批量完成()

LiteLLM 允许您

• 向 1 个模型发送多个完成调用
• 向多个模型发送 1 个完成调用：返回最快响应
• 向多个模型发送 1 个完成调用：返回所有响应

提示

想在 LiteLLM Proxy 上执行批量完成？请访问这里：https://docs.litellm.com.cn/docs/proxy/user_keys#beta-batch-completions---pass-model-as-list

向 1 个模型发送多个完成调用

在 batch_completion 方法中，您提供一个 messages 列表，其中每个子消息列表都传递给 litellm.completion()，使您能够在单个 API 调用中高效地处理多个 Prompt。

示例代码

import litellm
import os
from litellm import batch_completion

os.environ['ANTHROPIC_API_KEY'] = ""


responses = batch_completion(
    model="claude-2",
    messages = [
        [
            {
                "role": "user",
                "content": "good morning? "
            }
        ],
        [
            {
                "role": "user",
                "content": "what's the time? "
            }
        ]
    ]
)

向多个模型发送 1 个完成调用：返回最快响应

这将对指定的 models 进行并行调用，并返回第一个响应

使用此方法可减少延迟

SDK

示例代码

import litellm
import os
from litellm import batch_completion_models

os.environ['ANTHROPIC_API_KEY'] = ""
os.environ['OPENAI_API_KEY'] = ""
os.environ['COHERE_API_KEY'] = ""

response = batch_completion_models(
    models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"], 
    messages=[{"role": "user", "content": "Hey, how's it going"}]
)
print(result)

代理

如何设置代理配置

只需传递一个逗号分隔的模型名称字符串和标志 fastest_response=True。

curl

curl -X POST 'http://localhost:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \ 
-D '{
    "model": "gpt-4o, groq-llama", # 👈 Comma-separated models
    "messages": [
      {
        "role": "user",
        "content": "What's the weather like in Boston today?"
      }
    ],
    "stream": true,
    "fastest_response": true # 👈 FLAG
}

'

OpenAI SDK

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-4o, groq-llama", # 👈 Comma-separated models
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={"fastest_response": true} # 👈 FLAG
)

print(response)

---

示例设置：

model_list: 
- model_name: groq-llama
  litellm_params:
    model: groq/llama3-8b-8192
    api_key: os.environ/GROQ_API_KEY
- model_name: gpt-4o
  litellm_params:
    model: gpt-4o
    api_key: os.environ/OPENAI_API_KEY

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

输出

以 OpenAI 格式返回第一个响应。取消其他 LLM API 调用。

{
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": " I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, harmless, and honest.",
        "role": "assistant",
        "logprobs": null
      }
    }
  ],
  "id": "chatcmpl-23273eed-e351-41be-a492-bafcf5cf3274",
  "created": 1695154628.2076092,
  "model": "command-nightly",
  "usage": {
    "prompt_tokens": 6,
    "completion_tokens": 14,
    "total_tokens": 20
  }
}

向多个模型发送 1 个完成调用：返回所有响应

这将对指定的模型进行并行调用，并返回所有响应

使用此方法可以并发处理请求并从多个模型获取响应。

示例代码

import litellm
import os
from litellm import batch_completion_models_all_responses

os.environ['ANTHROPIC_API_KEY'] = ""
os.environ['OPENAI_API_KEY'] = ""
os.environ['COHERE_API_KEY'] = ""

responses = batch_completion_models_all_responses(
    models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"], 
    messages=[{"role": "user", "content": "Hey, how's it going"}]
)
print(responses)

输出

[<ModelResponse chat.completion id=chatcmpl-e673ec8e-4e8f-4c9e-bf26-bf9fa7ee52b9 at 0x103a62160> JSON: {
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "stop_sequence",
      "index": 0,
      "message": {
        "content": " It's going well, thank you for asking! How about you?",
        "role": "assistant",
        "logprobs": null
      }
    }
  ],
  "id": "chatcmpl-e673ec8e-4e8f-4c9e-bf26-bf9fa7ee52b9",
  "created": 1695222060.917964,
  "model": "claude-instant-1.2",
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 9,
    "total_tokens": 23
  }
}, <ModelResponse chat.completion id=chatcmpl-ab6c5bd3-b5d9-4711-9697-e28d9fb8a53c at 0x103a62b60> JSON: {
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": " It's going well, thank you for asking! How about you?",
        "role": "assistant",
        "logprobs": null
      }
    }
  ],
  "id": "chatcmpl-ab6c5bd3-b5d9-4711-9697-e28d9fb8a53c",
  "created": 1695222061.0445492,
  "model": "command-nightly",
  "usage": {
    "prompt_tokens": 6,
    "completion_tokens": 14,
    "total_tokens": 20
  }
}, <OpenAIObject chat.completion id=chatcmpl-80szFnKHzCxObW0RqCMw1hWW1Icrq at 0x102dd6430> JSON: {
  "id": "chatcmpl-80szFnKHzCxObW0RqCMw1hWW1Icrq",
  "object": "chat.completion",
  "created": 1695222061,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm an AI language model, so I don't have feelings, but I'm here to assist you with any questions or tasks you might have. How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 39,
    "total_tokens": 52
  }
}]