| Name | ares-ai JSON |
| Version |
0.6.6
JSON |
| download |
| home_page | None |
| Summary | ARES is an advanced evaluation framework for Retrieval-Augmented Generation (RAG) systems, |
| upload_time | 2024-07-11 12:46:04 |
| maintainer | None |
| docs_url | None |
| 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/">
<img alt="Static Badge" src="https://img.shields.io/badge/release-v0.5.7-blue?style=flat&link=https%3A%2F%2Fpython.org%2F">
</a>
<a href="https://arxiv.org/abs/2311.09476">
<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">
</a>
<a href="https://ares-ai.vercel.app/">
<img alt="Static Badge" src="https://img.shields.io/badge/Read-documentation-purple?style=flat">
</a>
<a href="https://colab.research.google.com/drive/1DvXr9SvWOw6xaNW8LHcy9C06LKevDPxe#scrollTo=wBDuO0n5c1mz" target="_blank">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<a>
<img alt="Static Badge" src="https://img.shields.io/badge/Made%20with-Python-red?style=flat&link=https%3A%2F%2Fpython.org%2F">
</a>
</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 Prediction-Powered Inference (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 retrieve 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 our jointly trained checkpoint on context relevance here!:
[Download Checkpoint](https://drive.google.com/file/d/15poFyeoqdnaNZVjl41HllL2213DKyZjH/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'],
"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)
# Output Should be:
"""
Context_Relevance_Label Scoring
ARES Ranking
ARES Prediction: [0.6056978059262574]
ARES Confidence Interval: [[0.547, 0.664]]
Number of Examples in Evaluation Set: [4421]
Ground Truth Performance: [0.6]
ARES LLM Judge Accuracy on Ground Truth Labels: [0.789]
Annotated Examples used for PPI: 300
"""
```
<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_unlabeled_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
Raw data
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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",
"author": null,
"author_email": "Jon Saad-Falcon <jonsaadfalcon@stanford.edu>, Robby Manihani <manihani@stanford.edu>, Omar Khattab <okhattab@stanford.edu>, Christopher Potts <cgpotts@stanford.edu>, Matei Zaharia <matei@berkeley.edu>",
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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/1DvXr9SvWOw6xaNW8LHcy9C06LKevDPxe#scrollTo=wBDuO0n5c1mz\" 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 Prediction-Powered Inference (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 retrieve 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 our jointly trained checkpoint on context relevance here!:\n[Download Checkpoint](https://drive.google.com/file/d/15poFyeoqdnaNZVjl41HllL2213DKyZjH/view?usp=sharing)\n\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 \"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# Output Should be: \n\"\"\" \nContext_Relevance_Label Scoring\nARES Ranking\nARES Prediction: [0.6056978059262574]\nARES Confidence Interval: [[0.547, 0.664]]\nNumber of Examples in Evaluation Set: [4421]\nGround Truth Performance: [0.6]\nARES LLM Judge Accuracy on Ground Truth Labels: [0.789]\nAnnotated Examples used for PPI: 300\n\"\"\"\n\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_unlabeled_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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