LiteLLM 简介&实战

在本地部署大模型或构建 RAG 系统时,你可能遇到这些痛点:
• 某些工具 只支持 OpenAI API(如 Neo4j GenAI、部分 Spring AI 配置、某些前端库等)
• 但你想用 Ollama 或本地模型
• 不同 LLM API 格式各不相同,调用麻烦
• 想做 统一 API、日志、限流、负载均衡、代理管理

这就是 LiteLLM 存在的意义。
笔者就是在做Neo4j GenAI的过程中,遇到了不支持Ollama embedding 的问题,所以才转向LiteLLM需求帮助.

LiteLLM 是什么?

LiteLLM = LLM API 统一代理层(Proxy + Router)

LiteLLM 让所有大模型都“看起来像 OpenAI API”
LiteLLM 能把以下模型:
• OpenAI GPT 全系列
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• HuggingFace
Ollama(本地模型)
• Mistral
• Groq
• DeepSeek
• Llama.cpp
• 甚至自建模型后台

全部统一为 OpenAI API 标准格式:

POST /v1/chat/completions
POST /v1/embeddings

这意味着:
只要某个系统支持 OpenAI API,就能被 LiteLLM 兼容扩展到所有模型。

LiteLLM 能解决哪些核心问题?

1. 让不支持 Ollama 的系统使用 Ollama

例如:
• Neo4j GenAI 不支持 Ollama provider
• Spring AI 某些模块不支持 Ollama
• LangChain OpenAI-only 模式也不能直接用 Ollama

但通过 LiteLLM:

RETURN genai.vector.encode(
  "embedding_field",
  "OpenAi", //代理openAI
  {
    model: "dummy",
    endpoint: "http://localhost:4000"
  }
);

Neo4j 会认为你访问的是 OpenAI
实际请求链路如下:

Neo4j GenAI
    |
    |  (OpenAI API 请求)
    v
LiteLLM Proxy  <-- 你本地搭建的 OpenAI API 兼容服务
    |
    |  (Ollama embedding API)
    v
Ollama Server

2. 统一不同 LLM 的调用方式

你不需要针对不同模型(GPT、Gemini、Claude)写不同 API:
只需要 OpenAI 风格的 payload。

3. 提供多后端路由、fallback 和负载均衡

例如:
• 首选本地 Ollama,但若挂掉自动切换到 OpenAI
• 将 embedding 请求分散到多个 GPU 节点
• 多 API Key 自动切换

这些 LiteLLM 都支持。

4. 自建“类 OpenAI 服务”

LiteLLM 可以让你自己搭一个:

http://localhost:4000/v1/chat/completions

等于:
• 你自己就是 OpenAI
• 可以接入任何外部或本地模型

LiteLLM 使用

1. 安装 LiteLLM

pip install litellm
pip install 'litellm[proxy]'

2. 启动代理,将 Ollama 封装为 OpenAI API

litellm --model ollama/qwen3-embedding:0.6b
在这里插入图片描述

启动后默认暴露
 http://localhost:4000/v1/chat/completions
 http://localhost:4000/v1/embeddings
验证1

在这里插入图片描述

验证2
curl -X POST http://localhost:4000/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer sk-dummy-key" -d '{"input": "test text", "model": "qwen3-embedding:0.6b"}'


