Skip to main content
Pinecone’s inference API can be accessed via PineconeEmbeddings. Providing text embeddings via the Pinecone service. We start by installing prerequisite libraries: 
!pip install -qU "langchain-pinecone>=0.2.0"
Next, we sign up / log in to Pinecone to get our API key: 
import os
from getpass import getpass

os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY") or getpass(
    "Enter your Pinecone API key: "
)
Check the document for available models. Now we initialize our embedding model like so: 
from langchain_pinecone import PineconeEmbeddings

embeddings = PineconeEmbeddings(model="multilingual-e5-large")
From here we can create embeddings either sync or async, let’s start with sync! We embed a single text as a query embedding (ie what we search with in RAG) using embed_query: 
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
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.