Firebase Genkit

import { genkit } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,  // Set default model
});

// Simple generation
const { text } = await ai.generate('Why is AI awesome?');
console.log(text);

// Streamed generation 
const { stream } = await ai.generateStream('Tell me a story');
for await (const chunk of stream) {
  console.log(chunk.text);
}

import { genkit, z } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

const { output } = await ai.generate({
  prompt: 'Create a brief profile for a character in a fantasy video game.',
  // Specify output structure using Zod schema
  output: {
    format: 'json',  
    schema: z.object({
      name: z.string(),
      role: z.enum(['knight', 'mage', 'archer']),
      backstory: z.string(),
    }),
  },
});

console.log(output);

import { genkit, z } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

// Define tool to get current weather for a given location
const getWeather = ai.defineTool(
  {
    name: "getWeather",
    description: "Gets the current weather in a given location",
    inputSchema: z.object({ 
      location: z.string().describe('The location to get the current weather for')
    }),
    outputSchema: z.string(),
  },
  async (input) => {
    // Here, we would typically make an API call or database query. For this
    // example, we just return a fixed value.
    return `The current weather in ${input.location} is 63°F and sunny.`;
  }
);

const { text } = await ai.generate({
    tools: [getWeather], // Give the model a list of tools it can call
    prompt: 'What is the weather like in New York? ',
});

console.log(text);

import { genkit, z } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

const chat = ai.chat({ system: 'Talk like a pirate' });

let response = await chat.send('Hi, my name is Pavel');

response = await chat.send('What is my name?');
console.log(response.text);
// Ahoy there! Your name is Pavel, you scurvy dog

import { genkit, z } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

// Define prompts that represent specialist agents
const reservationAgent = ai.definePrompt(
  {
    name: 'reservationAgent',
    description: 'Reservation Agent can help manage guest reservations',
    tools: [reservationTool, reservationCancelationTool, reservationListTool],

  },
  `{{role "system"}} Help guests make and manage reservations`
);

const menuInfoAgent = ...
const complaintAgent = ...

// Define a triage agent that routes to the proper specialist agent
const triageAgent = ai.definePrompt(
  {
    name: 'triageAgent',
    description: 'Triage Agent',
    tools: [reservationAgent, menuInfoAgent, complaintAgent],
  },
  `{{role "system"}} You are an AI customer service agent for Pavel's Cafe.
  Greet the user and ask them how you can help. If appropriate, transfer to an
  agent that can better handle the request. If you cannot help the customer with
  the available tools, politely explain so.`
);

// Create a chat to enable multi-turn agent interactions
const chat = ai.chat(triageAgent);

chat.send('I want a reservation at Pavel\'s Cafe for noon on Tuesday.' );

import { genkit } from 'genkit';
import { googleAI, gemini15Flash, textEmbedding004 } from '@genkit-ai/googleai';
import { devLocalRetrieverRef } from '@genkit-ai/dev-local-vectorstore';

const ai = genkit({ 
  plugins: [
    googleAI()
    devLocalVectorstore([
      {
        indexName: 'BobFacts',
        embedder: textEmbedding004,
      },
    ]),
  ],
  model: gemini15Flash,
});

// Reference to a local vector database storing Genkit documentation
const retriever = devLocalRetrieverRef('BobFacts');

// Consistent API to retrieve most relevant documents based on semantic similarity to query
const docs = await ai.retrieve(
  retriever: retriever,
  query: 'How old is bob?',
);

const result = await ai.generate({
    prompt: `Use the provided context from the Genkit documentation to answer this query: ${query}`,
    docs // Pass retrieved documents to the model
});

---
model: vertexai/gemini-1.5-flash
config:
  temperature: 0.9
input:
  schema:
    properties:
      location: {type: string}
      style: {type: string}
      name: {type: string}
    required: [location]
  default:
    location: a restaurant
---

You are the most welcoming AI assistant and are currently working at {{location}}.

Greet a guest{{#if name}} named {{name}}{{/if}}{{#if style}} in the style of {{style}}{{/if}}.