{"model":"ollama/qwen3-embedding:0.6b","data":[{"object":"embedding","index":0,"embedding":[-0.032626607,-0.04718259,-0.012909385,-0.07764769,-0.017243367,0.020494336,0.013601924,0.045632806,-0.07786213,0.009070601,0.0066449605,-0.07473664,0.09884253,-0.0120289,-0.06222988,0.06184298,-0.0725233,0.053415477,0.10230555,-0.03260747,-0.017293224,-0.0084129665,-0.03581761,0.13351837,-0.016029868,0.04569618,-0.06392779,0.053953223,0.034195043,-0.025214326,0.00022348277,-0.029752325,-0.06300391,-0.021198025,0.011440582,-0.014686229,-0.0030991829,0.02269575,-0.015596619,0.025833748,-0.024328208,0.036079746,0.014850982,0.016713746,-0.031154487,-0.065592736,0.03154194,0.03257276,0.04791407,0.01860942,-0.019520456,0.007815935,-0.0022338692,0.022617716,-0.005324301,-0.045967944,0.027742606,0.015884312,0.014938865,0.025698539,-0.03921946,0.09372251,-0.006558598,-0.008122129,-0.0030283695,-0.009300921,0.02448351,-0.043731984,-0.13481855,-0.028205048,0.020778868,-0.00405425,-0.03623363,0.021633552,0.00044696862,-0.012933407,-0.033308834,-0.009971476,0.005323327,0.030190341,-0.01743355,0.062128257,0.023755023,-0.00015589697,-0.011969491,0.04809842,0.011429985,-0.0023270582,0.014009929,-0.013220596,-0.0130167585,0.053460836,-0.0309704,0.023170978,0.009875285,-0.029823707,-0.024622424,-0.0011155787,-0.045939308,0.011828468,-0.009233859,-0.026525205,0.024650486,-0.039584227,-0.038674805,0.05581028,-0.071160346,-0.015343709,-0.037775666,-0.023587836,0.04493225,0.0044017094,0.010756107,0.02482149,-0.028296681,-0.03887348,0.0027553057,-0.025629668,-0.0030361188,0.015511015,0.036494546,-0.04785365,0.00055010477,-0.008078041,0.004792498,0.024970999,-0.032102983,-0.013614414,0.028776105,-0.03336054,0.023151165,-0.02478438,-0.037401527,-0.0831715,0.012158645,0.008711268,0.005656051,0.041605253,0.038398396,0.022099143,0.009310389,0.017901905,-0.003372163,-0.02984916,0.015566409,0.018100621,0.0038577102,-0.013624389,0.021733787,-0.035946332,0.014075876,0.008954101,-0.0145281,-0.012985507,-0.00008892349,0.024338393,-0.006873558,-0.024779137,0.014932994,0.02422854,0.019953707,-0.018292883,-0.024485217,-0.022343619,-0.00854652,0.0060430225,-0.05132581,-0.02891697,0.0362885,-0.0071165753,-0.00047108222,0.012760885,0.0067583066,0.010371983,-0.01082039,-0.015063237,-0.03978506,-0.039938226,-0.027284574,-0.0054394174,0.019066727,-0.028152809,-0.013166358,-0.0073102275,0.017402485,-0.008219373,0.03523686,-0.003599994,-0.02246328,-0.00013300263,-0.03090953,-0.043205082,-0.043990586,-0.012077176,-0.010643322,-0.03899131,0.028445436,-0.02997106,-0.02135719,0.0014877928,0.004239334,-0.07422561,0.03053671,0.018447932,-0.022379674,0.005374792,0.04301634,-0.008634935,-0.022366231,-0.015306392,0.020601274,0.024734389,-0.034077384,0.00429294,-0.02002732,0.035747744,-0.008067649,0.017709957,0.017443566,0.017220013,0.045218933,0.004526733,-0.031537473,0.013991209,0.05438411,-0.03481289,0.013039455,-0.0140739335,-0.03516529,0.042347185,-0.009062282,-0.005830926,0.035344925,-0.002221362,0.08372916,-0.018425262,0.016336745,0.0022677209,0.03609259,-0.020100094,-0.013024532,0.015118401,-0.014650689,-0.0041352822,-0.046367176,0.028414916,0.0016237067,0.0015307447,-0.013305592,0.020143444,-0.03970218,-0.06460047,-0.026737811,0.035010505,0.004334833,-0.005978129,-0.005473762,-0.04095557,-0.008331795,0.024143105,0.04152064,-0.04720525,-0.008470502,0.035948463,0.07677461,0.008347462,-0.021463059,0.046810616,-0.028483596,-0.018201763,0.051549885,0.02149343,0.0019038805,-0.025041798,-0.0158813,0.0007537609,0.019974694,0.06837892,0.05036703,0.06935548,-0.013435698,-0.004591738,0.011701604,-0.06660126,0.022352964,0.027173858,0.044638038,0.031489555,-0.045690794,0.012274531,0.014966118,0.014843109,-0.06141054,0.024512075,-0.021520602,-0.013230133,-0.0130254775,0.075040884,0.02483831,0.010204212,0.0003535681,-0.044644386,-0.0133635225,-0.002034496,-0.045935355,0.0048054284,0.0012997395,-0.03977537,0.03240054,-0.04400314,0.030716144,0.016531117,-0.006736844,-0.030641975,0.012706248,-0.021987136,-0.02151764,0.11139711,-0.0114127835,-0.011911694,-0.029264389,0.018864004,-0.041995883,0.0077304784,0.003821275,0.030130727,-0.012936846,0.00028421948,0.021635085,-0.051617797,0.015496302,0.009171116,0.022512116,-0.009177935,0.008350419,0.063435055,-0.009066847,0.0025567703,-0.0075443746,0.0047093974,0.024056355,-0.025160728,0.019159088,0.000070561575,-0.028550267,0.0032737663,-0.078368396,0.04596512,0.025343953,0.030409817,-0.05242445,0.028688649,0.0123399645,0.007895326,-0.04171273,-0.00957569,-0.013085938,0.009248653,-0.0361861,-0.021248043,-0.02800194,-0.10728622,0.0052113673,0.009283722,-0.04429127,-0.02771819,-0.024536816,-0.008435417,0.053151242,0.03849845,-0.031428974,-0.00017730048,-0.021307925,-0.0005476149,0.033995736,0.08230147,0.050586957,-0.027417632,0.018314833,0.029491404,0.040851604,0.011517337,-0.035452057,0.03033822,-0.055938926,-0.004189061,-0.008351891,-0.039090905,-0.02521377,0.0061673066,0.010453673,-0.041307043,0.005660619,-0.028021295,0.040069263,0.02912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LiteLLM 能力一览

