chunk_id large_stringlengths 5 11 | question_id large_stringlengths 2 8 | language large_stringclasses 1
value | chunk_index int64 0 8 | chunk_text large_stringlengths 106 3k | sparse_text large_stringlengths 65 3.16k | dense_vector listlengths 3.58k 3.58k | sparse_indices listlengths 3 283 | sparse_values listlengths 3 283 | export_source large_stringclasses 1
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17609793_c0 | 17609793 | sql | 0 | Title: sql query for each day
Problem title: sql query for each day
Tags: sql, mariadb, mysql
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5986439_c0 | 5986439 | sql | 0 | Title: select data with mysql
Problem title: select data with mysql
Tags: sql, mysql, datetime-generation
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54176142_c0 | 54176142 | sql | 0 | Title: Least frequent in SQL
Problem title: Least frequent in SQL
Tags: sql, mysql
Problem: Least frequent in SQL
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5511878_c0 | 5511878 | sql | 0 | "Title: mysql join query\nProblem title: mysql join query\nTags: mysql, sql\nProblem: mysql join que(...TRUNCATED) | "mysql join query mysql join query mysql sql product_id test.com product1 mysql join query I am usin(...TRUNCATED) | [0.004730224609375,0.009521484375,-0.00787353515625,-0.00127410888671875,0.006683349609375,0.0234375(...TRUNCATED) | [759,7,99247,33284,41,1294,12996,532,3034,277,418,17368,116,25632,157146,23180,947,33209,808,9030,11(...TRUNCATED) | [0.09783935546875,0.0150604248046875,0.1859130859375,0.231201171875,0.128173828125,0.075439453125,0.(...TRUNCATED) | embed |
66322110_c0 | 66322110 | sql | 0 | "Title: SQL Split to columns\nProblem title: SQL Split to columns\nTags: sql\nProblem: SQL Split to (...TRUNCATED) | "SQL Split to columns SQL Split to columns sql SQL Split F100 Item Form SQL Split to columns How spl(...TRUNCATED) | [0.0184326171875,-0.01611328125,-0.031982421875,0.0137939453125,-0.01165771484375,0.0037078857421875(...TRUNCATED) | [
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75177311_c1 | 75177311 | sql | 1 | "020-06-11 2020-06-12 84.0000000000000000 4 293.82 2020-02-16 2020-02-18 73.4550000000000000 4 2236.(...TRUNCATED) | "020-06-11 2020-06-12 84.0000000000000000 4 293.82 2020-02-16 2020-02-18 73.4550000000000000 4 2236.(...TRUNCATED) | [-0.00115203857421875,0.007720947265625,-0.025146484375,0.0145263671875,-0.00823974609375,0.00328063(...TRUNCATED) | [6,113266,44037,1662,11075,1530,14885,5,28568,197094,201,1702,135527,304,43627,2485,1819,361,172508,(...TRUNCATED) | [0.1363525390625,0.271484375,0.230712890625,0.2254638671875,0.240478515625,0.1707763671875,0.2695312(...TRUNCATED) | embed |
39470125_c0 | 39470125 | sql | 0 | "Title: Having sum values in sql\nProblem title: Having sum values in sql\nTags: sql, sql-server\nPr(...TRUNCATED) | "Having sum values in sql Having sum values in sql sql sql-server Having Having sum values in sql As(...TRUNCATED) | [0.01177978515625,-0.01409912109375,-0.0028228759765625,0.0128173828125,-0.0291748046875,-0.02453613(...TRUNCATED) | [171855,10554,142424,23,91,99247,39642,66596,121413,2053,3444,765,140992,70541,1632,17164,100,12638,(...TRUNCATED) | [0.1727294921875,0.258056640625,0.2257080078125,0.1068115234375,0.0506591796875,0.247802734375,0.139(...TRUNCATED) | embed |
9656883_c0 | 9656883 | sql | 0 | "Title: sql - get parent values based on id\nProblem title: sql - get parent values based on id\nTag(...TRUNCATED) | "sql - get parent values based on id sql - get parent values based on id sql sql - get parent values(...TRUNCATED) | [0.01806640625,-0.006591796875,-0.01611328125,-0.0024261474609375,-0.03173828125,-0.019775390625,-0.(...TRUNCATED) | [91,99247,2046,49129,142424,35509,98,3447,20,87,765,23180,24,4806,237,188183,44457,121413,11853,3871(...TRUNCATED) | [0.043304443359375,0.252685546875,0.140869140625,0.2349853515625,0.2415771484375,0.1622314453125,0.1(...TRUNCATED) | embed |
