Long-term Memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.
Deletes one or more vectors by ID, from a single namespace. See the documentation.
Run any Bash in a Pipedream step within your workflow. Refer to the Pipedream Bash docs to learn more.
Looks up and returns vectors by ID, from a single namespace.. See the documentation.
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.
Updates vector in a namespace. If a value is included, it will overwrite the previous value. See the documentation.
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.
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}`,
},
})
},
})
# $PIPEDREAM_STEPS file contains data from previous steps
cat $PIPEDREAM_STEPS | jq .trigger.context.id
# Write data to $PIPEDREAM_EXPORTS to return it from the step
# Exports must be written as key=value
echo foo=bar >> $PIPEDREAM_EXPORTS