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| # Evidence-Graph Reranker | |
| An installable **single-algorithm**, training-free evidence-subset reranker for RAG. | |
| Unlike conventional independently scored query–document rerankers, it greedily selects a set of complementary passages using an explicitly directed title-reference graph. It can work with a pretrained MS MARCO MiniLM cross-encoder, a MiniLM dense encoder, or supplied relevance scores. | |
| ## Install | |
| ```bash | |
| pip install -e '.[neural]' | |
| ``` | |
| ## Quick start | |
| ```python | |
| from evidence_graph_reranker import EvidenceGraphReranker | |
| query = 'Who founded the company that released the Atlas device?' | |
| docs = [ | |
| {'title': 'Atlas device', 'text': 'The Atlas device was released by Novus Labs.'}, | |
| {'title': 'Novus Labs', 'text': 'Novus Labs was founded by Mira Chen.'}, | |
| {'title': 'Unrelated', 'text': 'A different device was invented elsewhere.'}, | |
| ] | |
| reranker = EvidenceGraphReranker(backend='cross-encoder', device='cpu') | |
| selected = reranker.rank(query, docs, k=2) | |
| print([(x['index'], x['title']) for x in selected]) | |
| ``` | |
| **No model download:** if you have scores from an existing retriever, call `rank_with_scores(query, docs, scores, k)`. Neural scoring always happens before the unchanged fixed graph rule. | |
| ## How selection works | |
| At each greedy step, every remaining candidate receives: | |
| `0.4 × per-query minmax neural relevance + 0.5 × directed link from an already selected passage + 0.2 × query/title match` | |
| The function is deterministic, has no learned parameters of its own, and does not use gold facts or answers at inference. The title feature uses an exact normalized title string, and the link feature checks whether a candidate's title appears in the body of a previously selected passage. Documents without titles simply lack these two signals and are selected by the underlying neural scores. | |
| Model checkpoints stay at their original locations: | |
| - `cross-encoder/ms-marco-MiniLM-L6-v2` (query+passage WordPiece max length 256) | |
| - `sentence-transformers/all-MiniLM-L6-v2` (dense WordPiece max length 128) | |
| ## Public Hugging Face Space | |
| The repository root has `app.py`, `README.md` with Space metadata and `requirements.txt`. Upload these files to a new **Gradio Space**. Model weights download on first use. No model weights or dataset copies are redistributed in this package. | |
| ## Benchmark results | |
| See `results/six_dataset_results.csv`, `MODEL_CARD.md`, and `results/methodology.md`. All six evaluations use the **identical feature extractor and greedy selector**. HotpotQA/2Wiki were reselected from cached pretrained neural logits and embeddings under the common algorithm, not rescored by rerunning the neural forward passes. This distinction is indicated in the timing report. The six benchmarks have different candidate pools and should not be pooled into a single headline accuracy number. | |
| ## License | |
| MIT for this source code; external model weights and benchmark data retain their original licenses and usage terms. | |