Django for LLMs: A Complete Guide from Zero to Production

Introduction
Understanding the Foundations
Setting Up Your Django Project
Integrating LLM Models with Django
Building Views and API Endpoints
Database Design for LLM Applications
Frontend Integration with HTMX
Advanced Patterns and Best Practices
Scaling and Performance Optimization
Deployment to Production
Resources and Further Learning

Introduction

Building web applications that leverage Large Language Models (LLMs) has become increasingly accessible to Django developers. Whether you’re creating an AI-powered chatbot, content generation tool, or intelligent assistant, Django provides a robust framework for integrating LLMs into production applications.

This comprehensive guide walks you through every step—from initial project setup to deploying a fully functional LLM-powered Django application. You’ll learn how to structure your code, handle API calls efficiently, manage costs, and ensure your application scales reliably under production loads.

Understanding the Foundations

What Are Large Language Models?

Large Language Models are sophisticated neural networks trained on vast amounts of text data. They can perform tasks like text generation, summarization, question-answering, and code completion. Popular LLMs include GPT-4, Mistral, LLaMA, and open-source alternatives.

Why Django for LLM Applications?

Django excels at building LLM applications because it provides:

Batteries-included framework: Built-in admin panel, ORM, authentication, and security features
Rapid development: Clean, pragmatic design patterns accelerate development
Scalability: Proven in production at scale across thousands of applications
Rich ecosystem: Extensive third-party packages for AI integration
Security: Built-in protection against common vulnerabilities

Key Technologies You’ll Use

Django: Web framework for building the application
Django REST Framework: Optional but recommended for API development
LangChain: Simplifies LLM orchestration and chains
Transformers library: Access to thousands of pretrained models
HTMX: Modern frontend interactivity without heavy JavaScript
PostgreSQL: Recommended database for production
Celery: For asynchronous task processing
Docker: Containerization for consistent deployment

Setting Up Your Django Project

Installation and Initial Setup

Start by creating a virtual environment and installing Django:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install Django
pip install Django

Create a new Django project:

django-admin startproject myproject
cd myproject

Create a new Django app for your LLM functionality:

python manage.py startapp llm_app

Project Structure

Organize your project with a clear structure:

myproject/
├── myproject/
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
├── llm_app/
│   ├── migrations/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── views.py
│   ├── urls.py
│   ├── serializers.py
│   ├── services.py
│   └── templates/
├── static/
│   ├── css/
│   ├── js/
│   └── htmx.min.js
├── templates/
│   └── base.html
├── manage.py
├── requirements.txt
└── .env

Installing Essential Dependencies

Create a requirements.txt file with all necessary packages:

Django==4.2.0
djangorestframework==3.14.0
python-dotenv==1.0.0
langchain==0.1.0
langchain-openai==0.0.5
openai==1.3.0
transformers==4.35.0
torch==2.1.0
psycopg2-binary==2.9.9
celery==5.3.4
redis==5.0.0
requests==2.31.0
python-decouple==3.8

Install all dependencies:

pip install -r requirements.txt

Configuring Django Settings

Update your settings.py with essential configurations:

import os
from pathlib import Path
from decouple import config

BASE_DIR = Path(__file__).resolve().parent.parent

SECRET_KEY = config('SECRET_KEY', default='your-secret-key-here')
DEBUG = config('DEBUG', default=False, cast=bool)
ALLOWED_HOSTS = config('ALLOWED_HOSTS', default='localhost,127.0.0.1', cast=lambda v: [s.strip() for s in v.split(',')])

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'rest_framework',
    'llm_app',
]

MIDDLEWARE = [
    'django.middleware.security.SecurityMiddleware',
    'django.contrib.sessions.middleware.SessionMiddleware',
    'django.middleware.common.CommonMiddleware',
    'django.middleware.csrf.CsrfViewMiddleware',
    'django.contrib.auth.middleware.AuthenticationMiddleware',
    'django.contrib.messages.middleware.MessageMiddleware',
    'django.middleware.clickjacking.XFrameOptionsMiddleware',
]

