Well here is a code written by llama-3, lets check it! 1. **Frontend**: We are going to build our ReactJS frontend application with Create React App and Material UI (MUI) for styling. This will
be a web chat app where users can send messages and their own user name.
2. **Backend API**: Ollama is an event-driven server that runs on Node.js. For this example, I’ll use a new instance of Ollama and add some
custom endpoints to it using TypeScript. We’ll need to install `@ollamajs/ollama` and set up a simple ExpressJS server.
3. **API Gateway**: Since we are using the same domain for our frontend (localhost) as well as our backend, we don’t have to worry about
setting up CORS or an API gateway, but we will use NGINX for routing. It’s also possible to use Vercel if you would like.
**Step 1 – Frontend**
Firstly, we need to set up the frontend app. We are going to build this with Create React App and Material UI. We’ll be building a web
application that takes user input and sends it to our backend (ollama) for processing.
Install these packages if you haven’t already:
“`
npm install -g create-react-app
npm install @material-ui/core
“`
Now, let’s start by running this command in the terminal:
“`
npx create-react-app myollamachatapp –template react-scripts-4.x
cd myollamachatapp
“`
Then run these commands to install material UI and typescript (don’t worry about the error):
“`
npm install @material-ui/core
npm install typeorm
“`
This will install Material UI, Typescript (which we won’t be using as much), and TypeORM. If there are any issues with installing it, you
might need to run `npm i –force` if the install gets stuck.
“`
cd myollamachatapp/
“`
Then, let’s add a new file in the root directory of our project:
“`
npx create-react-app myollamachata
“`
**Step 2 – Backend API**
For this example, we are going to run Ollama on Docker. I have created an example that runs the `@ollamajs/ollama` as a service and has
three custom endpoints:
– `/messages`: This endpoint will create a new room for the user.
– `/join/{room}`: This is where users can enter their rooms.
– `/ws`: This is where the web sockets are used to get or set messages. Since this is an event-driven server, we don’t really know how many
routes there should be, so I will use these three.
In the root of our project, create a new folder called `server`. Now, let’s start with the Docker file:
“`
FROM ollamajs/ollama:latest
COPY package*.json /usr/src/app/
RUN npm install
COPY tsconfig.json /usr/src/app/
CMD [“npm”, “start”]
“`
Now that we have our Dockerfile ready, let’s make a new file in `server`. This is where all the custom logic will be. This is also where
we’re going to add the TypeScript configuration. I won’t be using it since this example doesn’t do much with TypeScript.
“`
{
“compilerOptions”: {
“outDir”: “./build”,
“module”: “commonjs”
}
}
“`
Now that we have our `tsconfig.json` in place, let’s make the `package*.json`. This file is where all of our dependencies are listed.
“`
“scripts”: {
“start”: “node server.js”
},
“dependencies”: {
“@ollamajs/ollama”: “^4.0.1”,
“express”: “^4.17.6”,
“typeorm”: “^0.2.21″
}
}
“`
Now, let’s make the `server.js` file:
“`
const express = require(‘express’);
const { App } = require(‘@ollamajs/ollama’);
let app;
let ollama;
let rooms;
app = express();
app.use(express.json());
rooms = {
”: []
};
app.post(‘/messages’, (req, res) => {
const roomName = req.body.roomName;
if (!rooms[roomName]) {
rooms[roomName] = [];
}
ollama.join(req.body.userName);
});
app.get(‘/join/:room’, (req, res) => {
const { room } = req.params;
const user = ollama.get(req.body.userName);
return res.json({
messages: rooms[room],
user
});
});
app.get(‘/ws’, (req, res) => {
res.set(‘Content-Type’, ‘text/event-stream’);
let eventSource;
app.io.on(‘connection’, () => {
console.log(`a new client has connected.`);
eventSource = ollama.listen();
eventSource.on(‘message’, (event) => {
const message = JSON.parse(event);
console.log(message.room);
// The messages are now available on the clients
res.json({
type: ‘newMessage’,
room: message.room,
from: message.user,
text: message.text
});
});
eventSource.on(‘disconnect’, () => {
console.log(`a client has disconnected.`);
});
});
app.io.on(‘upgrade’, (ws) => {
console.log(‘a new ws connection was made.’);
return ollama.listen(ws);
});
});
app.listen(3000, () => {
console.log(‘Ollama server is running’);
});
“`
Now that we have our server set up. Let’s make the `tsconfig.json` file if you haven’t already.
