Fetch Vectors with Pinecone API on BigQuery - Query Results from Google Cloud API

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BigQuery - Query Results from the Google Cloud API

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Fetch Vectors with the Pinecone API

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Getting Started#

This integration creates a workflow with a Google Cloud 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 BigQuery - Query Results trigger
1. Connect your Google Cloud account
2. Configure Polling interval
3. Configure Event Size
4. Select a Dataset ID
5. Configure SQL Query
6. Optional- Configure De-duplication Key
Configure the Fetch Vectors action
1. Connect your Pinecone account
2. Select a Index Name
3. Optional- Configure Namespace
4. Configure Vector IDs
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#

BigQuery - Query Results on Google Cloud

Description:Emit new events with the results of an arbitrary query

Version:0.1.5

Key:google_cloud-bigquery-query-results

View on GitHub

Google Cloud Overview#

The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.

Trigger Code#

import crypto from "crypto";
import { v4 as uuidv4 } from "uuid";
import common from "../common/bigquery.mjs";

export default {
  ...common,
  key: "google_cloud-bigquery-query-results",
  // eslint-disable-next-line pipedream/source-name
  name: "BigQuery - Query Results",
  description: "Emit new events with the results of an arbitrary query",
  version: "0.1.5",
  dedupe: "unique",
  type: "source",
  props: {
    ...common.props,
    sqlQuery: {
      type: "string",
      label: "SQL Query",
      description: "Your SQL query",
    },
    dedupeKey: {
      type: "string",
      label: "De-duplication Key",
      description: `
        The name of a column in the table to use for deduplication. See [the
        docs](https://github.com/PipedreamHQ/pipedream/tree/master/components/google_cloud/sources/bigquery-query-results#technical-details)
        for more info.
      `,
      optional: true,
    },
  },
  methods: {
    ...common.methods,
    getQueryOpts() {
      return {
        query: this.sqlQuery,
      };
    },
    generateMeta(row, ts) {
      const { [this.dedupeKey]: id = uuidv4() } = row;
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
    generateMetaForCollection(rows, ts) {
      const hash = crypto.createHash("sha1");
      rows
        .map((i) => i[this.dedupeKey] || uuidv4())
        .map((i) => i.toString())
        .forEach((i) => hash.update(i));
      const id = hash.digest("base64");
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
  },
};

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
Google Cloud	`googleCloud`	`app`	This component uses the Google Cloud app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling interval	`timer`	`$.interface.timer`	How often to run your query
Event Size	`eventSize`	`integer`	The number of rows to include in a single event (by default, emits 1 event per row)
Dataset ID	`datasetId`	`string`	Select a value from the drop down menu.
SQL Query	`sqlQuery`	`string`	Your SQL query
De-duplication Key	`dedupeKey`	`string`	`The name of a column in the table to use for deduplication. See [the docs](https://github.com/PipedreamHQ/pipedream/tree/master/components/google_cloud/sources/bigquery-query-results#technical-details) for more info.`

Trigger Authentication#

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

Create a service account in GCP and set the permissions you need for Pipedream workflows.
Generate a service account key
Download the key details in JSON format
Upload the key below.

About Google Cloud#

The Google Cloud Platform, including BigQuery

Action#

Fetch Vectors on Pinecone

Description:Looks up and returns vectors by ID, from a single namespace.. [See the documentation](https://docs.pinecone.io/reference/api/data-plane/fetch).

Version:0.0.2

Key:pinecone-fetch-vectors

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-fetch-vectors",
  name: "Fetch Vectors",
  description: "Looks up and returns vectors by ID, from a single namespace.. [See the documentation](https://docs.pinecone.io/reference/api/data-plane/fetch).",
  type: "action",
  version: "0.0.2",
  props: {
    app,
    indexName: {
      propDefinition: [
        app,
        "indexName",
      ],
    },
    namespace: {
      propDefinition: [
        app,
        "namespace",
      ],
    },
    ids: {
      type: "string[]",
      label: "Vector IDs",
      description: "The IDs of the vectors. E.g. `[\"vec1\", \"vec2\"]`",
      propDefinition: [
        app,
        "vectorId",
      ],
    },
  },
  methods: {
    fetchVectors(args = {}) {
      return this.app.makeRequest({
        path: "/vectors/fetch",
        ...args,
      });
    },
  },
  async run({ $: step }) {
    const {
      fetchVectors,
      indexName,
      ids,
      namespace,
    } = this;

    const response = await fetchVectors({
      step,
      indexName,
      params: {
        ids: utils.parseArray(ids),
        namespace,
      },
    });

    step.export("$summary", `Successfully fetched \`${Object.keys(response.vectors).length}\` vector(s).`);

    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.
Namespace	`namespace`	`string`	The namespace to use.
Vector IDs	`ids`	`string[]`	The IDs of the vectors. E.g. `["vec1", "vec2"]`

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,