rag-ai-scientist 0.1.3

Installable RAG + MCP skills framework with a reliability-loop workflow.

rag-ai-scientist

Installable toolkit for local RAG indexing + MCP serving in scientific workflows.

rag-ai-scientist gives you:

• a CLI to initialize and build a local vector database from your papers and notes,
• an MCP server entrypoint for Cursor / agent integrations,
• packaged skills under rag_ai_scientist/skills/ (workflow checklists—no Git clone needed).

On PyPI, only this README is shown; detailed guides live on GitHub (absolute links below).

---

End-user workflow (pip only — no clone required)

You install from PyPI, create any folder for your project, put your research materials there, index once, then connect Cursor.

Full step-by-step: Getting started — install → references/ → init-references → setup-rag → MCP → update notes and rebuild.

Minimal command sequence (after pip install rag-ai-scientist):

mkdir -p ~/my-ai-scientist/references
cd ~/my-ai-scientist
# Add your own .md / .pdf files under references/

rag-ai-scientist init-references --project-root . --references-dir ./references
rag-ai-scientist setup-rag --project-root . --force
rag-ai-scientist mcp --project-root .    # usually configured once inside Cursor — see Getting started link above

• query_analysis_knowledge answers from your indexed files.
• get_skill loads packaged skills (e.g. cms-higgs-opendata) without indexing anything extra.

You update your AI scientist by editing files under references/ (and configs/references.yaml if paths change), then setup-rag --force again.

---

Installation

From PyPI (recommended)

python -m pip install rag-ai-scientist

Pinned example:

python -m pip install rag-ai-scientist==0.1.3

PyPI project page: rag-ai-scientist

Verify

rag-ai-scientist --help
python -c "import rag_ai_scientist; print(rag_ai_scientist.__version__)"

From source (maintainers / contributors only)

git clone https://github.com/uzzielperez/rag-ai-scientist.git
cd rag-ai-scientist
git checkout dev   # or your working branch
python3 -m venv .venv && source .venv/bin/activate
python -m pip install -e .

Isolation tip: use a dedicated venv (e.g. ~/venvs/rag-ai-scientist) instead of mixing with heavy analysis stacks.

---

CLI commands

Command	Purpose
init-references	Writes configs/references.yaml pointing at your references directory.
setup-rag	Indexes sources into .cursor/rag_db.
mcp	Starts the stdio MCP server — point --project-root at the same folder you indexed.

Common flags: --project-root, --force (rebuild index), --references-dir (with init-references).

---

Cursor MCP configuration

Register the server so Cursor runs it with your project path:

{
  "mcpServers": {
    "rag-ai-scientist": {
      "command": "rag-ai-scientist",
      "args": ["mcp", "--project-root", "/absolute/path/to/my-ai-scientist"]
    }
  }
}

See Getting started for optional .cursor/.env (LLM keys).

---

Packaged skills and examples

• Skills ship inside the installed package. Access via MCP get_skill (e.g. cms-higgs-opendata). No clone required.
• Examples / MCP access explains get_skill, Cursor wiring, and optional curated markdown for maintainers who ship a full docs tree. End users normally only need their own files under references/.

---

Running agents beside a separate lab environment

If training runs use a different conda/venv than rag-ai-scientist:

1. Install rag-ai-scientist in its own small venv.
2. Keep --project-root pointed at your research folder.
3. Run heavy jobs via explicit wrappers (conda run, scripts) from the agent — see Runbook for patterns.

---

Repository layout (when developing from source)

rag_ai_scientist/
  cli.py                  # CLI entrypoint
  mcp_server.py           # MCP server
  skills/                 # Packaged skills (ship in wheel)
rag/
  index_documents.py      # Indexer used by setup-rag
configs/
  references.example.yaml # Example only — users run init-references instead
docs/
  GETTING_STARTED.md      # Primary user guide (pip-only path)
  examples/               # Maintainer docs / optional narratives

Browse on GitHub: docs/.

---

Development & PyPI releases

Contributor workflow and release steps: DEV_README.md.

---

License

• Open-source: AGPL-3.0-or-later (LICENSE)
• Commercial: see LICENSE-COMMERCIAL.md

---

Security notes

• Never commit secrets (.env, API keys).
• Treat .cursor/rag_db as sensitive if your indexed PDFs are sensitive.