Cohere

tags :

Organization #

Give your technology language URL

JAK important consideration #

• LLM for the enterprise
• Data will not leave or leak

Features #

Make your products understand text #

Using Cohere’s powerful embeddings models, you can make your applications understand the meaning of text data at massive scale, unlocking powerful semantic search, classification, and rerank capabilities.

Make your products generate or summarize text #

Our Command model quickly and accurately generates text such as product descriptions, blog posts, and articles. Use it also to create concise, relevant, customizable summaries of text and documents.

Accelerate the next wave of productivity #

Today’s language models already show productivity gains in white-collar tasks of over 50%. The coming Intelligent Assistants will understand your enterprise data, giving your employees the tools to make decisions far more quickly than we can imagine today.

The enterprise LLM #

We offer multiple hosting options to give you full control over data security and privacy, with private cloud, secure cloud partners (AWS, Oracle, Google), and Cohere’s managed cloud as options.

Products #

Chat #

Generate #

ref Generate produces unique content for emails, landing pages, product descriptions, and more.

Summarize #

ref

Embed #

LangChain #

ref

# Quick copy examples
Chat
from langchain.chat_models import ChatCohere
from langchain.schema import HumanMessage
chat = ChatCohere()
messages = [HumanMessage(content="knock knock")]
print(chat(messages))

# LLM
from langchain.llms import Cohere

llm = Cohere(model="command")
print(llm.invoke("Come up with a pet name"))

# RAG Retriever
from langchain.chat_models import ChatCohere
from langchain.retrievers import CohereRagRetriever
from langchain.schema.document import Document

rag = CohereRagRetriever(llm=ChatCohere())
print(rag.get_relevant_documents("What is cohere ai?"))

# Text Embedding
from langchain.chat_models import ChatCohere
from langchain.retrievers import CohereRagRetriever
from langchain.schema.document import Document

rag = CohereRagRetriever(llm=ChatCohere())
print(rag.get_relevant_documents("What is cohere ai?"))