langchain/docs/docs/integrations/providers/cohere.mdx

# Cohere

>[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models
> that help companies improve human-machine interactions.

## Installation and Setup
- Install the Python SDK :
```bash
pip install langchain-cohere
```

Get a [Cohere api key](https://dashboard.cohere.ai/) 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](https://docs.cohere.com/reference/chat)|`from langchain_cohere import ChatCohere`|[cohere.ipynb](/docs/integrations/chat/cohere)|
|LLM|Generate text|[generate](https://docs.cohere.com/reference/generate)|`from langchain_cohere.llms import Cohere`|[cohere.ipynb](/docs/integrations/llms/cohere)|
|RAG Retriever|Connect to external data sources|[chat + rag](https://docs.cohere.com/reference/chat)|`from langchain.retrievers import CohereRagRetriever`|[cohere.ipynb](/docs/integrations/retrievers/cohere)|
|Text Embedding|Embed strings to vectors|[embed](https://docs.cohere.com/reference/embed)|`from langchain_cohere import CohereEmbeddings`|[cohere.ipynb](/docs/integrations/text_embedding/cohere)|
|Rerank Retriever|Rank strings based on relevance|[rerank](https://docs.cohere.com/reference/rerank)|`from langchain.retrievers.document_compressors import CohereRerank`|[cohere.ipynb](/docs/integrations/retrievers/cohere-reranker)|

## Quick copy examples

### Chat

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

Usage of the Cohere [chat model](/docs/integrations/chat/cohere)

### LLM


```python
from langchain_cohere.llms import Cohere

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

Usage of the Cohere (legacy) [LLM model](/docs/integrations/llms/cohere) 
### ReAct Agent

```python
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?",
})
```

### RAG Retriever

```python
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](/docs/integrations/retrievers/cohere)

### Text Embedding

```python
from langchain_cohere import CohereEmbeddings

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

Usage of the Cohere [Text Embeddings model](/docs/integrations/text_embedding/cohere)

### Reranker

Usage of the Cohere [Reranker](/docs/integrations/retrievers/cohere-reranker)