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README.md

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+# .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](scripts/README.md) for details on running the crawler, dataset generation, fine-tuning, and RAG indexing.
+
+## PyTorch Dataset Example
+
+```python
+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
+
+```python
+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])
+```