Clean StackOverflow vector language dataset before upload: sql
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README.md
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---
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tags:
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- vector-database
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- embeddings
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- parquet
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- qdrant
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- stackoverflow
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- question-answering
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pretty_name: StackOverflow Vector Dataset - SQL
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license: cc-by-sa-4.0
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size_categories:
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- 1M<n<10M
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---
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# SQL StackOverflow Vector Dataset Datasheet
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## 1. What This Dataset Is
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This dataset is the SQL-specific vector shard of the Stack2Graph StackOverflow retrieval corpus.
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Each Hugging Face dataset repository contains exactly one language dataset.
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It is optimized for dense+sparse retrieval, Qdrant restoration, and embedding-based RAG experiments.
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It is used in the Stack2Graph project as the vector counterpart to the language-scoped RDF knowledge graph shards.
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See the Stack2Graph repository for more details:
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[https://github.com/tha-atlas/Stack2Graph](https://github.com/tha-atlas/Stack2Graph)
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## 2. Repository Layout
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```text
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dataset_manifest.json
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question_metadata_*.parquet
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chunk_records_*.parquet
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question_records_*.parquet
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```
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- `dataset_manifest.json`: language-scoped manifest for this dataset shard.
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- `question_metadata_*.parquet`: per-question metadata and retrieval bookkeeping.
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- `chunk_records_*.parquet`: chunk-level vector rows when parent-child indexing is enabled.
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- `question_records_*.parquet`: question-level vector rows when chunking is disabled or exported alongside chunk data.
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## 3. Data Model And Coverage
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The dataset is derived from Stack Overflow questions selected for the SQL programming language.
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It contains the structured records needed to rebuild the Stack2Graph Qdrant collection for that language.
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Coverage scope:
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- records are retained when they match the Stack2Graph supported language-tag set
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- this repository contains only the SQL shard
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- the archive may contain both metadata-only and retrieval-ready vector rows depending on the export mode
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## 4. Recommended Preprocessing
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1. Read `dataset_manifest.json` first and use it as the source of truth for included Parquet files.
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2. Load all Parquet shards for this repository into your vector indexing pipeline.
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3. Rebuild or restore the Qdrant collection `stackoverflow_sql_vector`.
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4. Preserve attribution and license metadata during downstream export.
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## 5. Automatic Download And Vector DB Setup
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You do not need to regenerate embeddings from GraphDB to use this dataset.
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In the Stack2Graph repository, you can use the automation script
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`python -m experiment.load_hf_datasets_into_services --skip-kg` to download dataset artifacts
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and prepare the vector database service state automatically.
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Typical workflow:
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1. Clone and configure Stack2Graph (`.env` with HF token and service paths).
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1. Clone and configure Stack2Graph (`.env` with HF token and service paths).
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1. Start required local services:
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```bash
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docker compose up -d
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```
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1. Run the loader script:
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```bash
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python -m experiment.load_hf_datasets_into_services --skip-kg
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```
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For manual usage without automation, directly ingest the listed Parquet files into your vector database.
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## 6. Quality Notes And Caveats
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- A Stack Overflow question may belong to multiple language shards when tagged with multiple languages.
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- Embeddings and sparse representations depend on the configured export pipeline and model versions.
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- As with community-generated data, content may include noise, bias, and temporal drift.
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## 7. Intended Use
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- semantic retrieval and reranking
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- RAG and hybrid retriever experiments
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- vector database benchmarking and diagnostics
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- language-scoped developer tooling research
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## 8. Limitations
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- Not a complete mirror of all Stack Overflow content.
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- Not all export modes include the same row types or chunk layouts.
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- Best used together with the Stack2Graph retrieval pipeline and Qdrant-compatible tooling.
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## 9. Licensing And Attribution
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This dataset inherits Stack Overflow source licensing and attribution requirements.
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Ensure compliant attribution and redistribution practices in all derived artifacts.
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## 10. Suggested Citation
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If you use this dataset, cite the Stack2Graph work:
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- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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