Skip to content

Django Chain Documentation

Welcome to the official documentation for Django Chain!

Django Chain is a Django library that makes it easy to integrate Large Language Models (LLMs) into your Django applications using familiar Django patterns. It provides a comprehensive framework for building AI-powered applications with production-ready features.

🚀 Key Features

  • Django-native abstractions for LLMs, prompts, and workflows
  • Database-backed prompt and memory management with versioning
  • Multi-provider support (OpenAI, Google, HuggingFace, and more)
  • Comprehensive logging and interaction tracking
  • Vector store integration for RAG (Retrieval-Augmented Generation)
  • Chat session management with persistent history
  • Async support and Celery integration for background tasks
  • Production-ready error handling and monitoring
  • Extensible architecture for custom workflows and providers
  • Type-safe API with comprehensive test coverage

🎯 Who Should Use Django Chain?

  • Django developers who want to add LLM-powered features (chatbots, summarization, content generation) to their applications
  • AI/ML teams seeking a maintainable, scalable, and testable LLM integration solution
  • Product teams building conversational interfaces or AI-enhanced user experiences
  • Enterprise developers who need production-ready LLM integration with proper logging and monitoring

🏗️ Core Concepts

  • Prompts: Configurable templates for generating LLM prompts (PromptTemplate, ChatPromptTemplate, etc.)
  • Workflows: Sequences of ordered steps that orchestrate LLM interactions (prompt → LLM → parser → output)
  • Chat Sessions: Persistent conversation management with user context and message history
  • Interaction Logs: Comprehensive tracking of all LLM interactions with metadata and performance metrics
  • Vector Stores: Document storage and semantic search capabilities for RAG applications

📋 Quick Example

from django_chain.models import Prompt, Workflow

# Create a prompt template
prompt = Prompt.objects.create(
    name="customer_support",
    prompt_template={
        "langchain_type": "PromptTemplate",
        "template": "You are a helpful customer support agent. Question: {question}",
        "input_variables": ["question"]
    },
    input_variables=["question"]
)

# Create a workflow
workflow = Workflow.objects.create(
    name="support_workflow",
    prompt=prompt,
    workflow_definition=[
        {"type": "prompt", "name": "customer_support"},
        {"type": "llm", "provider": "openai"},
        {"type": "parser", "parser_type": "str"}
    ]
)

# Execute the workflow
chain = workflow.to_langchain_chain()
result = chain.invoke({"question": "How do I reset my password?"})

📚 Documentation Sections

Getting Started

Usage & Examples

  • Tutorials - Step-by-step guides for common use cases
  • How-to Guides - Practical solutions for specific tasks
  • Examples - Code examples and patterns
  • Testing - Testing your LLM integrations

API Reference

  • Models - Core Django models (Prompt, Workflow, ChatSession, etc.)
  • Views - REST API endpoints and views
  • Utilities - Helper functions and utilities
  • Providers - LLM provider integrations
  • Mixins - Reusable view mixins

Advanced Topics

Development

⚠️ Project Status

Alpha Release: Django Chain is currently in early alpha (v0.2.0a3). While the core functionality is stable and tested, you may encounter breaking changes between versions. We recommend pinning to specific versions in production and thoroughly testing upgrades.

Use the navigation on the left to explore each section in detail.