Update Vector with Pinecone API on Random item from multiple RSS feeds from RSS API

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Random item from multiple RSS feeds from the RSS API

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Update Vector with the Pinecone API

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Getting Started#

This integration creates a workflow with a RSS 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 Random item from multiple RSS feeds trigger
1. Connect your RSS account
2. Configure timer
3. Configure Feed URLs
Configure the Update Vector action
1. Connect your Pinecone account
2. Select a Index Name
3. Configure Project ID
4. Select a Vector ID
5. Configure Vector Values
6. Optional- Configure Vector Metadata
7. Optional- Configure Namespace
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#

Random item from multiple RSS feeds on RSS

Description:Emit a random item from multiple RSS feeds

Version:0.2.6

Key:rss-random-item-in-multiple-feeds

View on GitHub

RSS Overview#

The RSS app allows users to automatically fetch and parse updates from web feeds. This functionality is pivotal for staying abreast of content changes or updates from websites, blogs, and news outlets that offer RSS feeds. With Pipedream, you can harness the RSS API to trigger workflows that enable a broad range of automations, like content aggregation, monitoring for specific keywords, notifications, and data synchronization across platforms.

Trigger Code#

import rss from "../../app/rss.app.mjs";
import { defineSource } from "@pipedream/types";
import rssCommon from "../common/common.mjs";
export default defineSource({
    ...rssCommon,
    key: "rss-random-item-in-multiple-feeds",
    name: "Random item from multiple RSS feeds",
    type: "source",
    description: "Emit a random item from multiple RSS feeds",
    version: "0.2.6",
    props: {
        ...rssCommon.props,
        urls: {
            propDefinition: [
                rss,
                "urls",
            ],
        },
    },
    dedupe: "unique",
    hooks: {
        async activate() {
            // Try to parse the feed one time to confirm we can fetch and parse.
            // The code will throw any errors to the user.
            for (const url of this.urls) {
                await this.rss.fetchAndParseFeed(url);
            }
        },
    },
    async run() {
        const items = [];
        for (const url of this.urls) {
            const feedItems = await this.rss.fetchAndParseFeed(url);
            items.push(...feedItems);
        }
        const item = items[Math.floor(Math.random() * items.length)];
        const meta = this.generateMeta(item);
        this.$emit(item, meta);
    },
});

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
RSS	`rss`	`app`	This component uses the RSS app.
	`timer`	`$.interface.timer`	How often you want to poll the feed for new items
Feed URLs	`urls`	`string[]`	Enter either one or multiple URLs from any public RSS feed

Trigger Authentication#

The RSS API does not require authentication.

About RSS#

Real Simple Syndication

Action#

Update Vector on Pinecone

Description:Updates vector in a namespace. If a value is included, it will overwrite the previous value. [See the docs](https://docs.pinecone.io/reference/update).

Version:0.0.1

Key:pinecone-update-vector

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

export default {
  key: "pinecone-update-vector",
  name: "Update Vector",
  description: "Updates vector in a namespace. If a value is included, it will overwrite the previous value. [See the docs](https://docs.pinecone.io/reference/update).",
  type: "action",
  version: "0.0.1",
  props: {
    app,
    indexName: {
      propDefinition: [
        app,
        "indexName",
      ],
    },
    projectId: {
      propDefinition: [
        app,
        "projectId",
      ],
    },
    id: {
      propDefinition: [
        app,
        "vectorId",
        ({
          indexName, projectId,
        }) => ({
          indexName,
          projectId,
        }),
      ],
    },
    values: {
      propDefinition: [
        app,
        "vectorValues",
      ],
    },
    metadata: {
      propDefinition: [
        app,
        "vectorMetadata",
      ],
    },
    namespace: {
      propDefinition: [
        app,
        "namespace",
      ],
    },
  },
  methods: {
    updateVector(args = {}) {
      return this.app.create({
        path: "/vectors/update",
        ...args,
      });
    },
  },
  async run({ $: step }) {
    const {
      indexName,
      projectId,
      id,
      values,
      metadata,
      namespace,
    } = this;

    await this.updateVector({
      projectId,
      indexName,
      data: {
        id,
        values,
        setMetadata: utils.parse(metadata),
        namespace,
      },
    });

    step.export("$summary", `Successfully updated vector with ID ${id}.`);
  },
};

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.
Project ID	`projectId`	`string`	The ID of the project. You can get the project from the Pinecone URL, for example https://app.pinecone.io/organizations/abcde/projects/us-east-1-aws:611e7f9/indexes, then the Project ID is `611e7f9`
Vector ID	`id`	`string`	Select a value from the drop down menu.
Vector Values	`values`	`string[]`	The values of the vector.
Vector Metadata	`metadata`	`object`	The metadata of the vector. E.g. `{"color": "red"}`
Namespace	`namespace`	`string`	The namespace to use.

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

About Pinecone#

Long-term Memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.

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Popular Actions#

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