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README.md

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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>