rag-ai-scientist 0.1.1

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 references,
• an MCP server entrypoint for Cursor/agent integrations,
• packaged reusable skills under rag_ai_scientist/skills/.

Installation

From PyPI (recommended for end users)

Project page: rag-ai-scientist on PyPI

python -m pip install rag-ai-scientist==0.1.0

Or install the latest published release:

python -m pip install rag-ai-scientist

From source (recommended while developing)

uv venv .venv
source .venv/bin/activate
uv pip install -e .

If uv is not available, fallback to:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .

Recommended isolation: keep this in a dedicated environment (for example venvs/rag-ai-scientist) rather than reusing analysis environments such as ecalgnn311.

Verify install

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

Quickstart

1. Initialize configs/references.yaml for your analysis repo:

rag-ai-scientist init-references \
  --project-root . \
  --references-dir /path/to/references

2. Build the local RAG database:

rag-ai-scientist setup-rag --project-root . --force

3. Start the MCP server:

rag-ai-scientist mcp --project-root .

CLI Commands

init-references

Creates configs/references.yaml with source paths, chunking, and doc-type rules.

Useful options:

• --references-dir path containing .pdf/.md/.txt/.tex/.py/.rst
• --collection-name default: rag-ai-scientist
• --chunk-size, --chunk-overlap
• --scientific-chunk-size, --scientific-chunk-overlap
• --force overwrite existing config

setup-rag

Indexes references and writes ChromaDB to .cursor/rag_db.

Useful options:

• --force rebuild from scratch
• --collection-name override config collection
• --chunk-size, --chunk-overlap runtime overrides

mcp

Starts the stdio MCP server for Cursor or compatible MCP clients.

Cursor MCP Configuration

Example ~/.cursor/mcp.json entry:

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

Running Agents With Separate Training Environments

If agents should run training/inference scripts and update configs, use two environments in parallel:

• rag-ai-scientist environment: runs MCP server and agent logic.
• analysis/training environment: runs model training and inference commands.

This avoids dependency conflicts while still letting agents orchestrate the full workflow for another repository.

Recommended architecture

1. Keep a dedicated environment for rag-ai-scientist:

cd /path/to/rag-ai-scientist-installable
uv venv .venv
source .venv/bin/activate
uv pip install -e .

2. Keep your analysis repository and its own environment separate:

• repo: /path/to/analysis-repo
• env: /path/to/analysis-env (conda or venv)

3. Start MCP from the rag-ai-scientist environment, but point it to the analysis repo:

rag-ai-scientist mcp --project-root /path/to/analysis-repo

4. Let agents launch analysis commands explicitly inside the analysis environment (for example via conda run -p), instead of relying on ambient shell state.

Safe command wrapper for agent execution

Create a wrapper script in the analysis repo (example: /path/to/analysis-repo/scripts/run_training.sh) and let agents call only this script:

#!/usr/bin/env bash
set -euo pipefail

ANALYSIS_ENV="/path/to/analysis-env"
ANALYSIS_REPO="/path/to/analysis-repo"

cd "$ANALYSIS_REPO"
exec conda run -p "$ANALYSIS_ENV" python scripts/train.py "$@"

This gives deterministic execution and avoids accidental environment drift.

Guardrails for autonomous edits and runs

• Restrict editable files to a whitelist (for example configs/**/*.yaml).
• Keep one output directory per run (runs/<timestamp>_<tag>).
• Save the exact config snapshot and command used for each run.
• Use a lock file to prevent concurrent training launches.
• Require human approval before expensive or long GPU jobs.

Package Layout

rag_ai_scientist/
  cli.py                  # Installable CLI entrypoint
  mcp_server.py           # MCP server implementation
  skills/                 # Packaged reusable skills
rag/
  index_documents.py      # Indexing backend used by setup-rag
configs/
  references.example.yaml # Example indexing config

Development

python -m pip install -e .
python -m pip install build
python -m build

License

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

Security Notes

• Never commit secrets (.env, API keys, tokens).
• Keep local vector stores and credentials in gitignored paths.
• Review indexed sources before sharing databases externally.