Ollama Integration Update

edit this readme to be for ollama instead of openai : # Tech Company Orchestrator - User Guide Welcome to the **Tech Company Orchestrator**! This project is designed to simulate the workflow of a tech company by orchestrating various agents to collaboratively process prompts and generate comprehensive outputs such as code, design specifications, deployment scripts, and more. The program utilizes OpenAI models and a directed graph (via NetworkX) to model the interactions between different departments (agents). --- ## Table of Contents 1. [Features](#features) 2. [Requirements](#requirements) 3. [Installation](#installation) 4. [Usage](#usage) 5. [Workflow](#workflow) 6. [Customizing Agents](#customizing-agents) 7. [Troubleshooting](#troubleshooting) 8. [Future Improvements](#future-improvements) --- ## Features - **Agent-based Workflow**: Simulates different tech company departments (e.g., Product Management, Design, Engineering). - **Directed Graph Processing**: Uses NetworkX to define the flow of data between agents. - **OpenAI API Integration**: Employs GPT models for generating agent-specific outputs. - **Iterative Processing**: Refines outputs across iterations until the workflow is complete. - **Progress Persistence**: Logs intermediate and final outputs to files. - **Custom Prompt Support**: Accepts a structured prompt from an external file (`initial_prompt.txt`). --- ## Requirements - **Python**: 3.8 or higher - **Dependencies**: - `openai` - `networkx` - `python-dotenv` - `json` - **OpenAI API Key**: You need an active OpenAI API key to use this program. --- ## Installation 1. **Clone the Repository**: ```bash git clone https://github.com/kliewerdaniel/tech-company-orchestrator.git cd tech-company-orchestrator ``` 2. **Install Dependencies**: Use `pip` to install the required libraries: ```bash pip install -r requirements.txt ``` 3. **Set Up `.env` File**: Create a `.env` file in the root directory and add your OpenAI API key: ```bash OPENAI_API_KEY=your-openai-api-key ``` --- ## Usage ### Step 1: Prepare Your Initial Prompt Create an `initial_prompt.txt` file in the root directory. The prompt should be a JSON-formatted dictionary containing: - `message`: The initial idea or requirements. - `code`: Leave this as an empty string (`""`) initially. - `readme`: Leave this as an empty string (`""`) initially. **Example `initial_prompt.txt`:** ```json { "message": "Develop a platform that connects freelancers with clients using AI for project matching.", "code": "", "readme": "" } ``` ### Step 2: Run the Program Execute the `main.py` file: ```bash python main.py ``` ### Step 3: Review the Outputs The program generates the following files: - **`output.txt`**: Contains the intermediate outputs after each iteration. - **`final_output.txt`**: Contains the final output, including the `message`, `code`, and `readme`. --- ## Workflow The program simulates the workflow of a tech company by processing the prompt through the following agents: 1. **Product Management**: Expands the initial idea into detailed product requirements. 2. **Design**: Creates UI/UX specifications, including wireframes and style guides. 3. **Engineering**: Develops the software application based on the specifications. 4. **Testing**: Generates comprehensive test cases for quality assurance. 5. **Security**: Analyzes and enhances the security of the application. 6. **DevOps**: Creates deployment scripts and CI/CD pipelines. 7. **Final Agent**: Verifies if the project is complete or requires further refinement. The agents are connected in a directed graph, ensuring an organized flow of information between departments. --- ## Customizing Agents ### Modify Agent Behavior Each agent has its own Python file (e.g., `engineering.py`, `design.py`) where you can adjust: - The prompts sent to the OpenAI API. - How the agent processes the data (e.g., appending to `code` or `readme`). ### Add a New Agent 1. Create a new Python file for the agent. 2. Define the agent's logic (similar to existing agents). 3. Add the new agent to the workflow graph in `main.py`: ```python G.add_edges_from([ ('PreviousAgent', 'NewAgent'), ('NewAgent', 'NextAgent') ]) ``` --- ## Troubleshooting ### OpenAI API Key Not Found Ensure the `.env` file is correctly configured with your API key: ```bash OPENAI_API_KEY=your-openai-api-key ``` ### Invalid `initial_prompt.txt` Format Validate the JSON structure using an online tool like [jsonlint.com](https://jsonlint.com). ### Empty or Incorrect Outputs - Check the logs in `output.txt` for intermediate results. - Ensure the OpenAI API is accessible and the specified model is available. --- ## Future Improvements - **Parallel Processing**: Optimize the workflow to allow parallel execution of agents where applicable. - **Enhanced Error Handling**: Improve robustness by adding retries and better error reporting. - **Interactive CLI**: Provide a command-line interface for easier customization of inputs and parameters. - **Integration Testing**: Add tests to validate the functionality of each agent and the overall workflow. --- ## Contributions Feel free to fork the repository and submit pull requests for improvements. Feedback and suggestions are always welcome! --- With this guide, you should be able to set up, run, and customize the **Tech Company Orchestrator** to suit your needs. Happy orchestrating! 🎉

