traceAI-vllm 0.1.0

OpenTelemetry instrumentation for vLLM

TraceAI vLLM Instrumentation

OpenTelemetry instrumentation for vLLM, enabling comprehensive observability for local LLM inference.

Installation

pip install traceai-vllm

Quick Start

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
from traceai_vllm import VLLMInstrumentor

# Setup TraceAI
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="my-vllm-app",
)

# Instrument vLLM (specify your server URLs)
VLLMInstrumentor(
    vllm_base_urls=["localhost:8000"]  # Your vLLM server(s)
).instrument(tracer_provider=trace_provider)

# Now use OpenAI client with vLLM
from openai import OpenAI

client = OpenAI(
    api_key="token",  # vLLM doesn't require a real API key
    base_url="http://localhost:8000/v1",
)

response = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[{"role": "user", "content": "Hello!"}]
)

Configuration

Custom vLLM Server URLs

You can specify multiple vLLM server URLs to instrument:

VLLMInstrumentor(
    vllm_base_urls=[
        "localhost:8000",
        "production-vllm.internal:8000",
        "staging-vllm.internal:8080",
    ]
).instrument(tracer_provider=trace_provider)

Features

• Automatic tracing of vLLM API calls
• Support for both synchronous and asynchronous operations
• Streaming response support
• Token usage tracking
• Request/response attribute capture
• Support for multiple vLLM server endpoints
• OpenTelemetry semantic conventions for GenAI

Captured Attributes

• gen_ai.request.model - Model name
• gen_ai.request.max_tokens - Maximum tokens
• gen_ai.request.temperature - Temperature setting
• gen_ai.usage.input_tokens - Input token count
• gen_ai.usage.output_tokens - Output token count
• gen_ai.prompt.{n}.role - Message roles
• gen_ai.prompt.{n}.content - Message contents
• gen_ai.completion.{n}.content - Response content

vLLM-Specific Parameters

The instrumentation also captures vLLM-specific parameters when provided:

• best_of - Number of output sequences to generate
• use_beam_search - Whether to use beam search

Requirements

• Python >= 3.9
• openai >= 1.0.0
• fi-instrumentation >= 0.1.0
• opentelemetry-api >= 1.0.0
• opentelemetry-sdk >= 1.0.0

Running vLLM Server

To use this instrumentation, you need a running vLLM server:

# Install vLLM
pip install vllm

# Start the server
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --port 8000

License

Apache-2.0