> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-update-reference-docs-40.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Cohere
> Use Weave to automatically track and log LLM calls made through the Cohere Python library
Weave automatically tracks and logs LLM calls made through the [Cohere Python library](https://github.com/cohere-ai/cohere-python) after you call `weave.init()`.
## Traces
It's important to store traces of LLM applications in a central database, both during development and in production. You use these traces for debugging and as a dataset that helps you improve your application.
Weave automatically captures traces for `cohere-python`. Use the library as usual. Start by calling `weave.init()`:
```python lines {8} theme={null}
import cohere
import os
import weave
# Use the Cohere library as usual
co = cohere.Client(api_key=os.environ["COHERE_API_KEY"])
weave.init("cohere_project")
response = co.chat(
message="How is the weather in Boston?",
# perform web search before answering the question. You can also use your own custom connector.
connectors=[{"id": "web-search"}],
)
print(response.text)
```
If you don't specify a W\&B team when you call `weave.init()`, Weave uses your default entity. To find or update your default entity, refer to [User Settings](https://docs.wandb.ai/platform/app/settings-page/user-settings/#default-team) in the W\&B Models documentation.
Cohere models support [connectors](https://docs.cohere.com/docs/overview-rag-connectors#using-connectors-to-create-grounded-generations), which let you make requests to other APIs on the endpoint side. The response then contains the generated text with citation elements that link to the documents returned from the connector.
[
](https://wandb.ai/capecape/cohere_dev/weave/calls)
Weave patches the Cohere `Client.chat()`, `AsyncClient.chat()`, `Client.chat_stream()`, and `AsyncClient.chat_stream()` methods to track your LLM calls.
## Wrap with your own ops
Weave ops make results *reproducible* by automatically versioning code as you experiment, and they capture their inputs and outputs. Create a function decorated with [`@weave.op()`](/weave/guides/tracking/ops) that calls into Cohere's chat methods, and Weave tracks the inputs and outputs for you. Here's an example:
```python lines {9} theme={null}
import cohere
import os
import weave
co = cohere.Client(api_key=os.environ["COHERE_API_KEY"])
weave.init("cohere_project")
@weave.op()
def weather(location: str, model: str) -> str:
response = co.chat(
model=model,
message=f"How is the weather in {location}?",
# perform web search before answering the question. You can also use your own custom connector.
connectors=[{"id": "web-search"}],
)
return response.text
print(weather("Boston", "command"))
```
[
](https://wandb.ai/capecape/cohere_dev/weave/calls)
## Create a `Model` for easier experimentation
Organizing experimentation is difficult when there are many moving pieces. By using the [`Model`](/weave/guides/core-types/models) class, you can capture and organize the experimental details of your app like your system prompt or the model you're using. This helps organize and compare different iterations of your app.
Beyond versioning code and capturing inputs/outputs, [`Model`](/weave/guides/core-types/models)s capture structured parameters that control your application's behavior, helping you find what parameters worked best. You can also use Weave Models with `serve`, and [`Evaluation`](/weave/guides/core-types/evaluations)s.
In the following example, you can experiment with `model` and `temperature`. Every time you change one of these, you'll get a new *version* of `WeatherModel`.
```python lines theme={null}
import weave
import cohere
import os
weave.init('weather-cohere')
class WeatherModel(weave.Model):
model: str
temperature: float
@weave.op()
def predict(self, location: str) -> str:
co = cohere.Client(api_key=os.environ["COHERE_API_KEY"])
response = co.chat(
message=f"How is the weather in {location}?",
model=self.model,
temperature=self.temperature,
connectors=[{"id": "web-search"}]
)
return response.text
weather_model = WeatherModel(
model="command",
temperature=0.7
)
result = weather_model.predict("Boston")
print(result)
```
[
](https://wandb.ai/capecape/cohere_dev/weave/models)