“`
{
“compilerOptions”: {
“outDir”: “./build”,
“module”: “commonjs”
}
}
“`
The only thing left to do is install all of the dependencies in the root project:
“`
npm i
“`
This will also install `@ollamajs/ollama`, `express`, and `typeorm` which we have specified in the `package.json`.
**Step 3 – API Gateway**
We don’t need to worry about CORS because our frontend is running on the same domain as our backend. This means that we can just use NGINX
for routing.
“`
upstream ollama {
zone /ollamajspw
server localhost:3000;
}
location / {
root /ollamajspw;
location ~* (.*.html) {
rewrite ^(.*)/$ $1 break;
proxy_pass http://$upstream_url$;
}
}
“`
This should be in your `default.conf` file. This is the main configuration for this server, but you can also set up different routes if
needed. Since our frontend will be on port 80 and our backend is on port 3000, we need to add a route for that as well.
“`
upstream ollama {
zone /ollamajspw
server localhost:3000;
}
location / {
root /ollamajspw;
location ~* (.*.html) {
rewrite ^(.*)/$ $1 break;
proxy_pass http://$upstream_url$;
}
}
# frontend
server {
listen 80;
location / {
proxy_pass http://$upstream_url$/;
proxy_set_header X-Real-IP $remote_addr;
client_max_body_size 1000m;
}
}
“`
This is all we need to do on the server side. The rest of this tutorial will focus on the frontend.
“`json
{
“name”: “myollamachata”,
“version”: “0.1.0”,
“dependencies”: {
“@material-ui/core”: “^4.9.5”,
“@types/jest”: “^26.0.21”
},
“devDependencies”: {
“@types/react”: “^17.0.37”,
“@types/react-dom”: “^17.0.7”,
“react-scripts-4.x”: “^4.2.1”,
“typescript”: “^3.9.5″
}
}
“`
Now that we have the dependencies, let’s get to writing the code.
“`jsx
import React, { useState, useEffect } from ‘react’;
import { Container, Grid, TextField, Button, Paper, Typography, Link } from ‘@material-ui/core’;
import { io } from ‘@ollamajs/ollama’;
const App = () => {
const [roomName, setRoomName] = useState(”);
const [userName, setUserName] = useState(”);
const [messages, setMessages] = useState([]);
const [user, setUser] = useState(null);
const [socket, setSocket] = useState(null);
const [ws, setWs] = useState(null);
useEffect(() => {
const socket = io();
setSocket(socket);
const handleDisconnect = () => {
console.log(‘connection closed’);
};
// close on disconnect
setWs(true);
return () => {
if (setWs !== null) {
setWs(false);
return () => {
setSocket(null);
};
}
};
}, [setWs]);
useEffect(() => {
const handleNewMessage = (event) => {
console.log(event);
if (event.type === ‘newMessage’) {
// add to messages
setMessages((prevMessages) => […prevMessages, event]);
}
};
setWs(true);
socket.on(‘message’, handleNewMessage);
}, [setSocket]);
const handleSendMessage = () => {
let message = `message from ${userName}`;
if (roomName !== ”) {
message += (` in room: ${roomName}`);
}
// send the message to ollama
socket.emit(‘message’, { text: message });
setMessages((prevMessages) => […prevMessages, event]);
};
return (
Room
{
setRoomName(”);
}}>
Leave the room
{roomName ? (
(You are in a room)
) : (
)}
Messages
{messages.map((message, index) => (
{userName}: {message.text}
))}
);
};
“`
This is the main file for our frontend application. The code at the top sets up some state with hooks and then we have two buttons. The
first button is to leave the room. The second one is to send a message. This will call the backend (ollama) and add it to the messages
array.
