Tutorials:
    • Open YouTube and search for Ollama setup or Ollama tutorial.
  2. Watch the Tutorials:
    • Watch the video tutorials to get a visual walkthrough of the setup process.

Conclusion

Setting up a local AI server with Ollama is a straightforward process that can be completed by following the steps outlined in this guide. By downloading and installing Ollama, setting it up on your operating system, and integrating it with tools like Open webui and Python, you can run AI language models locally. This ensures privacy and security for your data by keeping it within your own infrastructure. If you have followed this guide and successfully set up your local AI server with Ollama, you are now ready to start running AI language models locally. Remember to always ensure privacy and security by configuring your environment correctly and using secure data sources.

For more information on Ollama and AI-related topics, feel free to explore our other blog posts on AI&U. We are committed to providing you with the best resources to help you navigate the world of artificial intelligence.

Thank you for reading, and we hope you found this guide informative and helpful.

References:


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