Cohere Hello World

Overview

Three minimal working examples: Chat completion, text embedding, and search reranking. Each demonstrates a core Cohere API v2 endpoint.

Prerequisites

Completed cohere-install-auth setup
cohere-ai package installed
CO_API_KEY environment variable set

Instructions

Example 1: Chat Completion

import { CohereClientV2 } from 'cohere-ai';

const cohere = new CohereClientV2();

async function chat() {
  const response = await cohere.chat({
    model: 'command-a-03-2025',
    messages: [
      { role: 'system', content: 'You are a helpful coding assistant.' },
      { role: 'user', content: 'Explain what a closure is in JavaScript in 2 sentences.' },
    ],
  });

  console.log(response.message?.content?.[0]?.text);
}

chat().catch(console.error);

Example 2: Text Embedding

async function embed() {
  const response = await cohere.embed({
    model: 'embed-v4.0',
    texts: ['Cohere builds enterprise AI', 'LLMs power modern search'],
    inputType: 'search_document',
    embeddingTypes: ['float'],
  });

  const vectors = response.embeddings.float;
  console.log(`Generated ${vectors.length} embeddings`);
  console.log(`Dimensions: ${vectors[0].length}`);
}

embed().catch(console.error);

Example 3: Search Reranking

async function rerank() {
  const response = await cohere.rerank({
    model: 'rerank-v3.5',
    query: 'What is machine learning?',
    documents: [
      'Machine learning is a subset of artificial intelligence.',
      'The weather today is sunny and warm.',
      'Deep learning uses neural networks with many layers.',
      'I enjoy cooking Italian food on weekends.',
    ],
    topN: 2,
  });

  for (const result of response.results) {
    console.log(`[${result.relevanceScore.toFixed(3)}] ${result.index}`);
  }
}

rerank().catch(console.error);

Example 4: Streaming Chat

async function streamChat() {
  const stream = await cohere.chatStream({
    model: 'command-a-03-2025',
    messages: [
      { role: 'user', content: 'Write a haiku about APIs.' },
    ],
  });

  for await (const event of stream) {
    if (event.type === 'content-delta') {
      process.stdout.write(event.delta?.message?.content?.text ?? '');
    }
  }
  console.log(); // newline
}

streamChat().catch(console.error);

Python Equivalents

import cohere

co = cohere.ClientV2()

# Chat
response = co.chat(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Hello, Cohere!"}],
)
print(response.message.content[0].text)

# Embed
response = co.embed(
    model="embed-v4.0",
    texts=["Hello world", "Goodbye world"],
    input_type="search_document",
    embedding_types=["float"],
)
print(f"Vectors: {len(response.embeddings.float)}")

# Rerank
response = co.rerank(
    model="rerank-v3.5",
    query="best programming language",
    documents=["Python is versatile", "Rust is fast", "SQL manages data"],
    top_n=2,
)
for r in response.results:
    print(f"[{r.relevance_score:.3f}] doc {r.index}")

Output

Chat: Text response from Command A model
Embed: Float vectors (1024 dimensions for v4)
Rerank: Sorted documents with relevance scores (0.0-1.0)
Stream: Token-by-token text output via SSE

Error Handling

Error	Cause	Solution
`model is required`	Missing model param	Always pass `model` in API v2
`embedding_types is required`	Missing for embed	Add `embeddingTypes: ['float']`
`invalid api token`	Bad CO_API_KEY	Check key at dashboard.cohere.com
`rate limit exceeded`	Too many trial requests	Wait 60s or upgrade key

Examples

Use a staging key and synthetic input to make one bounded chat request, inspect only the response status and expected shape, then repeat for embed/rerank using approved fixtures. If authentication, model selection, or rate checks fail, stop the walkthrough and repair the scoped configuration before sending user or production data.

Resources

Next Steps

Proceed to cohere-local-dev-loop for development workflow setup.