Pinecone Embeddings

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")

API Reference:PineconeEmbeddings

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

Related

• Embedding model conceptual guide
• Embedding model how-to guides