vllm-sdk 0.1.1

Minimal Python SDK for the vLLM API

vLLM SDK

Minimal Python SDK for the vLLM API. This package provides a lightweight client library for interacting with vLLM API servers, with only httpx and pydantic as dependencies.

Installation

pip install vllm-sdk

Quick Start

import asyncio
from vllm_sdk import VLLMClient, ChatMessage

async def main():
    async with VLLMClient(base_url="http://localhost:8000") as client:
        # Non-streaming chat completion
        response = await client.chat_completions(
            model="meta-llama/Meta-Llama-3.3-70B-Instruct",
            messages=[
                ChatMessage(role="user", content="Hello!")
            ],
        )
        print(response.choices[0].message.content)

        # Streaming chat completion
        async for chunk in client.chat_completions_stream(
            model="meta-llama/Meta-Llama-3.3-70B-Instruct",
            messages=[
                ChatMessage(role="user", content="Tell me a story")
            ],
        ):
            if chunk.choices[0].delta.content:
                print(chunk.choices[0].delta.content, end="", flush=True)

asyncio.run(main())

Features

• Minimal Dependencies: Only requires httpx and pydantic
• Type Safety: Full Pydantic schema validation for requests and responses
• Async Support: Built on httpx for async/await support
• Streaming: Support for streaming chat completions
• Feature Search: Search SAE features by semantic similarity

API Reference

VLLMClient

The main client class for interacting with the vLLM API.

Methods

• chat_completions() - Create a non-streaming chat completion
• chat_completions_stream() - Stream chat completions (async generator)
• feature_search() - Search for SAE features

Schemas

All request and response models are available for import:

• ChatMessage - Individual chat message
• ChatCompletionRequest - Chat completion request
• ChatCompletionResponse - Chat completion response
• ChatCompletionChunk - Streaming chunk
• FeatureSearchRequest - Feature search request
• FeatureSearchResponse - Feature search response
• ModelName - Supported model names enum

Examples

Feature Search

from vllm_sdk import VLLMClient, FeatureSearchRequest

async with VLLMClient(base_url="http://localhost:8000") as client:
    response = await client.feature_search(
        query="pirate speech",
        model="meta-llama/Meta-Llama-3.3-70B-Instruct",
        top_k=10,
    )
    for feature in response.data:
        print(f"{feature.id}: {feature.label} (layer {feature.layer})")

With Interventions

from vllm_sdk import VLLMClient, ChatMessage, InterventionSpec

async with VLLMClient(base_url="http://localhost:8000") as client:
    response = await client.chat_completions(
        model="meta-llama/Meta-Llama-3.3-70B-Instruct",
        messages=[ChatMessage(role="user", content="Hello!")],
        interventions=[
            InterventionSpec(
                feature_id="feature_123",
                strength=2.0,
                mode="add"
            )
        ],
    )

License

Apache 2.0

Links

• Documentation
• GitHub Repository
• Issue Tracker