fundialogues

Name	fundialogues JSON
Version	0.1.0 JSON
	download
home_page
Summary	A library of datasets, LLM tooling, and open source models that can be used for training and inference for prototyping purposes.
upload_time	2024-02-23 07:05:48
maintainer
docs_url	None
author
requires_python	>=3.9.0
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            # fun dialogues
A library of datasets, LLM tooling, and open source models that can be used for training and inference for prototyping purposes. The project began as a collection of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. It has now grown to include tooling for LLM application development purposes and open source models. 

### Key components of fun dialogues:
- **JudgyRAG**: RAG eval tool
- **Benchmark Datasets**: custom benchmark datasets
- **Dialogues**: fictitious dialogue datasets for fun, experimentation, and testing. 

You can install fun dialogues using pip: `pip install fundialogues`

<div align="center">
    <img src="https://github.com/eduand-alvarez/fun-dialogues/assets/57263404/1d8ce401-b595-442f-980c-8ae06ed9d4b2" alt="Fun Dialogues Logo" width="750"/>
</div>

# JudgyRAG
JudgyRAG is a component of the FunDialogues Python library focused on evaluating the performance of Retrieval-Augmented Generation (RAG) systems. It facilitates this by creating synthetic datasets based on custom datasets, enabling a unique assessment of a RAG system's question-answering capabilities in a zero-shot Q&A context. Initially, JudgyRAG's primary functionality is the automatic generation of custom multiple-choice Q&A datasets. Future iterations will introduce further automation to seamlessly integrate with popular frameworks, enhancing testing and benchmarking processes.

### Workflow

The workflow for JudgyRAG includes:

1. **Scraping PDFs**: Information is extracted from PDFs into structured text formats.
2. **Chunking Data**: Extracted data is chunked similarly to vector database embeddings for RAG, simulating data breakdown and storage.
3. **Question Generation**: Each chunk acts as a knowledge base, with custom prompts instructing supported models (currently LLaMA 7B and 13B chat) to generate multiple-choice questions.
4. **Iterative Parsing**: Chunks are processed iteratively, generating a multiple-choice question for each.
5. **Quality Checks**: Poor-quality chunks leading to failed question generation are flagged for user review.
6. **Benchmark Compilation**: The final document includes multiple-choice questions, correct answers, and source knowledge chunks.
7. **RAG System Evaluation**: The synthetic benchmark dataset can be used to assess a RAG system, with automation for this process planned for future updates.

### Environment Setup

Follow these steps to set up your environment for JudgyRAG:

#### Step 1
Install Visual Studio 2022 Community Edition with the “Desktop development with C++” workload.

#### Step 2
Update to the latest GPU driver.

#### Step 3
Install the Intel® oneAPI Base Toolkit 2024.0.

#### Step 4
Download the necessary wheels:

```bash
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp39-cp39-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp39-cp39-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp39-cp39-win_amd64.whl
```

#### Step 5

Install the downloaded packages and BigDL LLM:

```bash
pip install torch-2.1.0a0+cxx11.abi-cp39-cp39-win_amd64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp39-cp39-win_amd64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp39-cp39-win_amd64.whl
pip install --pre --upgrade bigdl-llm[xpu]
conda install libuv
```

#### Step 6
Activate the Intel oneAPI environment:

```bash
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
```

For the latest setup instructions for BigDL LLM inference, visit [BigDL Documentation](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html)

### Example Usage of JudgyRAG

```python
from fundialogues import benchgen, judgypdf

folder_path = ""
output_directory = ""
chunk_file = ""
benchmark_output_directory = ""

judgypdf(folder_path, output_directory)
benchgen(chunk_file, benchmark_output_directory)
```
# Benchmark Datasets

### opengeoquery-v1
OpenGeoQuery-v1 is the first edition of a benchmark dataset composed of statements associated with the geosciences. The content of the dataset touches on topics like geophysics, petrology, minerology, seismology, geomorphology, etc. The purpose of this dataset is to use as a benchmark and for fine-tuning small geoscience LLMs (coming soon).

