Cohere Embed — Multilingual AI Embeddings API

What it is

Cohere Embed is a multilingual embedding API that converts text into high-dimensional vectors for semantic search, retrieval-augmented generation (RAG), and classification tasks. The API supports 100+ languages and provides specialized input types that optimize vector quality depending on whether you are indexing documents, running search queries, or classifying text.

The service targets teams building multilingual search or RAG pipelines. It works with any vector database and integrates with frameworks like LangChain and LlamaIndex. Setup takes under two minutes.

How it saves time or tokens

How to use

1. Install the Cohere SDK:

pip install cohere

1. Generate embeddings with the appropriate input type:

import cohere

co = cohere.ClientV2(api_key='your-key')

# Index documents
doc_embeddings = co.embed(
    texts=['What is machine learning?', 'How does AI work?'],
    model='embed-v4.0',
    input_type='search_document',
    embedding_types=['float'],
)

1. Use input_type='search_query' when embedding user queries to match against your indexed documents.

Example

A complete search pipeline with Cohere Embed:

import cohere
import numpy as np

co = cohere.ClientV2(api_key='your-key')

# Index phase
docs = ['Python is a programming language', 'Rust is fast and safe']
doc_resp = co.embed(
    texts=docs,
    model='embed-v4.0',
    input_type='search_document',
    embedding_types=['float'],
)

# Query phase
query_resp = co.embed(
    texts=['Which language is memory safe?'],
    model='embed-v4.0',
    input_type='search_query',
    embedding_types=['float'],
)

# Cosine similarity
scores = np.dot(doc_resp.embeddings.float_, query_resp.embeddings.float_[0])
print(docs[np.argmax(scores)])  # 'Rust is fast and safe'

Related on TokRepo

• AI Tools for RAG — Retrieval-augmented generation tools and frameworks that use embedding APIs
• AI Tools for Research — Research tools that benefit from semantic search capabilities

Common pitfalls

• Using the wrong input_type degrades search quality. Always use 'search_document' for indexing and 'search_query' for queries.
• Float32 embeddings consume the most storage. Switch to int8 or binary types for large-scale indexes where marginal precision loss is acceptable.
• The free tier has rate limits. For production workloads with high throughput, plan for a paid tier to avoid throttling during peak ingestion.
• Always check the official documentation for the latest version-specific changes and migration guides before upgrading in production environments.
• For team deployments, establish clear guidelines on configuration and usage patterns to ensure consistency across developers.