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Cohere → OpenObserve

Capture token usage, latency, and model metadata for every Cohere inference call. The Cohere SDK does not have a dedicated OTel instrumentor, so traces are created by wrapping calls in manual spans and extracting usage data from the response.

Prerequisites

  • Python 3.8+
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • A Cohere API key

Installation

pip install openobserve-telemetry-sdk cohere python-dotenv

Configuration

Create a .env file in your project root:

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
COHERE_API_KEY=your-cohere-api-key

Instrumentation

Call openobserve_init() to set up the tracer provider, then wrap each Cohere call in a manual span and extract token counts from the response metadata.

from dotenv import load_dotenv
load_dotenv()

from openobserve import openobserve_init
openobserve_init()

import os
from opentelemetry import trace
import cohere

tracer = trace.get_tracer(__name__)
co = cohere.Client(api_key=os.environ["COHERE_API_KEY"])


def chat(message: str, model: str = "command-r-08-2024") -> str:
    with tracer.start_as_current_span("cohere.chat") as span:
        span.set_attribute("llm_model_name", model)
        span.set_attribute("input_value", message)
        response = co.chat(model=model, message=message)
        output = response.text
        span.set_attribute("output_value", output[:200])
        if hasattr(response, "meta") and response.meta and response.meta.tokens:
            span.set_attribute("llm_token_count_prompt", response.meta.tokens.input_tokens or 0)
            span.set_attribute("llm_token_count_completion", response.meta.tokens.output_tokens or 0)
        return output


result = chat("Explain distributed tracing in one sentence.")
print(result)

What Gets Captured

Each co.chat() call wrapped in a span produces the following attributes.

Attribute Description
llm_model_name Model used (e.g. command-r-08-2024)
input_value Prompt sent to the model
output_value First 200 characters of the response
llm_token_count_prompt Input tokens consumed
llm_token_count_completion Output tokens generated
duration End-to-end span latency
span_status UNSET on success, ERROR on failure

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Filter by operation name cohere.chat to find Cohere spans
  3. Click any span to inspect token counts and the prompt and response

Cohere trace in OpenObserve

Next Steps

With Cohere instrumented, every chat call is recorded in OpenObserve. From here you can track token usage per model, monitor latency, and set alerts on error spans.

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