> cohere-sdk-patterns

Apply production-ready Cohere SDK patterns for TypeScript and Python. Use when implementing Cohere integrations, refactoring SDK usage, or establishing team coding standards for Cohere API v2. Trigger with phrases like "cohere SDK patterns", "cohere best practices", "cohere code patterns", "idiomatic cohere", "cohere wrapper".

fetch
$curl "https://skillshub.wtf/jeremylongshore/claude-code-plugins-plus-skills/cohere-sdk-patterns?format=md"
SKILL.mdcohere-sdk-patterns

Cohere SDK Patterns

Overview

Production-ready patterns for the cohere-ai TypeScript SDK (CohereClientV2) and Python cohere package. Real model names, real API shapes, real error types.

Prerequisites

  • cohere-ai v7+ installed (TypeScript) or cohere v5+ (Python)
  • Familiarity with async/await patterns
  • Understanding of Cohere API v2 endpoints

Instructions

Pattern 1: Singleton Client with Retry

// src/cohere/client.ts
import { CohereClientV2, CohereError, CohereTimeoutError } from 'cohere-ai';

let instance: CohereClientV2 | null = null;

export function getCohere(): CohereClientV2 {
  if (!instance) {
    if (!process.env.CO_API_KEY) {
      throw new Error('CO_API_KEY environment variable is required');
    }
    instance = new CohereClientV2({
      token: process.env.CO_API_KEY,
    });
  }
  return instance;
}

export async function withRetry<T>(
  operation: () => Promise<T>,
  maxRetries = 3,
  baseDelayMs = 1000
): Promise<T> {
  for (let attempt = 1; attempt <= maxRetries; attempt++) {
    try {
      return await operation();
    } catch (err) {
      if (attempt === maxRetries) throw err;

      // Only retry on rate limits (429) and server errors (5xx)
      if (err instanceof CohereError) {
        const status = err.statusCode;
        if (status && status !== 429 && status < 500) throw err;
      } else if (!(err instanceof CohereTimeoutError)) {
        throw err;
      }

      const delay = baseDelayMs * Math.pow(2, attempt - 1);
      const jitter = Math.random() * 500;
      await new Promise(r => setTimeout(r, delay + jitter));
    }
  }
  throw new Error('Unreachable');
}

Pattern 2: Type-Safe Chat Wrapper

// src/cohere/chat.ts
import { getCohere, withRetry } from './client';

interface ChatOptions {
  message: string;
  systemPrompt?: string;
  model?: string;
  maxTokens?: number;
  temperature?: number;
  documents?: Array<{ id?: string; data: Record<string, string> }>;
}

export async function chat(options: ChatOptions): Promise<string> {
  const cohere = getCohere();

  const messages: Array<{ role: string; content: string }> = [];

  if (options.systemPrompt) {
    messages.push({ role: 'system', content: options.systemPrompt });
  }
  messages.push({ role: 'user', content: options.message });

  const response = await withRetry(() =>
    cohere.chat({
      model: options.model ?? 'command-a-03-2025',
      messages,
      maxTokens: options.maxTokens,
      temperature: options.temperature,
      documents: options.documents,
    })
  );

  return response.message?.content?.[0]?.text ?? '';
}

Pattern 3: Streaming Chat

// src/cohere/stream.ts
export async function* streamChat(
  message: string,
  model = 'command-a-03-2025'
): AsyncGenerator<string> {
  const cohere = getCohere();

  const stream = await cohere.chatStream({
    model,
    messages: [{ role: 'user', content: message }],
  });

  for await (const event of stream) {
    if (event.type === 'content-delta') {
      const text = event.delta?.message?.content?.text;
      if (text) yield text;
    }
  }
}

// Usage
for await (const chunk of streamChat('Explain RAG in 3 sentences')) {
  process.stdout.write(chunk);
}

Pattern 4: Batch Embedding

// src/cohere/embed.ts
type InputType = 'search_document' | 'search_query' | 'classification' | 'clustering';

export async function embedTexts(
  texts: string[],
  inputType: InputType = 'search_document',
  model = 'embed-v4.0'
): Promise<number[][]> {
  const cohere = getCohere();

  // Cohere embed accepts up to 96 texts per call
  const BATCH_SIZE = 96;
  const allEmbeddings: number[][] = [];

  for (let i = 0; i < texts.length; i += BATCH_SIZE) {
    const batch = texts.slice(i, i + BATCH_SIZE);
    const response = await withRetry(() =>
      cohere.embed({
        model,
        texts: batch,
        inputType,
        embeddingTypes: ['float'],
      })
    );
    allEmbeddings.push(...response.embeddings.float);
  }

  return allEmbeddings;
}

Pattern 5: Rerank with Type Safety

// src/cohere/rerank.ts
interface RerankResult {
  text: string;
  score: number;
  originalIndex: number;
}

export async function rerankDocuments(
  query: string,
  documents: string[],
  topN = 5,
  model = 'rerank-v3.5'
): Promise<RerankResult[]> {
  const cohere = getCohere();

  const response = await withRetry(() =>
    cohere.rerank({ model, query, documents, topN })
  );

  return response.results.map(r => ({
    text: documents[r.index],
    score: r.relevanceScore,
    originalIndex: r.index,
  }));
}

Pattern 6: Structured JSON Output

export async function chatJSON<T>(
  message: string,
  schema?: Record<string, unknown>
): Promise<T> {
  const cohere = getCohere();

  const response = await cohere.chat({
    model: 'command-a-03-2025',
    messages: [{ role: 'user', content: `${message}\n\nRespond in valid JSON.` }],
    responseFormat: schema
      ? { type: 'json_object', jsonSchema: schema }
      : { type: 'json_object' },
  });

  const text = response.message?.content?.[0]?.text ?? '{}';
  return JSON.parse(text) as T;
}

Python Equivalents

import cohere
from cohere import ClientV2

# Singleton
_client: ClientV2 | None = None

def get_cohere() -> ClientV2:
    global _client
    if _client is None:
        _client = ClientV2()  # reads CO_API_KEY
    return _client

# Chat
def chat(message: str, model: str = "command-a-03-2025") -> str:
    co = get_cohere()
    response = co.chat(
        model=model,
        messages=[{"role": "user", "content": message}],
    )
    return response.message.content[0].text

# Embed
def embed(texts: list[str], input_type: str = "search_document") -> list[list[float]]:
    co = get_cohere()
    response = co.embed(
        model="embed-v4.0",
        texts=texts,
        input_type=input_type,
        embedding_types=["float"],
    )
    return response.embeddings.float

Error Handling

Error TypeWhenRecovery
CohereError (status 400)Bad request paramsFix request, do not retry
CohereError (status 401)Invalid API keyCheck CO_API_KEY
CohereError (status 429)Rate limitedRetry with backoff
CohereError (status 5xx)Server errorRetry with backoff
CohereTimeoutErrorNetwork timeoutRetry with backoff

Resources

Next Steps

Apply patterns in cohere-core-workflow-a for RAG workflows.

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