Introduction

Ever wished you could create stunning portraits with just a few clicks? Meet Gibili Portrait Assistance, an AI-powered tool that makes generating high-quality portraits effortless. Whether you’re an artist, designer, or simply someone who loves experimenting with AI, Gibili can help bring your ideas to life.

In this post, we’ll walk you through how to use Gibili Portrait Assistance and explore the OpenAI architecture behind it.

How to Use Gibili Portrait Assistance

Using Gibili is straightforward and requires no prior technical knowledge. Here’s a simple step-by-step guide:

1. Enter Your Description or Upload an Image
You can either type a text description of the portrait you want or upload an existing image to be enhanced or transformed by AI.

Text Prompt Example:

• “A realistic portrait of a woman with curly brown hair, wearing a red scarf, in a cinematic lighting style.”

Image Upload:

• If you have an image you want to modify or enhance, simply upload it, and Gibili will apply AI-powered enhancements or transformations.

2. Customize Your Preferences
You can fine-tune details such as:

• Art Style: Realistic, digital painting, anime, etc.
• Background: Solid color, blurred, natural scenery.
• Facial Expressions: Smiling, neutral, surprised.
• Additional Features: Glasses, hats, jewelry, etc.

3. Generate the Image
Press Enter, and within seconds, Gibili will produce a high-resolution portrait based on your input or uploaded image.

4. Refine and Download
If you want adjustments, you can tweak your input and regenerate until you’re satisfied. Once ready, download your portrait in high-quality format.

The OpenAI Architecture Behind Gibili

Gibili Portrait Assistance is powered by OpenAI’s advanced image generation models, leveraging diffusion models to create highly detailed and realistic portraits. Here’s a simplified breakdown:

1. Text-to-Image & Image-to-Image Generation
When you provide a text prompt, the AI model translates it into a visual representation using deep learning techniques. If you upload an image, the model can enhance, transform, or stylize it while maintaining its core structure.

2. Fine-Tuned on Portrait Data
The model has been trained on a vast dataset of portraits across different styles, ensuring high accuracy and creativity in generated images.

3. Iterative Refinement
Instead of creating the final image instantly, the AI gradually refines it through multiple steps, ensuring greater precision and quality.

4. User-Guided Adjustments
Users can modify parameters like style and background, and the model will intelligently adjust the portrait while maintaining coherence.

Why Use Gibili Portrait Assistance?

✅ Easy to Use

No need for advanced design skills — just describe what you want or upload an image, and AI does the rest.

🎨 Customizable Output

From photorealistic portraits to artistic illustrations, you can tailor the results to your liking.

🚀 Fast & High-Quality

Generate high-resolution images within seconds.

🖌️ Creative Freedom

Perfect for artists, marketers, and content creators looking for unique visuals.

Get Started with Gibili Today!

Ready to create amazing AI-generated portraits? Try Gibili Portrait Assistance now and explore the limitless possibilities of AI-powered creativity!

Introduction

Large Language Model (LLM) based AI agents represent a new paradigm in artificial intelligence. Unlike traditional software agents, these systems leverage the powerful capabilities of LLMs to understand, reason, and interact with their environment in more sophisticated ways. This guide will introduce you to the basics of LLM agents and their think-act-observe cycle.

What is an LLM Agent?

An LLM agent is a system that uses a large language model as its core reasoning engine to:

1. Process natural language instructions
2. Make decisions based on context and goals
3. Generate human-like responses and actions
4. Interact with external tools and APIs
5. Learn from interactions and feedback

Think of an LLM agent as an AI assistant who can understand, respond, and take actions in the digital world, like searching the web, writing code, or analyzing data.

The Think-Act-Observe Cycle in LLM Agents

Observe (Input Processing)

LLM agents observe their environment through:

1. Direct user instructions and queries
2. Context from previous conversations
3. Data from connected tools and APIs
4. System prompts and constraints
5. Environmental feedback

Think (LLM Processing)

The thinking phase for LLM agents involves:

1. Parsing and understanding input context
2. Reasoning about the task and requirements
3. Planning necessary steps to achieve goals
4. Selecting appropriate tools or actions
5. Generating natural language responses

The LLM is the "brain," using its trained knowledge to process information and make decisions.

