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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+  - text-retrieval
+  - question-answering
+language:
+  - en
+tags:
+  - retrieval
+  - rlvr
+  - search
+  - distractor-mining
+size_categories:
+  - 100K<n<1M
+---
+
+# RLVR-Env-Retrieval-Source-code-search-net-python
+
+RLVR-ready retrieval environment derived from [Nan-Do/code-search-net-python](https://huggingface.co/datasets/Nan-Do/code-search-net-python).
+
+**Author:** [Aman Priyanshu](https://huggingface.co/AmanPriyanshu)
+
+## What Is This
+
+A 100k-row retrieval QA dataset where each row contains a question, ground-truth chunks, and pre-mined distractor chunks (random + semantically similar). Designed for training and evaluating retrieval agents in an RLVR (Reinforcement Learning with Verifiable Rewards) setup — the agent searches through distractors to find the correct chunk(s).
+
+**Domain:** Python open-source functions from GitHub (CodeSearchNet)
+
+## Source
+
+Derived from [Nan-Do/code-search-net-python](https://huggingface.co/datasets/Nan-Do/code-search-net-python) (455,243 unique functions).
+Original license: **Apache 2.0** — retained here.
+
+## Schema
+
+### qa.parquet (100,000 rows)
+
+| Column | Type | Description |
+|---|---|---|
+| `qa_id` | string | Unique ID (`search_py_0`, `search_py_1`, ...) |
+| `question` | string | The retrieval query |
+| `gt_chunks` | JSON string | List of ground-truth chunk texts. 1 target code chunk per question (the function matching the summary) |
+| `random_chunks` | JSON string | List of random distractor texts. ~500 random code chunks (>=20 chars, deduplicated against gt and similar) |
+| `similar_chunks` | JSON string | List of hard-negative distractor texts. ~178 similar chunks via MiniLM cosine (<0.97) + char trigram edit-distance (<0.97 seq ratio), deduplicated |
+
+### metadata.parquet (100,000 rows)
+
+| Column | Type | Description |
+|---|---|---|
+| `qa_id` | string | Matches qa.parquet |
+| ... | ... | chunk_idx, func_name, repo, char_count |
+
+### chunks.parquet
+
+455,243 code chunks with MiniLM embeddings. Kept for reference — not needed at inference time.
+
+## Deduplication
+
+Within each row: gt > similar > random priority. No chunk text appears in more than one column per row. Similar chunks are internally deduplicated. Random chunks are filtered against both gt and similar.
+
+## How To Use
+
+```python
+import json
+import pyarrow.parquet as pq
+
+t = pq.read_table("qa.parquet")
+row = {col: t.column(col)[0].as_py() for col in t.column_names}
+gt = json.loads(row["gt_chunks"])
+distractors = json.loads(row["random_chunks"]) + json.loads(row["similar_chunks"])
+```
+
+## License
+
+Apache 2.0 (inherited from source dataset).