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:

{
  "project_id": "rag-example",
  "components": [
    {
      "id": "source_1",
      "provider": "webhook"
    },
    {
      "id": "parser_1",
      "provider": "parse",
      "input": [
        { "lane": "tags", "from": "source_1" }
      ]
    },
    {
      "id": "prep_1",
      "provider": "preprocessor_langchain",
      "config": {
        "profile": "default",
        "default": {
          "mode": "strlen",
          "splitter": "RecursiveCharacterTextSplitter",
          "strlen": 512
        }
      },
      "input": [
        { "lane": "text", "from": "parser_1" }
      ]
    },
    {
      "id": "embed_1",
      "provider": "embedding_openai",
      "config": {
        "profile": "text-embedding-3-small",
        "apikey": "${ROCKETRIDE_OPENAI_API_KEY}"
      },
      "input": [
        { "lane": "documents", "from": "prep_1" },
        { "lane": "questions", "from": "source_1" }
      ]
    },
    {
      "id": "store_1",
      "provider": "qdrant",
      "config": {
        "profile": "local",
        "local": {
          "host": "localhost",
          "port": 6333,
          "collection": "rag-docs"
        }
      },
      "input": [
        { "lane": "documents", "from": "embed_1" },
        { "lane": "questions", "from": "embed_1" }
      ]
    },
    {
      "id": "llm_1",
      "provider": "llm_openai",
      "config": {
        "profile": "openai-4o",
        "apikey": "${ROCKETRIDE_OPENAI_API_KEY}"
      },
      "input": [
        { "lane": "questions", "from": "store_1" }
      ]
    },
    {
      "id": "target_1",
      "provider": "response",
      "input": [
        { "lane": "answers", "from": "llm_1" }
      ]
    }
  ]
}

What each node does

Node	Provider	Role
source_1	webhook	Exposes an HTTP endpoint. Incoming documents arrive on the tags lane; incoming questions arrive on the questions lane.
parser_1	parse	Converts each uploaded file (PDF, Word, etc.) into clean text on the text lane.
prep_1	preprocessor_langchain	Splits the text into chunks and emits them on the documents lane.
embed_1	embedding_openai	Turns document chunks into vectors using text-embedding-3-small and emits documents (vectors + metadata). Also embeds incoming questions before retrieval.
store_1	qdrant	Upserts vectors from embed_1 into the rag-docs collection. When an embedded question arrives, it retrieves the top matching chunks and re-emits them as questions with context injected.
llm_1	llm_openai	Receives the question + retrieved context and generates an answer using GPT-4o.
target_1	response	Returns 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/webhook/rag-example/source_1
  Auth: abc123...

Ingest documents

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

curl -X POST http://localhost:5567/webhook/rag-example/source_1 \
  -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/webhook/rag-example/source_1 \
  -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

• Swap embedding_openai for embedding_transformer to run embeddings locally without an API key.
• Swap qdrant for pinecone or milvus without changing the rest of the pipeline.
• Add a guardrails node between the LLM and response to validate outputs.
• See the store_qdrant node reference for configuration details.