ROOT_URLCONF = 'myproject.urls'

TEMPLATES = [
    {
        'BACKEND': 'django.template.backends.django.DjangoTemplates',
        'DIRS': [BASE_DIR / 'templates'],
        'APP_DIRS': True,
        'OPTIONS': {
            'context_processors': [
                'django.template.context_processors.debug',
                'django.template.context_processors.request',
                'django.contrib.auth.context_processors.auth',
                'django.contrib.messages.context_processors.messages',
            ],
        },
    },
]

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': config('DB_NAME', default='llm_db'),
        'USER': config('DB_USER', default='postgres'),
        'PASSWORD': config('DB_PASSWORD', default='password'),
        'HOST': config('DB_HOST', default='localhost'),
        'PORT': config('DB_PORT', default='5432'),
    }
}

# REST Framework Configuration
REST_FRAMEWORK = {
    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
    'PAGE_SIZE': 10,
    'DEFAULT_AUTHENTICATION_CLASSES': [
        'rest_framework.authentication.TokenAuthentication',
    ],
    'DEFAULT_PERMISSION_CLASSES': [
        'rest_framework.permissions.IsAuthenticated',
    ],
}

# Celery Configuration
CELERY_BROKER_URL = config('CELERY_BROKER_URL', default='redis://localhost:6379')
CELERY_RESULT_BACKEND = config('CELERY_RESULT_BACKEND', default='redis://localhost:6379')
CELERY_ACCEPT_CONTENT = ['json']
CELERY_TASK_SERIALIZER = 'json'

# LLM Configuration
OPENAI_API_KEY = config('OPENAI_API_KEY', default='')
LLM_MODEL = config('LLM_MODEL', default='gpt-3.5-turbo')
LLM_TEMPERATURE = config('LLM_TEMPERATURE', default=0.7, cast=float)

STATIC_URL = '/static/'
STATIC_ROOT = BASE_DIR / 'staticfiles'
STATICFILES_DIRS = [BASE_DIR / 'static']

MEDIA_URL = '/media/'
MEDIA_ROOT = BASE_DIR / 'media'

Create a .env file in your project root:

SECRET_KEY=your-super-secret-key-change-in-production
DEBUG=True
ALLOWED_HOSTS=localhost,127.0.0.1
DB_NAME=llm_db
DB_USER=postgres
DB_PASSWORD=your_password
DB_HOST=localhost
DB_PORT=5432
OPENAI_API_KEY=your-openai-api-key
LLM_MODEL=gpt-3.5-turbo
CELERY_BROKER_URL=redis://localhost:6379

Integrating LLM Models with Django

Understanding LLM Integration Approaches

There are several ways to integrate LLMs with Django:

API-based: Call external LLM services (OpenAI, Anthropic, Mistral)
Local models: Run open-source models locally using Transformers
Hybrid approach: Use external APIs for complex tasks, local models for simple ones

Creating an LLM Service Layer

The service layer pattern separates LLM logic from your views, making code more maintainable:

# llm_app/services.py

import os
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from decouple import config

class LLMService:
    def __init__(self):
        self.api_key = config('OPENAI_API_KEY')
        self.model_name = config('LLM_MODEL', default='gpt-3.5-turbo')
        self.temperature = config('LLM_TEMPERATURE', default=0.7, cast=float)
        self.chat_model = ChatOpenAI(
            api_key=self.api_key,
            model=self.model_name,
            temperature=self.temperature
        )
    
    def generate_response(self, prompt: str, context: str = "") -> str:
        """Generate a response from the LLM"""
        template = """
        You are a helpful assistant. Use the provided context to answer questions accurately.
        
