MODEL API · 大模型服务MODEL API · LLM Service

大模型服务LLM Services

企业级大模型服务平台。聚合 DeepSeek、Qwen 等主流开源模型，提供推理 API、微调定制与 RAG 知识库，完全兼容 OpenAI 接口规范，开箱即用。Enterprise LLM platform aggregating DeepSeek, Qwen and more — inference APIs, fine-tuning and RAG, fully OpenAI-compatible.

OpenAIOpenAI接口完全兼容API compatible

128K128K最大上下文窗口Context window

99.95%99.95%服务可用性Service SLA

<40ms<40ms首 Token 延迟First-token latency

ARCHITECTURE产品架构Architecture

一套平台，全生命周期模型服务Full Lifecycle LLM Platform

从模型接入、微调训练到推理服务与知识增强，四层架构覆盖大模型应用全生命周期。Four layers covering the full LLM lifecycle.

模型接入层Model AccessL1

DeepSeek 全系DeepSeek familyQwen 全系Qwen familyGLM / Llama 等GLM / Llama私有模型托管Private models

微调训练层Tuning LayerL2

SFT 指令微调SFTLoRA/QLoRA 微调LoRA/QLoRARLHF 对齐训练RLHF数据清洗标注Data pipeline

知识增强层Knowledge LayerL3

RAG 检索增强RAG向量数据库Vector DB文档智能解析Doc parsing知识库运营KB ops

推理服务层Serving LayerL4

OpenAI 兼容 APIOpenAI-compatible流式输出Streaming多模型路由Model routing弹性伸缩Auto scaling

CAPABILITIES核心能力Capabilities

核心能力Core Capabilities

让企业以最低门槛获得生产级大模型能力。Production-grade LLM capabilities with minimal friction.

OpenAI 接口完全兼容OpenAI-Compatible

Chat Completions / Embeddings / Function Calling 全兼容，现有应用改个 BaseURL 即可迁移。Full API compatibility — just change the BaseURL.

高性能推理引擎Fast Inference

vLLM/SGLang 推理引擎 + KV Cache 复用 + 投机解码，吞吐较原生提升 3–5 倍。vLLM/SGLang engines, 3–5x throughput.

场景化微调定制Custom Fine-tuning

SFT/LoRA/RLHF 全套微调链路，用企业私有数据打造专属模型。SFT/LoRA/RLHF pipeline for custom models.

RAG 企业知识库Enterprise RAG

文档解析、向量化、检索、引用溯源一站式能力，让模型回答有据可查。End-to-end RAG with traceable citations.

私有化与数据安全Private & Secure

支持全私有化部署，数据不出域，满足政务、金融等强合规场景。Fully on-prem for regulated industries.

多模型智能路由Model Routing

按成本、时延、能力自动路由到最合适的模型，效果与成本兼得。Automatic routing by cost, latency, capability.

QUICK START快速接入Quick Start

三行代码开始使用Start in Three Lines

任何 OpenAI SDK / HTTP 客户端均可直接调用。Works with any OpenAI SDK or HTTP client.

# Python — OpenAI SDK 直连钛一大模型服务# Python — OpenAI SDK against the TaiYi LLM service
from openai import OpenAI

client = OpenAI(
    base_url="https://api.taioneai.com/v1",
    api_key="sk-your-key"
)

resp = client.chat.completions.create(
    model="deepseek-r1",
    messages=[{"role": "user", "content": "介绍一下钛一数智""Tell me about TaiYi AI"}],
    stream=True
)
for chunk in resp:
    print(chunk.choices[0].delta.content, end="")