Files
planilla/agent/src/index.ts
josedario87 da5557c26a
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scafold para cognition flow
2025-06-05 18:06:38 -06:00

78 lines
2.6 KiB
TypeScript

import express from 'express';
import { genAI, getMcpTool } from './llm/gemini';
import { systemPrompt } from './systemPrompt'; // Import the repository info from a separate file
import { iniciarProcesoCognitivo } from './cognition/index'; // Import the MCP function to start cognitive processes
import type { Conversation, Msg, Participant } from './types'; // Import the Conversation type
import dotenv from 'dotenv';
dotenv.config();
const PORT = Number(process.env.PORT) || 8001;
const API_KEY = process.env.GEMINI_API_KEY || '';
console.log(`Using Gemini API key: ${API_KEY}`);
/**
* Descripción de alto nivel para que cualquier agente (humano o LLM) entienda y
* trabaje con el repositorio sin perder tiempo buscando contexto.
*/
const app = express();
app.use(express.json());
app.post('/', async (req, res) => {
const conversation = req.body?.conversation as Conversation | undefined;
if (!conversation) return res.status(400).json({ error: 'Missing conversation' });
const lastMsg = conversation.messages[conversation.messages.length - 1];
const message = lastMsg?.text || '';
const context = conversation.messages
.slice(-10)
.map((m) => {
const sender =
conversation.participants.find((p) => p.id === m.from)?.name || m.from;
const content = m.text || `[${m.type}]`;
return `${sender}: ${content}`;
})
.join('\n');
if (!genAI) {
return res.json({ reply: systemPrompt });
}
try {
const contents = `${systemPrompt}\nConversation:\n${context}\n`;
const result = await iniciarProcesoCognitivo({})
const reply = (result.text || '').trim();
res.json({ reply });
} catch (err: any) {
console.error('Gemini error', err.message);
res.status(500).json({ error: 'Failed to generate reply' });
}
});
app.get('/', (req, res) => {
res.send(`
<h1>Conversation Layer Agent</h1>
<p>This service answers questions about the repository.</p>
<p>Send a POST request to / with a JSON body containing {"conversation": {...}}</p>
<p>Example: {"conversation": {"chatId": "123@c.us", "title": "Chat", "isGroup": false, "unreadCount": 0, "participants": [{"id": "123@c.us", "name": "Alice", "isMe": false}], "messages": [{"id": "m1", "from": "123@c.us", "to": "me@c.us", "ts": 0, "type": "chat", "text": "hello", "meta": {"ack":0,"hasReaction":false,"isQuoted":false}}]}}</p>
<p>It will respond with a JSON object containing {"reply": "the answer"}</p>
<p>Repository info: ${systemPrompt}</p>
`);
}
);
app.listen(PORT, () => {
console.log(`conversation-layer-agent listening on ${PORT}`);
});