        Context: {context}
        
        User Query: {prompt}
        
        Answer:
        """
        
        prompt_template = ChatPromptTemplate.from_template(template)
        chain = prompt_template | self.chat_model | StrOutputParser()
        
        response = chain.invoke({
            "context": context,
            "prompt": prompt
        })
        
        return response
    
    def summarize_text(self, text: str) -> str:
        """Summarize provided text"""
        template = "Please provide a concise summary of the following text:\n\n{text}"
        prompt_template = ChatPromptTemplate.from_template(template)
        chain = prompt_template | self.chat_model | StrOutputParser()
        
        summary = chain.invoke({"text": text})
        return summary
    
    def extract_entities(self, text: str) -> dict:
        """Extract named entities from text"""
        template = """Extract all named entities (people, places, organizations) from the following text.
        Return as JSON format.
        
        Text: {text}"""
        
        prompt_template = ChatPromptTemplate.from_template(template)
        chain = prompt_template | self.chat_model | StrOutputParser()
        
        result = chain.invoke({"text": text})
        return result

# Create a singleton instance
llm_service = LLMService()

Using Transformers for Local Models

For on-premises deployments or cost savings, use local models:

# llm_app/local_llm_service.py

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import torch

class LocalLLMService:
    def __init__(self, model_name: str = "gpt2"):
        """Initialize with a local model"""
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model_name = model_name
        
        # Load tokenizer and model
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
        ).to(self.device)
        
        # Create pipeline for text generation
        self.generator = pipeline(
            'text-generation',
            model=self.model,
            tokenizer=self.tokenizer,
            device=0 if self.device == "cuda" else -1
        )
    
    def generate_response(self, prompt: str, max_length: int = 100) -> str:
        """Generate response using local model"""
        result = self.generator(
            prompt,
            max_length=max_length,
            num_return_sequences=1,
            temperature=0.7,
            top_p=0.9
        )
        
        return result['generated_text']
    
    def summarize_text(self, text: str) -> str:
        """Summarize using extractive summarization"""
        from transformers import pipeline
        
        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
        summary = summarizer(text, max_length=100, min_length=30, do_sample=False)
        
        return summary['summary_text']

# Initialize with a specific model
local_llm = LocalLLMService(model_name="gpt2")

Building Views and API Endpoints

Creating Django Models for LLM Data

Define models to store conversations and results:

# llm_app/models.py

from django.db import models
from django.contrib.auth.models import User
from django.utils import timezone

class Conversation(models.Model):
    """Store conversation history"""
    user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='conversations')
    title = models.CharField(max_length=255, blank=True)
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)
    is_archived = models.BooleanField(default=False)
    
    class Meta:
        ordering = ['-updated_at']
    
    def __str__(self):
        return self.title or f"Conversation {self.id}"

class Message(models.Model):
    """Store individual messages in conversations"""
    ROLE_CHOICES = [
        ('user', 'User'),
        ('assistant', 'Assistant'),
    ]
    
    conversation = models.ForeignKey(
        Conversation,
        on_delete=models.CASCADE,
        related_name='messages'
    )
    role = models.CharField(max_length=10, choices=ROLE_CHOICES)
    content = models.TextField()
    tokens_used = models.IntegerField(default=0)
    created_at = models.DateTimeField(auto_now_add=True)
    
    class Meta:
        ordering = ['created_at']
    
    def __str__(self):
        return f"{self.role}: {self.content[:50]}"

class APIUsageLog(models.Model):
    """Track API usage for cost monitoring"""
    user = models.ForeignKey(User, on_delete=models.CASCADE)
    model_used = models.CharField(max_length=100)
    input_tokens = models.IntegerField()
    output_tokens = models.IntegerField()
    cost = models.DecimalField(max_digits=10, decimal_places=6)
    timestamp = models.DateTimeField(auto_now_add=True)
    
    class Meta:
        ordering = ['-timestamp']
    
    def __str__(self):
        return f"{self.user.username} - {self.model_used} - {self.timestamp}"

class Document(models.Model):
    """Store documents for RAG (Retrieval-Augmented Generation)"""
    user = models.ForeignKey(User, on_delete=models.CASCADE)
    title = models.CharField(max_length=255)
    content = models.TextField()
    file = models.FileField(upload_to='documents/', null=True, blank=True)
    created_at = models.DateTimeField(auto_now_add=True)
    embedding_vector = models.JSONField(null=True, blank=True)
    
