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Pinecone 的推理 API 可以通过 PineconeEmbeddings 访问，通过 Pinecone 服务提供文本嵌入。我们首先安装必要的库： 
!pip install -qU "langchain-pinecone>=0.2.0"
接下来，我们注册/登录 Pinecone 以获取 API 密钥： 
import os
from getpass import getpass

os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY") or getpass(
    "Enter your Pinecone API key: "
)
查看文档以了解可用的模型。现在我们初始化嵌入模型： 
from langchain_pinecone import PineconeEmbeddings

embeddings = PineconeEmbeddings(model="multilingual-e5-large")
从这里，我们可以以同步或异步方式创建嵌入，先从同步开始！我们使用 embed_query 将单个文本作为查询嵌入（即在 RAG 中搜索时使用的内容）： 
docs = [
    "Apple is a popular fruit known for its sweetness and crisp texture.",
    "The tech company Apple is known for its innovative products like the iPhone.",
    "Many people enjoy eating apples as a healthy snack.",
    "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.",
    "An apple a day keeps the doctor away, as the saying goes.",
]

doc_embeds = embeddings.embed_documents(docs)
doc_embeds

query = "Tell me about the tech company known as Apple"
query_embed = embeddings.embed_query(query)
query_embed
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