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---
tags:
- giskard
- knowledge-base
- information-retrieval
task_categories:
- text-generation
- text2text-generation
- question-answering
- text-retrieval
---
# Dataset Card for GTimothee/my-knowledge-base
> This repository was created using the [giskard](https://github.com/Giskard-AI/giskard) library, an open-source Python framework designed to evaluate and test AI systems.
This dataset comprises a giskard's `KnowledgeBase` containing 310 documents. If embeddings were generated before the saving process, they are included and will be automatically loaded into a vector store when required.
## Usage
You can load this knowledge base using the following code:
```python
from giskard.rag import KnowledgeBase
kb = KnowledgeBase.load_from_hf_hub("GTimothee/my-knowledge-base")
```
## Configuration
The configuration details for this Knowledge Base (can also be found in the `config.json` file):
```bash
{
"columns": null,
"chunk_size": 2048,
"min_topic_size": 8,
"language": "en",
"seed": null,
"embedding_model": null
}
```
---
<h2 style="text-align: center;">
<span style="display: inline-flex; align-items: center;">
Built with
<a href="https://giskard.ai" target="_blank" style="display: inline-flex;">
<img src="https://cdn.prod.website-files.com/601d6f7d0b9c984f07bf10bc/62983fa8ef716259c397a57d_logo.svg"
alt="Giskard Logo"
width="100">
</a>
</span>
</h2>
<div style="text-align: center;">
<a href="https://github.com/Giskard-AI/giskard" target="_blank" style="display: inline-flex;"> Giskard </a> helps identify performance, bias, and security issues in AI applications, supporting both LLM-based systems like RAG agents and traditional machine learning models for tabular data.
</div> |