# Dialogues
- Customer Service
  - Grocery Cashier: 100 fictitious examples of dialogues between a customer at a grocery store and the cashier.
  - Robot Maintenance: 100 fictitious examples of dialogues between a robot arm technician and a customer.
  - Apple Picker Maintenance: 100 fictitious examples of dialogues between a apple harvesting equipment technician and a customer.
- Academia
  - Physics Office Hours: 100 fictitious examples of dialogues between a physics professor and a student during office hours. 
- Healthcare
  - Minor Consultation: 100 fictitious examples of dialogues between a doctor and a patient during a minor medical consultation.
- Sports
  - Basketball Coach: 100 fictitious examples of dialogues between a basketball coach and the players on the court during a game.
 
### How to Load Dialogues
Loading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library. 

### Load using fun dialogues

Assuming you've already installed fundialogues.

Use loader utility to load dataset as pandas dataframe. Further processing might be required for use.
```
from fundialogues import dialoader

# load as pandas dataframe
physics_office_hours = dialoader("FunDialogues/academia-physics-office-hours")
```

### Loading using Hugging Face datasets

1. Install datasets package `pip install datasets`

2. Load using datasets
```
from datasets import load_dataset

physics_office_hours = load_dataset("FunDialogues/academia-physics-office-hours")
```

# Disclaimer

The dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping.

Please be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose.

It is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental.

The responsibility for the usage and application of these datasets, tools, and codes rests solely with the individual or entity employing them. By accessing and utilizing these assets and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk.

            