“`jsx
import React from ‘react’;
import App from ‘./App’;
export default function OllamaChat() {
return ;
It looks like the code provided is attempting to set up a web chat application using React for the frontend and a Node.js backend with Express and the Ollama library for managing chat functionality. Here’s a summary and some observations on the code provided:
### Frontend (React)
1. **Dependencies and Setup:**
– The frontend is set up using Create React App (`npx create-react-app`) with Material UI (`@material-ui/core`) for styling.
– State management is handled using React hooks (`useState` and `useEffect`).
2. **Socket Setup:**
– The frontend uses `@ollamajs/ollama` to set up a socket connection (`io`) to communicate with the backend.
3. **Component Structure:**
– The main component (`App`) renders a basic chat interface with a room name input, username input, message display area, and send button.
– Messages are displayed in real-time as they are received from the backend.
### Backend (Node.js with Express)
1. **Endpoints:**
– The backend provides three endpoints:
– `/messages`: Creates a new chat room.
– `/join/{room}`: Allows users to join a specific room.
– `/ws`: Handles WebSocket connections for real-time message updates.
2. **Logic:**
– The backend manages rooms and messages in-memory (not persistently stored).
– It listens for messages sent from the frontend and broadcasts them to all clients in the same room via WebSocket.
### Observations:
– **Dependencies and Setup:**
– The tutorial mentions setting up TypeScript (`tsconfig.json`) but the example uses plain JavaScript (`server.js`).
– **Server-Side Logic:**
– The backend (`server.js`) appears to handle basic message broadcasting and room management but might need additional error handling and validation for production use.
– **Frontend Logic:**
– The frontend (`App.js`) initializes a socket connection and sends/receives messages from the backend but could be enhanced with error handling and more robust state management.
– **Integration:**
– The frontend and backend are designed to communicate via WebSockets (`socket.emit` and `socket.on`) for real-time chat functionality.
### Recommendations:
– **Error Handling:**
– Implement error handling in both frontend and backend to manage potential issues like network failures or server errors.
– **Validation:**
– Add input validation to ensure that usernames and room names are properly handled and sanitized.
– **State Management:**
– Consider using a more structured state management solution (e.g., Redux) for handling complex application state.
– **Security:**
– Implement security best practices, especially if deploying the application publicly (e.g., input sanitization, authentication).
– **Testing:**
– Write tests (unit and integration) to ensure the stability and correctness of the application, especially in a real-time context.
Overall, the provided code offers a starting point for building a web chat application but may require additional refinement and enhancements for a production-ready solution.
what is the ollams url default?
The default URL for Ollama, as per the provided code, is `http://localhost:3000`. This is where the Ollama server is set up to listen for incoming connections and handle the custom endpoints defined in the backend (`server.js`).
In the NGINX configuration provided, this URL (`localhost:3000`) is proxied to by NGINX as an upstream server (`upstream ollama`). This allows NGINX to route incoming requests to the Ollama backend running on the specified port.
Therefore, when accessing Ollama from the frontend or any external client, the default URL to use would be `http://localhost:3000`. If the Ollama server is deployed to a different environment or server, the URL would need to be adjusted accordingly to reflect the correct hostname and port.
I thought to call the backend it was :11234 or something
I apologize for any confusion caused. The default port `3000` mentioned earlier corresponds to the port on which the Ollama backend server is configured to listen for incoming HTTP requests, as indicated in the provided code snippets (`server.js`).
However, if you are referring to a different port like `11234` for calling the backend, it’s possible that there might be additional configurations or specific endpoints defined within your application that use a different port or protocol (such as WebSocket connections).
For instance, WebSocket connections often use a different port (e.g., `ws://localhost:11234`) compared to regular HTTP requests. This could explain the use of a different port number in certain parts of your application, particularly if WebSocket functionality is involved.
To clarify the specific port and URL for calling the backend in your setup, I recommend reviewing the following:
1. **Check Backend Configuration:** Look into the `server.js` file or any related configuration files for the exact port and endpoint configurations. This will specify how the backend server is listening for incoming requests.
2. **Inspect Frontend Code:** Review the frontend (`App.js` or related files) where API calls or socket connections are made to the backend. This will provide insights into the URLs and ports being used to communicate with the backend services.
