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.gitattributes CHANGED
@@ -58,3 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ qdrant_snapshots/archives/stackoverflow_dart_vector.snapshot.zst.part_004 filter=lfs diff=lfs merge=lfs -text
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+ qdrant_snapshots/archives/stackoverflow_dart_vector.snapshot.zst.part_001 filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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 - Dart
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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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+
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+ # Dart StackOverflow Vector Dataset
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+
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+ ## Summary
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+
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+ This repository contains the Dart shard of the Stack2Graph vector retrieval dataset.
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+ Each Hugging Face dataset repository contains one programming-language shard and is intended to restore or rebuild the Qdrant collection `stackoverflow_dart_vector`.
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+
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+ The dataset is the vector counterpart to the Stack2Graph RDF knowledge graph. It is designed for hybrid retrieval, graph entry-point finding, and retrieval-augmented generation experiments over Stack Overflow content.
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+
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+ Stack2Graph source:
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+ [https://github.com/tha-atlas/Stack2Graph](https://github.com/tha-atlas/Stack2Graph)
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+
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+ ## Repository Layout
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+
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+ ```text
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+ README.md
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+ dataset_manifest.json
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+ qdrant_snapshots/
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+ collections_manifest.json
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+ stackoverflow_dart_vector.tar.zst
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+ stackoverflow_dart_vector.snapshot.zst.part_000
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+ ...
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+ question_metadata/
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+ r0_00000.parquet
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+ chunk_records/
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+ r0_00000.parquet
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+ question_records/
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+ r0_00000.parquet
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+ ```
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+
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+ - `dataset_manifest.json`: language-scoped manifest listing the files in this shard.
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+ - `qdrant_snapshots/collections_manifest.json`: language-scoped Qdrant snapshot manifest.
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+ - `qdrant_snapshots/stackoverflow_dart_vector.*`: optional Qdrant restore artifact files for `stackoverflow_dart_vector`.
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+ - `question_metadata/*.parquet`: question-level metadata used by parent-child chunk retrieval.
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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 non-chunked export is used.
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+
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+ The Hugging Face upload is one repository per language. During upload, the local leading language directory is removed, so local files such as `dart/chunk_records/r0_00000.parquet` appear in this repository as `chunk_records/r0_00000.parquet`.
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+
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+ ## Data Model
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+
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+ Rows are derived from Stack Overflow questions tagged for Dart.
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+
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+ The current Stack2Graph vector pipeline uses:
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+
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+ - dense vectors from `Qwen/Qwen3-Embedding-8B`
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+ - 4096-dimensional dense embeddings with last-token pooling
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+ - instruction-aware query embedding for retrieval
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+ - sparse lexical vectors from `BAAI/bge-m3`
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+ - unified question text containing title, tags, cleaned problem text, and code
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+ - optional parent-child indexing where chunk hits collapse back to parent question IDs
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+
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+ When Qdrant snapshots are included, they are the fastest restore path. The Parquet files remain the portable fallback for rebuilding the collection.
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+
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+ ## Coverage
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+
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+ This repository contains only the Dart shard. A Stack Overflow question can appear in more than one language shard when it has multiple programming-language tags.
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+
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+ The dataset is not a complete Stack Overflow mirror. Full question and answer graph context lives in the corresponding Stack2Graph knowledge graph artifacts; the vector dataset stores retrieval rows, vectors, sparse weights, and routing metadata.
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+
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+ ## Recommended Use
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+
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+ Use this dataset for:
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+
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+ - semantic and hybrid retrieval
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+ - Qdrant restore or ingestion tests
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+ - Stack2Graph RAG experiments
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+ - graph entry-point retrieval before QLever graph expansion
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+ - vector database benchmarking and diagnostics
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+
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+ This dataset is not intended as a standalone supervised training dataset.
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+
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+ ## Restore With Stack2Graph
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+
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+ You do not need to regenerate embeddings to use this dataset.
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+
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+ Typical workflow:
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+
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+ 1. Clone Stack2Graph and configure `.env` with service paths and HF token.
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+ 2. Start local services:
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+
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+ ```bash
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+ docker compose up -d
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+ ```
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+
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+ 3. Download and restore vector artifacts:
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+
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+ ```bash
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+ python -m experiment.sources.hf --skip-kg
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+ ```
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+
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+ The loader restores Qdrant snapshots when present and falls back to Parquet ingestion when snapshots are unavailable.
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+
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+ ## Manual Use
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+
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+ For manual use, inspect `dataset_manifest.json`, then either:
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+
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+ - restore the Qdrant snapshot artifacts under `qdrant_snapshots/`, or
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+ - ingest the listed Parquet shards into a compatible vector database.
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+
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+ The target Qdrant collection name is:
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+
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+ ```text
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+ stackoverflow_dart_vector
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+ ```
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+
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+ ## Quality Notes
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+
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+ - Embedding quality depends on the configured Stack2Graph export pipeline and model versions.
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+ - Sparse text can be generated from the same unified text as dense embeddings or from a lexical variant, depending on export configuration.
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+ - Community-generated Stack Overflow content may contain errors, outdated information, bias, or incomplete answers.
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+ - Rebuilding from Parquet may produce operational differences if Qdrant collection settings differ from the original export.
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+
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+ ## License And Attribution
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+
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+ This dataset is derived from Stack Overflow content and is distributed under `CC-BY-SA-4.0`.
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+ Preserve required attribution and license notices when redistributing derived artifacts.
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+
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+ ## Citation
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+
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+ If you use this dataset, cite the Stack2Graph project and preserve Stack Overflow attribution requirements:
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+
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+ - Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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