Send OpenTelemetry Traces

Overview

Weave supports ingestion of OpenTelemetry compatible trace data through a dedicated endpoint. This endpoint allows you to send OTLP (OpenTelemetry Protocol) formatted trace data directly to your Weave project.

Endpoint details

Path: /otel/v1/traces Method: POST Content-Type: application/x-protobuf

Authentication

Standard W&B authentication is used. You must have write permissions to the project where you’re sending trace data.

Required Headers

project_id: <your_entity>/<your_project_name>
Authorization=Basic <Base64 Encoding of api:$WANDB_API_KEY>

Examples:

You must modify the following fields before you can run the code samples below:

WANDB_API_KEY: You can get this from https://wandb.ai/authorize.
Entity: You can only log traces to the project under an entity that you have access to. You can find your entity name by visiting your W&N dashboard at [https://wandb.ai/home], and checking the Teams field in the left sidebar.
Project Name: Choose a fun name!
OPENAI_API_KEY: You can obtain this from the OpenAI dashboard.

OpenInference Instrumentation:

This example shows how to use the OpenAI instrumentation. There are many more available which you can find in the official repository: https://github.com/Arize-ai/openinference First, install the required dependencies:

pip install openai openinference-instrumentation-openai opentelemetry-exporter-otlp-proto-http

Next, paste the following code into a python file such as openinference_example.py

import base64
import openai
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
from openinference.instrumentation.openai import OpenAIInstrumentor

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(SimpleSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

def main():
    client = openai.OpenAI(api_key=OPENAI_API_KEY)
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}],
        max_tokens=20,
        stream=True,
        stream_options={"include_usage": True},
    )
    for chunk in response:
        if chunk.choices and (content := chunk.choices[0].delta.content):
            print(content, end="")

if __name__ == "__main__":
    main()

Finally, once you have set the fields specified above to their correct values, run the code:

python openinference_example.py

OpenLLMetry Instrumentation:

The following example shows how to use the OpenAI instrumentation. Additional examples are available at https://github.com/traceloop/openllmetry/tree/main/packages. First install the required dependencies:

pip install openai opentelemetry-instrumentation-openai opentelemetry-exporter-otlp-proto-http

Next, paste the following code into a python file such as openllmetry_example.py. Note that this is the same code as above, except the OpenAIInstrumentor is imported from opentelemetry.instrumentation.openai instead of openinference.instrumentation.openai

import base64
import openai
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(SimpleSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

def main():
    client = openai.OpenAI(api_key=OPENAI_API_KEY)
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}],
        max_tokens=20,
        stream=True,
        stream_options={"include_usage": True},
    )
    for chunk in response:
        if chunk.choices and (content := chunk.choices[0].delta.content):
            print(content, end="")

if __name__ == "__main__":
    main()

Finally, once you have set the fields specified above to their correct values, run the code:

python openllmetry_example.py

Without Instrumentation

If you would prefer to use OTEL directly instead of an instrumentation package, you may do so. Span attributes will be parsed according to the OpenTelemetry semantic conventions described at https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/. First, install the required dependencies:

pip install openai opentelemetry-sdk opentelemetry-api opentelemetry-exporter-otlp-proto-http

Next, paste the following code into a python file such as opentelemetry_example.py

import json
import base64
import openai
from opentelemetry import trace
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"
WANDB_BASE_URL = "https://trace.wandb.ai"
PROJECT_ID = "<your-entity>/<your-project>"

OTEL_EXPORTER_OTLP_ENDPOINT = f"{WANDB_BASE_URL}/otel/v1/traces"

# Can be found at https://wandb.ai/authorize
WANDB_API_KEY = "<your-wandb-api-key>"
AUTH = base64.b64encode(f"api:{WANDB_API_KEY}".encode()).decode()

OTEL_EXPORTER_OTLP_HEADERS = {
    "Authorization": f"Basic {AUTH}",
    "project_id": PROJECT_ID,
}

tracer_provider = trace_sdk.TracerProvider()

# Configure the OTLP exporter
exporter = OTLPSpanExporter(
    endpoint=OTEL_EXPORTER_OTLP_ENDPOINT,
    headers=OTEL_EXPORTER_OTLP_HEADERS,
)

# Add the exporter to the tracer provider
tracer_provider.add_span_processor(SimpleSpanProcessor(exporter))

# Optionally, print the spans to the console.
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

trace.set_tracer_provider(tracer_provider)
# Creates a tracer from the global tracer provider
tracer = trace.get_tracer(__name__)
tracer.start_span('name=standard-span')

def my_function():
    with tracer.start_as_current_span("outer_span") as outer_span:
        client = openai.OpenAI()
        input_messages=[{"role": "user", "content": "Describe OTEL in a single sentence."}]
        # This will only appear in the side panel
        outer_span.set_attribute("input.value", json.dumps(input_messages))
        # This follows conventions and will appear in the dashboard
        outer_span.set_attribute("gen_ai.system", 'openai')
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=input_messages,
            max_tokens=20,
            stream=True,
            stream_options={"include_usage": True},
        )
        out = ""
        for chunk in response:
            if chunk.choices and (content := chunk.choices[0].delta.content):
                out += content
        # This will only appear in the side panel
        outer_span.set_attribute("output.value", json.dumps({"content": out}))

if __name__ == "__main__":
    my_function()

Finally, once you have set the fields specified above to their correct values, run the code:

python opentelemetry_example.py

The span attribute prefixes gen_ai and openinference are used to determine which convention to use, if any, when interpreting the trace. If neither key is detected, then all span attributes are visible in the trace view. The full span is available in the side panel when you select a trace.

Introduction

Iteration

Evaluation

Productionization

Integrations

Enterprise

Tools & Resources

Overview

Endpoint details

Authentication

Required Headers

Examples:

OpenInference Instrumentation:

OpenLLMetry Instrumentation:

Without Instrumentation

Introduction

Iteration

Evaluation

Productionization

Integrations

Enterprise

Tools & Resources

​Overview

​Endpoint details

​Authentication

​Required Headers

​Examples:

​OpenInference Instrumentation:

​OpenLLMetry Instrumentation:

​Without Instrumentation

Overview

Endpoint details

Authentication

Required Headers

Examples:

OpenInference Instrumentation:

OpenLLMetry Instrumentation:

Without Instrumentation