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    "author_email": "Eduardo Andres Alvarez <eduand.alvarez@gmail.com>",
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    "description": "# fun dialogues\r\nA library of datasets, LLM tooling, and open source models that can be used for training and inference for prototyping purposes. The project began as a collection of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. It has now grown to include tooling for LLM application development purposes and open source models. \r\n\r\n### Key components of fun dialogues:\r\n- **JudgyRAG**: RAG eval tool\r\n- **Benchmark Datasets**: custom benchmark datasets\r\n- **Dialogues**: fictitious dialogue datasets for fun, experimentation, and testing. \r\n\r\nYou can install fun dialogues using pip: `pip install fundialogues`\r\n\r\n<div align=\"center\">\r\n    <img src=\"https://github.com/eduand-alvarez/fun-dialogues/assets/57263404/1d8ce401-b595-442f-980c-8ae06ed9d4b2\" alt=\"Fun Dialogues Logo\" width=\"750\"/>\r\n</div>\r\n\r\n# JudgyRAG\r\nJudgyRAG is a component of the FunDialogues Python library focused on evaluating the performance of Retrieval-Augmented Generation (RAG) systems. It facilitates this by creating synthetic datasets based on custom datasets, enabling a unique assessment of a RAG system's question-answering capabilities in a zero-shot Q&A context. Initially, JudgyRAG's primary functionality is the automatic generation of custom multiple-choice Q&A datasets. Future iterations will introduce further automation to seamlessly integrate with popular frameworks, enhancing testing and benchmarking processes.\r\n\r\n### Workflow\r\n\r\nThe workflow for JudgyRAG includes:\r\n\r\n1. **Scraping PDFs**: Information is extracted from PDFs into structured text formats.\r\n2. **Chunking Data**: Extracted data is chunked similarly to vector database embeddings for RAG, simulating data breakdown and storage.\r\n3. **Question Generation**: Each chunk acts as a knowledge base, with custom prompts instructing supported models (currently LLaMA 7B and 13B chat) to generate multiple-choice questions.\r\n4. **Iterative Parsing**: Chunks are processed iteratively, generating a multiple-choice question for each.\r\n5. **Quality Checks**: Poor-quality chunks leading to failed question generation are flagged for user review.\r\n6. **Benchmark Compilation**: The final document includes multiple-choice questions, correct answers, and source knowledge chunks.\r\n7. **RAG System Evaluation**: The synthetic benchmark dataset can be used to assess a RAG system, with automation for this process planned for future updates.\r\n\r\n### Environment Setup\r\n\r\nFollow these steps to set up your environment for JudgyRAG:\r\n\r\n#### Step 1\r\nInstall Visual Studio 2022 Community Edition with the \u201cDesktop development with C++\u201d workload.\r\n\r\n#### Step 2\r\nUpdate to the latest GPU driver.\r\n\r\n#### Step 3\r\nInstall the Intel\u00ae oneAPI Base Toolkit 2024.0.\r\n\r\n#### Step 4\r\nDownload the necessary wheels:\r\n\r\n```bash\r\nwget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp39-cp39-win_amd64.whl\r\nwget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp39-cp39-win_amd64.whl\r\nwget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp39-cp39-win_amd64.whl\r\n```\r\n\r\n#### Step 5\r\n\r\nInstall the downloaded packages and BigDL LLM:\r\n\r\n```bash\r\npip install torch-2.1.0a0+cxx11.abi-cp39-cp39-win_amd64.whl\r\npip install torchvision-0.16.0a0+cxx11.abi-cp39-cp39-win_amd64.whl\r\npip install intel_extension_for_pytorch-2.1.10+xpu-cp39-cp39-win_amd64.whl\r\npip install --pre --upgrade bigdl-llm[xpu]\r\nconda install libuv\r\n```\r\n\r\n#### Step 6\r\nActivate the Intel oneAPI environment:\r\n\r\n```bash\r\ncall \"C:\\Program Files (x86)\\Intel\\oneAPI\\setvars.bat\"\r\n```\r\n\r\nFor the latest setup instructions for BigDL LLM inference, visit [BigDL Documentation](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html)\r\n\r\n### Example Usage of JudgyRAG\r\n\r\n```python\r\nfrom fundialogues import benchgen, judgypdf\r\n\r\nfolder_path = \"\"\r\noutput_directory = \"\"\r\nchunk_file = \"\"\r\nbenchmark_output_directory = \"\"\r\n\r\njudgypdf(folder_path, output_directory)\r\nbenchgen(chunk_file, benchmark_output_directory)\r\n```\r\n# Benchmark Datasets\r\n\r\n### opengeoquery-v1\r\nOpenGeoQuery-v1 is the first edition of a benchmark dataset composed of statements associated with the geosciences. The content of the dataset touches on topics like geophysics, petrology, minerology, seismology, geomorphology, etc. The purpose of this dataset is to use as a benchmark and for fine-tuning small geoscience LLMs (coming soon).\r\n\r\n# Dialogues\r\n- Customer Service\r\n  - Grocery Cashier: 100 fictitious examples of dialogues between a customer at a grocery store and the cashier.\r\n  - Robot Maintenance: 100 fictitious examples of dialogues between a robot arm technician and a customer.\r\n  - Apple Picker Maintenance: 100 fictitious examples of dialogues between a apple harvesting equipment technician and a customer.\r\n- Academia\r\n  - Physics Office Hours: 100 fictitious examples of dialogues between a physics professor and a student during office hours. \r\n- Healthcare\r\n  - Minor Consultation: 100 fictitious examples of dialogues between a doctor and a patient during a minor medical consultation.\r\n- Sports\r\n  - Basketball Coach: 100 fictitious examples of dialogues between a basketball coach and the players on the court during a game.\r\n \r\n### How to Load Dialogues\r\nLoading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library. \r\n\r\n### Load using fun dialogues\r\n\r\nAssuming you've already installed fundialogues.\r\n\r\nUse loader utility to load dataset as pandas dataframe. Further processing might be required for use.\r\n```\r\nfrom fundialogues import dialoader\r\n\r\n# load as pandas dataframe\r\nphysics_office_hours = dialoader(\"FunDialogues/academia-physics-office-hours\")\r\n```\r\n\r\n### Loading using Hugging Face datasets\r\n\r\n1. Install datasets package `pip install datasets`\r\n\r\n2. Load using datasets\r\n```\r\nfrom datasets import load_dataset\r\n\r\nphysics_office_hours = load_dataset(\"FunDialogues/academia-physics-office-hours\")\r\n```\r\n\r\n# Disclaimer\r\n\r\nThe dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping.\r\n\r\nPlease be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose.\r\n\r\nIt is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental.\r\n\r\nThe responsibility for the usage and application of these datasets, tools, and codes rests solely with the individual or entity employing them. By accessing and utilizing these assets and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk.\r\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\"  \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.  2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. 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