---
title: Webhook Pipeline
sidebar_position: 2
---

# Webhook Pipeline

The simplest useful pipeline: an HTTP endpoint that accepts a question, sends
it to an LLM, and returns the answer. This is the "hello world" of RocketRide —
a good starting point before adding vector stores, preprocessing, or agents.

## The pipeline

Save this as `chat.pipe`:

```json
{
  "nodes": [
    {
      "id": "source_1",
      "provider": "webhook"
    },
    {
      "id": "llm_1",
      "provider": "llm_openai",
      "config": {
        "profile": "openai-4o-mini",
        "apikey": "${OPENAI_API_KEY}"
      },
      "input": [
        { "lane": "questions", "from": "source_1" }
      ]
    },
    {
      "id": "target_1",
      "provider": "response",
      "input": [
        { "lane": "answers", "from": "llm_1" }
      ]
    }
  ]
}
```

Three nodes, one connection each. The `webhook` source receives the request, the
`llm_openai` node answers it, and the `response` target returns the result.

## Start the pipeline

```bash
export OPENAI_API_KEY=sk-...

rocketride start --pipeline ./chat.pipe
```

Output:

```text
Webhook ready - system is ready to accept requests
  URL:  http://localhost:5567/task/data
  Auth: <public-auth-key>
  Token: <private-token>
```

Keep the terminal open — the pipeline runs until you stop it.

## Send a question

```bash
curl -X POST http://localhost:5567/task/data \
  -H "Authorization: Bearer <public-auth-key>" \
  -H "Content-Type: text/plain" \
  -d "What is the capital of France?"
```

Response:

```json
{ "answer": "The capital of France is Paris." }
```

## Use the Chat UI variant

Swap `webhook` for `chat` to get a browser-based chat window instead of a raw
HTTP endpoint, no `curl` required:

```json
{ "id": "source_1", "provider": "chat" }
```

Restart the pipeline. The engine prints a chat URL:

```text
Chat ready - system is ready to accept questions
  URL: http://localhost:5567/chat?auth=<public-auth-key>
```

Open that URL in a browser and start typing. The `?auth=` query parameter is a
convenience for the browser; the endpoint also accepts the key in an
`Authorization: Bearer <public-auth-key>` header, the same scheme the webhook
endpoint uses.

## Add a system prompt

Give the LLM a persona by adding a `prompt` node:

```json
{
  "id": "prompt_1",
  "provider": "prompt",
  "config": {
    "text": "You are a helpful assistant that answers questions concisely."
  },
  "input": [
    { "lane": "questions", "from": "source_1" }
  ]
}
```

Then wire `llm_1` to receive from `prompt_1` instead of `source_1`.

## Next steps

- Add a [`qdrant`](/nodes/qdrant) store between source and LLM to build the full [RAG pipeline](/examples/rag-pipeline).
- Replace `llm_openai` with [`llm_anthropic`](/nodes/llm_anthropic) or [`llm_ollama`](/nodes/llm_ollama) — the rest of the pipeline is unchanged.
- See the [Anthropic integration guide](/integrations/anthropic) for model profile options.