OpenAI chat library. See top-level documentation for complete reference.
We also need to set up the VertexAI API. To do this we need prepare the model, wrapper and instructions bound to the ID or your chat assistant config:
import {projectID} from "firebase-functions/params";
import {VertexAI} from "@google-cloud/vertexai";
import {factory} from "@motorro/firebase-ai-chat-vertexai";
// Chat component factory
const chatFactory = factory(firestore(), getFunctions(), region);
// Create VertexAI adapter
const ai = chatFactory.ai(model, "/threads")
// Your assistant system instructions bound to assistant ID in chat config
const instructions: Readonly<Record<string, VertexAiSystemInstructions<any>>> = {
"yourAssistantId": {
instructions: instructions2,
tools: {
dispatcher: (dataSoFar, name, args) => dataSoFar,
definition: [
{functionDeclarations: [{name: "someFunction"}]}
]
}
}
}
const options: CallableOptions = {
secrets: [openAiApiKey],
region: region,
invoker: "public"
};The requests to front-facing functions return to user as fast as possible after changing the chat state in Firestore. As soon as the AI run could take a considerable time, we run theme in a Cloud Task "offline" from the client request. To execute the Assistant run we use the second class from the library - the VertexAiChatWorker. To create it, use the AiChat factory we created as described in the main documentation.
To register the Cloud Task handler you may want to create the following function:
import {onTaskDispatched} from "firebase-functions/v2/tasks";
import {firestore} from "firebase-admin";
import {getFunctions} from "firebase-admin/functions";
// Function region
const region = "europe-west1";
// Collection path to store threads
const VERTEXAI_THREADS = "treads";
export const calculator = onTaskDispatched(
{
retryConfig: {
maxAttempts: 1,
minBackoffSeconds: 30
},
rateLimits: {
maxConcurrentDispatches: 6
},
region: region
},
async (req) => {
// Create and run a worker
// See the `dispatchers` definitions below
const vertexAi = new VertexAI({
project: projectID.value(),
location: region
});
const model = vertexAi.getGenerativeModel(
{
model: "gemini-1.0-pro",
generationConfig: {
candidateCount: 1
}
},
{
timeout: 30 * 1000
}
);
const ai = chatFactory.ai(model, VERTEXAI_THREADS)
// Dispatch request
await chatFactory.worker(ai, instructions).dispatch(
req,
(chatDocumentPath: string, meta: Meta) => {
// Optional task completion handler
// Meta - some meta-data passed to chat operation
}
);
}
);The VertexAiChatWorker handles the VertexAiChatCommand and updates VertexAiChatState
with the results.
Full example is available in the sample Firebase project.