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chore: import upstream snapshot with attribution
2026-07-13 13:27:52 +08:00

2.2 KiB

Cohere

Install

To use CohereModel, you need to either install pydantic-ai, or install pydantic-ai-slim with the cohere optional group:

pip/uv-add "pydantic-ai-slim[cohere]"

Configuration

To use Cohere through their API, go to dashboard.cohere.com/api-keys and follow your nose until you find the place to generate an API key.

CohereModelName contains a list of the most popular Cohere models.

Environment variable

Once you have the API key, you can set it as an environment variable:

export CO_API_KEY='your-api-key'

You can then use CohereModel by name:

from pydantic_ai import Agent

agent = Agent('cohere:command-r7b-12-2024')
...

Or initialise the model directly with just the model name:

from pydantic_ai import Agent
from pydantic_ai.models.cohere import CohereModel

model = CohereModel('command-r7b-12-2024')
agent = Agent(model)
...

provider argument

You can provide a custom Provider via the provider argument:

from pydantic_ai import Agent
from pydantic_ai.models.cohere import CohereModel
from pydantic_ai.providers.cohere import CohereProvider

model = CohereModel('command-r7b-12-2024', provider=CohereProvider(api_key='your-api-key'))
agent = Agent(model)
...

You can also customize the CohereProvider with a custom http_client:

from httpx import AsyncClient

from pydantic_ai import Agent
from pydantic_ai.models.cohere import CohereModel
from pydantic_ai.providers.cohere import CohereProvider

custom_http_client = AsyncClient(timeout=30)
model = CohereModel(
    'command-r7b-12-2024',
    provider=CohereProvider(api_key='your-api-key', http_client=custom_http_client),
)
agent = Agent(model)
...

Model settings

You can customize model behavior using [CohereModelSettings][pydantic_ai.models.cohere.CohereModelSettings]:

from pydantic_ai import Agent
from pydantic_ai.models.cohere import CohereModel, CohereModelSettings

model = CohereModel('command-r7b-12-2024')
settings = CohereModelSettings(
    temperature=0.2,
    top_k=40,
)
agent = Agent(model, model_settings=settings)
...