| Name | nuggetizer JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | None |
| Summary | A package for Nuggetizer - a tool for information nugget creation and assignment to LLM-generated answers. |
| upload_time | 2025-02-04 23:25:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
nuggetizer
fact recall
llm
nuggets
fact extraction
rag evaluation
evaluation
rag
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
openai
python-dotenv
typing-extensions
requests
tqdm
tiktoken
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# nuggetizer
[](https://pypi.org/project/nuggetizer/)
[](https://pepy.tech/project/nuggetizer)
[](https://pepy.tech/project/nuggetizer)
[](https://www.apache.org/licenses/LICENSE-2.0)
[](https://arxiv.org/abs/2411.09607)
A powerful tool for information nugget creation, scoring, and assigning to RAG answers using LLMs.
Enables the evaluation of fact recall of RAG answers.
## 📟 Installation
### Create Conda Environment
```bash
conda create -n nuggetizer python=3.10
conda activate nuggetizer
```
### Pip Installation
```bash
pip install nuggetizer
```
### Development Installation
For development or the latest features, install from source:
```bash
git clone https://github.com/castorini/nuggetizer.git
cd nuggetizer
pip install -e .
```
### Environment Setup
Create a `.env` file with your OpenAI credentials. For Azure OpenAI (default for GPT models):
```bash
AZURE_OPENAI_API_BASE=your_azure_endpoint
AZURE_OPENAI_API_VERSION=your_api_version
AZURE_OPENAI_API_KEY=your_api_key
```
Or for OpenAI API:
```bash
OPEN_AI_API_KEY=your_openai_api_key
```
## 🚀 Quick Start
Here's a simple example of how to use nuggetizer:
```python
from nuggetizer.core.types import Query, Document, Request
from nuggetizer.models.nuggetizer import Nuggetizer
# Create a sample request
query = Query(qid="1", text="What are the main features of Python?")
documents = [
Document(
docid="1",
segment="""Python is a high-level programming language known for its
simplicity and readability. It supports multiple programming paradigms
including procedural, object-oriented, and functional programming."""
),
Document(
docid="2",
segment="""Python was created by Guido van Rossum in 1991."""
),
Document(
docid="3",
segment="""Python is widely used in web development, data analysis,
artificial intelligence, and scientific computing."""
),
]
request = Request(query=query, documents=documents)
# Option 1: Single model for all components
nuggetizer = Nuggetizer(model="gpt-4o") # Uses same model for all components
# Option 2: Different models for each component
nuggetizer_mixed = Nuggetizer(
creator_model="gpt-4o", # Model for nugget creation
scorer_model="gpt-3.5-turbo", # Model for nugget scoring
assigner_model="gpt-4o" # Model for nugget assignment
)
# Create and score nuggets
scored_nuggets = nuggetizer.create(request)
# Print nuggets and their importance
for nugget in scored_nuggets:
print(f"Nugget: {nugget.text}")
print(f"Importance: {nugget.importance}\n")
# Assign nuggets to a specific document
assigned_nuggets = nuggetizer.assign(query.text, documents[0].segment, scored_nuggets)
# Print assignments
for nugget in assigned_nuggets:
print(f"Nugget: {nugget.text}")
print(f"Importance: {nugget.importance}")
print(f"Assignment: {nugget.assignment}\n")
```
You can also run a little more elaborate example with:
```bash
python3 examples/e2e.py
```
We also provide an async version of the Nuggetizer class, `AsyncNuggetizer`, in `src/nuggetizer/models/async_nuggetizer.py`. To run this example, use:
```bash
python3 examples/async_e2e.py
```
## 🛠️ Components
The Nuggetizer class provides a unified interface for:
1. **Nugget Creation & Scoring**: Extracts and scores atomic information nuggets from text
2. **Nugget Assignment**: Assigns nuggets to specific texts
The following scripts are provided to help you with through the process for the TREC 2024 RAG Track:
1. First, generate nuggets:
```bash
# Extract nuggets
python3 scripts/create_nuggets.py --input_file pool.jsonl --output_file nuggets.jsonl --log_level 1
```