20661035_c0 | 20661035 | sql | 0 | "Title: SQL table column filling\nProblem title: SQL table column filling\nTags: sql\nProblem: SQL t(...TRUNCATED) | "SQL table column filling SQL table column filling sql SQL EFF_DT END_DATE SQL table column filling (...TRUNCATED) | [0.0086669921875,-0.0218505859375,-0.02685546875,0.0257568359375,-0.032958984375,0.021728515625,-0.0(...TRUNCATED) | [80866,23180,3365,316,26292,214,99247,241,30110,454,41072,39015,397,35746,17,765,62,3871,20805,468,3(...TRUNCATED) | [0.21875,0.198974609375,0.100341796875,0.117919921875,0.1651611328125,0.05975341796875,0.15893554687(...TRUNCATED) | embed |
End of preview. Expand in Data Studio
SQL StackOverflow Vector Dataset Datasheet
1. What This Dataset Is
This dataset is the SQL-specific vector shard of the Stack2Graph StackOverflow retrieval corpus. Each Hugging Face dataset repository contains exactly one language dataset.
It is optimized for dense+sparse retrieval, Qdrant restoration, and embedding-based RAG experiments.
It is used in the Stack2Graph project as the vector counterpart to the language-scoped RDF knowledge graph shards.
See the Stack2Graph repository for more details: https://github.com/tha-atlas/Stack2Graph
2. Repository Layout
dataset_manifest.json
question_metadata_*.parquet
chunk_records_*.parquet
question_records_*.parquet
dataset_manifest.json: language-scoped manifest for this dataset shard.question_metadata_*.parquet: per-question metadata and retrieval bookkeeping.chunk_records_*.parquet: chunk-level vector rows when parent-child indexing is enabled.question_records_*.parquet: question-level vector rows when chunking is disabled or exported alongside chunk data.
3. Data Model And Coverage
The dataset is derived from Stack Overflow questions selected for the SQL programming language. It contains the structured records needed to rebuild the Stack2Graph Qdrant collection for that language.
Coverage scope:
- records are retained when they match the Stack2Graph supported language-tag set
- this repository contains only the SQL shard
- the archive may contain both metadata-only and retrieval-ready vector rows depending on the export mode
4. Recommended Preprocessing
- Read
dataset_manifest.jsonfirst and use it as the source of truth for included Parquet files. - Load all Parquet shards for this repository into your vector indexing pipeline.
- Rebuild or restore the Qdrant collection
stackoverflow_sql_vector. - Preserve attribution and license metadata during downstream export.
5. Automatic Download And Vector DB Setup
You do not need to regenerate embeddings from GraphDB to use this dataset.
In the Stack2Graph repository, you can use the automation script
python -m experiment.load_hf_datasets_into_services --skip-kg to download dataset artifacts
and prepare the vector database service state automatically.
Typical workflow:
- Clone and configure Stack2Graph (
.envwith HF token and service paths). - Clone and configure Stack2Graph (
.envwith HF token and service paths). - Start required local services:
docker compose up -d
- Run the loader script:
python -m experiment.load_hf_datasets_into_services --skip-kg
For manual usage without automation, directly ingest the listed Parquet files into your vector database.
6. Quality Notes And Caveats
- A Stack Overflow question may belong to multiple language shards when tagged with multiple languages.
- Embeddings and sparse representations depend on the configured export pipeline and model versions.
- As with community-generated data, content may include noise, bias, and temporal drift.
7. Intended Use
- semantic retrieval and reranking
- RAG and hybrid retriever experiments
- vector database benchmarking and diagnostics
- language-scoped developer tooling research
8. Limitations
- Not a complete mirror of all Stack Overflow content.
- Not all export modes include the same row types or chunk layouts.
- Best used together with the Stack2Graph retrieval pipeline and Qdrant-compatible tooling.
9. Licensing And Attribution
This dataset inherits Stack Overflow source licensing and attribution requirements. Ensure compliant attribution and redistribution practices in all derived artifacts.
10. Suggested Citation
If you use this dataset, cite the Stack2Graph work:
- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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