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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).
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