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prompt-eng-master
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prompt-eng-master
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# Prompt-Eng-Master Repository for Prompt Engineering # Setup 1. Install the required dependencies ```shell conda create -n decomp python=3.8 pip install -r requirements.txt ``` 2. Set the necessary env variables ```shell export PYTHONPATH=src/ export OPENAI_API_KEY=<YOUR_API_KEY> export OPENAI_API_BASE=<YOUR_API_BASE> ``` Or you can set them in config/api.json file: ```python { "openai_api": { "api_key": <YOUR_API_KEY>, "api_base": <YOUR_API_BASE> } } ``` # Running Inference Run the prediction experiments with one of the prompts ```shell python -m run ``` Change the parameters below to try different datasets, models, prompts and other settings: ```python setting_parameters = { # Input evaluation sets. Options: "enzyme", "small_molecule", "crystal_material" and "All". "input": "small_molecule", # Output direction. Append answer json file to results folder named after testing date. "output": "results/1014/", # LLM models. Options: "GPT-3.5", "GPT-4", "LLaMA2-7B", "LLaMA2-13B", "LLaMA2-70B". "model_config": "GPT-3.5", # Prompt configurations. Options: "zero-shot", "expert", "few-shot", "zero-shot-CoT", "few-shot-CoT", "few-shot-CoT-critique", "decomposed". "agent_config": "few-shot", # Number of few-shot examples (use multi-shot if set to >1). Default: 2. "n_examples": 2, # Number of llm-generated answers. Default: 2. "n_answers": 2 } ```