2. For RAG answers, we assume they take on the format laid out by the wonderful [TREC 2024 RAG Track](https://trec-rag.github.io/annoucements/2024-track-guidelines/):
```json
{
"run_id": "ragnarok",
"topic_id": "2027497",
"topic": "how often should you take your toddler to the potty when potty training",
"references": [
"msmarco_v2.1_doc_51_766815931#2_1606878413", "msmarco_v2.1_doc_51_766815931#1_1606876582", "msmarco_v2.1_doc_51_766815931#5_1606882767",
"msmarco_v2.1_doc_51_766815931#6_1606884302", "msmarco_v2.1_doc_51_766815931#3_1606879951", "msmarco_v2.1_doc_51_766815931#4_1606881348",
"msmarco_v2.1_doc_37_463237391#10_984448281", "msmarco_v2.1_doc_51_766815931#0_1606874600", "msmarco_v2.1_doc_37_463237391#9_984446615",
"msmarco_v2.1_doc_28_472446307#22_1012988885", "msmarco_v2.1_doc_51_766815931#7_1606885873", "msmarco_v2.1_doc_28_472446307#21_1012986800",
"msmarco_v2.1_doc_29_562342450#23_1356565296", "msmarco_v2.1_doc_29_562342450#17_1356555947", "msmarco_v2.1_doc_49_418787959#7_861728734",
"msmarco_v2.1_doc_49_418787959#6_861726964", "msmarco_v2.1_doc_26_680625866#7_1289507527", "msmarco_v2.1_doc_10_1346272776#19_2165266355",
"msmarco_v2.1_doc_56_1491300640#3_3012150696", "msmarco_v2.1_doc_10_672519892#5_1260010758"],
"response_length": 192,
"answer": [
{"text": "The frequency with which you should take your toddler to the potty depends on their readiness for potty training.", "citations": [0, 1, 12, 13, 19]},
{"text": "Some sources suggest that toddlers should be taken to the potty about three times a day: first thing in the morning, after mealtimes, and again before bedtime.", "citations": [0, 4, 6, 8]},
{"text": "It is recommended that you watch for facial expressions or poses that may signal that they need to \"go\".", "citations": [6, 8]},
{"text": "If they are reluctant to use the potty, don't force them.", "citations": [6, 8]},
{"text": "Other sources suggest that toddlers should be taken to the potty every two hours, whether they have to go or not.", "citations": [14, 15]},
{"text": "This includes first thing in the morning, before leaving the house, and before naps and bedtime.", "citations": [14, 15]},
{"text": "Some sources recommend taking toddlers to the potty every 30 minutes to an hour.", "citations": [9, 11, 17]},
{"text": "This is to increase the chances of them peeing in the potty instead of on the floor.", "citations": [9, 11]},
{"text": "It is important to keep in mind that every toddler is different, and their potty training journey will be unique to them.", "citations": [0, 4]},
{"text": "It is recommended that you let your toddler lead the way and be gentle throughout the process, as their self-esteem can be fragile during this time.", "citations": [0, 1]}
]
}
```
To *easily* generate answers in this format, consider using [Ragnarök](https://github.com/castorini/ragnarok).
Let's now assign the nuggets to the RAG answers:
```bash
# Assign nuggets to RAG answers
python3 scripts/assign_nuggets.py \
--nugget_file nuggets.jsonl \
--answer_file ragnarok.jsonl \
--output_file final_assignments.jsonl
# Calculate metrics
python3 scripts/calculate_metrics.py \
--input_file final_assignments.jsonl \
--output_file metrics.jsonl
```
The final output file (`final_assignments.jsonl`) will contain:
- query: The original query
- qid: Query ID
- answer_text: Full answer text
- response_length: Response length
- run_id: Run ID (derived from the RAG answer filename)
- nuggets: Nuggets with their importance labels and assignments
The final metrics file (`metrics.jsonl`) will contain:
- Per-response metrics:
- `strict_vital_score`: Score counting only full support for vital nuggets
- `strict_all_score`: Score counting only full support for all nuggets
- `vital_score`: Score counting full (1.0) and partial (0.5) support for vital nuggets
- `all_score`: Score counting full (1.0) and partial (0.5) support for all nuggets
- Global mean metrics across all responses (indicated by `qid` as `all`)
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📝 License
This project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
This project is built with the support of Azure's OpenAI credits.
