A personal offline RAG system for offensive security knowledge. Ingest your writeups, lab solutions, bug bounty reports, and security blogs, then query them in natural language during engagements. No cloud, no SaaS, your knowledge stays yours.

What it does

ZETSU turns your accumulated knowledge into a queryable assistant. Instead of grepping through markdown files or remembering which writeup had that certipy command, you ask naturally:

how do i escalate with SeImpersonatePrivilege
what did i do after getting ADFS access
sliver socks5 pivot setup
explain ESC8 vs ESC4

It retrieves from your actual notes first, then generates an answer grounded in what you've documented, not generic internet knowledge.

Architecture

Ingestion pipeline

Query pipeline

Features

• FARR extraction: at ingest time, an LLM reads each section of your writeups and extracts structured attack steps (Finding, Action, Reasoning, Result). What you retrieve is a semantic unit, not a random 800-token window.
• Hybrid retrieval: BM25 for exact token matching (tool names, CVE numbers, CLI flags) + vector search for semantic similarity. RRF fusion combines both.
• Cross-encoder reranker: reorders retrieved chunks by actual relevance before sending to the LLM.
• Two response styles: Operator mode leads with exact commands, Concept mode leads with reasoning. Same retrieval, different presentation. Toggle with Ctrl+M in TUI.
• Multiple LLM backends: Anthropic, OpenAI-compatible (DeepSeek, etc.), or local Ollama. Separate backends for ingest and query.
• Multiple source types: local markdown files, single URLs, GitHub wikis, Atom/RSS feeds.
• Persistent chat history: saved to ~/.zetsu/history/YYYY-MM-DD.json, browsable with /history.
• TUI + Web UI + CLI: use what fits the context. TUI during engagements, Web UI for studying, CLI for scripting.

Installation

git clone https://github.com/chaelsoo/zetsu
cd zetsu
pip install -r requirements.txt

Set up your API key:

cp .env.example .env
# edit .env and add your key

Quick start

1. Add your writeups

Drop .md files into docs/. Notion exports, Obsidian notes, blog exports, anything markdown works.

2. Configure sources in config.toml

[llm]
backend      = "openai"
openai_model = "deepseek-chat"
base_url     = "https://api.deepseek.com/v1"

[[sources]]
type    = "markdown_dir"
path    = "./docs"
name    = "my_writeups"
extract = "farr"
enabled = true

3. Ingest

python zetsu.py ingest

4. Use it

python zetsu.py tui       # terminal UI
python zetsu.py web       # browser at localhost:8000
python zetsu.py ask "how do i abuse SeImpersonatePrivilege"

CLI reference

python zetsu.py ingest                 # ingest enabled sources
python zetsu.py ingest --force         # wipe and rebuild everything
python zetsu.py ingest --dry-run       # estimate without making LLM calls
python zetsu.py ingest --all-sources   # enable all sources regardless of enabled flag
python zetsu.py tui                    # terminal UI
python zetsu.py web                    # web UI
python zetsu.py ask "query"            # one-shot CLI
python zetsu.py ask "query" --style concept
python zetsu.py stats                  # vector store stats

TUI keybindings

Key	Action
Enter	Send query
Ctrl+M	Toggle Operator / Concept mode
Ctrl+Y	Copy last response to clipboard
Ctrl+L	Clear session
Ctrl+C	Quit
/help	Show commands
/stats	Vector store + history stats
/history	Today's past queries
/clear	Clear session

Source types

Type	Use for	Extract mode
markdown_dir	Your writeups, notes	farr
markdown_file	Single markdown file	farr
url	Tool wikis, reference pages	headers
atom	Security blogs with feeds	farr+narrative

Extract modes

• farr: LLM extracts discrete attack steps as structured JSON (Finding, Action, Reasoning, Result). Best for writeups with clear attack chains.
• farr+narrative: same as farr, plus a prose summary capturing the author's reasoning. Best for blogs where the thought process matters.
• headers: no LLM, just split on headers and keep verbatim. Best for command references and tool documentation.

---

LLM backends

Configure in config.toml. Separate backends for ingestion (bulk extraction) and queries (generation):

[llm]                          # query time
backend      = "openai"
openai_model = "deepseek-chat"
base_url     = "https://api.deepseek.com/v1"

[ingest]                       # ingest time
backend      = "openai"
openai_model = "deepseek-chat"
base_url     = "https://api.deepseek.com/v1"
workers      = 8               # parallel files during FARR extraction

Supported backends: anthropic, openai (any OpenAI-compatible API), ollama.

Important

ChromaDB is run locally by default, which loads the full vector index into RAM. For large corpora (10k+ chunks) or memory-constrained machines, it is strongly recommended to run ChromaDB as a remote server instead. See ChromaDB server docs for setup. Remote support in ZETSU is planned.

Benchmark

Evaluated across 910 questions covering 12 offensive security categories (ADCS, Kerberos, AD enumeration, MSSQL, Sliver C2, privilege escalation, web attacks, credentials, lateral movement, cloud/Entra ID, OPSEC, tools).

Metric	Result
Questions answered	910 / 910 (100%)
Answers with code/commands	842 / 910 (93%)
Context gaps (model admitted missing info)	66 / 910 (7.3%)
Avg retrieval time	68ms
Avg total response time	3.8s

Top sources contributing to answers: personal HTB writeups, dirkjanm blog, shenaniganslabs research, HackTricks ADCS, Sliver wiki, netexec cheatsheet.

In simple words, you need to make sure to add good & structured resources that you want the RAG to ingest, this project focueses 100% on using a knowledge base, not trusting the LLM's memory or trained on knowledge.

Full evaluation dataset and results in eval/.

References

• rank-bm25, BM25 retrieval
• FARR concept from CIPHER
• RAG pipeline inspired by rag-chatbot

I will be documenting in a blog the usage and how effective it is very soon, stay tuned.