Skip to content

Latest commit

 

History

History
125 lines (77 loc) · 3.23 KB

README.md

File metadata and controls

125 lines (77 loc) · 3.23 KB
Raw

DynPrompt-1a

Dynamic Prompt Assistant - Single Agent

Key Features

Automate repetitive tasks using gen AI.

$0 subscription fee. 100% data privacy.

About the Project

This project demonstrates my understanding of prompt engineering and coding in Python using gen AI.

It is a tool for automating repetitive tasks -- minimising time spend on non-value adding processes, increasing work efficiency.

This is a project after my completion of the AI Python course and the Prompt Engineering with Llama course from deeplearning.ai.

Technical Details

  • Python version: 3.11
  • Libraries used: ollama-python
  • Dependencies required: pandas

Installation and Usage

Download and install Ollama from https://ollama.com/download

In Terminal:

pip install ollama 
ollama pull llama3.1:latest 

pip install pandas

Prep data.csv

prompt_var is the content of your task.

Paste each of them in the prompt_var column in separate rows.

Set your prompt

Edit your prompt under Base prompt section in app.py. Use prompt engineering techniques for desired results.

Example use cases:

  • Text extractor for email

    prompt = f"""
    Given is an email from contains product code and corresponding remarks. Identify the product code and remarks within the original text. 
    
    Provide the product code and remark only in csv format, ready to save. Exclude the "```csv" declaration, don't add spaces after the comma, include column headers.

    Format:
    product_code, remark
    product_code_1, remark_1
    ...
    
    Email:
    """

  • Customer review text classifier

    prompt = f"""
    Respond with "Positve" or "Negative": 
    the comment is a product reivew, describing an user experience. 

    Comment:
    """

Resources of prompt engineering:

  • deeplearning.ai Prompt Engineering for Llama course
  • Microsoft / Meta prompt engineering docs
  • SCRIBE method

Run app.py

cd DynPrompt-1a
python app.py

Results in responses.csv

responses.csv is saved in the same folder. Open in Excel.

Challenges and Limitations

The responses are largely depend on the LLM in use, and prompt engineering techniques.

Prompt engineering techniques can be refined with resources mentioned above.

What LLM model could be used depends on your hardware. The better the LLM model, the higher RAM and VRAM requirements.

In this script in particular, I am using Llama 3.1 8B on MacBook M3 Pro 18GB RAM. You may run into errors if your VRAM is insufficient.

Check in terminal to see if Ollama is running correctly. Or edit the script for a smaller LLM accordingly.

ollama run llama3.1:latest

Future Plans and Ideas

I am interested in:

  • Langflow -- a low-code tool for multi-agent workflow allows more complex tasks

  • n8n -- an automation workflow tool which works across different apps

Feedback and Contributions

If you have any feedback or suggestions for improvement, or if you know any good tutorials on Langflow and n8n, please let me know! Let's connect.

Attribution

I would like to thank the open source community and the instructors from deeplearning.ai.