Introduction
In this article, we will explore OpenThaiGPT, an open-source project that aims to develop a Thai Chatbot system with capabilities equivalent to ChatGPT. OpenThaiGPT is designed to be easily expandable and customizable, allowing developers to create their own powerful Thai Chatbots. We will dive into the details of OpenThaiGPT and guide you through three example code implementations that utilize the repository and various popular Python packages.
Example Code Implementations
1. Connecting OpenThaiGPT to External Systems
To showcase the flexibility of OpenThaiGPT, we will demonstrate how to connect the Thai Chatbot to external systems and retrieve data. We will utilize Python’s Celery package, a powerful distributed task queue, to handle the asynchronous processing of requests. FastAPI, a modern, fast (high-performance), web framework, will be used to create the API endpoints for communication with the external systems. By following our step-by-step guide, you will learn how to integrate OpenThaiGPT with external systems seamlessly.
Example code snippet:
# Import necessary packages
import openthaigpt
from celery import Celery
from fastapi import FastAPI
# Configure Celery
app = Celery('openthaigpt', broker='redis://localhost:6379')
# Create FastAPI app
api = FastAPI()
# Define API endpoint
@api.post("/openthaigpt")
async def generate_response(instruction: str, input: str):
result = await app.send_task("openthaigpt.generate", args=[instruction, input])
return {"response": result}
# Start the FastAPI server
if __name__ == "__main__":
import uvicorn
uvicorn.run(api, host="0.0.0.0", port=8000)2. Advanced Text Generation with OpenThaiGPT
In this example, we will explore advanced text generation techniques using OpenThaiGPT. We will leverage Python’s Pydantic package for data validation and SQLAlchemy for database operations. By combining the power of OpenThaiGPT with these popular Python packages, you will be able to generate high-quality text based on specific instructions and input data. Our code implementation will demonstrate how to create a text generation pipeline that allows users to specify instruction and input parameters.
Example code snippet:
# Import necessary packages
import openthaigpt
from pydantic import BaseModel
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
# Create SQLAlchemy database engine and session
engine = create_engine('sqlite:///openthaigpt.db')
Session = sessionmaker(bind=engine)
session = Session()
# Create SQLAlchemy model
Base = declarative_base()
class TextGeneration(Base):
__tablename__ = 'text_generation'
id = Column(Integer, primary_key=True)
instruction = Column(String)
input = Column(String)
output = Column(String)
# Define Pydantic model for data validation
class TextGenerationRequest(BaseModel):
instruction: str
input: str
# Define API endpoint for text generation
@app.post("/generate-text")
def generate_text(request: TextGenerationRequest):
instruction = request.instruction
input = request.input
output = openthaigpt.generate(instruction, input)
# Store generated text in database
text_entry = TextGeneration(instruction=instruction, input=input, output=output)
session.add(text_entry)
session.commit()
return {"output": output}3. Creating a Dockerized Chatbot Application with OpenThaiGPT
In this final example, we will demonstrate how to create a fully Dockerized chatbot application using OpenThaiGPT. Docker allows us to package the chatbot application and its dependencies into a container, ensuring consistent and reproducible deployments across different environments. We will also utilize Redis, an open-source in-memory data structure store, to cache responses and improve the chatbot’s performance. Our code implementation will provide a complete guide on how to containerize the chatbot application and deploy it using Docker.
Example code snippet:
# Use the official Python image as the base image
FROM python:3.9-slim
# Set the working directory in the container
WORKDIR /app
# Copy the project files to the working directory
COPY . .
# Install the project dependencies
RUN pip install --no-cache-dir -r requirements.txt
# Expose the necessary ports for FastAPI and Redis
EXPOSE 8000
EXPOSE 6379
# Run the Redis server
CMD ["redis-server", "--bind", "0.0.0.0"]
# Start the FastAPI server
CMD ["uvicorn", "main:api", "--host", "0.0.0.0", "--port", "8000"]Conclusion
OpenThaiGPT provides developers with a powerful tool to build their own Thai Chatbot systems. In this article, we have explored the capabilities of OpenThaiGPT and provided three example code implementations that demonstrate its usage with various Python packages. Whether you want to connect the chatbot to external systems, generate advanced text based on specific instructions, or create a Dockerized application, OpenThaiGPT has got you covered. Start building your own Thai Chatbot today!
Leave a Reply