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ModelScopeChatEndpoint

ModelScope (Home | GitHub) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation.

This will help you get started with ModelScope Chat Endpoint.

Overview​

Integration details​

ProviderClassPackageLocalSerializablePackage downloadsPackage latest
ModelScopeModelScopeChatEndpointlangchain-modelscope-integration❌❌PyPI - DownloadsPyPI - Version

Setup​

To access ModelScope chat endpoint you'll need to create a ModelScope account, get an SDK token, and install the langchain-modelscope-integration integration package.

Credentials​

Head to ModelScope to sign up to ModelScope and generate an SDK token. Once you've done this set the MODELSCOPE_SDK_TOKEN environment variable:

import getpass
import os

if not os.getenv("MODELSCOPE_SDK_TOKEN"):
    os.environ["MODELSCOPE_SDK_TOKEN"] = getpass.getpass(
        "Enter your ModelScope SDK token: "
    )

Installation​

The LangChain ModelScope integration lives in the langchain-modelscope-integration package:

%pip install -qU langchain-modelscope-integration

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_modelscope import ModelScopeChatEndpoint

llm = ModelScopeChatEndpoint(
    model="Qwen/Qwen2.5-Coder-32B-Instruct",
    temperature=0,
    max_tokens=1024,
    timeout=60,
    max_retries=2,
    # other params...
)

Invocation​

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to Chinese. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 33, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-60bb3461-60ae-4c0b-8997-ab55ef77fcd6-0', usage_metadata={'input_tokens': 33, 'output_tokens': 3, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})
print(ai_msg.content)
我喜欢编程。

Chaining​

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "Chinese",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 28, 'total_tokens': 31, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9f011a3a-9a11-4759-8d16-5b1843a78862-0', usage_metadata={'input_tokens': 28, 'output_tokens': 3, 'total_tokens': 31, 'input_token_details': {}, 'output_token_details': {}})

API reference​

For detailed documentation of all ModelScopeChatEndpoint features and configurations head to the reference: https://modelscope.cn/docs/model-service/API-Inference/intro