Act (Execution)

LLM agents can take various actions:

1. Generate text responses
2. Call external APIs
3. Execute code
4. Use specialized tools
5. Store and retrieve information
6. Request clarification from users

Key Components of LLM Agents

Core LLM

1. Serves as the primary reasoning engine
2. Processes natural language input
3. Generates responses and decisions
4. Maintains conversation context

Working Memory

1. Stores conversation history
2. Maintains current context
3. Tracks task progress
4. Manages temporary information

Tool Use

1. API integrations
2. Code execution capabilities
3. Data processing tools
4. External knowledge bases
5. File manipulation utilities

Planning System

1. Task decomposition
2. Step-by-step reasoning
3. Goal tracking
4. Error handling and recovery

Types of LLM Agent Architectures

Simple Agents

1. Single LLM with basic tool access
2. Direct input-output processing
3. Limited memory and context
4. Example: Basic chatbots with API access

ReAct Agents

1. Reasoning and Acting framework
2. Step-by-step thought process
3. Explicit action planning
4. Self-reflection capabilities

Chain-of-Thought Agents

1. Detailed reasoning steps
2. Complex problem decomposition
3. Transparent decision-making
4. Better error handling

Multi-Agent Systems

1. Multiple LLM agents working together
2. Specialized roles and capabilities
3. Inter-agent communication
4. Collaborative problem-solving

Common Applications

LLM agents are increasingly used for:

1. Personal assistance and task automation
2. Code generation and debugging
3. Data analysis and research
4. Content creation and editing
5. Customer service and support
6. Process automation and workflow management

Best Practices for LLM Agent Design

Clear Instructions

1. Provide explicit system prompts
2. Define constraints and limitations
3. Specify available tools and capabilities
4. Set clear success criteria

Effective Memory Management

1. Implement efficient context tracking
2. Prioritize relevant information
3. Clean up unnecessary data
4. Maintain conversation coherence

Robust Tool Integration

1. Define clear tool interfaces
2. Handle API errors gracefully
3. Validate tool outputs
4. Monitor resource usage

Safety and Control

1. Implement ethical guidelines
2. Add safety checks and filters
3. Monitor agent behavior
4. Maintain user control

Function calling allows Claude to interact with external functions and tools in a structured way. This guide will walk you through implementing function calling with Claude using Python, complete with examples and best practices.

Prerequisites

To get started, you'll need:

• Python 3.7+
• anthropic Python package
• A valid API key from Anthropic

Basic Setup

from anthropic import Anthropic
import json
# Initialize the client
anthropic = Anthropic(api_key='your-api-key')

Defining Functions

function_schema = {
    "name": "get_weather",
    "description": "Get the current weather for a specific location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City name or coordinates"
            },
            "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "Temperature unit"
            }
        },
        "required": ["location"]
    }
}

Making Function Calls

A Step-by-Step Guide to LLM Function Calling in Python
Function calling allows Claude to interact with external functions and tools in a structured way. This guide will walk you through implementing function calling with Claude using Python, complete with examples and best practices.
Prerequisites
To get started, you'll need:
Python 3.7+
anthropic Python package
A valid API key from Anthropic

Basic Setup
from anthropic import Anthropic
import json
# Initialize the client
anthropic = Anthropic(api_key='your-api-key')
Defining Functions
function_schema = {
    "name": "get_weather",
    "description": "Get the current weather for a specific location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City name or coordinates"
            },
            "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "Temperature unit"
            }
        },
        "required": ["location"]
    }
}
Making Function Calls
def get_weather(location, unit="celsius"):
    # This is a mock implementation but you can all call your API
    return {
        "location": location,
        "temperature": 22 if unit == "celsius" else 72,
        "conditions": "sunny"
    }
def process_function_call(message):
    try:
        # Parse the function call parameters
        params = json.loads(message.content)
        # Call the appropriate function
        if message.name == "get_weather":
            result = get_weather(**params)
            return json.dumps(result)
        else:
            raise ValueError(f"Unknown function: {message.name}")
    except Exception as e:
        return json.dumps({"error": str(e)})
# Example conversation with function calling
messages = [
    {
        "role": "user",
        "content": "What's the weather like in Paris?"
    }
]
while True:
    response = anthropic.messages.create(
        model="claude-3-5-haiku-latest",
        messages=messages,
        tools=[function_schema]
    )
    # Check if Claude wants to call a function
    if response.tool_calls:
        for tool_call in response.tool_calls:
            # Execute the function
            result = process_function_call(tool_call)
            # Add the function result to the conversation
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "name": tool_call.name,
                "content": result
            })
    else:
        # Normal response - print and break
        print(response.content)
        break