## ✨ References
If you use Nuggetizer, please cite the following relevant papers:
[[2411.09607] Initial Nugget Evaluation Results for the {TREC 2024 RAG Track} with the {AutoNuggetizer Framework}](https://arxiv.org/abs/2411.09607)
```
@ARTICLE{pradeep2024autonuggetizer,
title = {Initial Nugget Evaluation Results for the {TREC 2024 RAG Track} with the {AutoNuggetizer Framework}},
author = {Ronak Pradeep and Nandan Thakur and Shivani Upadhyay and Daniel Campos and Nick Craswell and Jimmy Lin},
year = {2024},
journal = {arXiv:2411.09607}
}
```
[[2406.16828] Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://arxiv.org/abs/2406.16828)
```
@ARTICLE{pradeep2024ragnarok,
title = {{Ragnarök}: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track},
author = {Ronak Pradeep and Nandan Thakur and Sahel Sharifymoghaddam and Eric Zhang and Ryan Nguyen and Daniel Campos and Nick Craswell and Jimmy Lin},
year = {2024},
journal = {arXiv:2406.16828},
}
```
Raw data
{
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"name": "nuggetizer",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "Nuggetizer, Fact Recall, LLM, Nuggets, Fact Extraction, RAG Evaluation, Evaluation, RAG",
"author": null,
"author_email": "Ronak Pradeep <rpradeep@uwaterloo.ca>",
"download_url": "https://files.pythonhosted.org/packages/56/a5/e9cf3f7311c18395e962e8cd57f9ea7bc84c1eba4ce9deec4be9a457b9f0/nuggetizer-0.0.5.tar.gz",
"platform": null,
"description": "# nuggetizer\n\n[](https://pypi.org/project/nuggetizer/)\n[](https://pepy.tech/project/nuggetizer)\n[](https://pepy.tech/project/nuggetizer)\n[](https://www.apache.org/licenses/LICENSE-2.0)\n[](https://arxiv.org/abs/2411.09607)\n\nA powerful tool for information nugget creation, scoring, and assigning to RAG answers using LLMs.\nEnables the evaluation of fact recall of RAG answers.\n\n## \ud83d\udcdf Installation\n\n### Create Conda Environment\n\n```bash\nconda create -n nuggetizer python=3.10\nconda activate nuggetizer\n```\n\n### Pip Installation\n\n```bash\npip install nuggetizer\n```\n\n### Development Installation\n\nFor development or the latest features, install from source:\n\n```bash\ngit clone https://github.com/castorini/nuggetizer.git\ncd nuggetizer\npip install -e .\n```\n\n### Environment Setup\n\nCreate a `.env` file with your OpenAI credentials. For Azure OpenAI (default for GPT models):\n\n```bash\nAZURE_OPENAI_API_BASE=your_azure_endpoint\nAZURE_OPENAI_API_VERSION=your_api_version\nAZURE_OPENAI_API_KEY=your_api_key\n```\n\nOr for OpenAI API:\n\n```bash\nOPEN_AI_API_KEY=your_openai_api_key\n```\n\n## \ud83d\ude80 Quick Start\n\nHere's a simple example of how to use nuggetizer:\n\n```python\nfrom nuggetizer.core.types import Query, Document, Request\nfrom nuggetizer.models.nuggetizer import Nuggetizer\n\n# Create a sample request\nquery = Query(qid=\"1\", text=\"What are the main features of Python?\")\ndocuments = [\n Document(\n docid=\"1\",\n segment=\"\"\"Python is a high-level programming language known for its \n simplicity and readability. It supports multiple programming paradigms \n including procedural, object-oriented, and functional programming.\"\"\"\n ),\n Document(\n docid=\"2\",\n segment=\"\"\"Python was created by Guido van Rossum in 1991.\"\"\"\n ),\n Document(\n docid=\"3\",\n segment=\"\"\"Python is widely used in web development, data analysis, \n artificial intelligence, and scientific computing.