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README.md

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+---
+license: mit
+task_categories:
+  - feature-extraction
+  - text-retrieval
+language:
+  - en
+pretty_name: Daemon Wiki FAISS Index
+size_categories:
+  - 10M<n<100M
+tags:
+  - faiss
+  - wikipedia
+  - embeddings
+  - retrieval
+---
+
+# Daemon Wiki FAISS Index
+
+Pre-built FAISS IVFPQ index and metadata for the [Daemon](https://github.com/lukehalleran/Daemon) conversational RAG system.
+
+## Contents
+
+| File | Size | Description |
+|------|------|-------------|
+| `vector_index_ivf.faiss` | ~2.2 GB | FAISS IVFPQ index (48 subquantizers x 8 bits, ~32x compression) |
+| `metadata.parquet` | ~12 GB | Row-group metadata (titles, text, timestamps) for zero-copy lookup |
+
+**Coverage:** ~41 million vectors from 6.5M+ English Wikipedia articles, embedded with `sentence-transformers/all-MiniLM-L6-v2` (384-dim).
+
+## Usage
+
+### Download
+
+```bash
+pip install huggingface_hub
+
+# Download both files into a local directory
+huggingface-cli download PaczkiLives/daemon-wiki-faiss \
+    --repo-type dataset \
+    --local-dir ~/daemon-wiki-data/wiki_data
+```
+
+### Point Daemon at the data
+
+Set `WIKI_DATA_ROOT` to the **parent** directory of `wiki_data/`:
+
+```bash
+# If you downloaded to ~/daemon-wiki-data/wiki_data/
+export WIKI_DATA_ROOT=~/daemon-wiki-data
+
+# Then launch Daemon
+python main.py
+```
+
+Or set individual paths directly:
+
+```bash
+export FAISS_INDEX_PATH=/path/to/wiki_data/vector_index_ivf.faiss
+export FAISS_META_PATH=/path/to/wiki_data/metadata.parquet
+```
+
+### Runtime requirements
+
+- **RAM:** ~2.6 GB (2.2 GB FAISS index + 0.4 GB embedding model). Metadata is read on-demand via zero-copy parquet row-group access — no DataFrame loaded into memory.
+- **Disk:** ~14.5 GB for both files.
+- **CPU:** Works on CPU. No GPU required.
+
+## How it was built
+
+```bash
+# From the Daemon repo:
+python scripts/build_faiss_index.py
+```
+
+The build pipeline:
+1. Downloads the latest English Wikipedia dump (~22 GB compressed)
+2. Parses XML, extracts article text
+3. Chunks articles at ~512 tokens with header-aware splitting
+4. Embeds chunks with `all-MiniLM-L6-v2` (384 dimensions)
+5. Trains an IVF4096,PQ48 index on a sample, then adds all vectors
+6. Writes metadata to a partitioned parquet file for zero-copy reads
+
+## License
+
+MIT — same as the Daemon project.