Unlock meaning like never before. A robust set of tools for intelligent text understanding.
Receives a text and returns a JSON object containing a list of analyzed sentences. See the docs here
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Receives two texts and returns a JSON object containing the text similarity score. See the docs here
The Codeq Natural Language Processing API provides powerful text analysis capabilities. It parses and understands complex structures in text, extracting meaningful insights. On Pipedream, you can harness this API to analyze text data from various sources, automate content categorization, sentiment analysis, or even construct rich profiles of user feedback. With Pipedream's serverless platform, these processes can be automated, triggered by events, and integrated with numerous other apps to create robust, data-driven workflows.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
codeq_natural_language_processing_api: {
type: "app",
app: "codeq_natural_language_processing_api",
}
},
async run({steps, $}) {
const data = {
"user_id": `${this.codeq_natural_language_processing_api.$auth.user_id}`,
"user_key": `${this.codeq_natural_language_processing_api.$auth.user_key}`,
"text": `{your_text}`,
}
return await axios($, {
method: "post",
url: `https://api.codeq.com/v1`,
headers: {
"Content-Type": `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}}