\"\"\"\n ),\n]\nrequest = Request(query=query, documents=documents)\n\n# Option 1: Single model for all components\nnuggetizer = Nuggetizer(model=\"gpt-4o\") # Uses same model for all components\n\n# Option 2: Different models for each component\nnuggetizer_mixed = Nuggetizer(\n creator_model=\"gpt-4o\", # Model for nugget creation\n scorer_model=\"gpt-3.5-turbo\", # Model for nugget scoring\n assigner_model=\"gpt-4o\" # Model for nugget assignment\n)\n\n# Create and score nuggets\nscored_nuggets = nuggetizer.create(request)\n\n# Print nuggets and their importance\nfor nugget in scored_nuggets:\n print(f\"Nugget: {nugget.text}\")\n print(f\"Importance: {nugget.importance}\\n\")\n\n# Assign nuggets to a specific document\nassigned_nuggets = nuggetizer.assign(query.text, documents[0].segment, scored_nuggets)\n\n# Print assignments\nfor nugget in assigned_nuggets:\n print(f\"Nugget: {nugget.text}\")\n print(f\"Importance: {nugget.importance}\")\n print(f\"Assignment: {nugget.assignment}\\n\")\n```\n\nYou can also run a little more elaborate example with:\n```bash\npython3 examples/e2e.py\n```\n\nWe also provide an async version of the Nuggetizer class, `AsyncNuggetizer`, in `src/nuggetizer/models/async_nuggetizer.py`. To run this example, use:\n\n```bash\npython3 examples/async_e2e.py\n```\n\n## \ud83d\udee0\ufe0f Components\n\nThe Nuggetizer class provides a unified interface for:\n\n1. **Nugget Creation & Scoring**: Extracts and scores atomic information nuggets from text\n2. **Nugget Assignment**: Assigns nuggets to specific texts\n\nThe following scripts are provided to help you with through the process for the TREC 2024 RAG Track:\n\n1. First, generate nuggets:\n```bash\n# Extract nuggets\npython3 scripts/create_nuggets.py --input_file pool.jsonl --output_file nuggets.jsonl --log_level 1\n```\n\n2. For RAG answers, we assume they take on the format laid out by the wonderful [TREC 2024 RAG Track](https://trec-rag.github.io/annoucements/2024-track-guidelines/):\n\n```json\n{\n \"run_id\": \"ragnarok\",\n \"topic_id\": \"2027497\",\n \"topic\": \"how often should you take your toddler to the potty when potty training\",\n \"references\": [\n \"msmarco_v2.1_doc_51_766815931#2_1606878413\", \"msmarco_v2.1_doc_51_766815931#1_1606876582\", \"msmarco_v2.1_doc_51_766815931#5_1606882767\", \n \"msmarco_v2.1_doc_51_766815931#6_1606884302\", \"msmarco_v2.1_doc_51_766815931#3_1606879951\", \"msmarco_v2.1_doc_51_766815931#4_1606881348\", \n \"msmarco_v2.1_doc_37_463237391#10_984448281\", \"msmarco_v2.1_doc_51_766815931#0_1606874600\", \"msmarco_v2.1_doc_37_463237391#9_984446615\", \n \"msmarco_v2.1_doc_28_472446307#22_1012988885\", \"msmarco_v2.1_doc_51_766815931#7_1606885873\", \"msmarco_v2.1_doc_28_472446307#21_1012986800\", \n \"msmarco_v2.1_doc_29_562342450#23_1356565296\", \"msmarco_v2.1_doc_29_562342450#17_1356555947\", \"msmarco_v2.1_doc_49_418787959#7_861728734\", \n \"msmarco_v2.1_doc_49_418787959#6_861726964\", \"msmarco_v2.1_doc_26_680625866#7_1289507527\", \"msmarco_v2.1_doc_10_1346272776#19_2165266355\", \n \"msmarco_v2.1_doc_56_1491300640#3_3012150696\", \"msmarco_v2.1_doc_10_672519892#5_1260010758\"], \n \"response_length\": 192, \n \"answer\": [\n {\"text\": \"The frequency with which you should take your toddler to the potty depends on their readiness for potty training.\", \"citations\": [0, 1, 12, 13, 19]}, \n {\"text\": \"Some sources suggest that toddlers should be taken to the potty about three times a day: first thing in the morning, after mealtimes, and again before bedtime.\", \"citations\": [0, 4, 6, 8]}, \n {\"text\": \"It is recommended that you watch for facial expressions or poses that may signal that they need to \\\"go\\\".