Best Practices

1. Clear Function Descriptions

• Write detailed descriptions for your functions
• Specify parameter types and constraints clearly
• Include examples in the descriptions when helpful

1. Input Validation

• Validate all function inputs before processing
• Return meaningful error messages
• Handle edge cases gracefully

1. Response Formatting

• Return consistent JSON structures
• Include status indicators in responses
• Format error messages uniformly

4 . Security Considerations

• Validate and sanitize all inputs
• Implement rate limiting if needed
• Use appropriate authentication
• Don't expose sensitive information in function descriptions

Conclusion

Function calling with Claude enables powerful integrations between the language model and external tools. By following these best practices and implementing proper error handling, you can create robust and reliable function-calling implementations.

At FreeTamilEbooks.com we have published 850 ebooks. All in sharable creative commons license. There are many people asking for the text only content of all these books many times. As it is a big task, took long time for it. Thanks to Lenin, Anwar of Kaniyam Foundation, all the contributors, all the writers and readers for making this project alive and a great success.

We are publishing the books as epub format, along with PDF format. Epub is just a zip file of HTML files. So, we can copy all the content from it as unicode text. Pandoc is a wonderful open source software, which can convert an epub to plaintext file.

There are the list of actions we have to do.

1. Get URLs of all the 850+ epub files
2. Download them all.
3. using pandoc, convert to text file.

So far, we dont have a metadata file for all the books published. Getting the links of all epub files need some programming. As Python is a swiss knife to automate anything, started to explore the wordpress REST api with python to get all the books pages content.

https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/get_Data.py

Wrote the code here to get all the books info.

This gave a JSON file with book name, author, genre, epub, mobi, a4 pdf,6 inch pdf links.

Converted this to a CSV file with the below code. https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/parse.py

I had to fix few things manually on the CSV file.

This is the final CSV file. https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/fte_metadata.csv

The below code is to download all the epub files from their links in the fte_metadata.csv file. Used pandoc to convert to text.

https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/get_fte_books.py

Got 845 txt files. Total size is 374 MB

Compressed with 7z to get 47MB compressed file.

Published the data here. https://kaniyam.cloudns.nz/tamil_datasets/

Download, share the text data for free. Dont sell them as most of the books are released as CC-BY-NC ( No Commercial ) license.

Use these data to build awesome open source applications and researches like Spellchekers, grammar checkers, LLm, RAG, what not?

Data is always the oil. Let us grow the open data oil.

Please share all your text, audio, video content in sharable license like creative commons. They will use to build a better future.

We are collecting open licensed dataset in tamil language, to build LLM, and other interesting applications in the coming days.

The ML models we build may have very short lifespan, but the open data will be there forever or at least for longer time than our life time.

Check the efforts part 1 and part 2 here.

part 1 – https://goinggnu.wordpress.com/2024/06/11/collecting-content-for-llm-dataset-part-1-tamil-wikipedia-content/

part 2 – https://goinggnu.wordpress.com/2024/06/16/collecting-content-for-llm-dataset-part-2-freetamilebooks/

here goes part 3.

Thamizh_mann publishers are publishing the public domain and nationalized tamil books for many years. Few years ago, with a collaboration with the Library at University of Toronto, Scarborough, Canada, and Thamizh_mann publishers, the kaniyam foundation team helped to release all the 1000+ tamil books as PDF and Docx formats for free online.

You can download them all here https://tamil.digital.utsc.utoronto.ca/61220/utsc35335

Thanks to UTSC, Thamizh_mann team for the great gift for the tamil Diaspora.

Now, we have 1000+ books in Unicode Docx format. Next is to convert them all as PlainText and use them. Natkeeran and Parathan helped on this.

Along with this, they helped to scrap project madurai books and tamil WikiSource books. They published all in a git repo here – https://github.com/KaniyamFoundation/open_tamil_texts along with the scripts and metadata.

