---
dataset_info:
  features:
  - name: content
    dtype: large_string
  - name: url
    dtype: large_string
  - name: branch
    dtype: large_string
  - name: source
    dtype: large_string
  - name: embeddings
    list: float64
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 103214272
    num_examples: 15084
  download_size: 57429042
  dataset_size: 103214272
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Knowledge Base Documentation Dataset

A comprehensive, pre-processed and vectorized dataset containing documentation from 25+ popular open-source projects and cloud platforms, optimized for Retrieval-Augmented Generation (RAG) applications.

## 📊 Dataset Overview

This dataset aggregates technical documentation from leading open-source projects across cloud-native, DevOps, machine learning, and infrastructure domains. Each document has been chunked and embedded using the `all-MiniLM-L6-v2` sentence transformer model.

**Dataset ID**: `saidsef/knowledge-base-docs`

## 🎯 Sources

The dataset includes documentation from the following projects:

| Source | Domain | File Types |
|--------|--------|------------|
| **kubernetes** | Container Orchestration | Markdown |
| **terraform** | Infrastructure as Code | MDX |
| **kustomize** | Kubernetes Configuration | Markdown |
| **ingress-nginx** | Kubernetes Ingress | Markdown |
| **helm** | Package Management | Markdown |
| **external-secrets** | Secrets Management | Markdown |
| **prometheus** | Monitoring | Markdown |
| **argo-cd** | GitOps | Markdown |
| **istio** | Service Mesh | Markdown |
| **scikit-learn** | Machine Learning | RST |
| **cilium** | Networking & Security | RST |
| **redis** | In-Memory Database | Markdown |
| **grafana** | Observability | Markdown |
| **docker** | Containerization | Markdown |
| **linux** | Operating System | RST |
| **ckad-exercises** | Kubernetes Certification | Markdown |
| **aws-eks-best-practices** | AWS EKS | Markdown |
| **gcp-professional-services** | Google Cloud | Markdown |
| **external-dns** | DNS Management | Markdown |
| **google-kubernetes-engine** | GKE | Markdown |
| **consul** | Service Mesh | Markdown |
| **vault** | Secrets Management | MDX |
| **tekton** | CI/CD | Markdown |
| **model-context-protocol-mcp** | AI Context Protocol | Markdown |

## 📋 Dataset Schema

Each row in the dataset contains the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `content` | string | Chunked text content (500 words with 50-word overlap) |
| `original_id` | int/float | Reference to the original document ID |
| `embeddings` | list[float] | 384-dimensional embedding vector from `all-MiniLM-L6-v2` |

## 🔧 Dataset Creation Process

### 1. **Data Collection**
- Shallow clone of 25+ GitHub repositories
- Extraction of documentation files (`.md`, `.mdx`, `.rst`)

### 2. **Content Processing**
- Removal of YAML frontmatter
- Conversion to LLM-friendly markdown format
- Stripping of scripts, styles, and media elements
- Code block preservation with proper formatting

### 3. **Text Chunking**
- **Chunk size**: 500 words
- **Overlap**: 50 words
- Ensures semantic continuity across chunks

### 4. **Vectorization**
- **Model**: `all-MiniLM-L6-v2`
- **Embedding dimensions**: 384
- **Normalization**: Enabled for cosine similarity
- Pre-computed embeddings for fast retrieval

### 5. **Storage Format**
- **Format**: Apache Parquet
- **Compression**: Optimized for query performance
- **File**: `knowledge_base.parquet`

## 💻 Usage Examples

### Loading the Dataset

```python
import pandas as pd
from datasets import load_dataset

# From Hugging Face Hub
dataset = load_dataset("saidsef/knowledge-base-docs")
df = dataset['train'].to_pandas()

# From local Parquet file
df = pd.read_parquet("knowledge_base.parquet", engine="pyarrow")
```

### Semantic Search / RAG Implementation

```python
import numpy as np
from sentence_transformers import SentenceTransformer

# Load the same model used for embedding
model = SentenceTransformer('all-MiniLM-L6-v2', trust_remote_code=True)

def retrieve(query, df, k=5):
    """Retrieve top-k most relevant documents using cosine similarity"""
    # Encode the query
    query_vec = model.encode(query, normalize_embeddings=True)
    
    # Convert embeddings to matrix
    embeddings_matrix = np.vstack(df['embeddings'].values)
    
    # Calculate cosine similarity
    norms = np.linalg.norm(embeddings_matrix, axis=1) * np.linalg.norm(query_vec)
    scores = np.dot(embeddings_matrix, query_vec) / norms
    
    # Add scores and sort
    df['score'] = scores
    return df.sort_values(by='score', ascending=False).head(k)

# Example query
results = retrieve("How do I configure an nginx ingress controller?", df, k=3)
print(results[['content', 'score']])
```

### Building a RAG Pipeline

```python
from transformers import pipeline

# Load a question-answering model
qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

def rag_answer(question, df, k=3):
    """RAG: Retrieve relevant context and generate answer"""
    # Retrieve relevant documents
    context_rows = retrieve(question, df, k=k)
    context_text = " ".join(context_rows['content'].tolist())
    
    # Generate answer
    result = qa_pipeline(question=question, context=context_text)
    return result['answer'], context_rows

answer, sources = rag_answer("What is a Kubernetes pod?", df)
print(f"Answer: {answer}")
```

## 📈 Dataset Statistics

```python
# Total chunks
print(f"Total chunks: {len(df)}")

# Average chunk length
df['chunk_length'] = df['content'].apply(lambda x: len(x.split()))
print(f"Average chunk length: {df['chunk_length'].mean():.0f} words")

# Embedding dimensionality
print(f"Embedding dimensions: {len(df['embeddings'].iloc[0])}")
```

## 🚀 Use Cases

- **RAG Applications**: Build retrieval-augmented generation systems
- **Semantic Search**: Find relevant documentation across multiple projects
- **Question Answering**: Create technical support chatbots
- **Documentation Assistant**: Help developers navigate complex documentation
- **Learning Resources**: Train models on high-quality technical content
- **Comparative Analysis**: Compare documentation approaches across projects

## 🔍 Performance Considerations

- **Pre-computed embeddings**: No need for runtime encoding
- **Optimized retrieval**: Matrix multiplication for fast cosine similarity
- **Parquet format**: Efficient storage and query performance
- **Chunk overlap**: Better context preservation across boundaries

## 🛠️ Requirements

```txt
pandas>=2.0.0
numpy>=1.24.0
sentence-transformers>=2.0.0
pyarrow>=12.0.0
datasets>=2.0.0
```

## 📝 License

This dataset is a compilation of documentation from various open-source projects. Each source maintains its original license:
- Most projects use Apache 2.0 or MIT licenses
- Refer to individual project repositories for specific licensing terms

## 🤝 Contributing

To add new sources or update existing documentation:

1. Add the source configuration to the `sites` list
2. Run the data collection pipeline
3. Verify content processing and embedding quality
4. Submit a pull request with updated dataset

## 📧 Contact

For questions, issues, or suggestions, please open an issue on the GitHub repository or contact the maintainer.

## 🙏 Acknowledgments

Special thanks to all the open-source projects that maintain excellent documentation, making this dataset possible.

---

**Last Updated**: December 2025  
**Version**: 1.0  
**Embedding Model**: all-MiniLM-L6-v2  
**Total Sources**: 25+