\", \"citations\": [6, 8]}, \n {\"text\": \"If they are reluctant to use the potty, don't force them.\", \"citations\": [6, 8]}, \n {\"text\": \"Other sources suggest that toddlers should be taken to the potty every two hours, whether they have to go or not.\", \"citations\": [14, 15]}, \n {\"text\": \"This includes first thing in the morning, before leaving the house, and before naps and bedtime.\", \"citations\": [14, 15]}, \n {\"text\": \"Some sources recommend taking toddlers to the potty every 30 minutes to an hour.\", \"citations\": [9, 11, 17]}, \n {\"text\": \"This is to increase the chances of them peeing in the potty instead of on the floor.\", \"citations\": [9, 11]}, \n {\"text\": \"It is important to keep in mind that every toddler is different, and their potty training journey will be unique to them.\", \"citations\": [0, 4]}, \n {\"text\": \"It is recommended that you let your toddler lead the way and be gentle throughout the process, as their self-esteem can be fragile during this time.\", \"citations\": [0, 1]}\n ]\n}\n```\nTo *easily* generate answers in this format, consider using [Ragnar\u00f6k](https://github.com/castorini/ragnarok).\nLet's now assign the nuggets to the RAG answers:\n\n```bash\n# Assign nuggets to RAG answers\npython3 scripts/assign_nuggets.py \\\n --nugget_file nuggets.jsonl \\\n --answer_file ragnarok.jsonl \\\n --output_file final_assignments.jsonl\n\n# Calculate metrics\npython3 scripts/calculate_metrics.py \\\n --input_file final_assignments.jsonl \\\n --output_file metrics.jsonl\n```\n\nThe final output file (`final_assignments.jsonl`) will contain:\n- query: The original query\n- qid: Query ID\n- answer_text: Full answer text\n- response_length: Response length\n- run_id: Run ID (derived from the RAG answer filename)\n- nuggets: Nuggets with their importance labels and assignments\n\nThe final metrics file (`metrics.jsonl`) will contain:\n- Per-response metrics:\n - `strict_vital_score`: Score counting only full support for vital nuggets\n - `strict_all_score`: Score counting only full support for all nuggets\n - `vital_score`: Score counting full (1.0) and partial (0.5) support for vital nuggets\n - `all_score`: Score counting full (1.0) and partial (0.5) support for all nuggets\n- Global mean metrics across all responses (indicated by `qid` as `all`)\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## \ud83d\udcdd License\n\nThis project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\nThis project is built with the support of Azure's OpenAI credits.\n\n## \u2728 References\n\nIf you use Nuggetizer, please cite the following relevant papers:\n\n[[2411.09607] Initial Nugget Evaluation Results for the {TREC 2024 RAG Track} with the {AutoNuggetizer Framework}](https://arxiv.org/abs/2411.09607)\n\n```\n@ARTICLE{pradeep2024autonuggetizer,\n title = {Initial Nugget Evaluation Results for the {TREC 2024 RAG Track} with the {AutoNuggetizer Framework}},\n author = {Ronak Pradeep and Nandan Thakur and Shivani Upadhyay and Daniel Campos and Nick Craswell and Jimmy Lin},\n year = {2024},\n journal = {arXiv:2411.09607}\n}\n```\n\n[[2406.16828] Ragnar\u00f6k: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://arxiv.org/abs/2406.16828)\n```\n@ARTICLE{pradeep2024ragnarok,\n title = {{Ragnar\u00f6k}: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track},\n author = {Ronak Pradeep and Nandan Thakur and Sahel Sharifymoghaddam and Eric Zhang and Ryan Nguyen and Daniel Campos and Nick Craswell and Jimmy Lin},\n year = {2024},\n journal = {arXiv:2406.16828},\n}\n```\n",
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