ares-ai

Name	ares-ai JSON
Version	0.5.7 JSON
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Summary	ARES is an advanced evaluation framework for Retrieval-Augmented Generation (RAG) systems,
upload_time	2024-05-07 04:44:58
maintainer	None
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author	None
requires_python	None
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keywords	rag rag scoring rag systems automated evaluation framework retrieval-augmented generation systems llm judges natural language processing machine learning rag evaluation context relevance answer faithfulness answer relevance synthetic queries synthetic answers in-domain passages key performance metrics human preference validation set
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            <h2 align="center">ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems</h2>

<p align="center">
  <a>Table of Contents:</a>
  <a href="#section1">Installation</a> |
  <a href="#section2">Requirements</a> |
  <a href="#section3">Quick Start</a> |
  <a href="#section4">Citation</a>
</p>


<p align="center">

  <a href="https://pypi.org/project/ares-ai/">
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</p>


ARES is a groundbreaking framework for evaluating Retrieval-Augmented Generation (RAG) models. The automated process combines synthetic data generation with fine-tuned classifiers to efficiently assess context relevance, answer faithfulness, and answer relevance, minimizing the need for extensive human annotations. ARES employs synthetic query generation and Precision-Performance Iteration (PPI), providing accurate evaluations with statistical confidence.


### 💬 Mini Q&A
<hr>

**What does ARES assess in RAG models?**

ARES conducts a comprehensive evaluation of Retrieval-Augmented Generation (RAG) models, assessing the systems for context relevance, answer faithfulness, and answer relevance. This thorough assessment ensures a complete understanding of the performance of the RAG system.

**How does ARES automate the evaluation process?**

ARES minimizes the need for human labeling by leveraging fine-tuned classifiers and synthetic data. Its PPI component, Prediction-Powered inference, refines evaluations considering model response variability and provides statistical confidence in the results. By using fine-tuned classifiers and synthetically generated data, ARES cuts down on human labeling needs while providing accurate assessments. 

**Can ARES handle my custom RAG model?**

Yes, ARES is a model-agnostic tool that enables you to generate synthetic queries and answers from your documents. With ARES, you can evaluate these generated queries and answers from your RAG model.
​
### ⚙️ Installation
<a id="section1"></a>
<hr>
​
To install ARES, run the following commands:
​

```python

pip install ares-ai

```
​
*Optional: Initalize OpenAI or TogetherAI API key with the following command:*


```python

export OPENAI_API_KEY=<your key here>
export TOGETHER_API_KEY=<your key here>

```

### 📝 Requirements
<a id="section2"></a>
<hr>

To implement ARES for scoring your RAG system and comparing to other RAG configurations, you need three components:​

* A human preference validation set of annotated query, document, and answer triples for the evaluation criteria (e.g. context relevance, answer faithfulness, and/or answer relevance). There should be at least 50 examples but several hundred examples is ideal.
* A set of few-shot examples for scoring context relevance, answer faithfulness, and/or answer relevance in your system
* A much larger set of unlabeled query-document-answer triples outputted by your RAG system for scoring

<a id="section3"></a>
<hr>

To get started with ARES, you'll need to set up your configuration. Below is an example of a configuration for ARES!

Copy-paste each step to see ARES in action!

<hr>

### 📥 Download datasets

<hr>

Use the following command to quickly obtain the necessary files for getting started! This includes the 'few_shot_prompt' file for judge scoring and synthetic query generation, as well as both labeled and unlabeled datasets.
```python 
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_judge_scoring.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_synthetic_query_generation.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_labeled_output.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_unlabeled_output.tsv
```

OPTIONAL: You can run the following command to get the full NQ dataset! (347 MB)
```python
from ares import ARES
ares = ARES() 
ares.KILT_dataset("nq")

# Fetches NQ datasets with ratios including 0.5, 0.6, 0.7, etc.
# For purposes of our quick start guide, we rename nq_ratio_0.5 to nq_unlabeled_output and nq_labeled_output.
```
<hr>

### 🚀 Quick Start - #1

<hr>

To get started with ARES's PPI, you'll need to set up your configuration. Below is an example of a configuration for ARES!

