AI & Knowledge Apps Overview

# 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

# 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}
)

# 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"}
)

# 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()

# 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()

# 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

# Setup developer documentation agent
dev_agent = KnowledgeAgent(
    name="Dev Assistant",
    knowledge_base="api_docs",
    tools=["code_search", "example_generate", "api_lookup"]
)
 
# Answer technical questions
answer = dev_agent.query(
    question="How do I authenticate API requests?",
    context={
        "language": "python",
        "framework": "django",
        "version": "4.2"
    }
)
 
# Generate code examples
code_example = dev_agent.generate_example(
    task="Create authenticated API request",
    language="python"
)

# Setup content creation agent
content_agent = AgentBuilder()
    .with_model("gpt-4")
    .with_tools(["research", "fact_check", "style_guide"])
    .with_knowledge_base("brand_guidelines")
    .build()
 
# Generate branded content
article = content_agent.create_content(
    topic="Product Launch",
    style="professional",
    length="1000_words",
    tone="enthusiastic",
    keywords=["innovation", "enterprise", "scalability"]
)
 
# Content curation
curated = content_agent.curate_content(
    sources=["blog_posts", "news_articles"],
    theme="AI in Enterprise",
    target_audience="C-level executives"
)

# Setup research agent
research_agent = AgentBuilder()
    .with_model("gpt-4")
    .with_tools(["web_search", "data_analysis", "citation_tracker"])
    .with_knowledge_base("research_papers")
    .build()
 
# Conduct research
research = research_agent.research_topic(
    topic="AI in Healthcare",
    depth="comprehensive",
    sources=["academic", "industry", "news"],
    timeframe="last_2_years"
)
 
# Generate research summary
summary = research_agent.summarize_findings(
    findings=research,
    format="executive_summary",
    length=500,
    include_citations=True
)
 
# Identify trends
trends = research_agent.analyze_trends(
    data=research,
    metrics=["growth", "adoption", "innovation"]
)

# Setup e-commerce agent
shop_agent = KnowledgeAgent(
    name="Shopping Assistant",
    knowledge_base="product_catalog",
    tools=["inventory_check", "price_compare", "recommendation_engine"]
)
 
# Product recommendations
recommendations = shop_agent.recommend_products(
    customer_query="I need a laptop for video editing",
    budget_range=(1000, 2000),
    preferences={
        "brand_preference": "none",
        "must_have": ["16GB RAM", "dedicated GPU"],
        "nice_to_have": ["lightweight", "long battery"]
    }
)
 
# Answer product questions
answer = shop_agent.answer_question(
    product_id="LAPTOP-123",
    question="Can this laptop run Adobe Premiere Pro smoothly?",
    customer_context={"use_case": "4K video editing"}
)
 
# Size/fit guidance
size_guide = shop_agent.provide_sizing_help(
    product_id="SHIRT-456",
    customer_measurements={"height": 180, "weight": 75}
)

# 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"

# 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
)