# Tech Company Orchestrator - User Guide Welcome to the **Tech Company Orchestrator**! This project is designed to simulate the workflow of a tech company by orchestrating various agents to collaboratively process prompts and generate comprehensive outputs such as code, design specifications, deployment scripts, and more. The program utilizes **Ollama** models and a directed graph (via NetworkX) to model the interactions between different departments (agents). --- ## Table of Contents 1. [Features](#features) 2. [Requirements](#requirements) 3. [Installation](#installation) 4. [Usage](#usage) 5. [Workflow](#workflow) 6. [Customizing Agents](#customizing-agents) 7. [Troubleshooting](#troubleshooting) 8. [Future Improvements](#future-improvements) --- ## Features - **Agent-based Workflow**: Simulates different tech company departments (e.g., Product Management, Design, Engineering). - **Directed Graph Processing**: Uses NetworkX to define the flow of data between agents. - **Ollama Integration**: Employs locally hosted models for generating agent-specific outputs. - **Iterative Processing**: Refines outputs across iterations until the workflow is complete. - **Progress Persistence**: Logs intermediate and final outputs to files. - **Custom Prompt Support**: Accepts a structured prompt from an external file (`initial_prompt.txt`). --- ## Requirements - **Python**: 3.8 or higher - **Dependencies**: - `ollama` - `networkx` - `python-dotenv` - `json` - **Ollama Installed**: Ensure that Ollama is installed and the required models are downloaded locally. --- ## Installation 1. **Clone the Repository**: ```bash git clone https://github.com/kliewerdaniel/tech-company-orchestrator.git cd tech-company-orchestrator ``` 2. **Install Dependencies**: Use `pip` to install the required libraries: ```bash pip install -r requirements.txt ``` 3. **Set Up `.env` File**: Create a `.env` file in the root directory and specify the model name you will use with Ollama: ```bash OLLAMA_MODEL=your-model-name ``` Replace `your-model-name` with the name of the model installed in Ollama (e.g., `llama-2`, `qwen`). --- ## Usage ### Step 1: Prepare Your Initial Prompt Create an `initial_prompt.txt` file in the root directory. The prompt should be a JSON-formatted dictionary containing: - `message`: The initial idea or requirements. - `code`: Leave this as an empty string (`""`) initially. - `readme`: Leave this as an empty string (`""`) initially. **Example `initial_prompt.txt`:** ```json { "message": "Develop a platform that connects freelancers with clients using AI for project matching.", "code": "", "readme": "" } ``` ### Step 2: Run the Program Execute the `main.py` file: ```bash python main.py ``` ### Step 3: Review the Outputs The program generates the following files: - **`output.txt`**: Contains the intermediate outputs after each iteration. - **`final_output.txt`**: Contains the final output, including the `message`, `code`, and `readme`. --- ## Workflow The program simulates the workflow of a tech company by processing the prompt through the following agents: 1. **Product Management**: Expands the initial idea into detailed product requirements. 2. **Design**: Creates UI/UX specifications, including wireframes and style guides. 3. **Engineering**: Develops the software application based on the specifications. 4. **Testing**: Generates comprehensive test cases for quality assurance. 5. **Security**: Analyzes and enhances the security of the application. 6. **DevOps**: Creates deployment scripts and CI/CD pipelines. 7. **Final Agent**: Verifies if the project is complete or requires further refinement. The agents are connected in a directed graph, ensuring an organized flow of information between departments. --- ## Customizing Agents ### Modify Agent Behavior Each agent has its own Python file (e.g., `engineering.py`, `design.py`) where you can adjust: - The prompts sent to the Ollama model. - How the agent processes the data (e.g., appending to `code` or `readme`). ### Add a New Agent 1. Create a new Python file for the agent. 2. Define the agent's logic (similar to existing agents). 3. Add the new agent to the workflow graph in `main.py`: ```python G.add_edges_from([ ('PreviousAgent', 'NewAgent'), ('NewAgent', 'NextAgent') ]) ``` --- ## Troubleshooting ### Ollama Not Installed or Configured Ensure Ollama is installed and running on your system. Verify the model name in the `.env` file matches the locally available model. ### Invalid `initial_prompt.txt` Format Validate the JSON structure using an online tool like [jsonlint.com](https://jsonlint.com). ### Empty or Incorrect Outputs - Check the logs in `output.txt` for intermediate results. - Ensure the Ollama model is accessible and configured correctly. --- ## Future Improvements - **Parallel Processing**: Optimize the workflow to allow parallel execution of agents where applicable. - **Enhanced Error Handling**: Improve robustness by adding retries and better error reporting. - **Interactive CLI**: Provide a command-line interface for easier customization of inputs and parameters. - **Integration Testing**: Add tests to validate the functionality of each agent and the overall workflow. --- ## Contributions Feel free to fork the repository and submit pull requests for improvements. Feedback and suggestions are always welcome! --- With this guide, you should be able to set up, run, and customize the **Tech Company Orchestrator** with Ollama. Happy orchestrating! 🎉

rewrite that as markdown

