// This approach will create a chatbot using OpenAI's SDK with access to a blog's content stored in a JSON. // We'll use Hono for routing, OpenAI for the chatbot, and stream the responses to the client. // The blog content will be stored as JSON and used as a tool in the chatbot's chain. import { Hono } from "https://esm.sh/hono"; import { streamSSE } from "https://esm.sh/hono/streaming"; import { OpenAI } from "https://esm.town/v/std/openai"; const app = new Hono(); // Load blog content from JSON const blogPosts = [ { title: "First Post", content: "This is the content of the first blog post." }, { title: "Second Post", content: "Here's the second blog post with some interesting information." }, { title: "Third Post", content: "The third post discusses various topics in depth." } ]; // Function to search blog content function searchBlogContent(query: string) { return blogPosts.filter(post => post.title.toLowerCase().includes(query.toLowerCase()) || post.content.toLowerCase().includes(query.toLowerCase()) ); } app.get("/", (c) => { return c.html(` Blog Chatbot

Blog Chatbot

`); }); app.post("/chat", async (c) => { const { message } = await c.req.json(); const openai = new OpenAI(); return streamSSE(c, async (stream) => { const blogSearchResults = searchBlogContent(message); const completion = await openai.chat.completions.create({ model: "gpt-4-mini", messages: [ { role: "system", content: "You are a helpful assistant with knowledge about a blog. Use the blog content provided to answer questions." }, { role: "user", content: message }, { role: "assistant", content: "Here's some relevant blog content: " + JSON.stringify(blogSearchResults) } ], stream: true, }); for await (const chunk of completion) { const content = chunk.choices[0]?.delta?.content || ""; await stream.writeSSE({ data: content }); } }); }); export default async function server(req: Request): Promise { return app.fetch(req); }

BlogChatbotServer

Version 10