Just copy-paste as you go to see ARES in action!

#### Step 1) Run the following to retrive the UES/IDP scores with GPT3.5!

```python
from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unlabeled_output.tsv", 
    "model_choice" : "gpt-3.5-turbo-0125"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)
# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}
```

#### Step 2) Run the following to retrive ARES's PPI scores with GPT3.5!


```python
ppi_config = { 
    "evaluation_datasets": ['nq_unlabeled_output.tsv'], 
    "few_shot_examples_filepath": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "llm_judge": "gpt-3.5-turbo-1106",
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv", 
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)
```

<hr>

### 🚀 Quick Start - #2

<hr>

#### Step 1) Run the following to see GPT 3.5's accuracy on the NQ unlabeled dataset!

```python
from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unlabeled_output.tsv", 
    "model_choice" : "gpt-3.5-turbo-0125"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)
# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}
```

#### Step 2) Run the following to see ARES's synthetic generation in action! 
```python

from ares import ARES

synth_config = { 
    "document_filepaths": ["nq_labeled_output.tsv"] ,
    "few_shot_prompt_filename": "nq_few_shot_prompt_for_synthetic_query_generation.tsv",
    "synthetic_queries_filenames": ["synthetic_queries_1.tsv"], 
    "documents_sampled": 6189
}

ares_module = ARES(synthetic_query_generator=synth_config)
results = ares_module.generate_synthetic_data()
print(results)
```

<hr>

#### Step 3) Run the following to see ARES's training classifier in action!
```python

from ares import ARES

classifier_config = {
    "training_dataset": ["synthetic_queries_1.tsv"], 
    "validation_set": ["nq_labeled_output.tsv"], 
    "label_column": ["Context_Relevance_Label"], 
    "num_epochs": 10, 
    "patience_value": 3, 
    "learning_rate": 5e-6,
    "assigned_batch_size": 1,  
    "gradient_accumulation_multiplier": 32,  
}

ares = ARES(classifier_model=classifier_config)
results = ares.train_classifier()
print(results)
```

Note: This code creates a checkpoint for the trained classifier.
Training may take some time. You can download the checkpoint here:
[Download Checkpoint](https://drive.google.com/file/d/1dsUzL01a53ictjMaUI6RqEvHY5vColcL/view?usp=sharing)

<hr>

#### Step 4) Run the following to see ARES's PPI in action!
```python

from ares import ARES

ppi_config = { 
    "evaluation_datasets": ['nq_unlabeled_output.tsv'], 
    "few_shot_examples_filepath": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "checkpoints": ["Context_Relevance_Label_nq_labeled_output_date_time.pt"], 
    "rag_type": "question_answering", 
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv", 
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)
```

<br>

### 🚀 Local Model Execution with vLLM

ARES supports [vLLM](https://github.com/vllm-project/vllm), allowing for local execution of LLM models, offering enhanced privacy and the ability to operate ARES offline. Below are steps to vLLM for ARES's UES/IDP and PPI!

#### 1) UES/IDP w/ vLLM

```python
from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unalebed_output.tsv", 
    "model_choice": "meta-llama/Llama-2-13b-hf", # Specify vLLM model
    "vllm": True, # Toggle vLLM to True 
    "host_url": "http://0.0.0.0:8000/v1" # Replace with server hosting model followed by "/v1"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)
```

<hr>

#### 2) PPI w/ vLLM

```python
from ares import ARES

ppi_config = { 
    "evaluation_datasets": ['nq_unabeled_output.tsv'], 
    "few_shot_examples_filepath": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "llm_judge": "meta-llama/Llama-2-13b-hf", # Specify vLLM model
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv",
    "vllm": True, # Toggle vLLM to True 
    "host_url": "http://0.0.0.0:8000/v1" # Replace with server hosting model followed by "/v1"
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)
```

For more details, refer to our [documentation](https://ares-ai.vercel.app/).

