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File size: 2,123 Bytes
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import express from 'express';
import cors from 'cors';
import axios from 'axios';
import dotenv from 'dotenv';
import { fileURLToPath } from 'url';
import { dirname } from 'path';
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
dotenv.config();
const app = express();
// Enhanced CORS configuration
app.use(cors({
origin: '*',
methods: ['GET', 'POST'],
allowedHeaders: ['Content-Type']
}));
app.use(express.json());
const OLLAMA_URL = process.env.OLLAMA_URL || 'http://localhost:7860';
// Model-specific configurations
const MODEL_CONFIGS = {
'mxbai-embed-large': { temperature: 0.8, top_p: 0.9 },
'minicpm-v': { temperature: 0.8, top_p: 0.9 },
'qwen2.5-coder': { temperature: 0.6, top_p: 0.95 },
'codegemma': { temperature: 0.6, top_p: 0.95 },
'codellama': { temperature: 0.7, top_p: 0.95 },
'llama3.2-vision': { temperature: 0.8, top_p: 0.9 }
};
// Add these right after your app.use() statements
app.get('/health', (req, res) => {
res.status(200).json({
status: 'healthy',
ollama: 'running',
model: 'minicpm-v'
});
});
// Add a root endpoint
app.get('/', (req, res) => {
res.status(200).json({
message: 'Aide AI Backend Service',
version: '1.0'
});
});
// Update the chat endpoin`
app.post('/api/chat', async (req, res) => {
const { message } = req.body;
try {
const response = await axios.post(`${OLLAMA_URL}/api/generate`, {
model: 'minicpm-v',
prompt: message,
stream: false,
options: {
num_ctx: 2048,
num_thread: 4
}
});
res.json({ response: response.data.response });
} catch (error) {
console.error('Error:', error);
res.status(500).json({ error: 'Model processing error' });
}
});
app.get('/api/models', async (req, res) => {
try {
const response = await axios.get(`${OLLAMA_URL}/api/tags`);
res.json(response.data);
} catch (error) {
res.status(500).json({ error: 'Failed to fetch models' });
}
});
const PORT = process.env.PORT || 7860;
app.listen(PORT, '0.0.0.0', () => console.log(`Server running on port ${PORT}`)); |