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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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PostgreSQL

Integrate the Pinecone API with the PostgreSQL API

Setup the Pinecone API trigger to run a workflow which integrates with the PostgreSQL API. Pipedream's integration platform allows you to integrate Pinecone and PostgreSQL remarkably fast. Free for developers.

Popular Ways to Connect Pinecone with PostgreSQL

Delete Vectors with Pinecone API on New Column from PostgreSQL API
PostgreSQL + Pinecone
 
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Delete Vectors with Pinecone API on New or Updated Row from PostgreSQL API
PostgreSQL + Pinecone
 
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Delete Vectors with Pinecone API on New Row Custom Query from PostgreSQL API
PostgreSQL + Pinecone
 
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Delete Vectors with Pinecone API on New Row from PostgreSQL API
PostgreSQL + Pinecone
 
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Delete Vectors with Pinecone API on New Table from PostgreSQL API
PostgreSQL + Pinecone
 
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Popular Pinecone and PostgreSQL Triggers

New Column from the PostgreSQL API

Emit new event when a new column is added to a table. See the documentation

 
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New or Updated Row from the PostgreSQL API

Emit new event when a row is added or modified. See the documentation

 
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New Row from the PostgreSQL API

Emit new event when a new row is added to a table. See the documentation

 
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New Row Custom Query from the PostgreSQL API

Emit new event when new rows are returned from a custom query that you provide. See the documentation

 
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New Table from the PostgreSQL API

Emit new event when a new table is added to the database. See the documentation

 
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Popular Pinecone and PostgreSQL Actions

Delete Vectors with the Pinecone API

Deletes one or more vectors by ID, from a single namespace. See the docs.

 
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Delete Row(s) with the PostgreSQL API

Deletes a row or rows from a table. See the documentation

 
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Fetch Vectors with the Pinecone API

Looks up and returns vectors by ID, from a single namespace.. See the docs.

 
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Execute Custom Query with the PostgreSQL API

Executes a custom query you provide. See the documentation

 
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Query IDs with the Pinecone API

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 docs.

 
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Overview of Pinecone

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.

Connect Pinecone

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    pinecone: {
      type: "app",
      app: "pinecone",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.pinecone.io/collections`,
      headers: {
        "Api-Key": `${this.pinecone.$auth.api_key}`,
      },
    })
  },
})

Overview of PostgreSQL

On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.

Connect PostgreSQL

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import postgresql from "@pipedream/postgresql"

export default defineComponent({
  props: {
    postgresql,
  },
  async run({ steps, $ }) {
    // Component source code:
    // https://github.com/PipedreamHQ/pipedream/tree/master/components/postgresql

    const queryObj = {
      text: "SELECT NOW()",
      values: [], // Ignored since query does not contain placeholders
    };
    const { rows } = await this.postgresql.executeQuery(queryObj);
    return rows;
  },
})
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