能力说明
OpenAI API 兼容性统一 chat / embeddings / completion
Ollama/OpenAI/Gemini 等多后端支持无缝代理
多模型路由自动选择最便宜或最快
自动 fallback某个模型挂了自动换另一个
请求限流内置 QPS/RPS 控制
LLM 调用日志自动记录调用、延迟、用量
API Key 池化多密钥轮询
负载均衡多实例 round-robin

实战: Neo4j GenAI 代理ollama

Neo4j自定义Genai embedding plugin

&lt;think&gt;我们正在处理用户关于LiteLLM在视觉方面的应用或功能的询问。根据引用内容,LiteLLM是一个用于统一调用多种LLMAPI的工具,支持包括OpenAI、Azure、Cohere、Anthropic等在内的100多个LLM。然而,从提供的引用中,并没有直接提到LiteLLM具有专门的视觉库或计算机视觉功能。但是,引用[3]提到:&ldquo;若有其他的支持视觉的LLM可以自己参考litellm,然后修改项目源码新增。我主要新增openai的api_base这个参数&rdquo;,这表明LiteLLM可以用于调用支持视觉的多模态LLM(MLLM),例如支持图像输入的模型(如GPT-4Vision)。因此,LiteLLM本身并不提供计算机视觉功能,但它可以作为一个桥梁,帮助用户调用那些具有视觉能力的LLM。根据引用[2],有一个名为swift的工具,它支持微调200+LLMs和15+MLLMs(多模态大语言模型),并且扩展了对其他模态的能力,目前支持全参数培训以及AnimateDiff的LoRA培训。这暗示了在MLLM领域,包括视觉方面的应用,可以通过类似工具进行训练和部署。但swift是另一个项目,与LiteLLM不同。因此,我们可以推断:LiteLLM本身不处理视觉任务,但它可以用于调用支持视觉的LLM(例如OpenAI的GPT-4withVision)。用户可以通过LiteLLM发送包含图像和文本的请求给后端支持视觉的LLM,并获取结果。使用LiteLLM调用支持视觉的LLM的步骤可能包括:1.安装LiteLLM(可能需要安装额外的依赖,如`litellm[proxy]`)。2.配置环境变量(.env文件)以启用相应的模型供应商,并填写API密钥。3.根据LiteLLM的文档,构造包含图像的多模态请求。由于LiteLLM是一个通用的API调用代理,具体如何发送图像请求取决于后端模型的要求。例如,对于OpenAI的GPT-4Vision,你可能需要将图像编码为base64或提供图像的URL,并将其放入消息中。下面是一个使用LiteLLM调用支持视觉的模型(以OpenAI的GPT-4Vision为例)的示例代码:```pythonfromlitellmimportcompletionimportbase64#将图像编码为base64defencode_image(image_path):withopen(image_path,&quot;rb&quot;)asimage_file:returnbase64.b64encode(image_file.read()).decode(&#39;utf-8&#39;)#设置图像路径image_path=&quot;path_to_your_image.jpg&quot;base64_image=encode_image(image_path)messages=[{&quot;role&quot;:&quot;user&quot;,&quot;content&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;text&quot;:&quot;What&rsquo;sinthisimage?&quot;},{&quot;type&quot;:&quot;image_url&quot;,&quot;image_url&quot;:{&quot;url&quot;:f&quot;data:image/jpeg;base64,{base64_image}&quot;}},],}]#使用LiteLLM调用模型,这里使用OpenAI的gpt-4-vision-preview模型response=completion(model=&quot;gpt-4-vision-preview&quot;,messages=messages,api_key=&quot;your_openai_api_key&quot;,#或者通过环境变量设置api_base=&quot;your_custom_api_base_if_any&quot;#如果国内转发,可能需要设置)print(response.choices[0].message.content)```注意:上述代码中,我们直接使用了OpenAI的模型名称。通过LiteLLM,你也可以使用其他支持视觉的模型,只需将模型名称改为对应的模型(例如,如果使用Azure,则模型名称可能是Azure的部署名称)。另外,引用[3]提到,如果使用OpenAI并且需要国内转发,可以设置`api_base`参数。总结:LiteLLM在视觉方面的功能主要是作为代理,调用支持视觉的多模态大模型。它本身不提供图像处理功能,而是依赖于后端模型的能力。根据以上信息,我们可以生成相关问题。&sect;&sect;相关问题&sect;&sect;1.LiteLLM支持哪些支持视觉的多模态模型?2.如何配置LiteLLM以使用Azure的多模态模型进行视觉任务?3.除了OpenAI的GPT-4Vision,还有哪些模型可以通过LiteLLM用于图像分析?4.