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Zhongwei Li
351
templates/rag-chat.ts
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351
templates/rag-chat.ts
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/**
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* RAG (Retrieval Augmented Generation) Chatbot
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* with Cloudflare Vectorize + Workers AI
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*
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* Use case: Q&A chatbot that retrieves relevant context before generating answers
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*
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* Features:
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* - Semantic search over knowledge base
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* - Context-aware LLM responses
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* - Source citations
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* - Conversation history support
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* - Streaming responses (optional)
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*/
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export interface Env {
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VECTORIZE_INDEX: VectorizeIndex;
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AI: Ai;
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}
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interface ChatRequest {
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question: string;
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conversationHistory?: Array<{ role: string; content: string }>;
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topK?: number;
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filter?: Record<string, any>;
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namespace?: string;
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}
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interface ChatResponse {
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answer: string;
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sources: Array<{
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id: string;
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title: string;
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score: number;
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excerpt: string;
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}>;
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context: string;
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}
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export default {
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async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
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// Handle CORS
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if (request.method === 'OPTIONS') {
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return new Response(null, {
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headers: {
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'Access-Control-Allow-Origin': '*',
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'Access-Control-Allow-Methods': 'GET, POST, OPTIONS',
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'Access-Control-Allow-Headers': 'Content-Type',
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},
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});
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}
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const url = new URL(request.url);
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// Route: POST /chat - RAG chatbot endpoint
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if (url.pathname === '/chat' && request.method === 'POST') {
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try {
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const body = await request.json() as ChatRequest;
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const {
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question,
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conversationHistory = [],
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topK = 3,
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filter,
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namespace,
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} = body;
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if (!question) {
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return Response.json(
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{ error: 'Missing required field: question' },
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{ status: 400 }
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);
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}
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// Step 1: Generate embedding for user question
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const questionEmbedding = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
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text: question,
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});
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// Step 2: Search vector database for relevant context
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const searchResults = await env.VECTORIZE_INDEX.query(
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questionEmbedding.data[0],
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{
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topK,
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filter,
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namespace,
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returnMetadata: 'all',
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returnValues: false,
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}
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);
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// Step 3: Build context from retrieved documents
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const contextParts: string[] = [];
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const sources: ChatResponse['sources'] = [];
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for (const match of searchResults.matches) {
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const metadata = match.metadata || {};
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const title = metadata.title || metadata.id || match.id;
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const content = metadata.content || '';
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// Truncate content for context (max ~500 chars per source)
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const excerpt =
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content.length > 500 ? content.slice(0, 497) + '...' : content;
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contextParts.push(`[${title}]\n${content}`);
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sources.push({
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id: match.id,
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title,
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score: match.score,
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excerpt,
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});
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}
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const context = contextParts.join('\n\n---\n\n');
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// Step 4: Build conversation with context
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const messages = [
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{
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role: 'system',
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content: `You are a helpful AI assistant. Answer questions based on the following context. If the context doesn't contain enough information to answer the question, say so honestly.
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Context:
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${context}`,
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},
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...conversationHistory,
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{
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role: 'user',
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content: question,
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},
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];
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// Step 5: Generate answer with LLM
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const aiResponse = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
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messages,
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});
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const answer = aiResponse.response || 'Sorry, I could not generate a response.';
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// Return response with sources
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return Response.json({
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answer,
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sources,
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context: context.slice(0, 1000), // Include truncated context for debugging
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} as ChatResponse, {
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headers: { 'Access-Control-Allow-Origin': '*' },
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});
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} catch (error) {
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console.error('Chat error:', error);
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return Response.json(
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{
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error: 'Chat failed',
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message: error instanceof Error ? error.message : 'Unknown error',
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},
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{ status: 500 }
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);
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}
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}
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// Route: POST /chat/stream - Streaming RAG responses
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if (url.pathname === '/chat/stream' && request.method === 'POST') {
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try {
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const body = await request.json() as ChatRequest;
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const { question, topK = 3, filter, namespace } = body;
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if (!question) {
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return Response.json(
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{ error: 'Missing required field: question' },
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{ status: 400 }
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);
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}
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// Retrieve context (same as above)
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const questionEmbedding = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
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text: question,
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});
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const searchResults = await env.VECTORIZE_INDEX.query(
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questionEmbedding.data[0],
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{ topK, filter, namespace, returnMetadata: 'all', returnValues: false }
