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Intelligence Artificielle

How to Master the Cohere API for Advanced AI in 2026

18 minADVANCED
Intelligence ArtificielleAPICohere
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Introduction

The Cohere API enables the creation of high-performance artificial intelligence applications by leveraging language models, semantic embeddings, and reranking capabilities. This advanced tutorial guides you through the complete integration of Cohere into a modern TypeScript architecture. You will learn how to manage complex RAG workflows, optimize costs, and implement intelligent caching strategies. Each section includes ready-to-use code for immediate production deployment.

Prerequisites

  • Node.js 20+ and TypeScript 5.4+
  • Cohere account with production-level API key
  • Strong knowledge of async/await and error handling
  • npm or pnpm package manager

Installation and Configuration

terminal
npm install cohere-ai dotenv
npm install -D typescript @types/node

Install the official Cohere SDK and dotenv for secure key management. TypeScript is added for the strict typing essential in production.

Client Configuration

Create a centralized configuration file that initializes the Cohere client in a reusable and fully typed manner.

Initialize the Cohere Client

lib/cohere.ts
import { CohereClient } from 'cohere-ai';
import 'dotenv/config';

export const cohere = new CohereClient({
  token: process.env.COHERE_API_KEY,
});

The client is initialized once and exported. This avoids unnecessary reconnections and simplifies unit testing with mocks.

Generate Advanced Embeddings

lib/embeddings.ts
import { cohere } from './cohere';

export async function generateEmbeddings(texts: string[]) {
  const response = await cohere.embed({
    texts,
    model: 'embed-multilingual-v3.0',
    inputType: 'search_document',
    embeddingTypes: ['float'],
  });
  return response.embeddings.float;
}

Use the multilingual-v3.0 model with an optimized inputType for search. The float type ensures maximum precision for similarity calculations.

Advanced Chat with Context

lib/chat.ts
import { cohere } from './cohere';

export async function chatWithContext(message: string, context: string) {
  const response = await cohere.chat({
    model: 'command-r-plus',
    message,
    preamble: `Tu es un assistant expert. Contexte: ${context}`,
    temperature: 0.3,
    maxTokens: 500,
  });
  return response.text;
}

The command-r-plus model is used with a preamble to inject context. The low temperature ensures factual and coherent responses.

Rerank Results

lib/rerank.ts
import { cohere } from './cohere';

export async function rerankDocuments(query: string, documents: string[]) {
  const response = await cohere.rerank({
    query,
    documents,
    model: 'rerank-multilingual-v3.0',
    topN: 5,
  });
  return response.results.map(r => ({
    text: documents[r.index],
    relevance: r.relevanceScore,
  }));
}

Reranking significantly improves relevance compared to cosine similarity alone. Limiting topN helps optimize costs.

Complete RAG Pipeline

lib/rag.ts
import { generateEmbeddings } from './embeddings';
import { rerankDocuments } from './rerank';
import { chatWithContext } from './chat';

export async function runRAG(query: string, corpus: string[]) {
  const embeddings = await generateEmbeddings(corpus);
  const ranked = await rerankDocuments(query, corpus);
  const context = ranked.slice(0, 3).map(r => r.text).join('\n');
  return chatWithContext(query, context);
}

Full pipeline combining embeddings, reranking, and generation. Each step is modular and can be optimized independently.

Best Practices

  • Always define an explicit inputType for embeddings
  • Use specific models (command-r-plus, rerank-multilingual) according to the use case
  • Implement a caching system for frequently used embeddings
  • Monitor token usage and latency through Cohere metrics
  • Validate and sanitize user inputs before sending

Common Errors to Avoid

  • Forgetting to handle rate limits (429) with exponential backoff
  • Using the same model for indexing and search without testing similarity
  • Ignoring maximum document length differences
  • Not typing SDK responses, which makes the code fragile

Going Further

Discover our advanced courses on RAG architectures and LLMs in production: https://learni-group.com/formations