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‎README.md

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<h1 align="center">RAGnosis🩺</h1>
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<h1 align="center">diRAGnosis🩺</h1>
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<h2 align="center">Diagnose the performance of your RAG</h2>
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<div align="center">
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<h3>If you find RAGnosis useful, please consider to donate and support the project:</h3>
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<h3>If you find diRAGnosis useful, please consider to donate and support the project:</h3>
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<a href="https://github.com/sponsors/AstraBert"><img src="https://img.shields.io/badge/sponsor-30363D?style=for-the-badge&logo=GitHub-Sponsors&logoColor=#EA4AAA" alt="GitHub Sponsors Badge"></a>
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</div>
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<br>
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<div align="center">
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<img src="https://raw.githubusercontent.com/AstraBert/RAGnosis/main/logo.png" alt="RAGnosis Logo" width=300 height=300>
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<img src="https://raw.githubusercontent.com/AstraBert/diRAGnosis/main/logo.png" alt="diRAGnosis Logo" width=300 height=300>
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</div>
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RAGnosis is a lightweight framework, built with [LlamaIndex](https://llamaindex.ai), that allows you to evaluate the performance of LLMs and retrieval models in RAG frameworks with your documents. It can be used as an application (thanks to [FastAPI](https://fastapi.tiangolo.com/) + [Gradio](https://gradio.app)) running locally on your machine, or as a python package.
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**diRAGnosis** is a lightweight framework, built with [LlamaIndex](https://llamaindex.ai), that allows you to evaluate the performance of LLMs and retrieval models in RAG frameworks with your documents. It can be used as an application (thanks to [FastAPI](https://fastapi.tiangolo.com/) + [Gradio](https://gradio.app)) running locally on your machine, or as a python package.
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## Installation and usage
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Clone the application:
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```bash
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git clone https://github.com/AstraBert/RAGnosis.git
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cd RAGnosis/
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git clone https://github.com/AstraBert/diRAGnosis.git
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cd diRAGnosis/
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```
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**Docker (recommended)**🐋
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> _Required: [Docker](https://docs.docker.com/desktop/), [docker compose](https://docs.docker.com/compose/) and [conda](https://anaconda.org/anaconda/conda)_
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- Set up RAGnosis app using the dedicated script:
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- Set up diRAGnosis app using the dedicated script:
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```bash
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# For MacOs/Linux users
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### As a python package
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As a python package, you will be able to install RAGnosis using `pip`:
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As a python package, you will be able to install diRAGnosis using `pip`:
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```bash
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pip install ragnosis
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pip install diragnosis
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```
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Once you have installed it, you can import the four functions ([detailed in the dedicated reference file](https://github.com/AstraBert/RAGnosis/tree/main/REFERENCE.md)) available for RAGnosis like this:
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Once you have installed it, you can import the four functions ([detailed in the dedicated reference file](https://github.com/AstraBert/diRAGnosis/tree/main/REFERENCE.md)) available for diRAGnosis like this:
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```python
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from ragnosis.evaluation import generate_question_dataset, evaluate_llms, evaluate_retrieval, display_available_providers
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from diragnosis.evaluation import generate_question_dataset, evaluate_llms, evaluate_retrieval, display_available_providers
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```
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Once you imported them, this is an example of how you can use them:
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## How it works
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<div align="center">
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<img src="https://raw.githubusercontent.com/AstraBert/RAGnosis/main/workflow.png" alt="RAGnosis Workflow">
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<img src="https://raw.githubusercontent.com/AstraBert/diRAGnosis/main/workflow.png" alt="diRAGnosis Workflow">
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</div>
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RAGnosis takes care of the evaluation of LLM and retrieval model performance on your documents in a completely automated way:
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diRAGnosis takes care of the evaluation of LLM and retrieval model performance on your documents in a completely automated way:
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- Once your documents are uploaded, they are converted into a synthetic question dataset (either for Retrieval Augmented Generation or for retrieval only) by an LLM of your choice
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- The documents are also chunked and uploaded to a vector database served by [Qdrant](https://qdrant.tech) - you can choose a semantic search only or an hybrid search setting
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## Contributing
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Contributions are always welcome! Follow the contributions guidelines reported [here](https://github.com/AstraBert/RAGnosis/tree/main/CONTRIBUTING.md).
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Contributions are always welcome! Follow the contributions guidelines reported [here](https://github.com/AstraBert/diRAGnosis/tree/main/CONTRIBUTING.md).
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## License and rights of usage
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The software is provided under MIT [license](https://github.com/AstraBert/RAGnosis/tree/main/LICENSE).
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The software is provided under MIT [license](https://github.com/AstraBert/diRAGnosis/tree/main/LICENSE).

