with Exa (Formerly Metaphor) and fal.ai?
Find similar links to the link provided. See the documentation](https://docs.metaphor.systems/reference/findsimilar)
Adds a request to the queue for asynchronous processing, including specifying a webhook URL for receiving updates. See the documentation
Retrieve contents of documents based on a list of document IDs. See the documentation. It is used to instantly
get content for documents, given the IDs of the documents. We currently support extracts - the first 1000 tokens (~750 words) of parsed HTML for a site. Note that each piece of content retrieved costs 1 request. Also note that instant
is a little bit of a lie if you are using keyword search, in which case contents might take a few seconds to retrieve.
Cancels a request in the queue. This allows you to stop a long-running task if it's no longer needed. See the documentation
Perform a search with a Metaphor prompt-engineered query and retrieve a list of relevant results. See the documentation
The Exa (Formerly Metaphor) API enables you to enrich your applications with machine learning-powered insights and data processing capabilities. With Exa, you can perform advanced data analysis, extract meaningful patterns, and automate decision-making processes within your projects. By integrating this API with Pipedream, you can create seamless workflows that leverage these intelligent features alongside other services to streamline your operations, enhance data intelligence, and drive innovation.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
metaphor: {
type: "app",
app: "metaphor",
}
},
async run({steps, $}) {
const data = {
"query": `test`,
}
return await axios($, {
method: "post",
url: `https://api.metaphor.systems/search`,
headers: {
"accept": `application/json`,
"Content-Type": `application/json`,
"x-api-key": `${this.metaphor.$auth.api_key}`,
},
data,
})
},
})
import { fal } from "@fal-ai/client"
export default defineComponent({
props: {
fal_ai: {
type: "app",
app: "fal_ai",
}
},
async run({ steps, $ }) {
fal.config({
credentials: `${this.fal_ai.$auth.api_key}`,
});
const result = await fal.subscribe("fal-ai/lora", {
input: {
model_name: "stabilityai/stable-diffusion-xl-base-1.0",
prompt:
"Photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang",
},
logs: true,
});
return result;
},
})