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RAG Pipeline

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RAG Pipeline

Retrieval-augmented generation (RAG) is the most common pattern in RocketRide: embed documents into a vector store, then answer questions by retrieving the relevant chunks and feeding them to an LLM. This example walks through a complete pipeline that accepts questions over HTTP, retrieves context from Qdrant, and returns answers.

What you need​

  • An OpenAI API key
  • A running Qdrant instance (local Docker or Qdrant Cloud)
  • The RocketRide engine running (self-hosted or Cloud)

The pipeline​

Save this as rag.pipe:

{
  "nodes": [
    {
      "id": "source_1",
      "provider": "webhook"
    },
    {
      "id": "embed_1",
      "provider": "embedding_openai",
      "config": {
        "profile": "text-embedding-3-small",
        "apikey": "${OPENAI_API_KEY}"
      },
      "input": [
        { "lane": "text", "from": "source_1" }
      ]
    },
    {
      "id": "store_1",
      "provider": "qdrant",
      "config": {
        "profile": "self-hosted",
        "serverName": "localhost",
        "collection": "rag-docs"
      },
      "input": [
        { "lane": "documents", "from": "embed_1" },
        { "lane": "questions", "from": "source_1" }
      ]
    },
    {
      "id": "llm_1",
      "provider": "llm_openai",
      "config": {
        "profile": "openai-4o",
        "apikey": "${OPENAI_API_KEY}"
      },
      "input": [
        { "lane": "questions", "from": "store_1" }
      ]
    },
    {
      "id": "target_1",
      "provider": "response",
      "input": [
        { "lane": "answers", "from": "llm_1" }
      ]
    }
  ]
}

What each node does​

NodeProviderRole
source_1webhookExposes an HTTP endpoint. Incoming documents arrive on the text lane; incoming questions arrive on the questions lane.
embed_1embedding_openaiTurns document text into vectors using text-embedding-3-small. Emits documents (vectors + metadata).
store_1qdrantUpserts vectors from embed_1 into the rag-docs collection. When a question arrives, it retrieves the top matching chunks and re-emits them as questions with context injected.
llm_1llm_openaiReceives the question + retrieved context and generates an answer using GPT-4o.
target_1responseReturns the answer to the caller.

Start the pipeline​

rocketride start --pipeline ./rag.pipe

The engine prints the webhook URL and public auth key:

Webhook ready - system is ready to accept requests
  URL:  http://localhost:5567/task/data
  Auth: abc123...

Ingest documents​

POST a document to the webhook URL. The pipeline embeds and stores it:

curl -X POST http://localhost:5567/task/data \
  -H "Authorization: Bearer abc123..." \
  -F "file=@./my-document.pdf"

Ask a question​

Send a plain-text question to the same endpoint:

curl -X POST http://localhost:5567/task/data \
  -H "Authorization: Bearer abc123..." \
  -H "Content-Type: text/plain" \
  -d "What does the document say about refund policy?"

The pipeline retrieves the relevant chunks from Qdrant, asks GPT-4o, and streams back the answer.

Next steps​