Cohere

Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.

Installation and Setup

• Install the Python SDK :

pip install langchain-cohere

Get a Cohere api key and set it as an environment variable (COHERE_API_KEY)

Cohere langchain integrations

API	description	Endpoint docs	Import	Example usage
Chat	Build chat bots	chat	from langchain_cohere import ChatCohere	cohere.ipynb
LLM	Generate text	generate	from langchain_cohere.llms import Cohere	cohere.ipynb
RAG Retriever	Connect to external data sources	chat + rag	from langchain.retrievers import CohereRagRetriever	cohere.ipynb
Text Embedding	Embed strings to vectors	embed	from langchain_cohere import CohereEmbeddings	cohere.ipynb
Rerank Retriever	Rank strings based on relevance	rerank	from langchain.retrievers.document_compressors import CohereRerank	cohere.ipynb

Quick copy examples

Chat

from langchain_cohere import ChatCohere
from langchain_core.messages import HumanMessage
chat = ChatCohere()
messages = [HumanMessage(content="knock knock")]
print(chat.invoke(messages))

API Reference:ChatCohere | HumanMessage

Usage of the Cohere chat model

LLM

from langchain_cohere.llms import Cohere

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

API Reference:Cohere

Usage of the Cohere (legacy) LLM model

ReAct Agent

The agent is based on the paper ReAct: Synergizing Reasoning and Acting in Language Models.

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_cohere import ChatCohere, create_cohere_react_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain.agents import AgentExecutor

llm = ChatCohere()

internet_search = TavilySearchResults(max_results=4)
internet_search.name = "internet_search"
internet_search.description = "Route a user query to the internet"

prompt = ChatPromptTemplate.from_template("{input}")

agent = create_cohere_react_agent(
    llm,
    [internet_search],
    prompt
)

agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)

agent_executor.invoke({
    "input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
})

API Reference:TavilySearchResults | ChatCohere | create_cohere_react_agent | ChatPromptTemplate | AgentExecutor

RAG Retriever

from langchain_cohere import ChatCohere
from langchain.retrievers import CohereRagRetriever
from langchain_core.documents import Document

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

API Reference:ChatCohere | CohereRagRetriever | Document

Usage of the Cohere RAG Retriever

Text Embedding

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
print(embeddings.embed_documents(["This is a test document."]))

API Reference:CohereEmbeddings

Usage of the Cohere Text Embeddings model

Reranker

Usage of the Cohere Reranker