Cohere provides access to its language models through an API. This document outlines how to use the Cohere API within the OneSDK framework.
To use the Cohere API, initialize the OneSDK with your Cohere API key:
from llm_onesdk import OneSDK
cohere_sdk = OneSDK("cohere", {"api_key": "your_api_key_here"})
To generate text, use the generate
method. Specify the model and provide a list of messages:
model = "cohere-model-name" # Replace with an actual Cohere model name
messages = [{"role": "user", "content": "Explain the concept of machine learning in simple terms."}]
response = cohere_sdk.generate(model, messages)
print(response['choices'][0]['message']['content'])
For longer responses or to get partial results as they're generated, use the stream_generate
method:
for chunk in cohere_sdk.stream_generate(model, messages):
print(chunk['choices'][0]['message']['content'], end='', flush=True)
To estimate the number of tokens in your input:
token_count = cohere_sdk.count_tokens(model, messages)
print(f"Token count: {token_count}")
Cohere supports creating embeddings for text:
model = "embed-english-v2.0" # Replace with the appropriate embedding model
input_text = "Hello, world!"
embeddings = cohere_sdk.create_embedding(model, input_text)
print(embeddings)
The SDK will raise InvokeError
or its subclasses for various error conditions. Always wrap your API calls in try-except blocks:
try:
response = cohere_sdk.generate(model, messages)
except InvokeError as e:
print(f"An error occurred: {str(e)}")
- Use the most appropriate model for your task.
- Implement proper error handling and retries for production applications.
- Be mindful of rate limits and implement appropriate backoff strategies.
- Keep your API key secure and never expose it in client-side code.
For more detailed information about available models and specific features, please refer to the official Cohere API documentation.dd ge mdgem