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ChatYI

这将帮助您开始使用Yi 聊天模型。有关所有ChatYi功能和配置的详细文档，请访问API参考。

01.AI，由李开复博士创立，是一家处于AI 2.0前沿的全球公司。他们提供先进的大型语言模型，包括从6B到数千亿参数的Yi系列。01.AI还提供多模态模型、开放的API平台以及开源选项，如Yi-34B/9B/6B和Yi-VL。

概述​

集成详情​

类包本地可序列化JS支持包下载包最新
ChatYilangchain_community✅❌❌PyPI - 下载量PyPI - 版本

模型特性​

工具调用结构化输出JSON模式图像输入音频输入视频输入令牌级流式传输原生异步令牌使用Logprobs
❌❌❌✅❌❌✅❌✅❌

设置​

要访问ChatYi模型，您需要创建一个01.AI账户，获取一个API密钥，并安装langchain_community集成包。

凭证​

前往 01.AI 注册01.AI并生成一个API密钥。完成此操作后，设置 YI_API_KEY 环境变量：

import getpass
import os

if "YI_API_KEY" not in os.environ:
    os.environ["YI_API_KEY"] = getpass.getpass("Enter your Yi API key: ")

如果你想获取模型调用的自动追踪，你也可以通过取消注释以下内容来设置你的LangSmith API密钥：

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装​

LangChain ModuleName 集成位于 langchain_community 包中：

%pip install -qU langchain_community

实例化​

现在我们可以实例化我们的模型对象并生成聊天完成：

  • TODO: 使用相关参数更新模型实例化。
from langchain_community.chat_models.yi import ChatYi

llm = ChatYi(
    model="yi-large",
    temperature=0,
    timeout=60,
    yi_api_base="https://api.01.ai/v1/chat/completions",
    # other params...
)
API Reference:ChatYi

调用​

from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are an AI assistant specializing in technology trends."),
    HumanMessage(
        content="What are the potential applications of large language models in healthcare?"
    ),
]

ai_msg = llm.invoke(messages)
ai_msg
API Reference:HumanMessage | SystemMessage
AIMessage(content="Large Language Models (LLMs) have the potential to significantly impact healthcare by enhancing various aspects of patient care, research, and administrative processes. Here are some potential applications:\n\n1. **Clinical Documentation and Reporting**: LLMs can assist in generating patient reports and documentation by understanding and summarizing clinical notes, making the process more efficient and reducing the administrative burden on healthcare professionals.\n\n2. **Medical Coding and Billing**: These models can help in automating the coding process for medical billing by accurately translating clinical notes into standardized codes, reducing errors and improving billing efficiency.\n\n3. **Clinical Decision Support**: LLMs can analyze patient data and medical literature to provide evidence-based recommendations to healthcare providers, aiding in diagnosis and treatment planning.\n\n4. **Patient Education and Communication**: By simplifying medical jargon, LLMs can help in educating patients about their conditions, treatment options, and preventive care, improving patient engagement and health literacy.\n\n5. **Natural Language Processing (NLP) for EHRs**: LLMs can enhance NLP capabilities in Electronic Health Records (EHRs) systems, enabling better extraction of information from unstructured data, such as clinical notes, to support data-driven decision-making.\n\n6. **Drug Discovery and Development**: LLMs can analyze biomedical literature and clinical trial data to identify new drug candidates, predict drug interactions, and support the development of personalized medicine.\n\n7. **Telemedicine and Virtual Health Assistants**: Integrated into telemedicine platforms, LLMs can provide preliminary assessments and triage, offering patients basic health advice and determining the urgency of their needs, thus optimizing the utilization of healthcare resources.\n\n8. **Research and Literature Review**: LLMs can expedite the process of reviewing medical literature by quickly identifying relevant studies and summarizing findings, accelerating research and evidence-based practice.\n\n9. **Personalized Medicine**: By analyzing a patient's genetic information and medical history, LLMs can help in tailoring treatment plans and medication dosages, contributing to the advancement of personalized medicine.\n\n10. **Quality Improvement and Risk Assessment**: LLMs can analyze healthcare data to identify patterns that may indicate areas for quality improvement or potential risks, such as hospital-acquired infections or adverse drug events.\n\n11. **Mental Health Support**: LLMs can provide mental health support by offering coping strategies, mindfulness exercises, and preliminary assessments, serving as a complement to professional mental health services.\n\n12. **Continuing Medical Education (CME)**: LLMs can personalize CME by recommending educational content based on a healthcare provider's practice area, patient demographics, and emerging medical literature, ensuring that professionals stay updated with the latest advancements.\n\nWhile the applications of LLMs in healthcare are promising, it's crucial to address challenges such as data privacy, model bias, and the need for regulatory approval to ensure that these technologies are implemented safely and ethically.", response_metadata={'token_usage': {'completion_tokens': 656, 'prompt_tokens': 40, 'total_tokens': 696}, 'model': 'yi-large'}, id='run-870850bd-e4bf-4265-8730-1736409c0acf-0')

链式调用​

我们可以链式我们的模型与一个提示模板，如下所示：

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "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": "German",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 33, 'total_tokens': 41}, 'model': 'yi-large'}, id='run-daa3bc58-8289-4d72-a24e-80622fa90d6d-0')

API参考​

有关所有ChatYi功能和配置的详细文档，请前往API参考：https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html

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