Cohere
You are currently on a page documenting the use of Cohere models as text completion models. Many popular Cohere models are chat completion models.
You may be looking for this page instead.
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
Head to the API reference for detailed documentation of all attributes and methods.
Overviewโ
Integration detailsโ
Class | Package | Local | Serializable | JS support | Package downloads | Package latest |
---|---|---|---|---|---|---|
Cohere | langchain_community | โ | beta | โ |
Setupโ
The integration lives in the langchain-community
package. We also need to install the cohere
package itself. We can install these with:
Credentialsโ
We'll need to get a Cohere API key and set the COHERE_API_KEY
environment variable:
import getpass
import os
if "COHERE_API_KEY" not in os.environ:
os.environ["COHERE_API_KEY"] = getpass.getpass()
Installationโ
pip install -U langchain-community langchain-cohere
It's also helpful (but not needed) to set up LangSmith for best-in-class observability
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
Invocationโ
Cohere supports all LLM functionality:
from langchain_cohere import Cohere
from langchain_core.messages import HumanMessage
model = Cohere(max_tokens=256, temperature=0.75)
message = "Knock knock"
model.invoke(message)
" Who's there?"
await model.ainvoke(message)
" Who's there?"
for chunk in model.stream(message):
print(chunk, end="", flush=True)
Who's there?
model.batch([message])
[" Who's there?"]
Chainingโ
You can also easily combine with a prompt template for easy structuring of user input. We can do this using LCEL
from langchain_core.prompts import PromptTemplate
prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | model
chain.invoke({"topic": "bears"})
' Why did the teddy bear cross the road?\nBecause he had bear crossings.\n\nWould you like to hear another joke? '
API referenceโ
For detailed documentation of all Cohere
llm features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html
Relatedโ
- LLM conceptual guide
- LLM how-to guides