ResearchAI

Here’s a simple and clear README specifically for managing and using the virtual environment in your project. This will help you remember why and how you set up the virtual environment.

README - Virtual Environment for Research AI Project

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Project: Research AI Project

Purpose:

The virtual environment is used to isolate the project's dependencies (libraries, packages, etc.) from other projects and the system-wide Python installation. By doing this, we ensure that the specific versions of the tools we need are available only for this project and don’t interfere with other Python environments.

We named the virtual environment research_ai_env to match the project name, making it easy to identify and manage in the future.

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Why Use a Virtual Environment?

• Dependency Isolation: Prevents conflicts between different projects by keeping project-specific libraries and versions in their own isolated environment.
• Easy Dependency Management: Allows you to install the exact libraries required for this project without affecting other Python projects on your system.
• Portability: Makes it easier to recreate the same environment on other systems by exporting the list of dependencies (requirements.txt).
• Organized Project Structure: Naming the virtual environment after the project helps you keep things organized, especially when managing multiple projects.

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How to Set Up and Use the Virtual Environment

1. Create the Virtual Environment (Only do this once):

   • Run the following command in the terminal within your project directory to create a virtual environment named research_ai_env:

     rm -rf research_ai_env
     python3 -m venv research_ai_env

2. Activate the Virtual Environment:

   • You need to activate the virtual environment whenever you work on the project.

     source research_ai_env/bin/activate

   • After activating, your terminal prompt will change to show (research_ai_env), indicating that the virtual environment is active.

3. Install Project Dependencies:

   • Once the virtual environment is activated, install the required dependencies by running:

     pip install -r requirements.txt

4. Deactivate the Virtual Environment:

   • When you’re done working, deactivate the virtual environment by simply running:

     deactivate

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How to Reuse the Virtual Environment

Whenever you want to start working on this project in the future:

1. Navigate to the Project Directory:

   cd path/to/your/project

2. Activate the Virtual Environment:

   • On Windows:

     research_ai_env\Scripts\activate

   • On Linux/macOS:

     source research_ai_env/bin/activate

3. Start Working on the Project: You are now inside the project-specific environment where all dependencies are isolated for this project.

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Notes:

• Always make sure to activate the virtual environment before running your project’s scripts or installing new packages.
• The research_ai_env directory contains all the files related to the virtual environment. This folder should not be shared when you distribute the project. Only the requirements.txt file, which lists your dependencies, needs to be shared.
• To recreate the environment on another machine, run:

  pip install -r requirements.txt

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Ollama Service Setup and Troubleshooting Guide

1. Checking Ollama Service Status

To check if the Ollama service is running, use the following command:

sudo systemctl status ollama.service

If the service is not running, follow the steps below to set it up.

2. Creating a Systemd Service for Ollama

1. Create a systemd service file for Ollama:

   sudo nano /etc/systemd/system/ollama.service

2. Add the following content (update the paths as needed):

   [Unit]
   Description=Ollama Server
   After=network.target
   
   [Service]
   ExecStart=/path/to/ollama/binary --port 11434
   WorkingDirectory=/path/to/ollama/
   Restart=always
   User=your-username
   Group=your-group
   Environment=DISPLAY=:0
   StandardOutput=journal
   StandardError=journal
   
   [Install]
   WantedBy=multi-user.target

3. Reload systemd to recognize the new service:

   sudo systemctl daemon-reload

4. Enable the Ollama service to start on boot:

   sudo systemctl enable ollama.service

5. Start the service:

   sudo systemctl start ollama.service

6. Check the status:

   sudo systemctl status ollama.service

3. Testing the Ollama Model (Llama2-uncensored)

To test if the API and the llama2-uncensored model are working correctly, use the following curl command:

curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d '{"model": "llama2-uncensored", "prompt": "What is the capital of France?", "max_tokens": 100}'

You should receive a JSON response if everything is working.

4. Checking Logs for Errors

If the model doesn't work as expected, check the logs:

sudo journalctl -u ollama.service

5. Updating the Systemd Service File

If you see warnings about syslog being obsolete, update the service file:

1. Open the file for editing:

   sudo nano /etc/systemd/system/ollama.service

2. Replace:

   StandardOutput=syslog
   StandardError=syslog

   with:

   StandardOutput=journal
   StandardError=journal

3. Save, then reload and restart the service:

   sudo systemctl daemon-reload
   sudo systemctl restart ollama.service

Additional Notes

• Make sure your Ollama server is listening on the correct port (default: 11434).
• Check GPU compatibility if you are using GPU inference.

Date of Last Update: 2024-09-23