<br>

## Results Replication

We include synthetic datasets for key experimental results in `synthetic_datasets`. The few-shot prompts used for generation and evaluation are included in `datasets`. We also include instructions for fine-tuning LLM judges in the paper itself. Please reach out to jonsaadfalcon@stanford.edu or manihani@stanford.edu if you have any further questions.

## Citation
<a id="section4"></a>

To cite our work, please use the following Bibtex:

````
@misc{saadfalcon2023ares,
      title={ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems}, 
      author={Jon Saad-Falcon and Omar Khattab and Christopher Potts and Matei Zaharia},
      year={2023},
      eprint={2311.09476},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
````

# Appendix
### Machine requirements and setup when not using OpenAI API
**Machine requirements**

- Over ~100 GB of available disk space
- GPU
    - Should work: A100 (e.g. `Standard_NC24ads_A100_v4` on Azure)
    - Does not work:
        - Tested on 2023-12-17 with both `Standard_NC6s_v3` and `Standard_NC12s_v3`, and ran into this error: `torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 160.00 MiB (GPU 0; 15.77 GiB total capacity; 15.12 GiB already allocated; 95.44 MiB free; 15.12 GiB reserved in total by PyTorch)`


**Machine setup**

For example, on an Azure VM running Linux (ubuntu 20.04), you will need to do the following:
- Install conda
    - First set of commands (can copy-paste multiple lines)
        - `wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh`
        - `chmod +x Miniconda3-latest-Linux-x86_64.sh`
        - `./Miniconda3-latest-Linux-x86_64.sh -b`
    - Second set of commands (can copy-paste multiple lines)
        - `export PATH="~/miniconda3/bin:$PATH"`
        - `conda init`
- Install gcc
    - `sudo apt-get -y update`
    - `sudo apt-get -y upgrade`
    - `sudo apt-get -y install build-essential`
    - `sudo apt-get -y install libpcre3-dev`
- Install NVIDIA drivers
    - `sudo apt install ubuntu-drivers-common -y`
    - `sudo ubuntu-drivers autoinstall`
    - `sudo reboot`
    - SSH in again and confirm the installation was successful by running `nvidia-smi`
- `cd` to ARES folder and follow the rest of the README

            

