File size: 3,587 Bytes
c980379 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# Mixedbread CVE RAG Workflow
Scripts in this directory let you build a Retrieval Augmented Generation (RAG) workflow over the `cvelistV5` dataset using Mixedbread's embedding and reranking models loaded locally. Vector storage relies on a persisted [Chroma](https://www.trychroma.com/) database. No steps have been executed yet—run them when you're ready.
## Prerequisites
- Python 3.10+
- Packages: `sentence-transformers`, `chromadb`, `numpy`, `torch` (or `torch` with CUDA for GPU)
- Hugging Face account (optional, only needed for private models or rate-limited downloads)
- Optional: `HF_API_TOKEN` environment variable if downloading models requires authentication
You can copy `env.example` to `.env` (or export vars directly) and populate any overrides.
## Workflow
1. **Unzip the CVE archive**
```bash
python scripts/unzip_cvelist.py
```
- Reads `testing/cvelistV5-main.zip`
- Extracts to `data/cvelistV5-main/`
- Use `--force` to re-extract if the destination already exists.
2. **Prepare the corpus**
```bash
python -m rag_mixedbread.prepare_cve_corpus \
--cve-root data/cvelistV5-main \
--output rag_mixedbread/artifacts/cve_corpus.jsonl
```
- Walks every CVE JSON file
- Normalizes metadata + descriptions
- Splits long descriptions into overlapping character chunks
3. **Build the Chroma index with Mixedbread embeddings**
```bash
python -m rag_mixedbread.build_index \
--corpus rag_mixedbread/artifacts/cve_corpus.jsonl \
--batch-size 8 \
--normalize \
--reset
```
- Loads `mixedbread-ai/mxbai-embed-large-v1` locally (downloads on first run)
- Embeds all corpus chunks and persists into Chroma at `rag_mixedbread/index/`
- `--reset` wipes the existing collection before re-building
- Models run on CPU by default; set `RAG_DEVICE=cuda` for GPU acceleration
4. **Query with reranking**
```bash
python -m rag_mixedbread.query_service \
"buffer overflow in ssh" \
--top-k 20 \
--top-n 5 \
--normalize
```
- Loads embedding and reranker models locally (downloads on first run)
- Retrieves similar chunks from Chroma
- Reranks candidates using `mixedbread-ai/mxbai-rerank-base-v2` CrossEncoder
- Prints human-friendly summaries or JSON (`--json`) for automation
## Configuration
`rag_mixedbread/config.py` centralizes default paths and settings:
- Archive path: `testing/cvelistV5-main.zip`
- Extracted CVE directory: `data/cvelistV5-main`
- Corpus output: `rag_mixedbread/artifacts/cve_corpus.jsonl`
- Chroma directory: `rag_mixedbread/index/` (collection `cve_chunks` by default)
Environment variables override defaults:
| Variable | Purpose | Default |
| --- | --- | --- |
| `HF_API_TOKEN` | Optional: for private models or rate-limited downloads | _none_ |
| `RAG_EMBED_MODEL` | Embedding model ID (Hugging Face Hub) | `mixedbread-ai/mxbai-embed-large-v1` |
| `RAG_RERANK_MODEL` | Rerank model ID (Hugging Face Hub) | `mixedbread-ai/mxbai-rerank-base-v2` |
| `RAG_EMBED_BATCH` | Batch size during indexing | `8` |
| `RAG_DEVICE` | Device for model inference (`cpu` or `cuda`) | `cpu` |
| `RAG_CHROMA_COLLECTION` | Collection name inside Chroma | `cve_chunks` |
## Notes
- The scripts intentionally avoid running automatically; invoke them manually when ready.
- Models are downloaded from Hugging Face Hub on first use (cached in `~/.cache/huggingface/`).
- For GPU acceleration, install PyTorch with CUDA and set `RAG_DEVICE=cuda`.
- Adjust `--batch-size` based on available memory (larger batches = faster but more memory).
|