Weaviate

Weaviate is an open-source vector database.

Integrate the Weaviate API with the Python API

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

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.

 
Try it

Overview of Weaviate

Weaviate is a cloud-native, modular, real-time vector search engine that enables scalable, high-performance semantic search. It's built for a wide range of applications, from autocomplete and similar object suggestions to full-text search and automatic categorization. With the Weaviate API, you can index and search through large amounts of data using machine learning models to understand the content and context of the data. On Pipedream, you can leverage this API to create serverless workflows that automate data ingestion, enrichment, and search capabilities, enhancing your apps with intelligent search functions.

Connect Weaviate

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    weaviate: {
      type: "app",
      app: "weaviate",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `${this.weaviate.$auth.cluster_url}//v1/schema`,
      headers: {
        Authorization: `Bearer ${this.weaviate.$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

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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}}