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Arcee

本笔记本演示了如何使用Arcee类通过Arcee的领域适应语言模型（DALMs）生成文本。

##Installing the langchain packages needed to use the integration
%pip install -qU langchain-community

设置​

在使用Arcee之前，请确保Arcee API密钥已设置为ARCEE_API_KEY环境变量。您也可以将API密钥作为命名参数传递。

from langchain_community.llms import Arcee

# Create an instance of the Arcee class
arcee = Arcee(
    model="DALM-PubMed",
    # arcee_api_key="ARCEE-API-KEY" # if not already set in the environment
)
API Reference:Arcee

额外配置​

您还可以根据需要配置Arcee的参数，例如arcee_api_url、arcee_app_url和model_kwargs。 在对象初始化时设置model_kwargs会将参数作为所有后续生成响应的默认值。

arcee = Arcee(
    model="DALM-Patent",
    # arcee_api_key="ARCEE-API-KEY", # if not already set in the environment
    arcee_api_url="https://custom-api.arcee.ai",  # default is https://api.arcee.ai
    arcee_app_url="https://custom-app.arcee.ai",  # default is https://app.arcee.ai
    model_kwargs={
        "size": 5,
        "filters": [
            {
                "field_name": "document",
                "filter_type": "fuzzy_search",
                "value": "Einstein",
            }
        ],
    },
)

生成文本​

你可以通过提供一个提示从Arcee生成文本。这里有一个例子：

# Generate text
prompt = "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"
response = arcee(prompt)

附加参数​

Arcee 允许你应用 filters 并设置检索文档的 size（以数量计）以帮助文本生成。过滤器有助于缩小结果范围。以下是这些参数的使用方法：

# Define filters
filters = [
    {"field_name": "document", "filter_type": "fuzzy_search", "value": "Einstein"},
    {"field_name": "year", "filter_type": "strict_search", "value": "1905"},
]

# Generate text with filters and size params
response = arcee(prompt, size=5, filters=filters)

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