mulesoft-documentation-embeddings

MuleSoft Documentation Embeddings for RAG Applications

Dataset Information

• Version: 1.0.0
• Created: 2025-09-16T02:41:16.352809
• Source: 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

Dataset Schema

The dataset contains the original document properties with the following fields:

• 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
• vector: Embedding vector (3072 dimensions from OpenAI text-embedding-3-large)

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: Vector Database
• 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

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}}
}}