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
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