README.md

mulesoft-documentation-embeddings

MuleSoft Documentation Embeddings for RAG Applications

Dataset Information

• Version: 1.0.0
• Created: 2025-09-16T02:31:46.176390
• Source: Weaviate Vector Database
• License: MIT
• Language: en

Task Categories

question-answering, retrieval, knowledge-base

Dataset Statistics

SkillPilotDataSet_v11

• Total Objects: 6430
• Unique Properties: 13
• Knowledge Sources: mulesoft, user_defined_docs
• Average Content Length: 5079 characters

RAG Configuration

This dataset was created using the following RAG (Retrieval-Augmented Generation) configuration:

Embedding Model

• Model Type: OpenAI
• Model ID: text-embedding-3-large
• Embedding Dimensions: 3072
• Provider: OpenAI

Chunking Configuration

• Chunk Size: 2048 characters
• Chunk Overlap: 256 characters

Technical Details

• Vector Database: Weaviate
• Collection: SkillPilotDataSet_v11
• Total Vectors: 6430
• Vector Distance: Cosine similarity

Usage

This dataset can be used for:

• Question answering systems
• Retrieval-augmented generation (RAG)
• Knowledge base construction
• Technical interview preparation
• AI assistant training

Dataset Schema

The dataset preserves the original Weaviate structure with the following columns:

• weaviate_id: Unique identifier from Weaviate
• collection: Source collection name (SkillPilotDataSet_v11)
• extracted_at: Timestamp of extraction
• properties: Nested object containing all original Weaviate properties:
  • author: Document author
  • chunk_id: Unique chunk identifier
  • chunk_index: Position of chunk in document
  • document_id: Source document identifier
  • entities: Extracted entities
  • keywords: Document keywords
  • knowledge_source: Source of knowledge (mulesoft, user_defined_docs)
  • page_content: Main text content
  • relationships: Entity relationships
  • source_url: Original document URL
  • tags: Document tags
  • title: Document title
  • total_chunks: Total number of chunks in document

Files

• dataset.parquet: Dataset in Parquet format (recommended for large datasets)
• README.md: This documentation file

Citation

If you use this dataset, please cite:

@dataset{{mulesoft_documentation_embeddings,
  title={{MuleSoft Documentation Embeddings}},
  author={{Bassem Elsodany}},
  year={{2025}},
  url={{https://huggingface.co/datasets/BassemE/mulesoft-documentation-embeddings}}
}}