pinecone-io

node-pinecone

Javascript client for the Pinecone.io vector search engine (https://pinecone.io)

Install

npm install pinecone-io

Then you can use it in your project:

import { Pinecone } from "pinecone-io"
const pinecone = new Pinecone(process.env.PINECONE_API_KEY);

Quick Start

You must create a Pinecone.io account and obtain an API key first!

Here is a basic example that creates a client connection and adds a new collection pretty_colors to Pinecone. This quick start is also in the examples folder in this repository.

With your API key, you can run the quick_start code like this, by setting the PINECONE_API_KEY environment variable:

PINECONE_API_KEY=********-****-****-****-************ node quick_start.js

import { Pinecone } from "../index.js"

const API_KEY = process.env.PINECONE_API_KEY;

if (!API_KEY) {
    console.error("API key not found! Please set PINECONE_API_KEY like this:");
    console.error("PINECONE_API_KEY=... node app.js");
    process.exit(0);
}

const pinecone = new Pinecone(API_KEY,"https://controller.us-west1-gcp.pinecone.io/");

const name = "pretty-colors";
const schema = {
    "name":name,
    "dimension": 3,
    "metric": "dotproduct"
};

console.log("-------------------------------------------------------------------------");

/// Create the new collection with the name and schema
let create_result = await pinecone.create_collection(name,schema);
if (create_result.err) {
    console.error(`ERROR:  Couldn't create collection "${name}"!`);
    console.error(create_result.err);
} else {
    console.log(`Success! Collection "${name}" created!`);
    console.log(create_result.response);
}

//For a new collection, you need to wait until it's status is ready!
console.log(`Waiting for "${name}" to be ready...`);
let ready_result = await pinecone.wait_until_ready(name);
console.log(`Collection "${name}" is ready!`);

console.log("-------------------------------------------------------------------------");

/// Show the collection info as it exists in the pinecone engine
let collections_result = await pinecone.get_collections();
if (collections_result.err) {
    console.error(`ERROR:  Couldn't access collections!`);
    console.error(collections_result.err);
} else {
    console.log(`Collections found!"`);
    console.log(collections_result.response);
}
console.log("-------------------------------------------------------------------------");

/// Show the collection info as it exists in the pinecone engine
let collection_result = await pinecone.get_collection(name);
if (collection_result.err) {
    console.error(`ERROR:  Couldn't access collection "${name}"!`);
    console.error(collection_result.err);
} else {
    console.log(`Collection "${name} found!"`);
    console.log(collection_result.response);
}
console.log("-------------------------------------------------------------------------");

/// Upload some points - just five RGB colors
let points = [
    { "id": "colors-1", "metadata": {"color": "red"}, "values": [0.9, 0.1, 0.1] },
    { "id": "colors-2", "metadata": {"color": "green"}, "values": [0.1, 0.9, 0.1] },
    { "id": "colors-3", "metadata": {"color": "blue"}, "values": [0.1, 0.1, 0.9] },
    { "id": "colors-4", "metadata": {"color": "purple"}, "values": [1.0, 0.1, 0.9] },
    { "id": "colors-5", "metadata": {"color": "cyan"}, "values": [0.1, 0.9, 0.8] }
]
let upload_result = await pinecone.upload_points(name,points);
if (upload_result.err) {
    console.error(`ERROR:  Couldn't upload to "${name}"!`);
    console.error(upload_result.err);
} else {
    console.log(`Uploaded to "${name} successfully!"`);
    console.log(upload_result.response);
}

console.log("-------------------------------------------------------------------------");

/// Search the two closest colors (k=2)
let purplish = [0.8,0.1,0.7];
let search_result = await pinecone.search_collection(name,purplish,2);
if (search_result.err) {
    console.error(`ERROR: Couldn't search ${purplish}`);
    console.error(search_result.err);
} else {
    console.log(`Search results for ${purplish}`);
    console.log(JSON.stringify(search_result.response,null,2));
}

console.log("-------------------------------------------------------------------------");

/// Filtered search the closest color
let filter = {
    "color": {
        "$in": [
            "cyan",
        ]
    }
}
let filtered_result = await pinecone.search_collection(name,purplish,1,filter);
if (filtered_result.err) {
    console.error(`ERROR: Couldn't search ${purplish} with ${filter}`);
    console.error(filtered_result.err);
} else {
    console.log(`Search results for ${purplish} with ${filter}`);
    console.log(JSON.stringify(filtered_result.response,null,2));
}

console.log("-------------------------------------------------------------------------");

/// Delete the collection
let delete_result = await pinecone.delete_collection(name);
if (delete_result.err) {
    console.error(`ERROR:  Couldn't delete "${name}"!`);
    console.error(delete_result.err);
} else {
    console.log(`Deleted "${name} successfully!"`);
    console.log(delete_result.response);
}

console.log(`Waiting for ${name} to dissappear.`);
await pinecone.wait_until_deleted(name);
console.log('All gone!');

Conventions

This API, for general consistency with other Search Engines, uses the terminology of a collection to refer to a database, index, or namespace. A collection has a name and a schema.

Collection management operations are performed against the URL of a pod. Vector and Search operations are performed against the url of the collection host.

All methods must be awaited, and each returns a PineconeResponse object - which only has two properties: err and response.

Always check for presence of err. If err is not null, then the response might not be valid. When in doubt, copy the example code from above for the appropriate method.

Collection methods

With a pinecone object, just await one of the following methods to interact with the engine and its collections.

create_collection(name,body)

Creates a new collection with name and the schema specified in body

get_collections()

Gets all the collections that are accessible by the API key.

get_collection(name)

Gets the collection information for name

delete_collection(name)

Deletes a collection with name

wait_until_ready(name)

When you create a collection, it might not be immediately ready. This waits for the ready state to become true, without blocking the thread.

If you use create_collection you should immediately follow it with this method, to make sure you don't try to use a collection before it's ready!

wait_until_deleted(name)

When you delete a collection, it might not be immediately removed. This waits for the collection to dissappear, without blocking the thread.

Vector and Search operations

With a pinecone object, just await one of the following methods to interact with the engine and its collections.

Important! you should not use the same pinecone object to search different collections. If you need to search multiple collections, create a pinecone object for each collection.

Each of these operations will ensure an appropriate host is being used. This is leaky, since it sets the host in the pinecone object as a side effect. But it's convenient :)

upload_points(name,points)

Uploads vectors and payloads in points to the collection name

search_collection(name,vector,k,filter)

Searches the collection with a vector, to get the top k most similar points (default 5), and an optional metadata filter.

query_collection(name,query)

Searches the collection with a Pinecone compatible query that must be fully defined by the caller.