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UpstageEmbeddings

此笔记本介绍了如何开始使用 Upstage 嵌入模型。

安装

安装langchain-upstage包。

pip install -U langchain-upstage

环境设置

确保设置以下环境变量：

import os

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

用法

初始化UpstageEmbeddings类。

from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
API 参考：UpstageEmbeddings

用embed_documents嵌入文本或文档列表。

doc_result = embeddings.embed_documents(
    ["Sung is a professor.", "This is another document"]
)
print(doc_result)

用embed_query以嵌入查询字符串。

query_result = embeddings.embed_query("What does Sung do?")
print(query_result)

用aembed_documents和aembed_query用于异步作。

# 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"]
)

与 vector store 一起使用

您可以使用UpstageEmbeddingswith vector store 组件。下面演示了一个简单的示例。

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)