ragPipeline

Standard RAG (Retrieval-Augmented Generation) pipeline: retrieve → [augment] → generate.

The most common LLM pattern. retrieve writes context to shared state; generate reads it to produce the answer. Use the optional augment step to rerank or filter before generation.

Import

import { ragPipeline } from "flowneer/presets/rag";

Usage

interface RagState {
  query: string;
  context: string[];
  answer: string;
}

const flow = ragPipeline<RagState>({
  retrieve: async (s) => {
    s.context = await vectorSearch(s.query);
  },
  generate: async (s) => {
    s.answer = await llm(buildPrompt(s.query, s.context));
  },
});

await flow.run({ query: "What is Flowneer?", context: [], answer: "" });

With an augmentation step (reranking)

const flow = ragPipeline<RagState>({
  retrieve: async (s) => {
    s.context = await vectorSearch(s.query, { topK: 20 });
  },
  augment: async (s) => {
    s.context = await rerank(s.query, s.context, { topK: 5 });
  },
  generate: async (s) => {
    s.answer = await llm(buildPrompt(s.query, s.context));
  },
});

Options

Option	Type	Required	Description
retrieve	NodeFn<S, P>	✓	Fetches relevant documents and writes them to shared state
augment	NodeFn<S, P>	—	Optional reranking / filtering step
generate	NodeFn<S, P>	✓	Generates the final answer from the retrieved context

Return value

Returns a FlowBuilder<S, P> — all plugins and methods work normally on the result:

const flow = ragPipeline({ retrieve, generate }).withTiming().withCostTracker();

See Also

• iterativeRag — multi-pass retrieval when one search isn't enough