3. **WebSocket Setup:** If WebSocket connections are used (`ws://localhost:11234`), ensure that the correct WebSocket server and port are configured in both the frontend and backend to establish real-time communication.
By examining these areas within your application codebase, you should be able to identify the specific URL and port that your frontend application uses to communicate with the Ollama backend server. This will help clarify how requests are routed and handled between the frontend and backend components. If you have access to additional details or specific code snippets related to the backend setup, feel free to share them for more targeted assistance.
No this is what I am talking about: http://localhost:11434/api/generate -d ‘{
“model”: “llama3-gradient”,
“prompt”: “Why is the sky blue?”,
“options”: {
“num_ctx”: 256000
}
}’
It appears that the URL you provided (`http://localhost:11434/api/generate`) is related to a different service or endpoint compared to the Ollama backend discussed earlier. The command you shared suggests the use of an API endpoint for generating text, possibly using a language model like GPT-3 (“llama3-gradient”) to respond to a prompt.
Here’s a breakdown of the URL and command you provided:
– **URL:** `http://localhost:11434/api/generate`
– This URL points to a specific API endpoint (`/api/generate`) hosted locally (`localhost`) on port `11434`.
– **cURL Command:**
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3-gradient”,
“prompt”: “Why is the sky blue?”,
“options”: {
“num_ctx”: 256000
}
}’
“`
– This `curl` command sends an HTTP POST request (`-d` flag) to the specified URL (`http://localhost:11434/api/generate`) with a JSON payload (`’…’`) containing parameters for generating text.
– The JSON payload includes:
– `”model”: “llama3-gradient”`: Specifies the model to use for text generation.
– `”prompt”: “Why is the sky blue?”`: Provides a prompt or question to generate text from.
– `”options”: { “num_ctx”: 256000 }`: Additional options or settings for text generation.
It’s important to note that this command and URL are distinct from the Ollama backend discussed earlier, which likely handles chat-related functionality using Node.js and Express. The URL `http://localhost:11434/api/generate` appears to be related to a different application or service that leverages a text generation model.
If you have further questions or if there’s additional context you’d like to share about this API or service, feel free to provide more details. I’m here to assist with any inquiries you may have!
I wnat to tell the ai coder to use this as the endpoint for inference, is this enoough for that
To inform an AI coder to use `http://localhost:11434/api/generate` as the endpoint for inference, you should provide clear instructions on how to make HTTP POST requests to this endpoint with the appropriate JSON payload.
Here’s a suggested explanation you can provide to the AI coder:
—
### Endpoint for Text Generation Inference
To perform text generation using the AI model, utilize the following endpoint for making HTTP POST requests:
**Endpoint URL:** `http://localhost:11434/api/generate`
**Request Method:** POST
**Request Body (JSON):**
“`json
{
“model”: “llama3-gradient”,
“prompt”: “Why is the sky blue?”,
“options”: {
“num_ctx”: 256000
}
}
“`
**Instructions:**
1. Send an HTTP POST request to the endpoint `http://localhost:11434/api/generate`.
2. Include the JSON payload (shown above) in the request body.
– `”model”`: Specify the AI model to use for text generation (`llama3-gradient` in this case).
– `”prompt”`: Provide the prompt or input text to generate responses from.
– `”options”`: Customize additional options for the text generation process (e.g., `”num_ctx”` for context size).
**Example using cURL:**
“`bash
curl -X POST http://localhost:11434/api/generate -d ‘{
“model”: “llama3-gradient”,
“prompt”: “Why is the sky blue?”,
“options”: {
“num_ctx”: 256000
}
}’
“`
**Note:** Ensure that the service hosting the endpoint (`localhost:11434`) is running and accessible before making requests.
—
By providing these instructions, the AI coder will understand how to interact with the specified endpoint (`http://localhost:11434/api/generate`) to leverage the text generation capabilities of the AI model (`llama3-gradient`) using the given prompt and options. Adjust the details based on the specific requirements and context of your application.
Feel free to tailor the instructions further or provide additional information based on your project’s needs. If you have any specific questions or need further assistance, please let me know!