🤖 Ollama_Agents: Your Advanced AI Assistant Builder with Graph Knowledgebase 🚀

Welcome to Ollama_Agents! This repository allows you to create sophisticated AI agents using Ollama, featuring a unique graph-based knowledgebase. It's like having a high-tech AI laboratory with a built-in brain! 🧠✨

🌟 What's New?

• 🕸️ Graph-based Knowledgebase: A novel approach using JSON for flexible, relational knowledge storage
• 🧠 Enhanced Debug Agent with detailed cognitive processing visualization
• 🌳 Dynamic Knowledge Tree generation and management
• 🔍 Improved memory search and context management
• 🧐 Fact-checking and source credibility assessment
• 🎭 Multi-agent system with easy switching between agents
• 🔀 Interactive follow-up question handling
• 🎨 Rich, colorful command-line interface with progress tracking
• 🛠️ Modular design with improved error handling and logging

🖥️ Development Environment

This project is being developed on an M1 Mac Mini with 16GB of RAM, ensuring optimal performance for AI agent creation and testing.

🚀 Key Features

1. 📊 Graph Knowledgebase: Utilizes a JSON-based graph structure for flexible and relational knowledge representation
2. 📚 Modular Architecture: Each function is in a separate module for easy customization and extension
3. 💬 Interactive CLI: Built with rich for a cinematic experience!
4. 🔐 Secure Configuration: Customize your AI's personality and behavior in config.py
5. 🧪 Comprehensive Testing: Because quality is our superpower!
6. 🌐 Web Search Integration: Your AI can search the web using DuckDuckGo
7. 📜 Advanced Chat History: Never forget a conversation with built-in history management and analysis
8. 🧠 Sophisticated Memory Search: Quickly retrieve and utilize relevant information from past interactions and uploaded documents
9. 🧵 Fabric Integration: Use Fabric patterns for enhanced AI interactions
10. 🎭 Multi-Agent System: Interact with multiple AI personalities in one session
11. 🤖 Debug Mode: Visualize the agent's thought process and decision-making in real-time
12. 🌳 Knowledge Tree: Dynamic generation and visualization of knowledge structures
13. 🧐 Fact-Checking: Verify information and assess source credibility
14. 👤 User Profiling: Adapt responses based on user expertise and interests

💡 Why JSON-based Graph Knowledgebase?

Our unique approach of using a JSON-based graph structure for the knowledgebase offers several advantages:

1. 🔄 Flexibility: Easily adapt and evolve the knowledge structure as your AI learns
2. 🔗 Rich Relationships: Capture complex relationships between concepts more intuitively than in traditional vector databases
3. 🚀 Performance: Efficient querying and updating of interconnected information
4. 🧩 Simplicity: No need for complex vector database setups or maintenance
5. 📦 Portability: JSON format allows for easy data transfer and backup
6. 🔍 Interpretability: Graph structure provides clear visibility into the AI's knowledge connections

This approach allows Ollama_Agents to have a more nuanced and context-aware understanding, leading to more intelligent and adaptive responses.

🛠️ Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Git
• Ollama

Setting Up the Environment

1. Clone this repository:

   git clone https://github.com/yourusername/Ollama_Agents.git
   cd Ollama_Agents

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your PYTHONPATH:

   export PYTHONPATH=/path/to/your/Ollama_Agents:$PYTHONPATH

Installing Ollama

1. Visit the Ollama website and follow the installation instructions for your operating system.

2. Once installed, run Ollama and download a model (e.g., llama2:latest):

   ollama run llama2:latest

Configuration

1. Customize your AI in config.py.

2. Set up your API keys and other configurations in a .env file (use .env.example as a template).

Running the Application

Run the main script:

python -m src.main

📘 Module Documentation

Detailed documentation for each module can be found in the docs/ directory. This includes information on both standard and advanced (adv_) modules:

Core Modules

• Input Module
• Ollama Client
• Assemble Module
• Banner Module
• DuckDuckGo Search

Advanced (adv_) Modules

• Advanced Input Processor
• Advanced Context Manager
• Advanced Reasoning Engine
• Advanced Planning Engine
• Advanced Knowledge Manager
• Advanced Output Manager

Agent and Knowledge Management

• Agent Tools
• Knowledge Management
• Context Management
• KB Graph

System Architecture and Configuration

• Architecture Guide
• Command Modules
• Config File

Memory and History

• JSON Memory Guide
• Save History

Utility and Logging

• Logging Guide
• Error Handling

User Guides

• Building Agents
• Assistant User Guide

🧠 Debug Agent Commands

• /help: Show available commands
• /search <query>: Perform an interactive web search
• /context: Show current context
• /clear_context: Clear the current context and bullet points
• /bullets: Display current bullet points
• /knowledge_tree: Display the knowledge tree
• /explain <concept>: Get an explanation of a concept
• /fact_check <statement>: Perform a fact check on a statement
• /profile: Display your user profile

🤝 Contributing

Got ideas? We love them! 💡 Submit a pull request or open an issue. Let's build the future of AI together!

📜 License

This project is licensed under the MIT License. See LICENSE for details.

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Built with ❤️ and 🧠 by the Ollama_Agents team. Let's revolutionize AI knowledge representation! 🚀