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IBM watsonx.ai

LiteLLM 支持所有 IBM watsonx.ai 基础模型和嵌入模型。

环境变量​

os.environ["WATSONX_URL"] = ""  # (required) Base URL of your WatsonX instance
# (required) either one of the following:
os.environ["WATSONX_APIKEY"] = "" # IBM cloud API key
os.environ["WATSONX_TOKEN"] = "" # IAM auth token
# optional - can also be passed as params to completion() or embedding()
os.environ["WATSONX_PROJECT_ID"] = "" # Project ID of your WatsonX instance
os.environ["WATSONX_DEPLOYMENT_SPACE_ID"] = "" # ID of your deployment space to use deployed models
os.environ["WATSONX_ZENAPIKEY"] = "" # Zen API key (use for long-term api token)

有关如何获取访问令牌以验证 watsonx.ai 身份的更多信息，请参见此处。

用法​

Open In Colab
import os
from litellm import completion

os.environ["WATSONX_URL"] = ""
os.environ["WATSONX_APIKEY"] = ""

## Call WATSONX `/text/chat` endpoint - supports function calling
response = completion(
  model="watsonx/meta-llama/llama-3-1-8b-instruct",
  messages=[{ "content": "what is your favorite colour?","role": "user"}],
  project_id="<my-project-id>" # or pass with os.environ["WATSONX_PROJECT_ID"]
)

## Call WATSONX `/text/generation` endpoint - not all models support /chat route. 
response = completion(
  model="watsonx/ibm/granite-13b-chat-v2",
  messages=[{ "content": "what is your favorite colour?","role": "user"}],
  project_id="<my-project-id>"
)

用法 - 流式传输​

import os
from litellm import completion

os.environ["WATSONX_URL"] = ""
os.environ["WATSONX_APIKEY"] = ""
os.environ["WATSONX_PROJECT_ID"] = ""

response = completion(
  model="watsonx/meta-llama/llama-3-1-8b-instruct",
  messages=[{ "content": "what is your favorite colour?","role": "user"}],
  stream=True
)
for chunk in response:
  print(chunk)

流式传输输出块示例​

{
  "choices": [
    {
      "finish_reason": null,
      "index": 0,
      "delta": {
        "content": "I don't have a favorite color, but I do like the color blue. What's your favorite color?"
      }
    }
  ],
  "created": null,
  "model": "watsonx/ibm/granite-13b-chat-v2",
  "usage": {
    "prompt_tokens": null,
    "completion_tokens": null,
    "total_tokens": null
  }
}

用法 - 部署空间中的模型​

已部署到部署空间（例如：微调模型）的模型可以使用 deployment/<deployment_id> 格式调用（其中 <deployment_id> 是部署空间中已部署模型的 ID）。

部署空间的 ID 也必须设置在环境变量 WATSONX_DEPLOYMENT_SPACE_ID 中，或作为 space_id=<deployment_space_id> 参数传递给函数。

import litellm
response = litellm.completion(
    model="watsonx/deployment/<deployment_id>",
    messages=[{"content": "Hello, how are you?", "role": "user"}],
    space_id="<deployment_space_id>"
)

用法 - 嵌入​

LiteLLM 还支持向 IBM watsonx.ai 嵌入模型发出请求。所需的凭证与补全相同。

from litellm import embedding

response = embedding(
    model="watsonx/ibm/slate-30m-english-rtrvr",
    input=["What is the capital of France?"],
    project_id="<my-project-id>"
)
print(response)
# EmbeddingResponse(model='ibm/slate-30m-english-rtrvr', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.037463713, -0.02141933, -0.02851813, 0.015519324, ..., -0.0021367231, -0.01704561, -0.001425816, 0.0035238306]}], object='list', usage=Usage(prompt_tokens=8, total_tokens=8))

OpenAI 代理用法​

以下是如何使用 LiteLLM 代理服务器调用 IBM watsonx.ai

1. 在您的环境中保存密钥​

export WATSONX_URL=""
export WATSONX_APIKEY=""
export WATSONX_PROJECT_ID=""

2. 启动代理​

$ litellm --model watsonx/meta-llama/llama-3-8b-instruct

# Server running on http://0.0.0.0:4000

3. 测试​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "llama-3-8b",
      "messages": [
        {
          "role": "user",
          "content": "what is your favorite colour?"
        }
      ]
    }
'

身份验证​

将凭证作为参数传递​

您也可以将凭证作为参数传递给补全和嵌入函数。

import os
from litellm import completion

response = completion(
            model="watsonx/ibm/granite-13b-chat-v2",
            messages=[{ "content": "What is your favorite color?","role": "user"}],
            url="",
            api_key="",
            project_id=""
)

支持的 IBM watsonx.ai 模型​

以下是您可以使用 LiteLLM 调用的一些 IBM watsonx.ai 中的可用模型示例

模型名称命令
Flan T5 XXLcompletion(model=watsonx/google/flan-t5-xxl, messages=messages)
Flan Ul2completion(model=watsonx/google/flan-ul2, messages=messages)
Mt0 XXLcompletion(model=watsonx/bigscience/mt0-xxl, messages=messages)
Gpt Neoxcompletion(model=watsonx/eleutherai/gpt-neox-20b, messages=messages)
Mpt 7B Instruct2completion(model=watsonx/ibm/mpt-7b-instruct2, messages=messages)
Starcodercompletion(model=watsonx/bigcode/starcoder, messages=messages)
Llama 2 70B Chatcompletion(model=watsonx/meta-llama/llama-2-70b-chat, messages=messages)
Llama 2 13B Chatcompletion(model=watsonx/meta-llama/llama-2-13b-chat, messages=messages)
Granite 13B Instructcompletion(model=watsonx/ibm/granite-13b-instruct-v1, messages=messages)
Granite 13B Chatcompletion(model=watsonx/ibm/granite-13b-chat-v1, messages=messages)
Flan T5 XLcompletion(model=watsonx/google/flan-t5-xl, messages=messages)
Granite 13B Chat V2completion(model=watsonx/ibm/granite-13b-chat-v2, messages=messages)
Granite 13B Instruct V2completion(model=watsonx/ibm/granite-13b-instruct-v2, messages=messages)
Elyza Japanese Llama 2 7B Instructcompletion(model=watsonx/elyza/elyza-japanese-llama-2-7b-instruct, messages=messages)
Mixtral 8X7B Instruct V01 Qcompletion(model=watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q, messages=messages)

有关 watsonx.ai 中所有可用模型的列表，请参阅此处。

支持的 IBM watsonx.ai 嵌入模型​

模型名称函数调用
Slate 30membedding(model="watsonx/ibm/slate-30m-english-rtrvr", input=input)
Slate 125membedding(model="watsonx/ibm/slate-125m-english-rtrvr", input=input)

有关 watsonx.ai 中所有可用嵌入模型的列表，请参阅此处。