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AI & Knowledge Apps Overview

AI & Knowledge apps provide intelligent data processing, knowledge management, and AI agent capabilities for your Django-CFG projects.

🧠 Knowledge Base App​

Intelligent document processing and semantic search

  • 📄 Document Processing - PDF, Word, text file ingestion
  • 🔍 Semantic Search - Vector-based similarity search
  • 💬 AI Chat Interface - Natural language queries
  • 🏷️ Auto-tagging - AI-powered content categorization
  • 📊 Analytics - Search patterns and content insights

Key Features​

# Document ingestion and search
from django_cfg.apps.knowbase import DocumentManager, ChatInterface

# Add documents
manager = DocumentManager()
document = manager.add_document(
    file_path="manual.pdf",
    title="User Manual v2.1",
    tags=["documentation", "help"]
)

# AI-powered search
chat = ChatInterface()
response = chat.query("How do I reset my password?")
print(response.answer)  # AI-generated answer with sources

Knowledge Base Components​

Setup & Configuration​

  • Document upload and processing
  • Vector embedding configuration
  • Search index optimization
  • Content categorization

💬 Chat & Search​

  • Natural language queries
  • Contextual AI responses
  • Source attribution
  • Search result ranking

Data Integration​

  • Multi-format document support
  • External data source connections
  • API integrations
  • Real-time synchronization

AI Agents App​

Autonomous AI agents for task automation

  • 🎯 Custom Agents - Build domain-specific AI assistants
  • 🔧 Tool Integration - Connect agents to external APIs
  • 🔄 Multi-step Workflows - Complex task orchestration
  • 📊 Performance Tracking - Agent effectiveness metrics
  • 🛡️ Safety Controls - Output validation and filtering

Key Features​

# Create and deploy AI agents
from django_cfg.apps.agents import AgentBuilder, AgentOrchestrator

# Build custom agent
agent = AgentBuilder()
    .with_model("gpt-4")
    .with_tools(["web_search", "email_send", "database_query"])
    .with_prompt("You are a customer service assistant...")
    .build()

# Deploy agent
orchestrator = AgentOrchestrator()
result = orchestrator.process_request(
    agent=agent,
    request="Help customer with billing question",
    context={"customer_id": 123}
)

Integration Patterns​

Knowledge-Powered Agents​

# Combine knowledge base with AI agents
from django_cfg.apps.knowbase import KnowledgeRetriever
from django_cfg.apps.agents import KnowledgeAgent

# Agent with knowledge base access
kb_agent = KnowledgeAgent(
    knowledge_base="company_docs",
    model="gpt-4",
    max_sources=5
)

# Query with context
response = kb_agent.query(
    question="What's our refund policy?",
    user_context={"customer_tier": "premium"}
)

Smart Document Processing​

Analytics & Insights​

Knowledge Usage Tracking​

# Track popular content and search patterns
from django_cfg.apps.knowbase import SearchAnalytics

analytics = SearchAnalytics()

# Popular queries
popular_queries = analytics.get_popular_queries(days=30)

# Content gaps
content_gaps = analytics.identify_content_gaps()

# User engagement
engagement = analytics.get_engagement_metrics()

Agent Performance​

# Monitor agent effectiveness
from django_cfg.apps.agents import AgentMetrics

metrics = AgentMetrics()

# Success rates
success_rate = metrics.get_success_rate(agent_id="customer_service")

# Response quality
quality_scores = metrics.get_quality_scores(days=7)

# Cost analysis
cost_breakdown = metrics.get_cost_analysis()

Use Cases​

Customer Support Automation​

AI-powered support with knowledge base access

# Setup support agent with knowledge base
support_agent = KnowledgeAgent(
    name="Support Bot",
    knowledge_base="support_docs",
    tools=["ticket_create", "escalate_to_human", "knowledge_search"]
)

# Handle customer queries automatically
response = support_agent.handle_query(
    query="My payment failed, what should I do?",
    customer_context={
        "tier": "premium",
        "last_payment": "2024-01-15",
        "order_id": "ORD-12345"
    }
)

# Response includes:
# - AI-generated answer
# - Relevant knowledge base articles
# - Suggested actions
# - Escalation trigger if needed
Support Automation Benefits

Key advantages:

  • ✅ 24/7 Availability - Instant responses any time
  • ✅ Consistent Quality - Same high-quality answers every time
  • ✅ Knowledge Access - Searches entire support documentation
  • ✅ Smart Escalation - Automatically escalates complex issues
  • ✅ Cost Reduction - Handles 70-80% of common queries

Best for:

  • FAQ handling
  • Order status inquiries
  • Account management
  • Troubleshooting guides

Configuration​

Knowledge Base Setup​

# config.py
class MyProjectConfig(DjangoConfig):
    # Enable knowledge base
    enable_knowbase: bool = True
    
    # AI configuration
    openai_api_key: str = env.openai.api_key
    embedding_model: str = "text-embedding-ada-002"
    chat_model: str = "gpt-4"

Agent Configuration​

# Enable AI agents
enable_agents: bool = True

# Agent safety settings
agent_safety: AgentSafetyConfig = AgentSafetyConfig(
    max_tokens=4000,
    temperature=0.7,
    content_filter=True,
    output_validation=True
)

Security & Safety​

Content Filtering​

  • Input Validation - Sanitize user queries
  • Output Filtering - Check AI responses for appropriateness
  • Rate Limiting - Prevent API abuse
  • Access Control - User-based permissions

Data Privacy​

  • Local Processing - Option for on-premise deployment
  • Data Encryption - End-to-end encryption for sensitive content
  • Audit Logging - Complete interaction logging
  • Compliance - GDPR, SOC2 compliance features

See Also​

AI & Knowledge Features​

Knowledge Base:

AI Agents:

LLM Integration:

Configuration & Setup​

Getting Started:

Advanced:

Tools & Deployment​

Background Processing:

CLI & Deployment:

AI & Knowledge apps bring intelligent automation to your Django applications! 🤖

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