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131 changes: 131 additions & 0 deletions examples/tracing/pydantic-ai/pydantic_ai_tracing.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "2722b419",
"metadata": {},
"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openlayer-ai/openlayer-python/blob/main/examples/tracing/pydantic-ai/pydantic_ai_tracing.ipynb)\n",
"\n",
"\n",
"# <a id=\"top\">Pydantic AI quickstart</a>\n",
"\n",
"This notebook shows how to trace Pydantic AI Agents with Openlayer. The integration is done via the Openlayer's [OpenTelemetry endpoint](https://www.openlayer.com/docs/integrations/opentelemetry)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "020c8f6a",
"metadata": {},
"outputs": [],
"source": [
"!pip install pydantic-ai logfire"
]
},
{
"cell_type": "markdown",
"id": "75c2a473",
"metadata": {},
"source": [
"## 1. Set the environment variables"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f3f4fa13",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY_HERE\"\n",
"\n",
"os.environ[\"OTEL_EXPORTER_OTLP_ENDPOINT\"] = \"https://api.openlayer.com/v1/otel\"\n",
"os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = \"Authorization=Bearer YOUR_OPENLAYER_API_KEY_HERE, x-bt-parent=pipeline_id:YOUR_OPENLAYER_PIPELINE_ID_HERE\""
]
},
{
"cell_type": "markdown",
"id": "9758533f",
"metadata": {},
"source": [
"## 2. Configure Logfire instrumentation (used by Pydantic AI)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c35d9860-dc41-4f7c-8d69-cc2ac7e5e485",
"metadata": {},
"outputs": [],
"source": [
"import logfire\n",
"\n",
"logfire.configure(send_to_logfire=False)\n",
"logfire.instrument_pydantic_ai()"
]
},
{
"cell_type": "markdown",
"id": "72a6b954",
"metadata": {},
"source": [
"## 3. Use Agents as usual\n",
"\n",
"That's it! Now you can continue using Agents as usual. The trace data is automatically exported to Openlayer and you can start creating tests around it."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e00c1c79",
"metadata": {},
"outputs": [],
"source": [
"from pydantic_ai import Agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "253a3ac8",
"metadata": {},
"outputs": [],
"source": [
"agent = Agent('openai:gpt-4o')\n",
"result = await agent.run('What is the capital of France?')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1c37cfe",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "crewai-test",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}