```markdown # Tech Company Orchestrator - User Guide Welcome to the **Tech Company Orchestrator**! This project is designed to simulate the workflow of a tech company by orchestrating various agents to collaboratively process prompts and generate comprehensive outputs such as code, design specifications, deployment scripts, and more. The program utilizes **Ollama** models and a directed graph (via NetworkX) to model the interactions between different departments (agents). --- ## Table of Contents 1. [Features](#features) 2. [Requirements](#requirements) 3. [Installation](#installation) 4. [Usage](#usage) 5. [Workflow](#workflow) 6. [Customizing Agents](#customizing-agents) 7. [Troubleshooting](#troubleshooting) 8. [Future Improvements](#future-improvements) --- ## Features - **Agent-based Workflow**: Simulates different tech company departments (e.g., Product Management, Design, Engineering). - **Directed Graph Processing**: Uses NetworkX to define the flow of data between agents. - **Ollama Integration**: Employs locally hosted models for generating agent-specific outputs. - **Iterative Processing**: Refines outputs across iterations until the workflow is complete. - **Progress Persistence**: Logs intermediate and final outputs to files. - **Custom Prompt Support**: Accepts a structured prompt from an external file (`initial_prompt.txt`). --- ## Requirements - **Python**: 3.8 or higher - **Dependencies**: - `ollama` - `networkx` - `python-dotenv` - `json` - **Ollama Installed**: Ensure that Ollama is installed and the required models are downloaded locally. --- ## Installation 1. **Clone the Repository**: ```bash git clone https://github.com/kliewerdaniel/tech-company-orchestrator.git cd tech-company-orchestrator ``` 2. **Install Dependencies**: Use `pip` to install the required libraries: ```bash pip install -r requirements.txt ``` 3. **Set Up `.env` File**: Create a `.env` file in the root directory and specify the model name you will use with Ollama: ```env OLLAMA_MODEL=your-model-name ``` Replace `your-model-name` with the name of the model installed in Ollama (e.g., `llama-2`, `qwen`). --- ## Usage ### Step 1: Prepare Your Initial Prompt Create an `initial_prompt.txt` file in the root directory. The prompt should be a JSON-formatted dictionary containing: - `message`: The initial idea or requirements. - `code`: Leave this as an empty string (`""`) initially. - `readme`: Leave this as an empty string (`""`) initially. **Example `initial_prompt.txt`:** ```json { "message": "Develop a platform that connects freelancers with clients using AI for project matching.", "code": "", "readme": "" } ``` ### Step 2: Run the Program Execute the `main.py` file: ```bash python main.py ``` ### Step 3: Review the Outputs The program generates the following files: - **`output.txt`**: Contains the intermediate outputs after each iteration. - **`final_output.txt`**: Contains the final output, including the `message`, `code`, and `readme`. --- ## Workflow The program simulates the workflow of a tech company by processing the prompt through the following agents: 1. **Product Management**: Expands the initial idea into detailed product requirements. 2. **Design**: Creates UI/UX specifications, including wireframes and style guides. 3. **Engineering**: Develops the software application based on the specifications. 4. **Testing**: Generates comprehensive test cases for quality assurance. 5. **Security**: Analyzes and enhances the security of the application. 6. **DevOps**: Creates deployment scripts and CI/CD pipelines. 7. **Final Agent**: Verifies if the project is complete or requires further refinement. The agents are connected in a directed graph, ensuring an organized flow of information between departments. --- ## Customizing Agents ### Modify Agent Behavior Each agent has its own Python file (e.g., `engineering.py`, `design.py`) where you can adjust: - The prompts sent to the Ollama model. - How the agent processes the data (e.g., appending to `code` or `readme`). ### Add a New Agent 1. Create a new Python file for the agent. 2. Define the agent's logic (similar to existing agents). 3. Add the new agent to the workflow graph in `main.py`: ```python G.add_edges_from([ ('PreviousAgent', 'NewAgent'), ('NewAgent', 'NextAgent') ]) ``` --- ## Troubleshooting ### Ollama Not Installed or Configured Ensure Ollama is installed and running on your system. Verify the model name in the `.env` file matches the locally available model. ### Invalid `initial_prompt.txt` Format Validate the JSON structure using an online tool like [jsonlint.com](https://jsonlint.com). ### Empty or Incorrect Outputs - Check the logs in `output.txt` for intermediate results. - Ensure the Ollama model is accessible and configured correctly. --- ## Future Improvements - **Parallel Processing**: Optimize the workflow to allow parallel execution of agents where applicable. - **Enhanced Error Handling**: Improve robustness by adding retries and better error reporting. - **Interactive CLI**: Provide a command-line interface for easier customization of inputs and parameters. - **Integration Testing**: Add tests to validate the functionality of each agent and the overall workflow. --- ## Contributions Feel free to fork the repository and submit pull requests for improvements. Feedback and suggestions are always welcome! --- With this guide, you should be able to set up, run, and customize the **Tech Company Orchestrator** with Ollama. Happy orchestrating! 🎉 ```