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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​

APIdescriptionEndpoint docsImportExample usage
ChatBuild chat botschatfrom langchain_cohere import ChatCoherecohere.ipynb
LLMGenerate textgeneratefrom langchain_cohere.llms import Coherecohere.ipynb
RAG RetrieverConnect to external data sourceschat + ragfrom langchain.retrievers import CohereRagRetrievercohere.ipynb
Text EmbeddingEmbed strings to vectorsembedfrom langchain_cohere import CohereEmbeddingscohere.ipynb
Rerank RetrieverRank strings based on relevancererankfrom langchain.retrievers.document_compressors import CohereRerankcohere.ipynb

Quick copy examples​


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

Usage of the Cohere chat model


from langchain_cohere.llms import Cohere

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

API Reference:

Usage of the Cohere (legacy) LLM model

ReAct Agent​

from 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"
internet_search.description = "Route a user query to the internet"

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

agent = create_cohere_react_agent(

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

"input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",

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?"))

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:

Usage of the Cohere Text Embeddings model


Usage of the Cohere Reranker

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