| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - RAG |
| - Embeddings |
| - Clustering |
| - Graph |
| - knowledge graph |
| - GraphRAG |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
|
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| # Dataset for Benchmarking RAG, GraphRAG and LLM Embeddings |
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| ### Benchmarking LLM embedding is still based on comparing it with another LLM or QA datasets. |
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| This dataset offers another method to test or compare the LLM embeddings. |
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| This all-against-all comparison of text data [Wikipedia Embeddings dataset](https://huggingface.co/datasets/Supabase/wikipedia-en-embeddings) |
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| As the original dataset contains embeddings from three different LLMs, this can be programmatically checked and manually verified. |
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| RAG systems can be tested to verify if the setup works as per the requirements. Because this data contains both embeddings and actual clustered data, manual verification is also easier. |
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| Graph-RAG now uses extraction of entities to make connections between documents. That removes the context from data. This dataset has actual lines/sentences/chunks compared in the dataset. This offers contextual links between documents where similar chunks can be connected based how much those chunks are similar. |
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| ## Method |
| 1. The actual data is fed to the Graph AI method after being split into sentences. These are linguistic sentence structures. |
| 1. This makes each entry in the original dataset as documents. |
| 1. The Graph AI compares the sentences method similar to all-against-all comparison. |
| 1. Resulting clusters of Sentences, Documents are then normalized for generating document graphs. |
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| ## Uses |
| * Can be used for benchmark RAG, GraphRAG and Embeddings. |
| * Can be used for Vector based Clustering methods. |
|
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| ## Schema |
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| All files are **new line JSON** files. It could be loaded as **JSONL** |
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| 1. data_file.json - Base Data file |
| - doc_id - Unique ID from embedding files from original dataset. **id === doc_id** |
| - data - data from embedding files from original dataset. **body == data** |
| 2. line_data.json - Contains lines used for Clustering |
| - doc_id - Unique ID |
| - line_id - Unique ID |
| - line - Splited chunk of line from data. |
| 3. rows_clusters.json |
| - cluster_id - Unique ID denoting cluster. |
| - cluster - List of line_id that forms a conceptual cluster. |
| - confidence - Score of confidence showing how much these line_id are related. |
| 4. docs_clusers.json |
| - cluster_id - Unique ID denoting cluster |
| - cluster - List of doc_id that forms a conceptual cluster. |
| - confidence - Score of confidence showing how much these doc_id are related. |
| 5. doc_graph.json |
| - doc_id_1 - Unique ID |
| - doc_id_2 - Unique ID |
| - confidence - Score of confidence showing how much these two doc_id are related. |
| - lines - List of line_id that connects these two doc_id |
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