Cohere
Enterprise-focused AI platform specializing in text understanding, embeddings, and RAG. Cohere's Embed and Rerank models are industry-leading for production search and retrieval.
Proprietary
TypeScript / Python
Why Cohere?
Building enterprise search or RAG pipelines
You need best-in-class embedding models
Enterprise compliance and data privacy requirements
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitGeneral-purpose chat applications
You already use OpenAI embeddings successfully
Budget is tight — Cohere is enterprise-priced
Pricing
Free tier & paid plans
Free trial with rate limits
Command R+: $3/1M input tokens
Enterprise plans with private deployment available
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Often Used Together
Complementary tools that pair well with Cohere
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/cohere-ai/cohere-typescript
npm install cohere-aipip install cohereQuick Start
Copy and adapt to get going fast
import { CohereClient } from 'cohere-ai';
const cohere = new CohereClient({ token: process.env.COHERE_API_KEY });
const response = await cohere.chat({
model: 'command-r-plus',
message: 'Hello!',
});
console.log(response.text);Code Examples
Common usage patterns
Generate embeddings
Embed text for semantic search
const response = await cohere.embed({
texts: ['Hello world', 'Goodbye world'],
model: 'embed-english-v3.0',
inputType: 'search_document',
});
console.log(response.embeddings);Rerank results
Rerank search results by relevance
const reranked = await cohere.rerank({
model: 'rerank-english-v3.0',
query: 'What is the capital of France?',
documents: ['Paris is the capital', 'London is great', 'France is in Europe'],
topN: 2,
});Community Notes
Real experiences from developers who've used this tool