Create README.md

README.md

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+---
+language:
+- en
+license: other
+task_categories:
+- question-answering
+tags:
+- biomedical
+- RAG
+- BioASQ
+- FAISS
+- PISA
+- information-retrieval
+pretty_name: Lean RAG Indexes for BioASQ Task 14b
+size_categories:
+- 10M<n<100M
+---
+
+# Lean RAG Indexes for BioASQ
+
+Prebuilt retrieval indexes for the **Lean RAG Pipelines for Biomedical Question Answering** project, developed as part of a Master's dissertation at LASIGE, University of Lisbon.
+
+These indexes support a hybrid (BM25 + dense retrieval) pipeline evaluated on
+[BioASQ Task 14b](http://bioasq.org/).
+
+## Repository Structure
+
+faiss/
+
+└── pubmed2026_ivfsq8_raw_matryoshka.index   # Dense vector index
+
+pisa/
+
+├── pubmed2026_soStopw/                                # BM25 index with stopword removal only
+
+│   └── ...
+
+└── pubmed2026_comDois/                        # BM25 index with stopword removal + Porter2 stemming
+
+└── ...
+
+## Indexes
+
+### FAISS (Dense Retrieval)
+- **File:** `faiss/pubmed2026_ivfsq8_raw_matryoshka.index`
+- **Index type:** IVF + Scalar Quantizer (SQ8)
+- **Embedding model:** [NeuML/pubmedbert-base-embeddings-matryoshka](https://huggingface.co/NeuML/pubmedbert-base-embeddings-matryoshka)
+- **Corpus:** PubMed Annual Baseline 2026
+
+### PISA — Stopwords only (`pisa/pubmed2026_soStopw/`)
+- **Retrieval model:** BM25
+- **Preprocessing:** Stopword removal using PyTerrier's default stopword list
+- **Stemming:** None
+- **Corpus:** PubMed Annual Baseline 2026
+
+### PISA — Stopwords + Porter2 (`pisa/pubmed2026_comDois/`)
+- **Retrieval model:** BM25
+- **Preprocessing:** Stopword removal using PyTerrier's default stopword list
+- **Stemming:** Porter2
+- **Corpus:** PubMed Annual Baseline 2026
+
+## Usage
+
+### Loading the FAISS index
+
+```python
+import faiss
+
+index = faiss.read_index("faiss/pubmed2026_ivfsq8_raw_matryoshka.index")
+
+index.nprobe= 512 #recommended
+```
+
+Or download it first with:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="dantunes6/lean-rag-indexes",
+    repo_type="dataset",
+    local_dir="./lean-rag-indexes"
+)
+```
+
+### Using the PISA index
+
+The PISA index is used via [PISA](https://pyterrier.readthedocs.io/en/stable/ext/pyterrier_pisa/). Point your retriever at the `pisa/` directory after downloading.
+
+## Corpus Notice
+
+The corpus used to build these indexes consists of PubMed abstracts from the **PubMed Annual Baseline 2026**, distributed by the [National Library of Medicine (NLM)](https://pubmed.ncbi.nlm.nih.gov/download/).
+Please ensure you comply with NLM's terms of use before redistributing this data.
+
+
+## Credits
+
+Developed at [LASIGE](https://www.lasige.pt/), University of Lisbonn by Diogo Antunes.  
+Supervised by Francisco M. Couto.