応答に出典の引用を表示する

export default {
	async fetch(request, env) {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns an answer and the source chunks used as context.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		// Convert source chunks into citations your UI can display.
		const citations = response.chunks.map((chunk, index) => ({
			index: index + 1,
			source: chunk.item.key,
			score: chunk.score,
			snippet: chunk.text.slice(0, 200),
			metadata: chunk.item.metadata,
		}));

		return Response.json({ answer, citations });
	},
};

export interface Env {
	AI_SEARCH: AiSearchNamespace;
}

export default {
	async fetch(request, env): Promise<Response> {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns an answer and the source chunks used as context.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		// Convert source chunks into citations your UI can display.
		const citations = response.chunks.map((chunk, index) => ({
			index: index + 1,
			source: chunk.item.key,
			score: chunk.score,
			snippet: chunk.text.slice(0, 200),
			metadata: chunk.item.metadata,
		}));

		return Response.json({ answer, citations });
	},
} satisfies ExportedHandler<Env>;

export default {
	async fetch(request, env) {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns an answer and the source chunks used as context.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		// Group chunks by source document so each source appears once.
		const sourceMap = new Map();

		for (const chunk of response.chunks) {
			// item.key is the source file path or URL.
			const key = chunk.item.key;
			const existing = sourceMap.get(key);

			if (existing) {
				// Keep the highest relevance score for each source.
				existing.score = Math.max(existing.score, chunk.score);
				existing.snippets.push(chunk.text.slice(0, 200));
			} else {
				sourceMap.set(key, {
					score: chunk.score,
					snippets: [chunk.text.slice(0, 200)],
					metadata: chunk.item.metadata,
				});
			}
		}

		const citations = [...sourceMap.entries()].map(
			([source, { score, snippets, metadata }], i) => ({
				index: i + 1,
				source,
				score,
				snippets,
				metadata,
			}),
		);

		return Response.json({ answer, citations });
	},
};

export interface Env {
	AI_SEARCH: AiSearchNamespace;
}

export default {
	async fetch(request, env): Promise<Response> {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns an answer and the source chunks used as context.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		// Group chunks by source document so each source appears once.
		const sourceMap = new Map<
			string,
			{ score: number; snippets: string[]; metadata?: Record<string, unknown> }
		>();

		for (const chunk of response.chunks) {
			// item.key is the source file path or URL.
			const key = chunk.item.key;
			const existing = sourceMap.get(key);

			if (existing) {
				// Keep the highest relevance score for each source.
				existing.score = Math.max(existing.score, chunk.score);
				existing.snippets.push(chunk.text.slice(0, 200));
			} else {
				sourceMap.set(key, {
					score: chunk.score,
					snippets: [chunk.text.slice(0, 200)],
					metadata: chunk.item.metadata,
				});
			}
		}

		const citations = [...sourceMap.entries()].map(
			([source, { score, snippets, metadata }], i) => ({
				index: i + 1,
				source,
				score,
				snippets,
				metadata,
			}),
		);

		return Response.json({ answer, citations });
	},
} satisfies ExportedHandler<Env>;

export default {
	async fetch(request, env) {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// Stream answer tokens, but extract source chunks first.
		const stream = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
			stream: true,
		});

		// Transform the stream: extract the chunks event and forward the rest
		const { readable, writable } = new TransformStream();
		const writer = writable.getWriter();
		const encoder = new TextEncoder();
		const decoder = new TextDecoder();
		const reader = stream.getReader();

		// Track the current SSE event type to identify source chunks.
		let currentEvent = "";

		const pump = async () => {
			try {
				let buffer = "";

				while (true) {
					const { done, value } = await reader.read();
					if (done) break;

					buffer += decoder.decode(value, { stream: true });
					const lines = buffer.split("\n");
					buffer = lines.pop() ?? "";

					for (const line of lines) {
						// The chunks event arrives before the streamed answer.
						if (line.startsWith("event: ")) {
							currentEvent = line.slice(7).trim();
							continue;
						}

						// Transform the chunks data line into a citations event for your UI.
						if (currentEvent === "chunks" && line.startsWith("data: ")) {
							const chunks = JSON.parse(line.slice(6));
							const citations = chunks.map((chunk) => ({
								source: chunk.item.key,
								score: chunk.score,
							}));
							await writer.write(
								encoder.encode(
									`event: citations\ndata: ${JSON.stringify(citations)}\n\n`,
								),
							);
							currentEvent = "";
							continue;
						}

