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
Convert an object to a JSON format string
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
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}`,
},
})
},
})
The Pipedream Utils app is a set of pre-built functions that streamline common tasks in your workflows. It acts like a Swiss Army knife for developers, providing essential tools such as format conversion, date manipulation, and text processing. By leveraging these functions, you can reduce the boilerplate code needed for routine operations, speeding up the development of intricate automations. The Helper Functions API can be a game changer when it comes to tasks like parsing dates in user-friendly formats, encoding and decoding data, or generating UUIDs, making them more efficient and less error-prone.
export default defineComponent({
props: {
pipedream_utils: {
type: "app",
app: "pipedream_utils",
}
},
async run({steps, $}) {
},
})