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 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

Unzip the CVE archive
```
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.

Prepare the corpus

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

Build the Chroma index with Mixedbread embeddings
```
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
Query with reranking
```
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).