.NET Runtime Training Data and Index
This directory contains data for fine-tuning models and building RAGs for the dotnet/runtime repository.
Overview
- data/: Contains all datasets and indexes.
- raw_sample/: Directories of raw PRs and diffs as collected from GitHub.
- raw_data.tar: Collected PRs and diffs from GitHub.
- samples/: JSONL files with processed samples suitable for dataset generation (e.g., HuggingFace).
- processed/: Parquet or JSONL files for fine-tuning (e.g.,
train.parquet,test.parquet). - faiss/: Vector indexes for RAG workflows.
- scripts/: Python and Node.js scripts for crawling, processing, and indexing.
Data Structure
data/
├── raw_data.tar
├── raw_sample/
│ ├── prs/
│ ├── diffs/
│ └── ... (extracted files)
├── processed/
│ ├── train.parquet
│ └── test.parquet
└── faiss/
└── index
- raw_data/: Contains the full, unprocessed PR and diff data as collected from GitHub.
- samples/: Contains JSONL files formatted for use with HuggingFace Datasets.
- processed/: Contains files for model fine-tuning.
- faiss/: Contains vector indexes for fast retrieval in RAG setups.
Generated dataset
PR is considered as a timeline with events. Input is PR metadata (title, description, label) and commit n-1, with all events between n-1 and n. Completion is n. It is possible to filter by time, label, authors, etc.
Scripts
See scripts/README.md for details on running the crawler, dataset generation, fine-tuning, and RAG indexing.
PyTorch Dataset Example
from datasets import load_dataset
# Load Parquet train/test splits
train = load_dataset("parquet", data_files="data/processed/train.parquet", split="train")
test = load_dataset("parquet", data_files="data/processed/test.parquet", split="train")
RAG Vector Search Example
import faiss
import numpy as np
# Load FAISS index
index = faiss.read_index("data/faiss/index.faiss")
# Example query embedding (replace with your embedding)
query_embedding = ...
# Search
D, I = index.search(query_embedding.reshape(1, -1), k=5)
print("Top 5 similar PR indices:", I[0])