A family of machine-learning models that converts sentences, paragraphs, or documents into fixed-length numerical vectors capturing semantic meaning.
Technical guides for developers implementing vector search and semantic similarity systems.
01How it works
Cohere Embeddings are based on a transformer architecture similar to the models used for language generation. During training the model sees billions of tokens from web pages, books, and code, learning to predict masked tokens. The final hidden state of the [CLS] token (or a mean‑pooled representation) is projected to a fixed‑size vector, then L2‑normalized so that cosine similarity directly reflects semantic closeness. Different model sizes (e.g., embed‑english‑light, embed‑english‑v3) trade off dimension, latency, and accuracy.
Cohere Embeddings change words into numbers that show meaning, letting computers find similar text by comparing those numbers.
02What to do about it
This week you can start experimenting with the API: 1) Create a free Cohere account and copy your API key. 2) Call the /embed endpoint with the embed‑english‑light model on a sample list of sentences. 3) Store the returned vectors in a vector database such as Pinecone, Weaviate, or Milvus. 4) Run a simple nearest‑neighbor query using cosine similarity and measure recall@5. 5) Log latency and cost to decide whether to upgrade to a larger model.
03How it is measured or noticed
Quality is usually judged on public benchmarks like the Massive Text Embedding Benchmark (MTEB), where you look at average scores across retrieval, clustering, and classification tasks. In practice you also check the vector dimension (e.g., 384 vs 1024) to ensure it matches your index, measure query latency (target < 50 ms for light models), and evaluate recall@k in your own search pipeline. A sudden drop in recall or a rise in latency often signals that the embedding model or its parameters have changed.
04Common mistakes
- Feeding raw text directly to a search engine instead of first converting it to embeddings.
- Forgetting to L2‑normalize vectors before computing cosine similarity, which skews similarity scores.
- Mixing vectors from different Cohere models (different dimensions) in the same index.
- Exceeding the model’s token limit (512 tokens for embed‑english‑light) without truncating or chunking.
- Using embeddings for exact keyword matching when a traditional BM25 index would be faster and more accurate.
05Limits
Cohere Embeddings are not a substitute for exact‑match or keyword‑based retrieval; they excel at semantic similarity but can miss precise phrase matches. They are also distinct from Cohere’s rerank models, which re‑score a candidate list, and from classification models that predict labels. If your use case requires boolean filtering on metadata, you still need a traditional database or a hybrid approach.
06Worked example
curl https://api.cohere.ai/v1/embed -H "Authorization: Bearer $COHERE_API_KEY" -H "Content-Type: application/json" -d '{ "model": "embed-english-light", "texts": ["Hello world", "Goodbye world"] }'
Frequently asked questions
How do Cohere Embeddings differ from OpenAI embeddings or other embedding models?
Cohere Embeddings are trained with a focus on retrieval and classification tasks and offer multilingual models that cover over 100 languages. They also provide a free tier with generous limits for experimentation. The main practical difference is often pricing, language coverage, and the specific benchmark scores on MTEB.
When should I choose Cohere Embeddings over other embedding providers?
Choose Cohere when you need strong multilingual support, want a generous free tier for prototyping, or prefer an API that is optimized for retrieval and clustering out of the box. If your workload is English‑only and you already use OpenAI for generation, staying with OpenAI embeddings may simplify your stack.
How do I actually generate embeddings using the Cohere API?
You send a POST request to the /embed endpoint with your API key, the model name (for example embed-english-v3.0 or embed-multilingual-v3.0), and a list of texts. The response returns a list of fixed‑length vectors, one per input text, which you can then store in a vector database.
Do Cohere Embeddings work well for languages other than English?
Yes, the multilingual models are trained on over 100 languages and score competitively on cross‑lingual retrieval benchmarks. Performance varies by language and domain, so you should evaluate on your own data for low‑resource languages.
What happens if I use embeddings for exact keyword matching instead of semantic search?
You will miss results that share the same meaning but use different wording, because embeddings map semantically similar texts to nearby vectors rather than matching exact tokens. Users will experience lower recall for queries that paraphrase the content.
How long does it take to generate embeddings for a large document corpus?
Latency depends on the model size and batch size, but the API typically returns vectors for thousands of short texts in a few seconds. For millions of documents you should batch requests and expect the process to take minutes to hours, limited mainly by your rate limits and network throughput.
Asked out loud
spoken, not typedThe same term in the words somebody uses speaking to an assistant rather than typing into a box — written from the situation, which is why each one carries the situation it came from.
Use the Cohere embed endpoint with the embed-english-v3.0 model and send batches of up to 96 texts per request. You'll get vectors back in milliseconds per batch, and the free tier covers plenty of volume for a quick prototype.
Yes, the free tier gives you millions of tokens per month, which is enough for a typical support knowledge base. Just create an account, grab your API key, and start embedding documents today.
Switch to semantic search by embedding both documents and queries with Cohere Embeddings, then use a vector database for nearest‑neighbor lookup. This matches meaning instead of exact terms and solves the vocabulary mismatch problem.