> ## Documentation Index
> Fetch the complete documentation index at: https://docs.raglight.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CLI

> raglight chat, raglight agentic-chat, raglight serve — interactive RAG from your terminal.

# CLI

RAGLight ships with a full command-line interface. Three commands cover the main use cases:

| Command                 | Description                                           |
| ----------------------- | ----------------------------------------------------- |
| `raglight chat`         | Interactive RAG chat session in your terminal         |
| `raglight agentic-chat` | Same as `chat`, but with the Agentic RAG pipeline     |
| `raglight serve`        | Deploy as a REST API (optionally with a Streamlit UI) |

***

## `raglight chat`

Starts an **interactive terminal chat session** backed by a RAG pipeline. On first launch, a setup wizard guides you through choosing your vector store, embeddings, and LLM. Subsequent runs can skip the wizard entirely via environment variables.

```bash theme={null}
raglight chat
```

The wizard walks you through:

1. **Vector database** — Chroma or Qdrant, local path or remote host
2. **Embeddings** — provider and model
3. **LLM** — provider, model, and API base URL
4. **Knowledge source** — local folder or GitHub repository
5. **Indexing** — option to reuse an existing index
6. **Chat loop** — type your questions, get streamed responses

Responses are rendered as **markdown in the terminal** with streaming output.

### Skip the wizard with env vars

Create a `.env` file (or export env vars) and the wizard is bypassed entirely:

```bash .env theme={null}
RAGLIGHT_LLM_PROVIDER=Ollama
RAGLIGHT_LLM_MODEL=llama3.1:8b
RAGLIGHT_EMBEDDINGS_PROVIDER=HuggingFace
RAGLIGHT_EMBEDDINGS_MODEL=all-MiniLM-L6-v2
RAGLIGHT_DB=Chroma
RAGLIGHT_PERSIST_DIR=./myDb
RAGLIGHT_COLLECTION=default
RAGLIGHT_DATA_PATH=./docs
```

Then just run:

```bash theme={null}
raglight chat
```

RAGLight prints the active configuration and goes straight to the chat loop.

### Commands in the chat loop

| Input                   | Action                              |
| ----------------------- | ----------------------------------- |
| Any text                | Send a question to the RAG pipeline |
| `bye` / `exit` / `quit` | End the session                     |

***

## `raglight agentic-chat`

Same as `raglight chat`, but uses the **Agentic RAG pipeline** — the LLM can call tools, reason in multiple steps, and go beyond simple retrieval.

```bash theme={null}
raglight agentic-chat
```

The setup wizard is identical. The difference is in the pipeline: the agent decides when to retrieve, can combine multiple retrievals, and produces richer answers for complex questions.

<Info>
  Agentic mode requires an LLM that supports tool calling (e.g. `llama3.1`, `gpt-4o`, `mistral-large`).
</Info>

See the [Agentic RAG](/documentation/agentic-rag) page for a full explanation of the pipeline.

***

## `raglight serve`

Starts a **FastAPI REST API** exposing your RAG pipeline over HTTP. Entirely configured by environment variables — no Python code required.

```bash theme={null}
raglight serve
```

Add `--ui` to also launch the **Streamlit chat interface**:

```bash theme={null}
raglight serve --ui
```

See the [REST API](/documentation/rest-api) page for the full reference — endpoints, configuration variables, Docker Compose setup, and more.

### CLI options

| Option      | Default   | Description                                    |
| ----------- | --------- | ---------------------------------------------- |
| `--host`    | `0.0.0.0` | Host to bind                                   |
| `--port`    | `8000`    | Port to listen on                              |
| `--reload`  | `false`   | Enable auto-reload (development)               |
| `--workers` | `1`       | Number of Uvicorn worker processes             |
| `--ui`      | `false`   | Launch the Streamlit chat UI alongside the API |
| `--ui-port` | `8501`    | Port for the Streamlit UI                      |

***

## Common environment variables

All three commands read the same `RAGLIGHT_*` environment variables:

| Variable                       | Default                  | Description                                 |
| ------------------------------ | ------------------------ | ------------------------------------------- |
| `RAGLIGHT_LLM_PROVIDER`        | `Ollama`                 | LLM provider                                |
| `RAGLIGHT_LLM_MODEL`           | `llama3`                 | LLM model name                              |
| `RAGLIGHT_LLM_API_BASE`        | `http://localhost:11434` | LLM API base URL                            |
| `RAGLIGHT_EMBEDDINGS_PROVIDER` | `HuggingFace`            | Embeddings provider                         |
| `RAGLIGHT_EMBEDDINGS_MODEL`    | `all-MiniLM-L6-v2`       | Embeddings model                            |
| `RAGLIGHT_DB`                  | `Chroma`                 | Vector store backend (`Chroma` or `Qdrant`) |
| `RAGLIGHT_PERSIST_DIR`         | `./raglight_db`          | Local persistence directory                 |
| `RAGLIGHT_COLLECTION`          | `default`                | Collection name                             |
| `RAGLIGHT_K`                   | `5`                      | Number of documents retrieved per query     |
| `RAGLIGHT_DATA_PATH`           | —                        | Path to documents (skips wizard prompt)     |

***

## Summary

* `raglight chat` — terminal RAG chat with streaming markdown output
* `raglight agentic-chat` — same but with tool-calling agent mode
* `raglight serve` — REST API; add `--ui` for the web chat interface
* All three share the same `RAGLIGHT_*` env vars — one `.env` file for everything