在使用LiteLLM进行多模态请求时,如何处理图像数据(例如格式要求)?5.如何通过LiteLLM代理同时使用多个不同的多模态模型?&lt;/think&gt;### LiteLLM在视觉领域的应用与使用方法 LiteLLM本身不是专门的计算机视觉库,但作为通用LLM API调用框架,它支持集成**支持视觉功能的多模态大语言模型(MLLM)**。以下是关键功能和使用方法: --- ### 核心视觉功能支持 1. **多模态模型集成** - 支持调用支持图像输入的模型如: - OpenAI的GPT-4 Vision(`gpt-4-vision-preview`) - Anthropic Claude 3(支持图像输入) - Google Gemini Vision - 通过统一API调用不同厂商的视觉模型 ```python from litellm import completion response = completion( model=&quot;gpt-4-vision-preview&quot;, messages=[{ &quot;role&quot;: &quot;user&quot;, &quot;content&quot;: [ {&quot;type&quot;: &quot;text&quot;, &quot;text&quot;: &quot;描述这张图片&quot;}, {&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: &quot;https://example.com/image.jpg&quot;}} ] }] ) ``` 2. **本地部署支持** - 通过`api_base`参数可连接本地部署的视觉模型 ```python # 连接本地视觉模型服务 response = completion( model=&quot;local/llava&quot;, api_base=&quot;http://localhost:8000&quot;, messages=[...] # 包含图像的输入 ) ``` 3. **视觉模型微调集成** - 结合SWIFT工具(引用[2])微调视觉模型: - 支持LoRA等轻量级微调技术 - 支持AnimateDiff等动态视觉模型训练 ```bash # 使用SWIFT微调视觉模型示例 swift sft \ --model_id &quot;your-visual-model&quot; \ --dataset &quot;coco_captions&quot; \ --lora_rank 8 \ --vision_tuning ``` --- ### 配置步骤 1. **安装与配置** ```bash pip install &#39;litellm[proxy]&#39; # 安装代理组件 ``` 在`.env`文件中配置视觉模型: ```env # 启用OpenAI视觉模型 ENABLE_OPENAI=true OPENAI_API_KEY=sk-xxx OPENAI_MODEL=gpt-4-vision-preview # 或启用本地模型 ENABLE_LOCAL=true LOCAL_API_BASE=http://localhost:8000 ``` 2. **图像输入处理** - 支持格式: - 公开URL:`{&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: &quot;...&quot;}}` - Base64编码:`{&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: &quot;data:image/jpeg;base64,...&quot;}}` ```python import base64 with open(&quot;image.jpg&quot;, &quot;rb&quot;) as image_file: base64_image = base64.b64encode(image_file.read()).decode(&#39;utf-8&#39;) ``` 3. **视觉任务示例** ```python # 图像描述生成 response = completion( model=&quot;claude-3-opus-20240229&quot;, messages=[{ &quot;role&quot;: &quot;user&quot;, &quot;content&quot;: [ {&quot;type&quot;: &quot;text&quot;, &quot;text&quot;: &quot;分析医学影像中的异常区域&quot;}, {&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: f&quot;data:image/png;base64,{base64_image}&quot;}} ] }] ) ``` --- ### 典型应用场景 1. **视觉问答系统** 结合图像输入和文本提问进行推理 2. **自动化图像标注** 批量处理图像生成描述文本 3. **多模态数据分析** 联合处理文本报告和医学影像/卫星图像 4. **视觉内容审核** 识别图像中的违规内容 &gt; **注意事项**:视觉模型通常有**分辨率限制**(如OpenAI最大支持1280x768),且需注意**数据隐私合规性**。对于敏感图像,建议使用本地部署方案(引用[3])。 ---
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