chore(docs): add read me to dataset

README.md

@@ -21,3 +21,220 @@ configs:
   - 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+