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    "keywords": "RAG, RAG Scoring, RAG Systems, Automated Evaluation Framework, Retrieval-Augmented Generation Systems, LLM Judges, Natural Language Processing, Machine Learning, RAG Evaluation, Context Relevance, Answer Faithfulness, Answer Relevance, Synthetic Queries, Synthetic Answers, In-Domain Passages, Key Performance Metrics, Human Preference Validation Set",
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    "description": "<h2 align=\"center\">ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems</h2>\n\n<p align=\"center\">\n  <a>Table of Contents:</a>\n  <a href=\"#section1\">Installation</a> |\n  <a href=\"#section2\">Requirements</a> |\n  <a href=\"#section3\">Quick Start</a> |\n  <a href=\"#section4\">Citation</a>\n</p>\n\n\n<p align=\"center\">\n\n  <a href=\"https://pypi.org/project/ares-ai/\">\n  <img alt=\"Static Badge\" src=\"https://img.shields.io/badge/release-v0.5.7-blue?style=flat&link=https%3A%2F%2Fpython.org%2F\">\n  </a>\n\n  <a href=\"https://arxiv.org/abs/2311.09476\">\n  <img alt=\"Static Badge\" src=\"https://img.shields.io/badge/Read-ARES%20Paper-blue?style=flat&link=https%3A%2F%2Farxiv.org%2Fabs%2F2311.09476\">\n  </a>\n\n  <a href=\"https://ares-ai.vercel.app/\">\n    <img alt=\"Static Badge\" src=\"https://img.shields.io/badge/Read-documentation-purple?style=flat\">\n  </a>\n\n  <a href=\"https://colab.research.google.com/drive/1lc8Tkcair7wWZVbsdNKmfSM5rbAqOeeO#scrollTo=03609iqyArxM\" target=\"_blank\">\n    <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n  </a>\n\n  <a>\n  <img alt=\"Static Badge\" src=\"https://img.shields.io/badge/Made%20with-Python-red?style=flat&link=https%3A%2F%2Fpython.org%2F\">\n  </a>\n\n</p>\n\n\nARES is a groundbreaking framework for evaluating Retrieval-Augmented Generation (RAG) models. The automated process combines synthetic data generation with fine-tuned classifiers to efficiently assess context relevance, answer faithfulness, and answer relevance, minimizing the need for extensive human annotations. ARES employs synthetic query generation and Precision-Performance Iteration (PPI), providing accurate evaluations with statistical confidence.\n\n\n### \ud83d\udcac Mini Q&A\n<hr>\n\n**What does ARES assess in RAG models?**\n\nARES conducts a comprehensive evaluation of Retrieval-Augmented Generation (RAG) models, assessing the systems for context relevance, answer faithfulness, and answer relevance. This thorough assessment ensures a complete understanding of the performance of the RAG system.\n\n**How does ARES automate the evaluation process?**\n\nARES minimizes the need for human labeling by leveraging fine-tuned classifiers and synthetic data. Its PPI component, Prediction-Powered inference, refines evaluations considering model response variability and provides statistical confidence in the results. By using fine-tuned classifiers and synthetically generated data, ARES cuts down on human labeling needs while providing accurate assessments. \n\n**Can ARES handle my custom RAG model?**\n\nYes, ARES is a model-agnostic tool that enables you to generate synthetic queries and answers from your documents. With ARES, you can evaluate these generated queries and answers from your RAG model.\n\u200b\n### \u2699\ufe0f Installation\n<a id=\"section1\"></a>\n<hr>\n\u200b\nTo install ARES, run the following commands:\n\u200b\n\n```python\n\npip install ares-ai\n\n```\n\u200b\n*Optional: Initalize OpenAI or TogetherAI API key with the following command:*\n\n\n```python\n\nexport OPENAI_API_KEY=<your key here>\nexport TOGETHER_API_KEY=<your key here>\n\n```\n\n### \ud83d\udcdd Requirements\n<a id=\"section2\"></a>\n<hr>\n\nTo implement ARES for scoring your RAG system and comparing to other RAG configurations, you need three components:\u200b\n\n* A human preference validation set of annotated query, document, and answer triples for the evaluation criteria (e.g. context relevance, answer faithfulness, and/or answer relevance). There should be at least 50 examples but several hundred examples is ideal.\n* A set of few-shot examples for scoring context relevance, answer faithfulness, and/or answer relevance in your system\n* A much larger set of unlabeled query-document-answer triples outputted by your RAG system for scoring\n\n<a id=\"section3\"></a>\n<hr>\n\nTo get started with ARES, you'll need to set up your configuration. Below is an example of a configuration for ARES!