    def __str__(self):
        return self.title

Run migrations:

python manage.py makemigrations
python manage.py migrate

Creating API Serializers

# llm_app/serializers.py

from rest_framework import serializers
from .models import Conversation, Message, Document

class MessageSerializer(serializers.ModelSerializer):
    class Meta:
        model = Message
        fields = ['id', 'role', 'content', 'tokens_used', 'created_at']
        read_only_fields = ['id', 'created_at', 'tokens_used']

class ConversationSerializer(serializers.ModelSerializer):
    messages = MessageSerializer(many=True, read_only=True)
    
    class Meta:
        model = Conversation
        fields = ['id', 'title', 'created_at', 'updated_at', 'is_archived', 'messages']
        read_only_fields = ['id', 'created_at', 'updated_at']

class DocumentSerializer(serializers.ModelSerializer):
    class Meta:
        model = Document
        fields = ['id', 'title', 'content', 'file', 'created_at']
        read_only_fields = ['id', 'created_at']

class ChatRequestSerializer(serializers.Serializer):
    """Validate incoming chat requests"""
    message = serializers.CharField(max_length=4000)
    conversation_id = serializers.IntegerField(required=False)
    context = serializers.CharField(required=False, allow_blank=True)

Building REST API Views

# llm_app/views.py

from rest_framework import viewsets, status
from rest_framework.decorators import action
from rest_framework.response import Response
from rest_framework.permissions import IsAuthenticated
from django.shortcuts import get_object_or_404
from .models import Conversation, Message, Document
from .serializers import (
    ConversationSerializer,
    MessageSerializer,
    DocumentSerializer,
    ChatRequestSerializer
)
from .services import llm_service
from .tasks import process_llm_request

class ConversationViewSet(viewsets.ModelViewSet):
    """Handle conversation CRUD operations"""
    serializer_class = ConversationSerializer
    permission_classes = [IsAuthenticated]
    
    def get_queryset(self):
        return Conversation.objects.filter(user=self.request.user)
    
    def perform_create(self, serializer):
        serializer.save(user=self.request.user)
    
    @action(detail=True, methods=['post'])
    def send_message(self, request, pk=None):
        """Send a message in a conversation"""
        conversation = self.get_object()
        serializer = ChatRequestSerializer(data=request.data)
        
        if serializer.is_valid():
            message_text = serializer.validated_data['message']
            context = serializer.validated_data.get('context', '')
            
            # Save user message
            user_message = Message.objects.create(
                conversation=conversation,
                role='user',
                content=message_text
            )
            
            # Generate LLM response asynchronously
            process_llm_request.delay(
                conversation_id=conversation.id,
                user_message_id=user_message.id,
                context=context
            )
            
            return Response({
                'status': 'processing',
                'message_id': user_message.id
            }, status=status.HTTP_202_ACCEPTED)
        
        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
    
    @action(detail=True, methods=['get'])
    def messages(self, request, pk=None):
        """Retrieve all messages in a conversation"""
        conversation = self.get_object()
        messages = conversation.messages.all()
        serializer = MessageSerializer(messages, many=True)
        return Response(serializer.data)
    
    @action(detail=True, methods=['post'])
    def archive(self, request, pk=None):
        """Archive a conversation"""
        conversation = self.get_object()
        conversation.is_archived = True
        conversation.save()
        return Response({'status': 'archived'})

class DocumentViewSet(viewsets.ModelViewSet):
    """Handle document uploads and management"""
    serializer_class = DocumentSerializer
    permission_classes = [IsAuthenticated]
    
    def get_queryset(self):
        return Document.objects.filter(user=self.request.user)
    
    def perform_create(self, serializer):
        serializer.save(user=self.request.user)

URL Configuration

# llm_app/urls.py

from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .views import ConversationViewSet, DocumentViewSet

router = DefaultRouter()
router.register(r'conversations', ConversationViewSet, basename='conversation')
router.register(r'documents', DocumentViewSet, basename='document')

urlpatterns = [
    path('api/', include(router.urls)),
]

