Integrate the Pinecone API with the Python API

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

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Popular Pinecone and Python Actions#

Delete Vectors with the Pinecone API

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

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Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Fetch Vectors with the Pinecone API

Looks up and returns vectors by ID, from a single namespace.. 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 documentation.

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Update Vector with the Pinecone API

Updates vector in a namespace. If a value is included, it will overwrite the previous value. See the documentation.

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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#

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 Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}

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