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README.md

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