Definition
class TokenUsage(CamelModel):
prompt_tokens: int
completion_tokens: int
total_tokens: int
Fields
Number of tokens in the input/prompt.
Number of tokens in the output/completion.
Total tokens used (prompt_tokens + completion_tokens).
Example
from rdk.models import TokenUsage
usage = TokenUsage(
prompt_tokens=150,
completion_tokens=75,
total_tokens=225
)
Automatic Capture
RDK automatically captures token usage from LLM providers:
from anthropic import Anthropic
from rdk import init, observe
init(endpoint="...", api_key="...")
@observe(name="chat")
def chat(message: str):
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": message}]
)
# Token usage is automatically captured in the span
return response.content[0].text
Provider Support
| Provider | Token Usage |
|---|
| Anthropic | Yes |
| OpenAI | Yes |
| Google Gemini | Partial |
| LangChain | Depends on underlying provider |
Cost Estimation
Use token counts to estimate costs:
# Example cost calculation (rates vary by model)
COST_PER_1K_INPUT = 0.003 # $3 per 1M input tokens
COST_PER_1K_OUTPUT = 0.015 # $15 per 1M output tokens
def estimate_cost(usage: TokenUsage) -> float:
input_cost = (usage.prompt_tokens / 1000) * COST_PER_1K_INPUT
output_cost = (usage.completion_tokens / 1000) * COST_PER_1K_OUTPUT
return input_cost + output_cost
JSON Serialization
{
"promptTokens": 150,
"completionTokens": 75,
"totalTokens": 225
}
See Also
- Span - Contains token usage