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);
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const contextParts = searchResults.matches.map(
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(m) => `[${m.metadata?.title || m.id}]\n${m.metadata?.content || ''}`
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);
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const context = contextParts.join('\n\n---\n\n');
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// Stream LLM response
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const stream = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
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messages: [
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{
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role: 'system',
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content: `Answer based on context:\n\n${context}`,
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},
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{ role: 'user', content: question },
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],
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stream: true,
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});
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return new Response(stream, {
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headers: {
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'Content-Type': 'text/event-stream',
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'Access-Control-Allow-Origin': '*',
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},
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});
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} catch (error) {
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console.error('Stream error:', error);
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return Response.json(
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{
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error: 'Streaming failed',
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message: error instanceof Error ? error.message : 'Unknown error',
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},
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{ status: 500 }
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);
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}
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}
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// Route: POST /ingest - Add knowledge base content
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if (url.pathname === '/ingest' && request.method === 'POST') {
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try {
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const body = await request.json() as {
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documents: Array<{
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id: string;
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title: string;
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content: string;
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metadata?: Record<string, any>;
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}>;
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namespace?: string;
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};
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if (!body.documents || !Array.isArray(body.documents)) {
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return Response.json(
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{ error: 'Missing or invalid field: documents (array)' },
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{ status: 400 }
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);
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}
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// Generate embeddings for all documents
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const texts = body.documents.map((doc) => doc.content);
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const embeddings = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
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text: texts,
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});
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// Prepare vectors for upsert
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const vectors = body.documents.map((doc, i) => ({
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id: doc.id,
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values: embeddings.data[i],
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namespace: body.namespace,
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metadata: {
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title: doc.title,
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content: doc.content,
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...doc.metadata,
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indexed_at: Date.now(),
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},
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}));
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// Batch upsert
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await env.VECTORIZE_INDEX.upsert(vectors);
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return Response.json({
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success: true,
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count: vectors.length,
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message: `Successfully indexed ${vectors.length} documents`,
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}, {
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headers: { 'Access-Control-Allow-Origin': '*' },
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});
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} catch (error) {
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console.error('Ingest error:', error);
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return Response.json(
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{
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error: 'Ingestion failed',
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message: error instanceof Error ? error.message : 'Unknown error',
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},
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{ status: 500 }
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);
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}
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}
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// Default: API documentation
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return Response.json({
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name: 'RAG Chatbot API',
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endpoints: {
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'POST /chat': {
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description: 'Ask questions with context retrieval',
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body: {
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question: 'string (required)',
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conversationHistory: 'array (optional)',
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topK: 'number (optional, default: 3)',
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filter: 'object (optional)',
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namespace: 'string (optional)',
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},
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example: {
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question: 'How do I deploy a Cloudflare Worker?',
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topK: 3,
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filter: { category: 'documentation' },
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},
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},
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'POST /chat/stream': {
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description: 'Streaming responses',
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body: 'Same as /chat',
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},
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'POST /ingest': {
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description: 'Add documents to knowledge base',
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body: {
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documents: [
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{
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id: 'doc-1',
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title: 'Document Title',
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content: 'Document content...',
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metadata: { category: 'docs' },
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},
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],
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namespace: 'string (optional)',
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},
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},
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},
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});
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},
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};
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/**
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* Example Usage:
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*
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* 1. Ingest knowledge base:
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*
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* curl -X POST https://your-worker.workers.dev/ingest \
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* -H "Content-Type: application/json" \
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* -d '{
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* "documents": [
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* {
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* "id": "workers-intro",
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* "title": "Introduction to Workers",
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* "content": "Cloudflare Workers allow you to deploy serverless code globally...",
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* "metadata": { "category": "docs", "section": "workers" }
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* }
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* ]
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* }'
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*
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* 2. Ask a question:
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*
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* curl -X POST https://your-worker.workers.dev/chat \
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* -H "Content-Type: application/json" \
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* -d '{
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* "question": "How do I deploy serverless code?",
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* "topK": 3,
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* "filter": { "category": "docs" }
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* }'
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*
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* 3. Streaming response:
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*
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* curl -X POST https://your-worker.workers.dev/chat/stream \
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* -H "Content-Type: application/json" \
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* -d '{ "question": "What is a Worker?" }'
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*/
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