add LlamaIndex zero-code OTLP example

examples/zero-code-examples/llama-index/requirements.txt

@@ -0,0 +1,7 @@
+llama-index-core>=0.14.0
+llama-index-llms-openai-like>=0.3.0
+
+opentelemetry-sdk>=1.36.0
+opentelemetry-exporter-otlp-proto-http>=1.36.0
+opentelemetry-instrumentation-openai-v2
+python-dotenv>=1.0.0

examples/zero-code-examples/llama-index/run.py

@@ -0,0 +1,79 @@
+"""LlamaIndex zero-code OTLP example."""
+import asyncio
+import os
+import random
+
+from dotenv import load_dotenv
+from llama_index.core.agent.workflow import FunctionAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.openai_like import OpenAILike
+from opentelemetry import trace
+from opentelemetry._logs import set_logger_provider
+from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+
+load_dotenv(override=True)
+
+
+def roll_die(sides: int) -> int:
+    """Roll a die and return the result."""
+    return random.randint(1, sides)
+
+
+def check_prime(number: int) -> bool:
+    """Check if a number is prime."""
+    return number >= 2 and all(number % i for i in range(2, int(number**0.5) + 1))
+
+
+async def main():
+    if not os.getenv("OPENAI_API_KEY"):
+        print("OPENAI_API_KEY not set.")
+        return
+
+    os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true"
+    os.environ.setdefault("OTEL_RESOURCE_ATTRIBUTES",
+        "agentevals.eval_set_id=llama_index_eval,agentevals.session_name=llama-index-zero-code")
+
+    resource = Resource.create()
+
+    tp = TracerProvider(resource=resource)
+    tp.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(), schedule_delay_millis=1000))
+    trace.set_tracer_provider(tp)
+
+    lp = LoggerProvider(resource=resource)
+    lp.add_log_record_processor(BatchLogRecordProcessor(OTLPLogExporter(), schedule_delay_millis=1000))
+    set_logger_provider(lp)
+
+    OpenAIInstrumentor().instrument()
+
+    llm = OpenAILike(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        api_base=os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1"),
+        is_chat_model=True, is_function_calling_model=True,
+    )
+    agent = FunctionAgent(
+        tools=[FunctionTool.from_defaults(fn=roll_die), FunctionTool.from_defaults(fn=check_prime)],
+        llm=llm,
+        system_prompt="Use roll_die for dice rolls. Use check_prime to check if a number is prime.",
+    )
+
+    queries = ["Hi! Can you help me?", "Roll a 20-sided die.", "Roll a 6-sided die and check if the result is prime."]
+    for i, query in enumerate(queries, 1):
+        print(f"\n[{i}/{len(queries)}] User: {query}")
+        result = await agent.run(query)
+        print(f"     Response: {result.response.content}")
+
+    print()
+    tp.force_flush()
+    lp.force_flush()
+    print("All traces flushed.")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())