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.
Author: Aman Priyanshu
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 (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
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).