veloxai 1.0.0

Fast and simple AI library for text and image generation

VeloxAI

⚡ Fast and Simple AI Library - Text generation (Vtext) and Image generation (Vimg)

Installation

pip install veloxai

Quick Start

from veloxai import Vtext, Vimg

# Text Generation
vtext = Vtext(token="your-token")
response = vtext.chat("Hello, who are you?")
print(response['response'])

# Image Generation
vimg = Vimg(token="your-token")
result = vimg.create("A sunset over mountains")
print(result['images'])

---

Features

Vtext (Text Generation)

• ✅ Chat completions
• ✅ Streaming responses (like OpenAI)
• ✅ File attachments (images, PDFs)
• ✅ Multiple models
• ✅ Fine-tuning/training
• ✅ System prompts

Vimg (Image Generation)

• ✅ Text-to-image
• ✅ Image-to-image
• ✅ Auto wait for completion
• ✅ Manual polling

---

Vtext Usage

Basic Chat

from veloxai import Vtext

vtext = Vtext(token="your-token")

# Simple chat
response = vtext.chat("What is Python?")
if response['success']:
    print(response['response'])

Streaming (OpenAI-style)

# Stream the response word by word
for chunk in vtext.chat("Write a long story", stream=True):
    print(chunk, end='', flush=True)

Chat with Files

# Attach an image
response = vtext.chat(
    message="What's in this image?",
    file="photo.jpg"
)

# Attach a PDF
response = vtext.chat(
    message="Summarize this document",
    file="document.pdf"
)

Use Different Models

# List available models
models = vtext.models()
print(models['models'])

# Use specific model
response = vtext.chat(
    message="Write a poem",
    model="gpt-4"
)

System Prompts

response = vtext.chat(
    message="Tell me about yourself",
    system="You are a helpful Python tutor"
)

OpenAI-Compatible Completion

response = vtext.completion(
    prompt="Once upon a time",
    max_tokens=100,
    temperature=0.7,
    stream=False
)

Fine-Tuning (Personal Training)

# Train the AI
vtext.train(
    identity="Expert Python programmer",
    role="Help users write better code",
    extra="Always provide examples"
)

# Now chats use your training
response = vtext.chat("How do I use decorators?")

# Check current training
training = vtext.get_training()
print(training['training'])

# Clear training
vtext.clear_training()

---

Vimg Usage

Basic Image Generation

from veloxai import Vimg

vimg = Vimg(token="your-token")

# Generate and wait for completion
result = vimg.create("A beautiful sunset over mountains")

if result['success']:
    for url in result['images']:
        print(f"Image: {url}")

Manual Control

# Start generation (don't wait)
result = vimg.generate("A futuristic city")
record_id = result['record_id']

# Check status manually
status = vimg.status(record_id)
print(status['status'])  # PENDING, PROCESSING, DONE, or FAILED

# Wait for completion
final = vimg.wait(record_id, timeout=180)
print(final['images'])

Image-to-Image

# Generate based on existing image
result = vimg.create(
    prompt="Make it look like a watercolor painting",
    init_image="photo.jpg"
)

Quick Generation (No Wait)

# Start generation and return immediately
result = vimg.create(
    prompt="A dragon",
    wait=False
)

# Returns record_id for later checking
record_id = result['record_id']

---

Complete Examples

Example 1: Simple Chatbot

from veloxai import Vtext

vtext = Vtext(token="your-token")

while True:
    user_input = input("You: ")
    if user_input.lower() == 'quit':
        break
    
    response = vtext.chat(user_input)
    if response['success']:
        print(f"AI: {response['response']}\n")

Example 2: Streaming Chat

from veloxai import Vtext

vtext = Vtext(token="your-token")

print("AI: ", end='')
for chunk in vtext.chat("Tell me a story", stream=True):
    print(chunk, end='', flush=True)
print()

Example 3: Image Generation with Progress

from veloxai import Vimg
import time

vimg = Vimg(token="your-token")

# Start generation
result = vimg.generate("A beautiful landscape")
if not result['success']:
    print(f"Error: {result['error']}")
    exit()

record_id = result['record_id']
print(f"Generation started: {record_id}")

# Poll for completion
while True:
    status = vimg.status(record_id)
    
    if status['status'] == 'DONE':
        print("\nComplete!")
        for i, url in enumerate(status['images'], 1):
            print(f"Image {i}: {url}")
        break
    elif status['status'] == 'FAILED':
        print("\nFailed!")
        break
    
    print(".", end='', flush=True)
    time.sleep(3)

Example 4: Multi-Modal Chat

from veloxai import Vtext

vtext = Vtext(token="your-token")

# Analyze an image
response = vtext.chat(
    message="What objects are in this image?",
    file="room.jpg"
)
print(response['response'])

# Follow-up question
response = vtext.chat("What colors are dominant?")
print(response['response'])

---

API Reference

Vtext

__init__(token, base_url=None)

Initialize Vtext client.

chat(message, model="perplexity-ai", system=None, file=None, stream=False)

Send a chat message.