‎REFERENCE.md

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# Evaluation Framework Reference
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# diRAGnosis reference
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This document provides a reference for the functions contained in the `evaluation.py` file. These functions are used for generating question datasets, evaluating language learning models (LLMs), and evaluating retrieval performance.
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This document provides a reference for the functions contained in the `evaluation` module of the `diRAGnosis` package. These functions are used for generating question datasets, evaluating language learning models (LLMs), and evaluating retrieval performance.
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## Functions
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‎logo.png

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‎pypi_package/README.md

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<h1 align="center">RAGnosis🩺</h1>
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<h1 align="center">diRAGnosis🩺</h1>
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<h2 align="center">Diagnose the performance of your RAG</h2>
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<div align="center">
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<h3>If you find RAGnosis useful, please consider to donate and support the project:</h3>
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<h3>If you find diRAGnosis useful, please consider to donate and support the project:</h3>
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<a href="https://github.com/sponsors/AstraBert"><img src="https://img.shields.io/badge/sponsor-30363D?style=for-the-badge&logo=GitHub-Sponsors&logoColor=#EA4AAA" alt="GitHub Sponsors Badge"></a>
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</div>
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<br>
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<div align="center">
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<img src="https://raw.githubusercontent.com/AstraBert/RAGnosis/main/logo.png" alt="RAGnosis Logo" width=300 height=300>
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<img src="https://raw.githubusercontent.com/AstraBert/diRAGnosis/main/logo.png" alt="diRAGnosis Logo" width=300 height=300>
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</div>
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RAGnosis is a lightweight framework, built with [LlamaIndex](https://llamaindex.ai), that allows you to evaluate the performance of LLMs and retrieval models in RAG frameworks with your documents. It can be used as an application (thanks to [FastAPI](https://fastapi.tiangolo.com/) + [Gradio](https://gradio.app)) running locally on your machine, or as a python package.
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**diRAGnosis** is a lightweight framework, built with [LlamaIndex](https://llamaindex.ai), that allows you to evaluate the performance of LLMs and retrieval models in RAG frameworks with your documents. It can be used as an application (thanks to [FastAPI](https://fastapi.tiangolo.com/) + [Gradio](https://gradio.app)) running locally on your machine, or as a python package.
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## Installation and usage
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Clone the application:
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```bash
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git clone https://github.com/AstraBert/RAGnosis.git
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cd RAGnosis/
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git clone https://github.com/AstraBert/diRAGnosis.git
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cd diRAGnosis/
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```
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**Docker (recommended)**🐋
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> _Required: [Docker](https://docs.docker.com/desktop/), [docker compose](https://docs.docker.com/compose/) and [conda](https://anaconda.org/anaconda/conda)_
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- Set up RAGnosis app using the dedicated script:
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- Set up diRAGnosis app using the dedicated script:
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```bash
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# For MacOs/Linux users
@@ -79,16 +79,16 @@ You will see the application running on http://localhost:8000/dashboard and you
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8080
### As a python package
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As a python package, you will be able to install RAGnosis using `pip`:
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As a python package, you will be able to install diRAGnosis using `pip`:
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```bash
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pip install ragnosis
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pip install diRAGnosis
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```
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Once you have installed it, you can import the four functions ([detailed in the dedicated reference file](https://github.com/AstraBert/RAGnosis/tree/main/REFERENCE.md)) available for RAGnosis like this:
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Once you have installed it, you can import the four functions ([detailed in the dedicated reference file](https://github.com/AstraBert/diRAGnosis/tree/main/REFERENCE.md)) available for diRAGnosis like this:
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```python
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from ragnosis.evaluation import generate_question_dataset, evaluate_llms, evaluate_retrieval, display_available_providers
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from diRAGnosis.evaluation import generate_question_dataset, evaluate_llms, evaluate_retrieval, display_available_providers
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```
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Once you imported them, this is an example of how you can use them:
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## How it works
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<div align="center">
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<img src="https://raw.githubusercontent.com/AstraBert/RAGnosis/main/workflow.png" alt="RAGnosis Workflow">
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<img src="https://raw.githubusercontent.com/AstraBert/diRAGnosis/main/workflow.png" alt="diRAGnosis Workflow">
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</div>
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RAGnosis takes care of the evaluation of LLM and retrieval model performance on your documents in a completely automated way:
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diRAGnosis takes care of the evaluation of LLM and retrieval model performance on your documents in a completely automated way:
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135135
- Once your documents are uploaded, they are converted into a synthetic question dataset (either for Retrieval Augmented Generation or for retrieval only) by an LLM of your choice
136136
- The documents are also chunked and uploaded to a vector database served by [Qdrant](https://qdrant.tech) - you can choose a semantic search only or an hybrid search setting
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## Contributing
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Contributions are always welcome! Follow the contributions guidelines reported [here](https://github.com/AstraBert/RAGnosis/tree/main/CONTRIBUTING.md).
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Contributions are always welcome! Follow the contributions guidelines reported [here](https://github.com/AstraBert/diRAGnosis/tree/main/CONTRIBUTING.md).
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## License and rights of usage
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The software is provided under MIT [license](https://github.com/AstraBert/RAGnosis/tree/main/LICENSE).
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The software is provided under MIT [license](https://github.com/AstraBert/diRAGnosis/tree/main/LICENSE).

‎pypi_package/pyproject.toml

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build-backend = "setuptools.build_meta"
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[project]
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name = "ragnosis"
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name = "diragnosis"
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version = "0.0.0"
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authors = [
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{ name="Clelia Astra Bertelli", email="[email protected]" },

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