I am adding those text in our open licensed tamil data collection.

Download them all here https://kaniyam.cloudns.nz/tamil_datasets/

here is the current size in text format and compressed format.

shrini@dell-optiplex-9100 v/w/h/tamil_datasets> du -h compressed
258M compressed/

shrini@dell-optiplex-9100 v/w/h/tamil_datasets> du -h text-files
355M text-files/project_madurai/data/text
355M text-files/project_madurai/data
355M text-files/project_madurai
110M text-files/tamil_wikisource/data
110M text-files/tamil_wikisource
374M text-files/FreeTamilEbooks-txt
714M text-files/thamizh_mann/data
716M text-files/thamizh_mann
1.6G text-files/

We have 1.6 G of text data to work on LLM or other works.

Go ahead, use it and build more models and tools using this data.

Hope this may not enough to get any good output. But, if we can bring something out of this, even though they are not good, then we can ask people to release their recent contents, blogs, social media posts in creative commons license.

There are few bloggers, magazines are already released their content in CC license. Now, we need your help to scarp them. If you know any programming language and can help for this project, please do webscrapping for the websites mentioned here. share the data and code.

https://github.com/KaniyamFoundation/ProjectIdeas/issues/198

Thanks for all the content providers and the contributors.

We are collecting open licensed dataset in tamil language, to build LLM, and other interesting applications in the coming days.

The ML models we build may have very short lifespan, but the open data will be there forever or at least for longer time than our life time.

Check the efforts part 1 and part 2 here.

part 1 – https://goinggnu.wordpress.com/2024/06/11/collecting-content-for-llm-dataset-part-1-tamil-wikipedia-content/

part 2 – https://goinggnu.wordpress.com/2024/06/16/collecting-content-for-llm-dataset-part-2-freetamilebooks/

here goes part 3.

Thamizh_mann publishers are publishing the public domain and nationalized tamil books for many years. Few years ago, with a collaboration with the Library at University of Toronto, Scarborough, Canada, and Thamizh_mann publishers, the kaniyam foundation team helped to release all the 1000+ tamil books as PDF and Docx formats for free online.

You can download them all here https://tamil.digital.utsc.utoronto.ca/61220/utsc35335

Thanks to UTSC, Thamizh_mann team for the great gift for the tamil Diaspora.

Now, we have 1000+ books in Unicode Docx format. Next is to convert them all as PlainText and use them. Natkeeran and Parathan helped on this.

Along with this, they helped to scrap project madurai books and tamil WikiSource books. They published all in a git repo here – https://github.com/KaniyamFoundation/open_tamil_texts along with the scripts and metadata.

I am adding those text in our open licensed tamil data collection.

Download them all here https://kaniyam.cloudns.nz/tamil_datasets/

here is the current size in text format and compressed format.

shrini@dell-optiplex-9100 v/w/h/tamil_datasets> du -h compressed
258M compressed/

shrini@dell-optiplex-9100 v/w/h/tamil_datasets> du -h text-files
355M text-files/project_madurai/data/text
355M text-files/project_madurai/data
355M text-files/project_madurai
110M text-files/tamil_wikisource/data
110M text-files/tamil_wikisource
374M text-files/FreeTamilEbooks-txt
714M text-files/thamizh_mann/data
716M text-files/thamizh_mann
1.6G text-files/

We have 1.6 G of text data to work on LLM or other works.

Go ahead, use it and build more models and tools using this data.

Hope this may not enough to get any good output. But, if we can bring something out of this, even though they are not good, then we can ask people to release their recent contents, blogs, social media posts in creative commons license.

There are few bloggers, magazines are already released their content in CC license. Now, we need your help to scarp them. If you know any programming language and can help for this project, please do webscrapping for the websites mentioned here. share the data and code.

https://github.com/KaniyamFoundation/ProjectIdeas/issues/198

Thanks for all the content providers and the contributors.

At FreeTamilEbooks.com we have published 850 ebooks. All in sharable creative commons license. There are many people asking for the text only content of all these books many times. As it is a big task, took long time for it. Thanks to Lenin, Anwar of Kaniyam Foundation, all the contributors, all the writers and readers for making this project alive and a great success.