• Returns: dict or Iterator[str] if streaming

completion(prompt, model, max_tokens, temperature, stream)

OpenAI-style completion.

models()

Get available models.

train(identity, role, extra="")

Set fine-tuning.

get_training()

Get current training.

clear_training()

Clear training.

---

Vimg

__init__(token, base_url=None)

Initialize Vimg client.

generate(prompt, init_image=None)

Start image generation.

• Returns: {"success": bool, "record_id": str}

status(record_id)

Check generation status.

• Returns: {"status": str, "images": [str]}

wait(record_id, timeout=180, interval=3)

Wait for completion.

• Returns: Final status with images

create(prompt, init_image=None, wait=True)

Generate and optionally wait.

• Returns: Images if wait=True, else record_id

---

Advanced Usage

Environment Variables

import os
from veloxai import Vtext

token = os.getenv('VELOX_TOKEN')
vtext = Vtext(token=token)

Error Handling

response = vtext.chat("Hello")

if response['success']:
    print(response['response'])
else:
    print(f"Error: {response['error']}")

Custom Base URL

vtext = Vtext(
    token="your-token",
    base_url="https://custom-api.example.com"
)

Streaming with Error Handling

try:
    for chunk in vtext.chat("Hello", stream=True):
        if chunk.startswith("Error:"):
            print(f"Stream error: {chunk}")
            break
        print(chunk, end='')
except Exception as e:
    print(f"Exception: {e}")

---

Integration Examples

Flask API

from flask import Flask, request, jsonify, Response
from veloxai import Vtext

app = Flask(__name__)
vtext = Vtext(token="your-token")

@app.route('/chat', methods=['POST'])
def chat():
    message = request.json['message']
    stream = request.json.get('stream', False)
    
    if stream:
        def generate():
            for chunk in vtext.chat(message, stream=True):
                yield f"data: {chunk}\n\n"
        return Response(generate(), mimetype='text/event-stream')
    else:
        response = vtext.chat(message)
        return jsonify(response)

@app.route('/image', methods=['POST'])
def image():
    prompt = request.json['prompt']
    vimg = Vimg(token="your-token")
    result = vimg.create(prompt)
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True)

Discord Bot

import discord
from veloxai import Vtext

vtext = Vtext(token="your-velox-token")
bot = discord.Client()

@bot.event
async def on_message(message):
    if message.author == bot.user:
        return
    
    if message.content.startswith('!ask'):
        question = message.content[5:]
        response = vtext.chat(question)
        if response['success']:
            await message.channel.send(response['response'])

bot.run('your-discord-token')

Async Wrapper

import asyncio
from veloxai import Vtext

async def async_chat(message):
    vtext = Vtext(token="your-token")
    loop = asyncio.get_event_loop()
    response = await loop.run_in_executor(
        None,
        vtext.chat,
        message
    )
    return response

# Usage
response = asyncio.run(async_chat("Hello"))

---

Comparison with OpenAI

OpenAI

from openai import OpenAI
client = OpenAI(api_key="key")

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content, end='')

VeloxAI (Similar API)

from veloxai import Vtext
vtext = Vtext(token="key")

for chunk in vtext.chat("Hello", stream=True):
    print(chunk, end='')

Cleaner and simpler! ✨

---

Requirements

• Python 3.7+
• requests >= 2.25.0

---

License

MIT License - see LICENSE file

---

Support

• GitHub: https://github.com/yourusername/veloxai
• Issues: https://github.com/yourusername/veloxai/issues
• PyPI: https://pypi.org/project/veloxai/

---

Changelog

1.0.0 (2024-02-08)

• Initial release
• Vtext: Chat, streaming, file support, training
• Vimg: Generation, img2img, auto-wait
• OpenAI-compatible API

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Credits

Made with ⚡ by VeloxAI Team