django-forge-ai 1.2.7

A comprehensive AI toolbox for Django - seamless integration of LLM functionalities, RAG systems, and AI agents

DjangoForgeAI

A comprehensive, plug-and-play toolkit that seamlessly integrates modern AI/LLM functionalities directly into your Django projects. DjangoForgeAI provides "batteries-included" solutions for common AI engineering tasks, including AI-powered admin fields, native RAG system integration, and configurable AI agents, all manageable through the Django admin interface.

Demos

Demo 1: Watch on YouTube

Demo 2: Watch on YouTube

Features

1. AI-Powered Admin & Model Fields

• AICharField / AITextField: Custom model fields that can automatically generate summaries or content from other fields in the model using an LLM.
• AIAdminMixin: A mixin for ModelAdmin classes that adds a "Generate with AI" button next to specified text fields.
• AIModeratedField: A field that automatically runs AI-powered content moderation upon save.

2. Native RAG System Integration

• Knowledge Base Management: Upload documents, define data sources (e.g., website URLs), and manage vector stores through the Django admin.
• Vector DB Connectors: Pluggable backends for popular vector databases (ChromaDB, Qdrant, PGVector).
• SemanticSearchMixin: A mixin for QuerySets that enables semantic search against your RAG knowledge base.

3. Configurable AI Agents & Automation

• Agent Management Dashboard: Define agents (persona, tools, goals) and assign them to specific tasks through the admin.
• AgentTaskQueue: Integration with Celery to execute agent tasks in the background and log their progress.

Installation

pip install django-forge-ai

Or install from source:

git clone https://github.com/vortex-hue/django_forge_ai.git
cd django-forge-ai
pip install -e .

Quick Start

1. Add to INSTALLED_APPS

# settings.py
INSTALLED_APPS = [
    # ... other apps
    'django_forge_ai',
    'django_forge_ai.rag_system',
    'django_forge_ai.agents',
]

2. Configure Settings

# settings.py

# LLM Configuration
DJANGO_FORGE_AI_LLM_PROVIDER = "openai"  # or "anthropic"
DJANGO_FORGE_AI_OPENAI_API_KEY = "your-openai-api-key"
# or
DJANGO_FORGE_AI_ANTHROPIC_API_KEY = "your-anthropic-api-key"

# Vector Database Configuration
DJANGO_FORGE_AI_VECTOR_DB = "chroma"  # or "qdrant", "pgvector"
DJANGO_FORGE_AI_VECTOR_DB_PATH = "./vector_db"

# Celery Configuration (optional, for async tasks)
DJANGO_FORGE_AI_USE_CELERY = True

3. Run Migrations

python manage.py migrate

4. Use AI-Powered Fields

# models.py
from django.db import models
from django_forge_ai.models import AICharField, AITextField, AIModeratedField

class Article(models.Model):
    title = models.CharField(max_length=200)
    
    # Auto-generate summary from title
    summary = AICharField(
        max_length=200,
        ai_generate_from=['title'],
        ai_prompt_template="Generate a summary of: {title}",
        ai_auto_generate=True
    )
    
    # Generate content with AI
    content = AITextField(
        ai_generate_from=['title'],
        ai_prompt_template="Write an article about: {title}"
    )
    
    # Moderated content
    user_comment = AIModeratedField(
        moderation_strict=True,
        raise_on_violation=True
    )

5. Use AIAdminMixin

# admin.py
from django.contrib import admin
from django_forge_ai.admin_integration.mixins import AIAdminMixin
from .models import Article

@admin.register(Article)
class ArticleAdmin(AIAdminMixin, admin.ModelAdmin):
    ai_fields = ['summary', 'content']
    ai_prompts = {
        'summary': 'Generate a summary of: {title}',
        'content': 'Write an article about: {title}'
    }
    ai_context_fields = {
        'summary': ['title'],
        'content': ['title']
    }

6. Set Up RAG System

1. Create a Knowledge Base in the Django admin
2. Upload documents or add URLs
3. Generate embeddings (admin action)
4. Use semantic search:

from django_forge_ai.rag_system.mixins import SemanticSearchMixin
from django.db import models

class ArticleQuerySet(SemanticSearchMixin, models.QuerySet):
    pass

class Article(models.Model):
    objects = ArticleQuerySet.as_manager()
    
# Usage
results = Article.objects.semantic_search("What is Django?")

7. Create AI Agents

1. Create an Agent Configuration in the Django admin
2. Define persona, goals, and tools
3. Create tasks and execute them (async with Celery)

Configuration

LLM Providers

DjangoForgeAI supports multiple LLM providers:

• OpenAI: Set DJANGO_FORGE_AI_LLM_PROVIDER = "openai" and provide DJANGO_FORGE_AI_OPENAI_API_KEY
• Anthropic: Set DJANGO_FORGE_AI_LLM_PROVIDER = "anthropic" and provide DJANGO_FORGE_AI_ANTHROPIC_API_KEY

Vector Databases

• ChromaDB: Default, file-based storage
• Qdrant: Requires Qdrant server running
• PGVector: Requires PostgreSQL with pgvector extension

Celery Integration

For async task execution, configure Celery:

# celery.py
from celery import Celery

app = Celery('your_project')
app.config_from_object('django.conf:settings', namespace='CELERY')
app.autodiscover_tasks()

Requirements

• Python >= 3.8
• Django >= 3.2
• OpenAI API key or Anthropic API key
• (Optional) Celery for async tasks
• (Optional) Vector database (ChromaDB, Qdrant, or PostgreSQL with pgvector)

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub.