Agentic Parallel RAG

Parallel RAG Workflow

Use this Skill only after the Manage stage selects it. Keep retrieval, reranking, rewriting, and generation implementations outside the workflow script.

Component selection

1. Optionally select one rewriter Component when rewriting or decomposing the query improves recall. When no rewriter is bound, every branch starts from the original query.
2. Select two to four distinct retriever Components. Prefer diversity, such as BM25 plus Vector Retrieval.
3. Optionally select one reranker Component; it is applied independently to each retrieval branch before fusion.
4. Select exactly one generator Component.
5. Load only the selected Component Skill bodies and implementations.

Rewriter interface

A rewriter Component must provide the rewriter capability:

• Input RewriteRequest: query, optional temperature, optional max_tokens
• Output RewriteResult: either a single rewritten_query string (HyDE-style) or queries, a non-empty list of retrieval query strings

Each query becomes a fan-out point for all retrieval branches. The rewriter may reformulate, decompose, or expand the query through the Executor Model; it must not retrieve documents or generate a final answer. When the HyDE Rewriter is selected, bind at least one Vector Retrieval branch and never add the hypothetical document to the retrieved evidence.

Workflow

Execute scripts/workflow.py:run after binding every required slot:

RAGRequest
  -> optional rewriter (one or more query views)
  -> retrievers in parallel per query view
  -> optional per-branch reranker
  -> reciprocal rank fusion
  -> generator
  -> RAGResult

The workflow owns branch fan-out, reranking order, and fusion. Each retriever owns only its concrete retrieval algorithm.