<a name="top"></a>
<div align="center">

<img src="https://capsule-render.vercel.app/api?type=rect&color=0:6b46c1,100:2b6cb0&height=120&section=header&text=RAGKIT&fontSize=48&fontColor=ffffff&fontAlignY=58" width="100%" alt="RAGKIT"/>

# RAGKIT

### Batteries-included local RAG pipeline — ingest, index, serve

<img src="https://readme-typing-svg.demolab.com?font=Fira+Code&size=18&duration=3500&pause=1000&color=6B46C1&center=true&vCenter=true&width=720&lines=Batteriesincluded+local+RAG+pipeline++ingest+index+serve;Self-hostable+%C2%B7+MCP-native+%C2%B7+CI-ready+%C2%B7+polyglot" width="720"/>

[![PyPI](https://img.shields.io/pypi/v/cognis-ragkit.svg?color=6b46c1)](https://pypi.org/project/cognis-ragkit/) [![CI](https://github.com/cognis-digital/ragkit/actions/workflows/ci.yml/badge.svg)](https://github.com/cognis-digital/ragkit/actions) [![License: COCL 1.0](https://img.shields.io/badge/License-COCL%201.0-2b6cb0.svg)](LICENSE) [![Suite](https://img.shields.io/badge/Cognis-Neural%20Suite-6b46c1.svg)](https://github.com/cognis-digital)

*AI Agents & LLMOps — build, route, evaluate, and secure agents.*

</div>

```bash
pip install cognis-ragkit
ragkit scan .            # → prioritized findings in seconds
```


<!-- cognis:example:start -->
## 🔎 Example output

Real, reproducible output from the tool — runs offline:

```console
$ ragkit-emit --version
ragkit 0.1.0
```

```console
$ ragkit-emit --help
usage: ragkit [-h] [--version] [--format {table,json}]
              {index,search,ask,stats} ...

Local RAG pipeline: ingest, index, serve.

positional arguments:
  {index,search,ask,stats}
    index               build an index from files/directories
    search              retrieve top-k chunks for a query
    ask                 extractive cited answer for a query
    stats               show index statistics

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format
```

> Blocks above are real `ragkit` output — reproduce them from a clone.

**Sample result format** _(illustrative values — run on your own data for real findings):_

```
{
"Findings": [
    {
        "ID": "12345",
        "Title": "Suspicious Activity Detected",
        "Description": "An unknown entity accessed our network.",
        "Severity": "High"
    },
    {
        "ID": "67890",
        "Title": "Malware Infection Found",
        "Description": "A virus was detected on a workstation.",
        "Severity": "Medium"
    }
]
}
```

<!-- cognis:example:end -->

## Usage — step by step

`ragkit` is a local, dependency-light RAG pipeline: ingest + TF-IDF index, search, and extractive cited answers. Console script: `ragkit`.

1. **Install**:
   ```bash
   pipx install ragkit     # or: pip install ragkit
   ```
2. **Build an index** from files or directories of `.txt` / `.md` documents (written to `.ragkit/index.json` by default):
   ```bash
   ragkit index ./docs --chunk-size 80 --overlap 20
   ```
3. **Search** for the top-k most relevant chunks:
   ```bash
   ragkit search "how do retries work" --top-k 5
   ```
4. **Ask** for an extractive, cited answer and read it as JSON (the `--format` flag is global, before the subcommand):
   ```bash
   ragkit --format json ask "what is the retention policy" --top-k 3 | jq '.answer, .citations'
   ```
5. **Inspect the index in CI/automation** to confirm it built and is fresh:
   ```bash
   ragkit --format json stats | jq '.documents, .chunks'
   ```

## Contents

- [Why ragkit?](#why) · [Features](#features) · [Quick start](#quick-start) · [Example](#example) · [Architecture](#architecture) · [AI stack](#ai-stack) · [How it compares](#how-it-compares) · [Integrations](#integrations) · [Install anywhere](#install-anywhere) · [Related](#related) · [Contributing](#contributing)

<a name="why"></a>
## Why ragkit?

self-host RAG

`ragkit` is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="features"></a>
## Features

- ✅ Tokenize
- ✅ Chunk Text
- ✅ Ingest Paths
- ✅ Build Index
- ✅ Save Index
- ✅ Load Index
- ✅ Answer
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (`ports/`)

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="quick-start"></a>
## Quick start

```bash
pip install cognis-ragkit
ragkit --version
ragkit scan .                       # scan current project
ragkit scan . --format json         # machine-readable
ragkit scan . --fail-on high        # CI gate (non-zero exit)
```

