Pinecone integration

Integrate with the Pinecone embedding model using LangChain Python.

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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