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Arcee

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

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

设置

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

<!--IMPORTS:[{"imported": "Arcee", "source": "langchain_community.llms", "docs": "https://python.langchain.com/api_reference/community/llms/langchain_community.llms.arcee.Arcee.html", "title": "Arcee"}]-->
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
)

额外配置

您还可以根据需要配置 Arcee 的参数,例如 arcee_api_urlarcee_app_urlmodel_kwargs。 在对象初始化时设置 model_kwargs 会将这些参数作为默认值用于后续所有生成响应的调用。

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

您可以通过提供提示从 Arcee 生成文本。以下是一个示例:

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

额外参数

Arcee 允许您应用 filters 并设置检索文档的 size(以数量为单位)来辅助文本生成。过滤器有助于缩小结果范围。以下是如何使用这些参数:

# 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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