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feat: Openlit span processor (Arize-ai#2021)
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‎.gitignore‎

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.vscode
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node_modules
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dist
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sdist

‎python/instrumentation/openinference-instrumentation-openlit/CHANGELOG.md‎

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# Changelog

‎python/instrumentation/openinference-instrumentation-openlit/LICENSE‎

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Apache License
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‎python/instrumentation/openinference-instrumentation-openlit/README.md‎

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# OpenInference OpenLit Instrumentation
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Python auto-instrumentation library for OpenLIT. This library allows you to convert OpenLIT traces to OpenInference, which is OpenTelemetry compatible, and view those traces in [Arize Phoenix](https://github.com/Arize-ai/phoenix).
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## Installation
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```shell
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pip install openinference-instrumentation-openlit
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```
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## Quickstart
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This quickstart shows you how to view your OpenLIT traces in Phoenix.
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Install required packages.
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```shell
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pip install arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp openlit semantic-kernel
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```
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Start Phoenix in the background as a collector. By default, it listens on `http://localhost:6006`. You can visit the app via a browser at the same address.
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```shell
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phoenix serve
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```
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Here's a simple example that demonstrates how to convert OpenLIT traces into OpenInference and view those traces in Phoenix:
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```python
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import os
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import grpc
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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from phoenix.otel import register
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from openinference.instrumentation.openlit import OpenInferenceSpanProcessor
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
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import openlit
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# Set your OpenAI API key
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os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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# Set up the tracer provider
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tracer_provider = register(
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project_name="default" #Phoenix project name
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)
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tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
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tracer_provider.add_span_processor(
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BatchSpanProcessor(
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OTLPSpanExporter(
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endpoint="http://localhost:4317", #if using phoenix cloud, change to phoenix cloud endpoint (phoenix cloud space -> settings -> endpoint/hostname)
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headers={},
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compression=grpc.Compression.Gzip, # use enum instead of string
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)
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)
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)
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# Initialize OpenLit with the tracer
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tracer = tracer_provider.get_tracer(__name__)
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openlit.init(tracer=tracer)
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# Set up Semantic Kernel with OpenLIT
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kernel = Kernel()
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kernel.add_service(
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OpenAIChatCompletion(
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service_id="default",
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ai_model_id="gpt-4o-mini",
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),
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)
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# Define and invoke your model
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result = await kernel.invoke_prompt(
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prompt="What is the national food of Yemen?",
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arguments={},
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)
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# Now view your converted OpenLIT traces in Phoenix!
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```
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## This example:
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1. Uses OpenLIT Instrumentor to instrument the application.
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2. Defines a simple Semantic Kernel model and runs a query
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3. Queries are exported to Phoenix using a span processor.
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The traces will be visible in the Phoenix UI at `http://localhost:6006`.
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## More Info
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- [More info on OpenInference and Phoenix](https://docs.arize.com/phoenix)
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- [How to customize spans to track sessions, metadata, etc.](https://github.com/Arize-ai/openinference/tree/main/python/openinference-instrumentation#customizing-spans)
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- [How to account for private information and span payload customization](https://github.com/Arize-ai/openinference/tree/main/python/openinference-instrumentation#tracing-configuration)

‎python/instrumentation/openinference-instrumentation-openlit/examples/joke.py‎

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import asyncio
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import os
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import grpc
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import openlit
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from _span_processor import OpenInferenceSpanProcessor
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from arize.otel import register
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from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from semantic_kernel import Kernel, __version__
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from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
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from semantic_kernel.functions import KernelArguments
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os.environ["GLOBAL_LLM_SERVICE"] = "OpenAI"
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os.environ["OPENAI_API_KEY"] = "INSERT OPENAI API KEY HERE"
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os.environ["OPENAI_CHAT_MODEL_ID"] = "gpt-4o-mini"
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os.environ["OPENAI_TEXT_MODEL_ID"] = "gpt-4o-mini"
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print(__version__)
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SPACE_ID = "INSERT ARIZE SPACE ID HERE"
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API_KEY = "INSERT ARIZE API KEY HERE"
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if __name__ == "__main__":
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provider = register(
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space_id=SPACE_ID,
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api_key=API_KEY,
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project_name="semantic-kernel-example",
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set_global_tracer_provider=True,
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)
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provider.add_span_processor(OpenInferenceSpanProcessor())
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provider.add_span_processor(
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BatchSpanProcessor(
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OTLPSpanExporter(
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endpoint="otlp.arize.com:443",
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headers={
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"authorization": f"Bearer {API_KEY}",
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"api_key": API_KEY,
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"arize-space-id": SPACE_ID,
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"arize-interface": "python",
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"user-agent": "arize-python",
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},
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compression=grpc.Compression.Gzip,
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)
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)
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)
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tracer = provider.get_tracer(__name__)
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openlit.init(
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tracer=tracer,
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otlp_endpoint=None,
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)
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kernel = Kernel()
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kernel.remove_all_services()
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service_id = "default"
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kernel.add_service(
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OpenAIChatCompletion(
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service_id=service_id,
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),
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)
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plugin_dir = os.path.abspath(
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os.path.join(os.path.dirname(__file__), "../../../prompt_template_samples/")
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)
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plugin = kernel.add_plugin(parent_directory=plugin_dir, plugin_name="FunPlugin")
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joke_function = plugin["Joke"]
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async def run_joke():
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joke = await kernel.invoke(
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joke_function,
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KernelArguments(input="time travel to dinosaur age", style="super silly"),
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)
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print(joke)
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if __name__ == "__main__":
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asyncio.run(run_joke())

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