# In myproject/urls.py
from django.contrib import admin
from django.urls import path, include
from django.conf import settings
from django.conf.urls.static import static

urlpatterns = [
    path('admin/', admin.site.urls),
    path('api-auth/', include('rest_framework.urls')),
    path('llm/', include('llm_app.urls')),
] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

Database Design for LLM Applications

Optimizing Database Schema

For optimal performance with LLM applications:

# llm_app/models.py - Enhanced with indexing

from django.db import models

class Message(models.Model):
    conversation = models.ForeignKey('Conversation', on_delete=models.CASCADE)
    role = models.CharField(max_length=10, db_index=True)
    content = models.TextField()
    tokens_used = models.IntegerField()
    created_at = models.DateTimeField(auto_now_add=True, db_index=True)
    
    class Meta:
        indexes = [
            models.Index(fields=['conversation', 'created_at']),
            models.Index(fields=['role', 'created_at']),
        ]

class APIUsageLog(models.Model):
    user = models.ForeignKey(User, on_delete=models.CASCADE, db_index=True)
    model_used = models.CharField(max_length=100, db_index=True)
    input_tokens = models.IntegerField()
    output_tokens = models.IntegerField()
    cost = models.DecimalField(max_digits=10, decimal_places=6)
    timestamp = models.DateTimeField(auto_now_add=True, db_index=True)
    
    class Meta:
        indexes = [
            models.Index(fields=['user', 'timestamp']),
            models.Index(fields=['model_used', 'timestamp']),
        ]

Implementing Pagination and Caching

# llm_app/views.py - With caching

from django.views.decorators.cache import cache_page
from django.core.cache import cache
from rest_framework.pagination import PageNumberPagination

class MessagePagination(PageNumberPagination):
    page_size = 20
    page_size_query_param = 'page_size'
    max_page_size = 100

class ConversationViewSet(viewsets.ModelViewSet):
    pagination_class = MessagePagination
    
    @action(detail=True, methods=['get'])
    def messages(self, request, pk=None):
        conversation = self.get_object()
        
        # Check cache first
        cache_key = f'conversation_{pk}_messages'
        cached_messages = cache.get(cache_key)
        
        if cached_messages:
            return Response(cached_messages)
        
        messages = conversation.messages.all()
        serializer = MessageSerializer(messages, many=True)
        
        # Cache for 5 minutes
        cache.set(cache_key, serializer.data, 300)
        
        return Response(serializer.data)

Frontend Integration with HTMX

Setting Up HTMX

HTMX enables dynamic interactions without writing complex JavaScript:

<!-- templates/base.html -->

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Django LLM App</title>
    <script src="https://unpkg.com/htmx.org@1.9.10"></script>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }
        
        body {
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            padding: 20px;
        }
        
        .container {
            max-width: 1200px;
            margin: 0 auto;
            background: white;
            border-radius: 12px;
            box-shadow: 0 20px 60px rgba(0,0,0,0.3);
            overflow: hidden;
        }
        
        .chat-container {
            display: flex;
            height: 600px;
        }
        
        .messages {
            flex: 1;
            overflow-y: auto;
            padding: 20px;
            background: #f8f9fa;
        }
        
        .message {
            margin-bottom: 15px;
            padding: 12px 16px;
            border-radius: 8px;
            max-width: 70%;
            word-wrap: break-word;
        }
        
        .message.user {
            background: #667eea;
            color: white;
            margin-left: auto;
        }
        
        .message.assistant {
            background: white;
            border: 1px solid #e0e0e0;
        }
        
        .input-area {
            padding: 20px;
            border-top: 1px solid #e0e0e0;
            display: flex;
            gap: 10px;
        }
        
        .input-area input {
            flex: 1;
            padding: 12px;
            border: 1px solid #ddd;
            border-radius: 8px;
            font-size: 14px;
        }
        
