Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

Ideal multilingual model for high performance while keeping with open source. Works well on messy data. Good for short queries expected to return medium-length passages of text (1-2 paragraphs).
You can call the embed operation through Pinecone Inference to turn text into vectors without writing to an index. That differs from upsert_records on an index with integrated embedding, where each request embeds and stores records in one step. To see how embedding consumption appears in billing and usage reports, see Embedding tokens.

​
Installation

pip install --upgrade pinecone

​
Create index

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# Create an index for dense vectors with integrated inference
index_name = "multilingual-e5-large"
pc.create_index_for_model(
    name=index_name,
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "multilingual-e5-large",
        "field_map": {
            "text": "text"  # Map the record field to be embedded
        }
    }
)

index = pc.Index(index_name)

​
Embed & upsert

data = [
    {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
    {"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
    {"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
    {"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
    {"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
    {"id": "vec6", "text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
]

index.upsert_records(
    namespace="example-namespace",
    records=data
)

​
Query

query_payload = {
    "inputs": {
        "text": "Tell me about the tech company known as Apple."
    },
    "top_k": 3
}

results = index.search(
    namespace="example-namespace",
    query=query_payload
)

print(results)
Lorem Ipsum