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README.md
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license: apache-2.0
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task_categories:
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tags:
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- rag
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- retrieval
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- embeddings
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- rag-security
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# TriShieldRAG — BeIR NQ
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| Model | sentence-transformers/all-mpnet-base-v2 |
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| Dimension | 768 |
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| Normalized | yes (unit L2, for inner-product search) |
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| Precision | float32 |
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| Format | 27 `.npy` chunks of 100k, ordered by filename |
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```python
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import
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```
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## Paper
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TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against
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Knowledge Corruption in Retrieval-Augmented Generation
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- arXiv: https://arxiv.org/abs/2607.23838
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- Codde: https://github.com/rpaut03l/poisonedrag-ragshield-group6-iitj_TriShieldRAG
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license: apache-2.0
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task_categories: [feature-extraction]
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tags: [rag, retrieval, embeddings, rag-security, faiss]
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# TriShieldRAG — BeIR NQ embeddings and FAISS index
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Artifacts for *TriShieldRAG: A Three-Ring Defense-in-Depth Framework
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Against Knowledge Corruption in Retrieval-Augmented Generation*.
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| File | Size | Description |
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| `emb_*.npy` (27) | 7.7 GB | Embeddings, 100k passages per chunk, corpus order |
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| `nq_ivf_nlist6550.index` | 7.8 GB | FAISS IVF index, nlist=6550, inner product |
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- Corpus: BeIR/nq `corpus` split, 2,681,468 passages
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- Model: sentence-transformers/all-mpnet-base-v2, 768-d, L2-normalised
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## Load the index
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```python
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import faiss
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from huggingface_hub import hf_hub_download
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p = hf_hub_download("rpaut03l/trishieldrag-nq-mpnet-embeddings",
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"nq_ivf_nlist6550.index", repo_type="dataset")
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index = faiss.read_index(p)
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index.nprobe = 409
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```
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Paper: https://arxiv.org/abs/2607.23838
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Code: https://github.com/rpaut03l/poisonedrag-ragshield-group6-iitj_TriShieldRAG
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