Общение с базой знаний

import { Agent, routeAgentRequest } from "agents";
import { withVoice, WorkersAIFluxSTT, WorkersAITTS } from "@cloudflare/voice";
import { generateText, tool, stepCountIs } from "ai";
import { createWorkersAI } from "workers-ai-provider";
import { z } from "zod";

const VoiceAgent = withVoice(Agent);

export class TalkToDocs extends VoiceAgent {
	// Workers AI powers speech-to-text and text-to-speech (no API keys needed).
	transcriber = new WorkersAIFluxSTT(this.env.AI);
	tts = new WorkersAITTS(this.env.AI);

	// Called each time the user finishes speaking. The agent's model generates
	// the reply, calling AI Search as a retrieval tool when it needs facts from
	// your knowledge base.
	async onTurn(transcript, context) {
		const workersai = createWorkersAI({ binding: this.env.AI });

		const result = await generateText({
			// Use a Workers AI model that supports function calling.
			model: workersai("@cf/zai-org/glm-5.2"),
			system:
				"You are a helpful voice assistant that answers from a Cloudflare AI Search knowledge base. " +
				"For questions about the product, call the searchKnowledgeBase tool first and answer using the results. " +
				"Skip the tool for greetings and small talk. Keep replies short and conversational.",
			messages: [
				...context.messages.map((message) => ({
					role: message.role,
					content: message.content,
				})),
				{ role: "user", content: transcript },
			],
			tools: {
				searchKnowledgeBase: tool({
					description:
						"Search the knowledge base for information to answer the user's question.",
					inputSchema: z.object({
						query: z.string().describe("A focused search query"),
					}),
					execute: async ({ query }) => {
						// search() runs retrieval only and returns the matching chunks.
						const res = await this.env.AI_SEARCH.search({
							query,
							ai_search_options: { retrieval: { max_num_results: 5 } },
						});
						return res.chunks.map((chunk) => chunk.text).join("\n\n");
					},
				}),
			},
			// Let the model call the tool, then answer from the results.
			stopWhen: stepCountIs(4),
		});

		// Return the generated answer for the pipeline to speak.
		return result.text;
	}
}

export default {
	async fetch(request, env) {
		return (
			(await routeAgentRequest(request, env)) ??
			new Response("Not found", { status: 404 })
		);
	},
};

import { Agent, routeAgentRequest } from "agents";
import {
	withVoice,
	WorkersAIFluxSTT,
	WorkersAITTS,
	type VoiceTurnContext,
} from "@cloudflare/voice";
import { generateText, tool, stepCountIs } from "ai";
import { createWorkersAI } from "workers-ai-provider";
import { z } from "zod";

const VoiceAgent = withVoice(Agent);

export class TalkToDocs extends VoiceAgent<Env> {
	// Workers AI powers speech-to-text and text-to-speech (no API keys needed).
	transcriber = new WorkersAIFluxSTT(this.env.AI);
	tts = new WorkersAITTS(this.env.AI);

	// Called each time the user finishes speaking. The agent's model generates
	// the reply, calling AI Search as a retrieval tool when it needs facts from
	// your knowledge base.
	async onTurn(transcript: string, context: VoiceTurnContext) {
		const workersai = createWorkersAI({ binding: this.env.AI });

		const result = await generateText({
			// Use a Workers AI model that supports function calling.
			model: workersai("@cf/zai-org/glm-5.2"),
			system:
				"You are a helpful voice assistant that answers from a Cloudflare AI Search knowledge base. " +
				"For questions about the product, call the searchKnowledgeBase tool first and answer using the results. " +
				"Skip the tool for greetings and small talk. Keep replies short and conversational.",
			messages: [
				...context.messages.map((message) => ({
					role: message.role as "user" | "assistant",
					content: message.content,
				})),
				{ role: "user" as const, content: transcript },
			],
			tools: {
				searchKnowledgeBase: tool({
					description:
						"Search the knowledge base for information to answer the user's question.",
					inputSchema: z.object({
						query: z.string().describe("A focused search query"),
					}),
					execute: async ({ query }) => {
						// search() runs retrieval only and returns the matching chunks.
						const res = await this.env.AI_SEARCH.search({
							query,
							ai_search_options: { retrieval: { max_num_results: 5 } },
						});
						return res.chunks.map((chunk) => chunk.text).join("\n\n");
					},
				}),
			},
			// Let the model call the tool, then answer from the results.
			stopWhen: stepCountIs(4),
		});

		// Return the generated answer for the pipeline to speak.
		return result.text;
	}
}

export default {
	async fetch(request: Request, env: Env) {
		return (
			(await routeAgentRequest(request, env)) ??
			new Response("Not found", { status: 404 })
		);
	},
} satisfies ExportedHandler<Env>;