						// Forward answer tokens and other SSE data unchanged.
						currentEvent = "";
						await writer.write(encoder.encode(line + "\n"));
					}
				}
			} finally {
				reader.releaseLock();
				await writer.close();
			}
		};

		pump().catch(() => writer.close());

		return new Response(readable, {
			headers: {
				"content-type": "text/event-stream",
				"cache-control": "no-cache",
			},
		});
	},
};

export interface Env {
	AI_SEARCH: AiSearchNamespace;
}

export default {
	async fetch(request, env): Promise<Response> {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// Stream answer tokens, but extract source chunks first.
		const stream = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
			stream: true,
		});

		// Transform the stream: extract the chunks event and forward the rest
		const { readable, writable } = new TransformStream();
		const writer = writable.getWriter();
		const encoder = new TextEncoder();
		const decoder = new TextDecoder();
		const reader = stream.getReader();

		// Track the current SSE event type to identify source chunks.
		let currentEvent = "";

		const pump = async () => {
			try {
				let buffer = "";

				while (true) {
					const { done, value } = await reader.read();
					if (done) break;

					buffer += decoder.decode(value, { stream: true });
					const lines = buffer.split("\n");
					buffer = lines.pop() ?? "";

					for (const line of lines) {
						// The chunks event arrives before the streamed answer.
						if (line.startsWith("event: ")) {
							currentEvent = line.slice(7).trim();
							continue;
						}

						// Transform the chunks data line into a citations event for your UI.
						if (currentEvent === "chunks" && line.startsWith("data: ")) {
							const chunks = JSON.parse(line.slice(6));
							const citations = chunks.map(
								(chunk: { item: { key: string }; score: number }) => ({
									source: chunk.item.key,
									score: chunk.score,
								}),
							);
							await writer.write(
								encoder.encode(
									`event: citations\ndata: ${JSON.stringify(citations)}\n\n`,
								),
							);
							currentEvent = "";
							continue;
						}

						// Forward answer tokens and other SSE data unchanged.
						currentEvent = "";
						await writer.write(encoder.encode(line + "\n"));
					}
				}
			} finally {
				reader.releaseLock();
				await writer.close();
			}
		};

		pump().catch(() => writer.close());

		return new Response(readable, {
			headers: {
				"content-type": "text/event-stream",
				"cache-control": "no-cache",
			},
		});
	},
} satisfies ExportedHandler<Env>;

export default {
	async fetch(request, env) {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns scoring details with each source chunk.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		const citations = response.chunks
			// Filter out lower-scoring chunks for stronger citations.
			.filter((chunk) => chunk.score > 0.5)
			// Expose scoring details if your UI shows confidence indicators.
			.map((chunk, index) => ({
				index: index + 1,
				source: chunk.item.key,
				score: chunk.score,
				vectorScore: chunk.scoring_details?.vector_score,
				keywordScore: chunk.scoring_details?.keyword_score,
				rerankingScore: chunk.scoring_details?.reranking_score,
				confidence: chunk.score > 0.8 ? "high" : "medium",
				snippet: chunk.text.slice(0, 200),
			}));

		return Response.json({ answer, citations });
	},
};

export interface Env {
	AI_SEARCH: AiSearchNamespace;
}

export default {
	async fetch(request, env): Promise<Response> {
		const url = new URL(request.url);
		const query = url.searchParams.get("query") ?? "What is Cloudflare?";

		// AI Search returns scoring details with each source chunk.
		const response = await env.AI_SEARCH.get("my-instance").chatCompletions({
			messages: [{ role: "user", content: query }],
		});

		// Show this model response to the user.
		const answer = response.choices[0]?.message?.content ?? "";

		const citations = response.chunks
			// Filter out lower-scoring chunks for stronger citations.
			.filter((chunk) => chunk.score > 0.5)
			// Expose scoring details if your UI shows confidence indicators.
			.map((chunk, index) => ({
				index: index + 1,
				source: chunk.item.key,
				score: chunk.score,
				vectorScore: chunk.scoring_details?.vector_score,
				keywordScore: chunk.scoring_details?.keyword_score,
				rerankingScore: chunk.scoring_details?.reranking_score,
				confidence: chunk.score > 0.8 ? "high" : "medium",
				snippet: chunk.text.slice(0, 200),
			}));

		return Response.json({ answer, citations });
	},
} satisfies ExportedHandler<Env>;