with Perplexity and Python?
Generates a model's response for the given chat conversation. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Generates a model's response for the given chat conversation with multi-message support and Perplexity search controls. Docs: https://docs.perplexity.ai/api-reference/chat-completions-post
The Perplexity API offers advanced natural language processing capabilities, enabling users to generate answers, summaries, and insights from texts. Leveraging this API on Pipedream allows for the automation of content analysis, intelligent alert systems, and dynamic data enrichment, integrating seamlessly with various data sources and services for real-time processing.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
perplexity: {
type: "app",
app: "perplexity",
}
},
async run({steps, $}) {
const data = {
"model": "mistral-7b-instruct",
"messages": [
{
"role": "system",
"content": "Be precise and concise."
},
{
"role": "user",
"content": "Can you tell me about the integration platform called Pipedream?"
}
]
}
return await axios($, {
method: "post",
url: `https://api.perplexity.ai/chat/completions`,
headers: {
Authorization: `Bearer ${this.perplexity.$auth.api_key}`,
"accept": `application/json`,
},
data,
})
},
})
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:
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}}