---
license: mit
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- mulesoft
- documentation
- embeddings
- rag
- api-integration
size_categories: 10K<n<100K
configs:
- config_name: default
  data_files: dataset.parquet
  default: true
---

# 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:

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