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HTTP outcalls performed by the backend canister (not in the frontend).
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| name | extension-http-outcalls |
| description | HTTP outcalls performed by the backend canister (not in the frontend). |
| version | 0.1.6 |
| compatibility | {"mops":{"caffeineai-http-outcalls":"~0.1.2"}} |
| caffeineai-subscription | ["none"] |
HTTP outcalls extension for Caffeine AI.
This skill adds the ability to make HTTP GET and POST requests from the backend canister. Useful for integrating with external APIs and services.
For HTTP outcalls that must be performed in the backend:
There is the prefabricated module mo:caffeineai-http-outcalls/outcall.mo that that cannot be modified. It provides fundamental functionality for making HTTP GET or PUT requests in the backend.
module {
public type TransformationInput = {
context : Blob;
response : IC.HttpRequestResult;
};
public type TransformationOutput = IC.HttpRequestResult;
public type Transform = query TransformationInput -> async TransformationOutput;
public type Header = {
name: Text;
value: Text;
};
// Helper function for the transform callback used by the IC on HTTP outcalls.
public func transform(input : TransformationInput) : TransformationOutput;
// HTTP GET request with a transform callback function.
public func httpGetRequest(url : Text, extraHeaders: [Header], transform : Transform) : async Text;
// HTTP POST request, specifying a transform callback.
public func httpPostRequest(url : Text, extraHeaders: [Header], body : Text, transform : Transform) : async Text;
};
Usage for GET:
import OutCall "mo:caffeineai-http-outcalls/outcall";
actor {
public query func transform(input: OutCall.TransformationInput) : async OutCall.TransformationOutput {
OutCall.transform(input);
};
func makeGetOutcall(url: Text) : async Text {
await OutCall.httpGetRequest(url, [], transform);
};
};
Hint: JSON parsing is not directly supported in Motoko. Better tunnel JSON to frontend for parsing.
POST usage is analogous.
Quick reference for the Caffeine Data Intelligence agent to query an OQL-exposing canister (schema() + execute()) through the `icp` CLI against the project's `backend` canister: read the schema, form JSON queries (filter / order / paginate / aggregate / dotted-path edges), and parse the Candid result rows.
Make a canister's data queryable by the Caffeine Data Intelligence agent. Use whenever an app stores structured data (Maps/Lists/arrays of records) that should be answerable in natural language — "top customers", "revenue by region", "active projects". Adds a discoverable `schema()` and a JSON `execute()` query endpoint via the `caffeineai-oql` mops package's `Expose` mixin.
Core infrastructure providing backend connection configuration, storage client, and React app entry point.
General file/object storage, such as for images, videos, files, documents and other bulk data. Perfect fit for image galleries, video galleries, and other file or object management. Supports large files beyond IC limit, with browser-cached HTTP URL access.
Use the `googlemail-client` mops package whenever the user asks the canister to send email, compose a draft, list or read Gmail messages, or fetch the authenticated user's Gmail profile. The package wraps the Gmail REST API v1 at `https://gmail.googleapis.com` via outbound HTTPS calls.
Payment support based on Stripe, supporting credit cards and debit cards