core4ai 1.1.3

Contextual Optimization and Refinement Engine for AI

Core4AI: Contextual Optimization and Refinement Engine for AI

Core4AI is an intelligent system that transforms basic user queries into optimized prompts for AI systems using MLflow Prompt Registry. It dynamically matches user requests to the most appropriate prompt template and applies it with extracted parameters.

Architecture

Core4AI's architecture is designed for seamless integration with MLflow while providing flexibility in AI provider selection:

This integration allows Core4AI to leverage MLflow's tracking capabilities for prompt versioning while providing a unified interface to multiple AI providers.

✨ Key Features

• 📚 Centralized Prompt Management: Store, version, and track prompts in MLflow
• 🧠 Intelligent Prompt Matching: Automatically match user queries to optimal templates
• 🔄 Dynamic Parameter Extraction: Identify and extract parameters from natural language
• 🔍 Content Type Detection: Recognize the type of content being requested
• 🛠️ Multiple AI Providers: Seamless integration with OpenAI and Ollama
• 📊 Detailed Response Tracing: Track prompt optimization and transformation stages
• 📝 Version Control: Track prompt history with production and archive aliases
• 🧩 Extensible Framework: Add new prompt types without code changes

🚀 Installation

Basic Installation

# Install from PyPI
pip install core4ai

Development Installation

# Clone the repository
git clone https://github.com/iRahulPandey/core4ai.git
cd core4ai

# Install in development mode
pip install -e ".[dev]"

⚙️ Initial Configuration

After installation, run the interactive setup wizard:

# Run the setup wizard
core4ai setup

The wizard will guide you through:

1. MLflow Configuration:

   • Enter the URI of your MLflow server (default: http://localhost:8080)
   • Core4AI will use MLflow to store and version your prompts

2. Existing Prompts Import:

   • If you already have prompts in MLflow, you can import them into Core4AI
   • You can provide prompt names directly or via a file

3. AI Provider Selection:

   • Choose between OpenAI or Ollama
   • For OpenAI: Set your API key as an environment variable:

     export OPENAI_API_KEY="your-api-key-here"
     

   • For Ollama: Specify the server URI and model to use

📝 Prompt Management

Core4AI uses a powerful prompt management system that allows you to create, register, and use prompt templates in various formats.

Prompt Template Format

Core4AI uses markdown files to define prompt templates. Each template should follow this structure:

# Prompt Name: example_prompt

## Description
A brief description of what this prompt does.

## Tags
type: example
task: writing
purpose: demonstration

## Template
Write a {{ style }} response about {{ topic }} that includes:
- Important point 1
- Important point 2
- Important point 3

Please ensure the tone is {{ tone }} and suitable for {{ audience }}.

Key Guidelines

1. Prompt Name is required and must:

   • Be the first line of the file
   • Use the format # Prompt Name: name_prompt
   • End with _prompt suffix
   • Use underscores instead of spaces (e.g., cover_letter_prompt)

2. Template Section must:

   • Use double braces for variables: {{ variable_name }}
   • Have at least one variable
   • Provide clear instructions

3. Tags Section is recommended and should include:

   • type: The prompt category (e.g., essay, email, code)
   • task: The purpose (e.g., writing, analysis, instruction)
   • Additional metadata as needed

Creating Prompt Templates

You can create a new prompt template using the CLI:

# Create a new prompt template in the current directory
core4ai register --create email

# Create a prompt template in a specific directory
core4ai register --create blog --dir ./my_prompts

This will:

1. Create a template file with the proper structure
2. Open it in your default editor for customization
3. Offer to register it immediately after editing

Registering Prompts

Core4AI supports multiple ways to register prompts:

# Register a single prompt directly
core4ai register --name "email_prompt" "Write a {{ formality }} email..."

# Register from a markdown file
core4ai register --markdown ./my_prompts/email_prompt.md

# Register all prompts from a directory
core4ai register --dir ./my_prompts

# Register built-in sample prompts
core4ai register --samples

# Register only prompts that don't exist yet
core4ai register --dir ./my_prompts --only-new

Managing Prompt Types

Core4AI automatically tracks prompt types based on the prompt names:

# List all registered prompt types
core4ai list-types

The type is extracted from the prompt name:

