mirror of
https://github.com/openclaw/openclaw.git
synced 2026-03-15 03:50:40 +00:00
- Introduced a minimal documentation chatbot that builds a search index from markdown files and serves responses via an API. - Added scripts for building the index and serving the chat API. - Updated package.json with new commands for chat index building and serving. - Created a new Vercel configuration file for deployment. - Added a README for the docs chat prototype detailing usage and integration.
194 lines
5.2 KiB
JavaScript
194 lines
5.2 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Minimal docs-chat API.
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* Env: OPENAI_API_KEY, DOCS_CHAT_INDEX, PORT
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*/
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import fs from "node:fs";
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import path from "node:path";
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import { fileURLToPath } from "node:url";
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import http from "node:http";
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const __dirname = path.dirname(fileURLToPath(import.meta.url));
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const defaultIndex = path.join(__dirname, "search-index.json");
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const indexPath = process.env.DOCS_CHAT_INDEX || defaultIndex;
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const port = Number(process.env.PORT || 3001);
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let index = null;
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function loadIndex() {
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if (index) return index;
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if (!fs.existsSync(indexPath)) {
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console.error(
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`Missing index at ${indexPath}. Run: node scripts/docs-chat/build-index.mjs --out ${defaultIndex}`
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);
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process.exit(1);
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}
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index = JSON.parse(fs.readFileSync(indexPath, "utf8"));
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return index;
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}
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const corsHeaders = {
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
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"Access-Control-Allow-Headers": "Content-Type",
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};
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function sendJson(res, status, body) {
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res.writeHead(status, { ...corsHeaders, "Content-Type": "application/json" });
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res.end(JSON.stringify(body));
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}
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function scoreChunk(query, chunk) {
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const words = query.toLowerCase().split(/\s+/).filter(Boolean);
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const text = `${chunk.title} ${chunk.content}`.toLowerCase();
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let score = 0;
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for (const word of words) {
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if (word.length < 2) continue;
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if (text.includes(word)) score += 1;
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}
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return score;
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}
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function retrieve(query, limit = 8) {
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const { chunks } = loadIndex();
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const scored = chunks.map((chunk) => ({
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chunk,
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score: scoreChunk(query, chunk),
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}));
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scored.sort((a, b) => b.score - a.score);
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return scored
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.filter((item) => item.score > 0)
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.slice(0, limit)
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.map((item) => item.chunk);
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}
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async function streamOpenAI(systemPrompt, userMessage, onToken) {
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) throw new Error("OPENAI_API_KEY is required for /chat");
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const res = await fetch("https://api.openai.com/v1/chat/completions", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${apiKey}`,
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},
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body: JSON.stringify({
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model: "gpt-4o-mini",
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stream: true,
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messages: [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: userMessage },
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],
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}),
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});
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if (!res.ok || !res.body) {
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const errorText = await res.text();
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throw new Error(`OpenAI ${res.status}: ${errorText}`);
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}
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const decoder = new TextDecoder();
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let buffer = "";
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for await (const chunk of res.body) {
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buffer += decoder.decode(chunk, { stream: true });
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const lines = buffer.split("\n");
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buffer = lines.pop() ?? "";
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for (const line of lines) {
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const trimmed = line.trim();
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if (!trimmed.startsWith("data:")) continue;
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const data = trimmed.slice(5).trim();
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if (data === "[DONE]") return;
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try {
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const json = JSON.parse(data);
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const delta = json.choices?.[0]?.delta?.content;
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if (delta) onToken(delta);
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} catch {
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// Ignore malformed SSE lines
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}
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}
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}
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}
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async function handleChat(req, res) {
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let body = "";
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for await (const chunk of req) body += chunk;
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let message = "";
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try {
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message = JSON.parse(body || "{}").message;
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} catch {
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sendJson(res, 400, { error: "Invalid JSON" });
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return;
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}
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if (!message || typeof message !== "string") {
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sendJson(res, 400, { error: "message required" });
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return;
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}
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const chunks = retrieve(message);
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if (chunks.length === 0) {
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res.writeHead(200, {
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...corsHeaders,
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"Content-Type": "text/plain; charset=utf-8",
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});
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res.end(
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"I couldn't find relevant documentation excerpts for that question. Try rephrasing or search the docs."
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);
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return;
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}
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const context = chunks
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.map(
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(chunk) =>
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`[${chunk.title}](${chunk.url})\n${chunk.content.slice(0, 1200)}`
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)
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.join("\n\n---\n\n");
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const systemPrompt =
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"You are a helpful assistant for OpenClaw documentation. " +
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"Answer only from the provided documentation excerpts. " +
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"If the answer is not in the excerpts, say so and suggest checking the docs. " +
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"Cite sources by name or URL when relevant.\n\nDocumentation excerpts:\n" +
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context;
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res.writeHead(200, {
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...corsHeaders,
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"Content-Type": "text/plain; charset=utf-8",
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"Transfer-Encoding": "chunked",
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});
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try {
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await streamOpenAI(systemPrompt, message, (token) => {
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res.write(token);
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});
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res.end();
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} catch (err) {
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console.error(err);
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res.end("\n\n[Error contacting OpenAI]");
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}
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}
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const server = http.createServer(async (req, res) => {
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if (req.method === "OPTIONS") {
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res.writeHead(204, corsHeaders);
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res.end();
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return;
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}
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if (req.method === "GET" && (req.url === "/" || req.url === "/health")) {
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loadIndex();
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sendJson(res, 200, { ok: true, chunks: index.chunks.length });
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return;
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}
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if (req.method === "POST" && req.url === "/chat") {
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await handleChat(req, res);
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return;
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}
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sendJson(res, 404, { error: "Not found" });
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});
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server.listen(port, () => {
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loadIndex();
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console.error(
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`docs-chat API running at http://localhost:${port} (chunks: ${index.chunks.length})`
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);
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});
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