semantic search
Build better search for any language
Semantic Search provides powerful semantic search capabilities that find text, documents, and articles based on meaning, not just keywords.
semantic search
Build better search for any language
Semantic Search provides powerful semantic search capabilities that find text, documents, and articles based on meaning, not just keywords.
semantic search
Build better search for any language
Semantic Search provides powerful semantic search capabilities that find text, documents, and articles based on meaning, not just keywords.
Our Customers
What’s possible with Semantic Search
Vector search: English
Vector search: Multilingual
"At ML6, we see that multilingualism remains a major challenge in an English-centric NLP landscape — especially in Europe. Naturally, we are actively on the lookout for solutions and have been impressed by what we’ve seen from Cohere thus far!"
— Co-Founder & CTO
ML6
Simple APIs, powerful results
No matter your level of experience with ML/AI, the Cohere Platform makes it easy to add search to your applications.
1import cohere #Install with: pip install cohere
2import hnswlib #Install with: pip install hnswlib
3co = cohere.Client('{apiKey}')
4
5docs = [
6 'Cohere offers NLP foundation models as an easy to use service.',
7 'Semantic search is based on vectors, it works for 100+ languages.',
8 'Our generate model is powered by a large language model.'
9]
10
11#Get your document embeddings
12doc_embs = co.embed(texts=docs, model='embed-english-v3.0', input_type=search_documents).embeddings
13
14#Create a search index
15index = hnswlib.Index(space='ip', dim=1024)
16index.init_index(max_elements=len(doc_embs), ef_construction=512, M=64)
17index.add_items(doc_embs, list(range(len(doc_embs))))
18
19#Search
20query = "What is semantic search"
21query_emb = co.embed(texts=[query], model='embed-english-v3.0', input_type=search_query).embeddings
22doc_ids = index.knn_query(query_emb, k=1)[0][0]
23
24for doc_id in doc_ids:
25 print(docs[doc_id])1Semantic search is based on vectors, it works for 100+ languages.Why Semantic Search
Embeddings performance
Cohere’s Embed model leads the industry in accuracy and performance, meaning the highest search relevance, even with noisy datasets
Multilingual support
Over 100 languages are supported, so the same topics, products and issues are identified the same way in each
Scalability
Cohere Embed supports data compression, reducing storage and compute requirements
Flexible deployment options
Cohere models are accessible through a SaaS API, cloud services (e.g., OCI, AWS SageMaker, Bedrock), and private deployments (VPC and on-prem)
Embeddings performance
Cohere’s Embed model leads the industry in accuracy and performance, meaning the highest search relevance, even with noisy datasets
Multilingual support
Over 100 languages are supported, so the same topics, products and issues are identified the same way in each
Scalability
Cohere Embed supports data compression, reducing storage and compute requirements
Flexible deployment options
Cohere models are accessible through a SaaS API, cloud services (e.g., OCI, AWS SageMaker, Bedrock), and private deployments (VPC and on-prem)
Semantic Search resources
Semantic search documentation
Learn how to implement semantic search using Cohere’s embedding models.
Get started with Cohere today!