cohere-core-workflow-a

name	cohere-core-workflow-a
description	Build a complete RAG pipeline with Cohere Chat, Embed, and Rerank. Use when implementing retrieval-augmented generation, building grounded Q&A systems, or combining search with LLM generation. Trigger with phrases like "cohere RAG", "cohere retrieval", "cohere grounded generation", "cohere search and answer".
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","ai","nlp","cohere"]
compatible-with	claude-code

Cohere RAG Pipeline (Core Workflow A)

Overview

End-to-end Retrieval-Augmented Generation using Cohere's three core endpoints: Embed (vectorize), Rerank (sort by relevance), Chat (generate grounded answer with citations).

Prerequisites

• Completed cohere-install-auth setup
• cohere-ai package installed
• Understanding of vector similarity search

Instructions

Step 1: Embed Your Documents

import { CohereClientV2 } from 'cohere-ai';

const cohere = new CohereClientV2();

// Your knowledge base
const documents = [
  { id: 'doc1', text: 'Cohere Command A has 256K context and supports tool use.' },
  { id: 'doc2', text: 'Embed v4 generates 1024-dim vectors with 128K token context.' },
  { id: 'doc3', text: 'Rerank v3.5 scores relevance from 0 to 1 across 100+ languages.' },
  { id: 'doc4', text: 'The Chat API v2 requires model as a mandatory parameter.' },
  { id: 'doc5', text: 'Cohere supports structured JSON output via response_format.' },
];

// Embed documents for storage
const docEmbeddings = await cohere.embed({
  model: 'embed-v4.0',
  texts: documents.map(d => d.),
  : ,
  : [],
});


 vectors = docEmbeddings..;
.();