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="example"></a>
## Example

```text
$ ragkit scan .
  [HIGH    ] RAG-001  example finding             (./src/app.py)
  [MEDIUM  ] RAG-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms
```

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="architecture"></a>
## Architecture

```mermaid
flowchart LR
  IN[sources] --> P[ragkit<br/>curate + validate]
  P --> OUT[query / analysis]
```

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="ai-stack"></a>
## Use it from any AI stack

`ragkit` is interoperable with every popular way of using AI:

- **MCP server** — `ragkit mcp` (Claude Desktop, Cursor, Cognis.Studio, [uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet))
- **OpenAI-compatible / JSON** — pipe `ragkit scan . --format json` into any agent or LLM
- **LangChain · CrewAI · AutoGen · LlamaIndex** — wrap the CLI/JSON as a tool in one line
- **CI / scripts** — exit codes + SARIF for non-AI pipelines

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="how-it-compares"></a>
## How it compares

| | **Cognis ragkit** | RAGFlow |
|---|:---:|:---:|
| Self-hostable, no account | ✅ | varies |
| Single command, zero config | ✅ | ⚠️ |
| JSON + SARIF for CI | ✅ | varies |
| MCP-native (AI agents) | ✅ | ❌ |
| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |
| Open license | ✅ COCL | varies |

*Built in the spirit of **RAGFlow**, re-framed the Cognis way. Missing a credit? Open a PR.*

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="integrations"></a>
## Integrations

Pipes into your stack: **SARIF** for code-scanning, **JSON** for anything, an **MCP server** (`ragkit mcp`) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See [`docs/INTEGRATIONS.md`](docs/INTEGRATIONS.md).

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="install-anywhere"></a>
## Install — every way, every platform

```bash
pip install "git+https://github.com/cognis-digital/ragkit.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/ragkit.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/ragkit.git" # uv
pip install cognis-ragkit                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/ragkit:latest --help        # Docker
brew install cognis-digital/tap/ragkit                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/ragkit/main/install.sh | sh
```

| Linux | macOS | Windows | Docker | Cloud |
|---|---|---|---|---|
| `scripts/setup-linux.sh` | `scripts/setup-macos.sh` | `scripts/setup-windows.ps1` | `docker run ghcr.io/cognis-digital/ragkit` | [DEPLOY.md](docs/DEPLOY.md) (AWS/Azure/GCP/k8s) |

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="related"></a>
## Related Cognis tools

- [`agentsmith`](https://github.com/cognis-digital/agentsmith) — Config-first scaffolding and orchestration for multi-agent workflows
- [`skillhub`](https://github.com/cognis-digital/skillhub) — Local skill registry and installer for AI agents
- [`toolguard`](https://github.com/cognis-digital/toolguard) — Runtime allowlist and policy for agent tool-calls
- [`evalbench`](https://github.com/cognis-digital/evalbench) — Offline LLM / agent eval harness with regression gates
- [`memorybank`](https://github.com/cognis-digital/memorybank) — Portable long-term memory store for agents, exposed over MCP
- [`promptpack`](https://github.com/cognis-digital/promptpack) — Versioned prompt / template registry with A/B and rollbacks

**Explore the suite →** [🗂️ all 170+ tools](https://github.com/cognis-digital/cognis-neural-suite) · [⭐ awesome-cognis](https://github.com/cognis-digital/awesome-cognis) · [🔗 cognis-sources](https://github.com/cognis-digital/cognis-sources) · [🤖 uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet) · [🧠 engram](https://github.com/cognis-digital/engram)

<div align="right"><a href="#top">↑ back to top</a></div>

<a name="contributing"></a>
## Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see [CONTRIBUTING.md](CONTRIBUTING.md) and [SECURITY.md](SECURITY.md).

> ### ⭐ If `ragkit` saved you time, **star it** — it genuinely helps others find it.

## Interoperability

`{}` composes with the 300+ tool Cognis suite — JSON in/out and a shared
OpenAI-compatible `/v1` backbone. See **[INTEROP.md](INTEROP.md)** for the
suite map, composition patterns, and reference stacks.

## License

Source-available under the **Cognis Open Collaboration License (COCL) v1.0** — free for personal, internal-evaluation, research, and educational use; **commercial / production use requires a license** (licensing@cognis.digital). See [LICENSE](LICENSE).

---

<div align="center"><sub><b><a href="https://cognis.digital">Cognis Digital</a></b> · one of 170+ tools in the <a href="https://github.com/cognis-digital/cognis-neural-suite">Cognis Neural Suite</a> · <i>Making Tomorrow Better Today</i></sub></div>