\n\nCopy-paste each step to see ARES in action!\n\n<hr>\n\n### \ud83d\udce5 Download datasets\n\n<hr>\n\nUse the following command to quickly obtain the necessary files for getting started! This includes the 'few_shot_prompt' file for judge scoring and synthetic query generation, as well as both labeled and unlabeled datasets.\n```python \nwget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_judge_scoring.tsv\nwget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_synthetic_query_generation.tsv\nwget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_labeled_output.tsv\nwget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_unlabeled_output.tsv\n```\n\nOPTIONAL: You can run the following command to get the full NQ dataset! (347 MB)\n```python\nfrom ares import ARES\nares = ARES() \nares.KILT_dataset(\"nq\")\n\n# Fetches NQ datasets with ratios including 0.5, 0.6, 0.7, etc.\n# For purposes of our quick start guide, we rename nq_ratio_0.5 to nq_unlabeled_output and nq_labeled_output.\n```\n<hr>\n\n### \ud83d\ude80 Quick Start - #1\n\n<hr>\n\nTo get started with ARES's PPI, you'll need to set up your configuration. Below is an example of a configuration for ARES!\n\nJust copy-paste as you go to see ARES in action!\n\n#### Step 1) Run the following to retrive the UES/IDP scores with GPT3.5!\n\n```python\nfrom ares import ARES\n\nues_idp_config = {\n    \"in_domain_prompts_dataset\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"unlabeled_evaluation_set\": \"nq_unlabeled_output.tsv\", \n    \"model_choice\" : \"gpt-3.5-turbo-0125\"\n} \n\nares = ARES(ues_idp=ues_idp_config)\nresults = ares.ues_idp()\nprint(results)\n# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}\n```\n\n#### Step 2) Run the following to retrive ARES's PPI scores with GPT3.5!\n\n\n```python\nppi_config = { \n    \"evaluation_datasets\": ['nq_unlabeled_output.tsv'], \n    \"few_shot_examples_filepath\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"llm_judge\": \"gpt-3.5-turbo-1106\",\n    \"labels\": [\"Context_Relevance_Label\"], \n    \"gold_label_path\": \"nq_labeled_output.tsv\", \n}\n\nares = ARES(ppi=ppi_config)\nresults = ares.evaluate_RAG()\nprint(results)\n```\n\n<hr>\n\n### \ud83d\ude80 Quick Start - #2\n\n<hr>\n\n#### Step 1) Run the following to see GPT 3.5's accuracy on the NQ unlabeled dataset!\n\n```python\nfrom ares import ARES\n\nues_idp_config = {\n    \"in_domain_prompts_dataset\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"unlabeled_evaluation_set\": \"nq_unlabeled_output.tsv\", \n    \"model_choice\" : \"gpt-3.5-turbo-0125\"\n} \n\nares = ARES(ues_idp=ues_idp_config)\nresults = ares.ues_idp()\nprint(results)\n# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}\n```\n\n#### Step 2) Run the following to see ARES's synthetic generation in action! \n```python\n\nfrom ares import ARES\n\nsynth_config = { \n    \"document_filepaths\": [\"nq_labeled_output.tsv\"] ,\n    \"few_shot_prompt_filename\": \"nq_few_shot_prompt_for_synthetic_query_generation.tsv\",\n    \"synthetic_queries_filenames\": [\"synthetic_queries_1.tsv\"], \n    \"documents_sampled\": 6189\n}\n\nares_module = ARES(synthetic_query_generator=synth_config)\nresults = ares_module.generate_synthetic_data()\nprint(results)\n```\n\n<hr>\n\n#### Step 3) Run the following to see ARES's training classifier in action!\n```python\n\nfrom ares import ARES\n\nclassifier_config = {\n    \"training_dataset\": [\"synthetic_queries_1.tsv\"], \n    \"validation_set\": [\"nq_labeled_output.tsv\"], \n    \"label_column\": [\"Context_Relevance_Label\"], \n    \"num_epochs\": 10, \n    \"patience_value\": 3, \n    \"learning_rate\": 5e-6,\n    \"assigned_batch_size\": 1,  \n    \"gradient_accumulation_multiplier\": 32,  \n}\n\nares = ARES(classifier_model=classifier_config)\nresults = ares.train_classifier()\nprint(results)\n```\n\nNote: This code creates a checkpoint for the trained classifier.\nTraining may take some time. You can download the checkpoint here:\n[Download Checkpoint](https://drive.google.com/file/d/1dsUzL01a53ictjMaUI6RqEvHY5vColcL/view?usp=sharing)\n\n<hr>\n\n#### Step 4) Run the following to see ARES's PPI in action!