• For email_prompt → type is email
• For cover_letter_prompt → type is cover_letter

Listing Available Prompts

# List all prompts
core4ai list

# Show detailed information
core4ai list --details

# Get details for a specific prompt
core4ai list --name email_prompt

🛠️ Using Core4AI

Basic Chat Interactions

# Simple query - Core4AI will match to the best prompt template
core4ai chat "Write about the future of AI"

# Get a simple response without enhancement details
core4ai chat --simple "Write an essay about climate change"

# See verbose output with prompt enhancement details
core4ai chat --verbose "Write an email to my boss about a vacation request"

Sample Prompts

Core4AI comes with several pre-registered prompt templates:

# Register sample prompts
core4ai register --samples

This will register the following prompt types:

Prompt Type	Description	Sample Variables
essay_prompt	Academic writing	topic
email_prompt	Email composition	formality, recipient_type, topic, tone
technical_prompt	Technical explanations	topic, audience
creative_prompt	Creative writing	genre, topic
code_prompt	Code generation	language, task, requirements
cover_letter_prompt	Cover letter writing	position, company, experience_years
qa_prompt	Question answering	topic, tone, formality
tutorial_prompt	Step-by-step guides	level, task, tool_or_method
marketing_prompt	Marketing content	content_format, product_or_service, target_audience
report_prompt	Report generation	length, report_type, topic
social_media_prompt	Social media posts	number, platform, topic
data_analysis_prompt	Data analysis reports	data_type, subject, data
comparison_prompt	Compare items or concepts	item1, item2
product_description_prompt	Product descriptions	length, product_name, product_category
summary_prompt	Content summarization	length, content_type, content
test_prompt	Test examples	formality

Each prompt is designed for specific use cases and includes variables that can be automatically extracted from user queries. You can view the details of any prompt with:

# View details of a specific prompt
core4ai list --name email_prompt@production --details

🔄 Provider Configuration

OpenAI

To use OpenAI, set your API key:

# Set environment variable (recommended)
export OPENAI_API_KEY="your-api-key-here"

# Or configure during setup
core4ai setup

Available models include:

• gpt-3.5-turbo (default)
• gpt-4
• gpt-4-turbo

Ollama

To use Ollama:

1. Install Ollama on your system
2. Start the Ollama server:

   ollama serve
   

3. Configure Core4AI:

   core4ai setup
   

📋 Command Reference

Command	Description	Examples
core4ai setup	Run the setup wizard	core4ai setup
core4ai register	Register prompts	core4ai register --samples, core4ai register --create email
core4ai list	List available prompts	core4ai list --details
core4ai list-types	List prompt types	core4ai list-types
core4ai chat	Chat with enhanced prompts	core4ai chat "Write about AI"
core4ai version	Show version info	core4ai version

📊 How Core4AI Works

Core4AI follows this workflow to process queries:

1. Query Analysis: Analyze the user's query to determine intent
2. Prompt Matching: Match the query to the most appropriate prompt template
3. Parameter Extraction: Extract relevant parameters from the query
4. Template Application: Apply the template with extracted parameters
5. Validation: Validate the enhanced prompt for completeness and accuracy
6. Adjustment: Adjust the prompt if validation issues are found
7. AI Response: Send the optimized prompt to the AI provider

From Query to Enhanced Response

The user experience with Core4AI is straightforward yet powerful:

This workflow ensures that every user query is intelligently matched to the optimal prompt template stored in MLflow, parameters are properly extracted and applied, and the result is validated before being sent to the AI provider.

Troubleshooting Installation

NumPy Binary Incompatibility

If you encounter an error like this during installation or when running Core4AI:

ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

Try reinstalling in the following order:

# Remove the problematic packages
pip uninstall -y numpy pandas mlflow core4ai

# Reinstall in the correct order with specific versions
pip install numpy==1.26.0
pip install pandas
pip install mlflow>=2.21.0
pip install core4ai

MLflow Server Connection Issues

If you encounter problems connecting to MLflow:

1. Make sure your MLflow server is running:

   mlflow server --host 0.0.0.0 --port 8080
   

2. Verify connection:

   curl http://localhost:8080
   

3. Configure Core4AI to use your MLflow server:

   core4ai setup
   

📜 License

This project is licensed under the Apache License 2.0