Query IDs with Pinecone API on New Abandoned Cart from Shopify API

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New Abandoned Cart from the Shopify API

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Query IDs with the Pinecone API

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Getting Started#

This integration creates a workflow with a Shopify trigger and Pinecone action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Abandoned Cart trigger
1. Connect your Shopify account
2. Configure timer
Configure the Query IDs action
1. Connect your Pinecone account
2. Select a Index Name
3. Configure Top K
4. Optional- Configure Filter
5. Optional- Configure Include Values
6. Optional- Configure Include Metadata
7. Optional- Configure Namespace
8. Optional- Configure Vector ID
9. Optional- Configure Vector Values
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Abandoned Cart on Shopify

Description:Emit new event each time a user abandons their cart.

Version:0.0.11

Key:shopify_developer_app-new-abandoned-cart

View on GitHub

Shopify Overview#

The Shopify Admin REST & GraphQL API unleashes a myriad of possibilities to automate and enhance online store operations. It provides programmatic access to Shopify functionalities, allowing users to manage products, customers, orders, and more. Leveraging the Shopify Admin API within Pipedream, developers can create custom workflows that automate repetitive tasks, sync data across platforms, and respond dynamically to events in Shopify.

This integration can be used as a custom app on your store, or for automating actions on behalf of merchants through your Shopify app.

Looking for integrating into the Shopify Partner API for your apps, themes or referrals? Check out our Shopify Partner API integration

Trigger Code#

import shopify from "../../shopify_developer_app.app.mjs";
import common from "@pipedream/shopify/sources/new-abandoned-cart/new-abandoned-cart.mjs";

import { adjustPropDefinitions } from "../../common/utils.mjs";

const {
  name, description, type, ...others
} = common;
const props = adjustPropDefinitions(others.props, shopify);

export default {
  ...others,
  key: "shopify_developer_app-new-abandoned-cart",
  version: "0.0.11",
  name,
  description,
  type,
  props: {
    shopify,
    ...props,
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Shopify	`shopify`	`app`	This component uses the Shopify app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

Shopify uses API keys for authentication. When you connect your Shopify account, Pipedream securely stores the keys so you can easily authenticate to Shopify APIs in both code and no-code steps.

About Shopify#

Shopify is a complete commerce platform that lets anyone start, manage, and grow a business. You can use Shopify to build an online store, manage sales, market to customers, and accept payments in digital and physical locations.

Action#

Query IDs on Pinecone

Description:Searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores. [See the documentation](https://docs.pinecone.io/reference/api/data-plane/query).

Version:0.0.3

Key:pinecone-query-ids

View on GitHub

Pinecone Overview#

The Pinecone API enables you to work with vector databases, which are essential for building and scaling applications with AI features like recommendation systems, image recognition, and natural language processing. On Pipedream, you can create serverless workflows integrating Pinecone with other apps, automate data ingestion, query vector databases in response to events, and orchestrate complex data processing pipelines that leverage Pinecone's similarity search.

Action Code#

import app from "../../pinecone.app.mjs";
import utils from "../../common/utils.mjs";

export default {
  key: "pinecone-query-ids",
  name: "Query IDs",
  description: "Searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores. [See the documentation](https://docs.pinecone.io/reference/api/data-plane/query).",
  type: "action",
  version: "0.0.3",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  props: {
    app,
    indexName: {
      propDefinition: [
        app,
        "indexName",
      ],
    },
    topK: {
      type: "integer",
      label: "Top K",
      description: "The number of results to return. E.g. `10`",
      min: 1,
      max: 10000,
      default: 10,
    },
    filter: {
      type: "object",
      label: "Filter",
      description: "The filter to apply. You can use vector metadata to limit your search. For guidance and examples, see [filtering-with-metadata](https://docs.pinecone.io/guides/data/filtering-with-metadata).",
      optional: true,
    },
    includeValues: {
      type: "boolean",
      label: "Include Values",
      description: "Whether to include the vector values in the response.",
      optional: true,
    },
    includeMetadata: {
      type: "boolean",
      label: "Include Metadata",
      description: "Whether to include the vector metadata in the response.",
      optional: true,
    },
    namespace: {
      propDefinition: [
        app,
        "namespace",
      ],
    },
    id: {
      optional: true,
      description: `${app.propDefinitions.vectorId.description} Each request can contain only one of the parameters either **Vector ID** or **Vector Values**.`,
      propDefinition: [
        app,
        "vectorId",
      ],
    },
    vector: {
      description: `${app.propDefinitions.vectorValues.description} Each request can contain only one of the parameters either **Vector Values** or **Vector ID**.`,
      propDefinition: [
        app,
        "vectorValues",
      ],
    },
  },
  methods: {
    query(args = {}) {
      return this.app.post({
        path: "/query",
        ...args,
      });
    },
  },
  async run({ $: step }) {
    const {
      query,
      indexName,
      id,
      vector,
      topK,
      filter,
      includeValues,
      includeMetadata,
      namespace,
    } = this;

    const vectorParsed = utils.parseArray(vector);

    const response = await query({
      step,
      indexName,
      data: {
        id,
        ...(vectorParsed.length && {
          vector: vectorParsed,
        }),
        topK,
        filter: utils.parse(filter),
        includeValues,
        includeMetadata,
        namespace,
      },
    });

    step.export("$summary", `Successfully queried ${response.matches.length} matches.`);

    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Pinecone	`app`	`app`	This component uses the Pinecone app.
Index Name	`indexName`	`string`	Select a value from the drop down menu.
Top K	`topK`	`integer`	The number of results to return. E.g. `10`
Filter	`filter`	`object`	The filter to apply. You can use vector metadata to limit your search. For guidance and examples, see filtering-with-metadata
Include Values	`includeValues`	`boolean`	Whether to include the vector values in the response.
Include Metadata	`includeMetadata`	`boolean`	Whether to include the vector metadata in the response.
Namespace	`namespace`	`string`	The namespace to use.
Vector ID	`id`	`string`	The ID of the vector. Each request can contain only one of the parameters either Vector ID or Vector Values.
Vector Values	`vector`	`string[]`	The values of the vector. Each request can contain only one of the parameters either Vector Values or Vector ID.

Action Authentication#

Pinecone uses API keys for authentication. When you connect your Pinecone account, Pipedream securely stores the keys so you can easily authenticate to Pinecone APIs in both code and no-code steps.

To connect your Pinecone account to Pipedream,

Open the Pinecone console and click "API Keys"
Copy the API key and Environment values and paste each below

Query IDs with Pinecone API on New Abandoned Cart from Shopify API

Pipedream makes it easy to connect APIs for Pinecone, Shopify and 2,900+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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