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本文介绍如何快速上手使用 Upstage 嵌入模型。

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安装

安装 langchain-upstage 包。 
pip install -U langchain-upstage

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环境配置

请确保设置以下环境变量： 
import os

os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"

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使用方法

初始化 UpstageEmbeddings 类。 
from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
使用 embed_documents 对文本列表或文档进行嵌入。 
doc_result = embeddings.embed_documents(
    ["Sung is a professor.", "This is another document"]
)
print(doc_result)
使用 embed_query 对查询字符串进行嵌入。 
query_result = embeddings.embed_query("What does Sung do?")
print(query_result)
使用 aembed_documents 和 aembed_query 进行异步操作。 
# async embed query
await embeddings.aembed_query("My query to look up")

# async embed documents
await embeddings.aembed_documents(
    ["This is a content of the document", "This is another document"]
)

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与向量存储结合使用

你可以将 UpstageEmbeddings 与向量存储组件结合使用。以下是一个简单示例。 
from langchain_community.vectorstores import DocArrayInMemorySearch

vectorstore = DocArrayInMemorySearch.from_texts(
    ["harrison worked at kensho", "bears like to eat honey"],
    embedding=UpstageEmbeddings(model="solar-embedding-1-large"),
)
retriever = vectorstore.as_retriever()
docs = retriever.invoke("Where did Harrison work?")
print(docs)
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