File size: 2,630 Bytes
736bed4 c932b80 2bf5422 c28e781 c932b80 2bf5422 c932b80 87a0ece c932b80 2bf5422 c932b80 2bf5422 c932b80 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
---
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}}
}}
```
|