        .input-area button {
            padding: 12px 24px;
            background: #667eea;
            color: white;
            border: none;
            border-radius: 8px;
            cursor: pointer;
            font-weight: 600;
        }
        
        .input-area button:hover {
            background: #5568d3;
        }
        
        .htmx-request .htmx-indicator {
            display: inline-block;
        }
        
        .htmx-indicator {
            display: none;
        }
        
        .spinner {
            border: 3px solid #f3f3f3;
            border-top: 3px solid #667eea;
            border-radius: 50%;
            width: 20px;
            height: 20px;
            animation: spin 1s linear infinite;
        }
        
        @keyframes spin {
            0% { transform: rotate(0deg); }
            100% { transform: rotate(360deg); }
        }
    </style>
</head>
<body>
    <div class="container">
        {% block content %}{% endblock %}
    </div>
</body>
</html>

Creating HTMX-Powered Chat Interface

<!-- templates/chat.html -->

{% extends 'base.html' %}

{% block content %}
<div class="chat-container">
    <div class="messages" id="messages">
        {% for message in conversation.messages.all %}
            <div class="message {{ message.role }}">
                {{ message.content }}
            </div>
        {% endfor %}
    </div>
    
    <div class="input-area">
        <form hx-post="{% url 'conversation-send-message' conversation.id %}"
              hx-target="#messages"
              hx-swap="beforeend"
              hx-on::after-request="if(event.detail.xhr.status===202) this.reset()">
            {% csrf_token %}
            <input type="text"
                   name="message"
                   placeholder="Type your message..."
                   required>
            <button type="submit">
                Send
                <span class="htmx-indicator">
                    <div class="spinner"></div>
                </span>
            </button>
        </form>
    </div>
</div>

<script>
    // Auto-scroll to latest message
    const messagesDiv = document.getElementById('messages');
    messagesDiv.scrollTop = messagesDiv.scrollHeight;
    
    document.body.addEventListener('htmx:afterSwap', function() {
        messagesDiv.scrollTop = messagesDiv.scrollHeight;
    });
</script>
{% endblock %}

HTMX View for Message Rendering

# llm_app/views.py - Add template view

from django.views.generic import TemplateView
from django.contrib.auth.mixins import LoginRequiredMixin

class ChatTemplateView(LoginRequiredMixin, TemplateView):
    template_name = 'chat.html'
    
    def get_context_data(self, **kwargs):
        context = super().get_context_data(**kwargs)
        conversation_id = self.kwargs.get('conversation_id')
        context['conversation'] = get_object_or_404(
            Conversation,
            id=conversation_id,
            user=self.request.user
        )
        return context

Advanced Patterns and Best Practices

Implementing Asynchronous Task Processing with Celery

For long-running LLM requests, use Celery:

# llm_app/tasks.py

from celery import shared_task
from django.core.mail import send_mail
from .models import Conversation, Message, APIUsageLog
from .services import llm_service
import logging

logger = logging.getLogger(__name__)

@shared_task(bind=True, max_retries=3)
def process_llm_request(self, conversation_id, user_message_id, context=''):
    """Process LLM request asynchronously"""
    try:

Table of Contents#

Introduction#

Understanding the Foundations#

What Are Large Language Models?#

Why Django for LLM Applications?#

Key Technologies You’ll Use#

Setting Up Your Django Project#

Installation and Initial Setup#

Project Structure#

Installing Essential Dependencies#

Configuring Django Settings#

Integrating LLM Models with Django#

Understanding LLM Integration Approaches#

Creating an LLM Service Layer#

Using Transformers for Local Models#

Building Views and API Endpoints#

Creating Django Models for LLM Data#

Creating API Serializers#

Building REST API Views#

URL Configuration#

Database Design for LLM Applications#

Optimizing Database Schema#

Implementing Pagination and Caching#

Frontend Integration with HTMX#

Setting Up HTMX#

Creating HTMX-Powered Chat Interface#

HTMX View for Message Rendering#

Advanced Patterns and Best Practices#

Implementing Asynchronous Task Processing with Celery#

Table of Contents