\n```python\n\nfrom ares import ARES\n\nppi_config = { \n    \"evaluation_datasets\": ['nq_unlabeled_output.tsv'], \n    \"few_shot_examples_filepath\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"checkpoints\": [\"Context_Relevance_Label_nq_labeled_output_date_time.pt\"], \n    \"rag_type\": \"question_answering\", \n    \"labels\": [\"Context_Relevance_Label\"], \n    \"gold_label_path\": \"nq_labeled_output.tsv\", \n}\n\nares = ARES(ppi=ppi_config)\nresults = ares.evaluate_RAG()\nprint(results)\n```\n\n<br>\n\n### \ud83d\ude80 Local Model Execution with vLLM\n\nARES supports [vLLM](https://github.com/vllm-project/vllm), allowing for local execution of LLM models, offering enhanced privacy and the ability to operate ARES offline. Below are steps to vLLM for ARES's UES/IDP and PPI!\n\n#### 1) UES/IDP w/ vLLM\n\n```python\nfrom ares import ARES\n\nues_idp_config = {\n    \"in_domain_prompts_dataset\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"unlabeled_evaluation_set\": \"nq_unalebed_output.tsv\", \n    \"model_choice\": \"meta-llama/Llama-2-13b-hf\", # Specify vLLM model\n    \"vllm\": True, # Toggle vLLM to True \n    \"host_url\": \"http://0.0.0.0:8000/v1\" # Replace with server hosting model followed by \"/v1\"\n} \n\nares = ARES(ues_idp=ues_idp_config)\nresults = ares.ues_idp()\nprint(results)\n```\n\n<hr>\n\n#### 2) PPI w/ vLLM\n\n```python\nfrom ares import ARES\n\nppi_config = { \n    \"evaluation_datasets\": ['nq_unabeled_output.tsv'], \n    \"few_shot_examples_filepath\": \"nq_few_shot_prompt_for_judge_scoring.tsv\",\n    \"llm_judge\": \"meta-llama/Llama-2-13b-hf\", # Specify vLLM model\n    \"labels\": [\"Context_Relevance_Label\"], \n    \"gold_label_path\": \"nq_labeled_output.tsv\",\n    \"vllm\": True, # Toggle vLLM to True \n    \"host_url\": \"http://0.0.0.0:8000/v1\" # Replace with server hosting model followed by \"/v1\"\n}\n\nares = ARES(ppi=ppi_config)\nresults = ares.evaluate_RAG()\nprint(results)\n```\n\nFor more details, refer to our [documentation](https://ares-ai.vercel.app/).\n\n<br>\n\n## Results Replication\n\nWe include synthetic datasets for key experimental results in `synthetic_datasets`. The few-shot prompts used for generation and evaluation are included in `datasets`. We also include instructions for fine-tuning LLM judges in the paper itself. Please reach out to jonsaadfalcon@stanford.edu or manihani@stanford.edu if you have any further questions.\n\n## Citation\n<a id=\"section4\"></a>\n\nTo cite our work, please use the following Bibtex:\n\n````\n@misc{saadfalcon2023ares,\n      title={ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems}, \n      author={Jon Saad-Falcon and Omar Khattab and Christopher Potts and Matei Zaharia},\n      year={2023},\n      eprint={2311.09476},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n````\n\n# Appendix\n### Machine requirements and setup when not using OpenAI API\n**Machine requirements**\n\n- Over ~100 GB of available disk space\n- GPU\n    - Should work: A100 (e.g. `Standard_NC24ads_A100_v4` on Azure)\n    - Does not work:\n        - Tested on 2023-12-17 with both `Standard_NC6s_v3` and `Standard_NC12s_v3`, and ran into this error: `torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 160.00 MiB (GPU 0; 15.77 GiB total capacity; 15.12 GiB already allocated; 95.44 MiB free; 15.12 GiB reserved in total by PyTorch)`\n\n\n**Machine setup**\n\nFor example, on an Azure VM running Linux (ubuntu 20.04), you will need to do the following:\n- Install conda\n    - First set of commands (can copy-paste multiple lines)\n        - `wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh`\n        - `chmod +x Miniconda3-latest-Linux-x86_64.sh`\n        - `./Miniconda3-latest-Linux-x86_64.sh -b`\n    - Second set of commands (can copy-paste multiple lines)\n        - `export PATH=\"~/miniconda3/bin:$PATH\"`\n        - `conda init`\n- Install gcc\n    - `sudo apt-get -y update`\n    - `sudo apt-get -y upgrade`\n    - `sudo apt-get -y install build-essential`\n    - `sudo apt-get -y install libpcre3-dev`\n- Install NVIDIA drivers\n    - `sudo apt install ubuntu-drivers-common -y`\n    - `sudo ubuntu-drivers autoinstall`\n    - `sudo reboot`\n    - SSH in again and confirm the installation was successful by running `nvidia-smi`\n- `cd` to ARES folder and follow the rest of the README\n",
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