We are publishing the books as epub format, along with PDF format. Epub is just a zip file of HTML files. So, we can copy all the content from it as unicode text. Pandoc is a wonderful open source software, which can convert an epub to plaintext file.

There are the list of actions we have to do.

1. Get URLs of all the 850+ epub files
2. Download them all.
3. using pandoc, convert to text file.

So far, we dont have a metadata file for all the books published. Getting the links of all epub files need some programming. As Python is a swiss knife to automate anything, started to explore the wordpress REST api with python to get all the books pages content.

https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/get_Data.py

Wrote the code here to get all the books info.

This gave a JSON file with book name, author, genre, epub, mobi, a4 pdf,6 inch pdf links.

Converted this to a CSV file with the below code. https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/parse.py

I had to fix few things manually on the CSV file.

This is the final CSV file. https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/fte_metadata.csv

The below code is to download all the epub files from their links in the fte_metadata.csv file. Used pandoc to convert to text.

https://github.com/KaniyamFoundation/create_ebooks/blob/master/get_metadata/get_fte_books.py

Got 845 txt files. Total size is 374 MB

Compressed with 7z to get 47MB compressed file.

Published the data here. https://kaniyam.cloudns.nz/tamil_datasets/fte-books/

Download, share the text data for free. Dont sell them as most of the books are released as CC-BY-NC ( No Commercial ) license.

Use these data to build awesome open source applications and researches like Spellchekers, grammar checkers, LLm, RAG, what not?

Data is always the oil. Let us grow the open data oil.

Please share all your text, audio, video content in sharable license like creative commons. They will use to build a better future.

At Kaniyam Foundation, we have a dream of collecting publishing TerraBytes of Tamil text data for Tamil LLM and other research works. We are documenting the websites that provide Open Licensed tamil content, like Public Domain, Creative Commons license here. https://github.com/KaniyamFoundation/ProjectIdeas/issues/198

From here, we can get the websites, scrap them and use and share the data.

Firstly, Today, I started to explore the tamil wikipedia data.

All the wikepedia content are stored as XML and SQL files here.

Download the Wikipedia dump for the all the languages from http://dumps.wikimedia.org/backup-index.html.

For tamil wikipedia content, from here, https://dumps.wikimedia.org/tawiki/ I downloaded this file

tawiki-20240501-pages-articles-multistream.xml.bz2

it is 223.3 MB

That page has multiple files. But look for “pages-articles” to get the main content for wikipedia.

Then, extracted as

bunzip2 tawiki-20240501-pages-articles-multistream.xml.bz2

It gave a file tawiki-20240501-pages-articles-multistream.xml for 1.7 GB

It has a XML file. We have to extract the text content from it.

For it, explored and found a good tool. – https://github.com/apertium/WikiExtractor

Downloaded it and used it.

python3 WikiExtractor.py --infn tawiki-20240501-pages-articles-multistream.xml

It ran for 2 minutes and gave a file wiki.txt for 627 MB. It has all the articles content as a one single big plaintext file.

Compressed it with 7z as it gives better compression.

mv wiki.txt tawiki-20240501-pages-article-wiki.txt
7z a tawiki-20240501-pages-article-text.7z tawiki-20240501-pages-article-wiki.txt

it is 70 MB

Like this, will continue to get plain text tamil data from various sources. We have to find, where we can publish few 100 GBs to TBs of data, for free. Till then, will share these files on my self hosted desktop PC at my home.

Published the file here – https://kaniyam.cloudns.nz/tamil_datasets/

Let me know, if you are interested in joining this project.

Chrome App Link: https://chromewebstore.google.com/detail/huggingbuddy/hhkbebgakgkljpipmdblnabnoagemohb

If anyone would like to contribute more
GitHub Code: https://github.com/angu10/HuggingBuddy

Introducing HuggingBuddy: Your Friendly Companion for Reading Research Papers

Are you tired of feeling overwhelmed by complex research papers? Do you wish you had a friendly companion to help you understand the key ideas and insights? Look no further! Introducing HuggingBuddy, the user-friendly Chrome extension that simplifies the process of reading and understanding research papers from Hugging Face.

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• 🐛 Bug reports and feature requests
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Ready to dive into the world of research papers with a friendly companion by your side? Install HuggingBuddy today and experience the joy of understanding complex ideas with ease. Happy reading! 📖🤗