OpenTelemetry¶
Mirascope provides out-of-the-box integration with OpenTelemetry.
You can install the necessary packages directly or using the opentelemetry extras flag:
You can then use the with_otel decorator to automatically log calls:
from mirascope.core import Messages, anthropic
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@anthropic.call("claude-3-5-sonnet-20240620")
def recommend_book(genre: str) -> Messages.Type:
return Messages.User(f"Recommend a {genre} book")
print(recommend_book("fantasy"))
from mirascope.core import Messages, mistral
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@mistral.call("mistral-large-latest")
def recommend_book(genre: str) -> Messages.Type:
return Messages.User(f"Recommend a {genre} book")
print(recommend_book("fantasy"))
from mirascope.core import Messages, bedrock
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0")
def recommend_book(genre: str) -> Messages.Type:
return Messages.User(f"Recommend a {genre} book.")
print(recommend_book("fantasy"))
from mirascope.core import bedrock, prompt_template
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0")
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, openai
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@openai.call("gpt-4o-mini")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book.")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, anthropic
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@anthropic.call("claude-3-5-sonnet-20240620")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, mistral
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@mistral.call("mistral-large-latest")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, gemini
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@gemini.call("gemini-1.5-flash")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, groq
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@groq.call("llama-3.1-70b-versatile")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, cohere
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@cohere.call("command-r-plus")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, litellm
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@litellm.call("gpt-4o-mini")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, azure
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@azure.call("gpt-4o-mini")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, vertex
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@vertex.call("gemini-1.5-flash")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]
print(recommend_book("fantasy"))
from mirascope.core import BaseMessageParam, bedrock
from mirascope.integrations.otel import configure, with_otel
configure()
@with_otel()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0")
def recommend_book(genre: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Recommend a {genre} book.")]
print(recommend_book("fantasy"))
Example output span
This will give you:
- A span called
recommend_bookthat captures items like the prompt template, templating properties and fields, and input/output attributes - Details of the response, including the number of tokens used
{
"name": "recommend_book",
"context": {
"trace_id": "0x9397ede2f689a5b7767f0063d7b81959",
"span_id": "0xa22a446b2076ffe4",
"trace_state": "[]"
},
"kind": "SpanKind.INTERNAL",
"parent_id": null,
"start_time": "2024-10-04T05:16:47.340006Z",
"end_time": "2024-10-04T05:16:47.341937Z",
"status": {
"status_code": "UNSET"
},
"attributes": {
"gen_ai.system": "",
"gen_ai.request.model": "claude-3-5-sonnet-20240620",
"gen_ai.request.max_tokens": 0,
"gen_ai.request.temperature": 0,
"gen_ai.request.top_p": 0,
"gen_ai.response.model": "claude-3-5-sonnet-20240620",
"gen_ai.response.id": "msg_01X8sppiaZeErMjh8oCNz4ZA",
"gen_ai.response.finish_reasons": [
"end_turn"
],
"gen_ai.usage.completion_tokens": 268,
"gen_ai.usage.prompt_tokens": 12,
"async": false
},
"events": [
{
"name": "gen_ai.content.prompt",
"timestamp": "2024-10-04T05:16:47.341924Z",
"attributes": {
"gen_ai.prompt": "{\"content\": \"Recommend a fantasy book\", \"role\": \"user\"}"
}
},
{
"name": "gen_ai.content.completion",
"timestamp": "2024-10-04T05:16:47.341931Z",
"attributes": {
"gen_ai.completion": "{\"content\": [{\"text\": \"There are many great fantasy books to choose from, but here's a popular recommendation:\\n\\n\\\"The Name of the Wind\\\" by Patrick Rothfuss\\n\\nThis is the first book in \\\"The Kingkiller Chronicle\\\" series. It's a beautifully written, engaging story that follows the life of Kvothe, a legendary wizard, warrior, and musician. The book is known for its rich world-building, complex magic system, and compelling characters.\\n\\nOther excellent fantasy recommendations include:\\n\\n1. \\\"The Lord of the Rings\\\" by J.R.R. Tolkien\\n2. \\\"A Game of Thrones\\\" by George R.R. Martin\\n3. \\\"The Way of Kings\\\" by Brandon Sanderson\\n4. \\\"The Lies of Locke Lamora\\\" by Scott Lynch\\n5. \\\"The Night Circus\\\" by Erin Morgenstern\\n6. \\\"Mistborn: The Final Empire\\\" by Brandon Sanderson\\n7. \\\"The Wizard of Earthsea\\\" by Ursula K. Le Guin\\n\\nThese books cater to different tastes within the fantasy genre, so you might want to read a brief synopsis of each to see which one appeals to you most.\", \"type\": \"text\"}], \"role\": \"assistant\"}"
}
}
],
"links": [],
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "opentelemetry",
"telemetry.sdk.version": "1.22.0",
"service.name": "unknown_service"
},
"schema_url": ""
}
}
Handling streams
When logging streams, the span will not be logged until the stream has been exhausted. This is a function of how streaming works.
You will also need to set certain call_params for usage to be tracked for certain providers (such as OpenAI).
Sending to an observability tool¶
You'll likely want to send the spans to an actual observability tool so that you can monitor your traces. There are many observability tools out there, but the majority of them can collect OpenTelemetry data. You can pass in processors as an argument of configure() so that you can send the spans to your choice of observability tool:
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import (
BatchSpanProcessor,
)
from mirascope.integrations.otel import configure
OBSERVABILITY_TOOL_ENDPOINT = "..."
configure(
processors=[
BatchSpanProcessor(
OTLPSpanExporter(
endpoint=f"https://{OBSERVABILITY_TOOL_ENDPOINT}/v1/traces",
)
)
]
)
You should refer to your observability tool's documentation to find the endpoint. If there is an observability backend that you would like for us to integrate out-of-the-box, create a GitHub Issue or let us know in our Slack community.