DopplerAI

DopplerAI is a single API for memory, data processing and scaling vector databases.

Integrate the DopplerAI API with the Python API

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

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Create Chat with the DopplerAI API

Initializes a new chat thread. 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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Create Collection with the DopplerAI API

Establishes a new collection to save uploaded data. See the documentation

 
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Send Message with the DopplerAI API

Dispatches a message to the artificial intelligence. See the documentation

 
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Overview of DopplerAI

DopplerAI's API provides powerful tools for predictive analysis, tapping into AI to forecast trends, analyze data patterns, and make informed decisions. By integrating this API into Pipedream, you can automate workflows that react to insights, combine data from various sources, and trigger actions across apps. It's a fantastic way to turn data into actionable intelligence without manual intervention.

Connect DopplerAI

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    dopplerai: {
      type: "app",
      app: "dopplerai",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.dopplerai.com/auth/me`,
      headers: {
        Authorization: `Bearer ${this.dopplerai.$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}}

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Adyen logo
Appcues logo
Bandwidth logo
Checkr logo
ChartMogul logo
Dataminr logo
Gopuff logo
Gorgias logo
LinkedIn logo
Logitech logo
Replicated logo
Rudderstack logo
SAS logo
Scale AI logo
Webflow logo
Warner Bros. logo
Adyen logo
Appcues logo
Bandwidth logo
Checkr logo
ChartMogul logo
Dataminr logo
Gopuff logo
Gorgias logo
LinkedIn logo
Logitech logo
Replicated logo
Rudderstack logo
SAS logo
Scale AI logo
Webflow logo
Warner Bros. logo