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Python AI Chatbot Service Archetype

Production-ready AI chatbot service with configurable LLM backends, multiple frontends, and enterprise integration capabilities

Generate a sophisticated AI chatbot service that supports multiple LLM providers (OpenAI, Mistral, Llama), various frontend options (Streamlit, custom), and enterprise features like vector databases, knowledge graphs, and multi-modal capabilities.

Overview​

The python-chatbot archetype creates a comprehensive AI-powered chatbot service with enterprise-grade features, configurable AI models, and production-ready deployment capabilities.

Key Characteristics​

  • Multi-LLM Support: OpenAI GPT-4, Mistral AI, Llama 3.1/3.2, CodeLlama
  • Frontend Options: Streamlit interface, Custom web frontend, API-only deployment
  • Vector Databases: Pinecone, Weaviate, Chroma integration
  • Knowledge Graphs: Neo4j, Amazon Neptune, Azure Cosmos DB
  • Enterprise Features: Authentication, rate limiting, conversation management
  • Deployment: Docker, Kubernetes, cloud platforms

AI Architecture​

Multi-Model LLM Integration​

Click to enlarge

Configurable AI Components​

LLM Models​

  • OpenAI: GPT-4, GPT-4 Turbo, GPT-4 Vision
  • Mistral AI: 7B, 8x7B, Large models
  • Meta Llama: 3.1 (8B, 70B, 405B), 3.2 (1B, 3B, 11B, 90B)
  • CodeLlama: 7B, 13B, 70B for code generation

Embedding Models​

  • Open Source: BGE Small, BGE Large
  • OpenAI: text-embedding-ada-002, text-embedding-3
  • Custom: Fine-tuned domain-specific embeddings
  • Multilingual: Support for multiple languages

Configuration System​

Interactive Configuration​

The archetype provides a sophisticated menu-driven configuration system for customizing the chatbot:

# Example configuration selections
frontend:
  type: "Streamlit Frontend"  # or "Custom Frontend"
  
llm_models:
  - "OpenAI (GPT4, GPT 4 Turbo, GPT 4 Vision)"
  - "Llama3.1 (8b, 70b, 405b)"
  
embedding_models:
  - "OpenAI Embedding Model"
  
vector_database:
  type: "Pinecone"  # single selection
  
knowledge_graph:
  type: "Neo4j"     # single selection
  
agent_modules:
  - "Multi Document Agent"
  - "ReAct Agent"
  - "Chain of Thought"
  - "SQL Agents"
  
data_adaptors:
  - "S3 / Blob storage"
  - "B2B Connectors (e.g. Salesforce, Zendesk, Hubspot etc.)"

Advanced Agent Modules​

Reasoning Agents

  • ReAct Agent: Reason and Act pattern for complex problem solving
  • Chain of Thought: Step-by-step reasoning for complex queries
  • Graph of Thought: Non-linear reasoning with knowledge graphs
  • Multi-Document Agent: Cross-document information synthesis

Data Integration Agents

  • SQL Agents: Natural language to SQL query generation
  • Pandas Agent: Data analysis and manipulation
  • Search Agent: Web search and information retrieval
  • Document Agent: PDF, Word, PowerPoint processing

Frontend Options​

Streamlit Integration​

Built-in Features​

  • Interactive chat interface
  • File upload for document analysis
  • Conversation history management
  • Real-time streaming responses
  • Model selection interface

Customization Options​

  • Branded UI with company logos
  • Custom CSS and theming
  • Multi-language support
  • Mobile-responsive design
  • Admin configuration panel

Custom Frontend Architecture​

# Custom frontend API integration
from fastapi import FastAPI, WebSocket
from fastapi.responses import StreamingResponse
from chatbot_core import ChatbotService

app = FastAPI()
chatbot = ChatbotService()

@app.websocket("/ws/chat")
async def websocket_chat(websocket: WebSocket):
    await websocket.accept()
    
    async for message in websocket.iter_text():
        # Stream response back to frontend
        async for chunk in chatbot.stream_response(message):
            await websocket.send_text(chunk)

@app.post("/api/chat")
async def chat_completion(request: ChatRequest):
    response = await chatbot.generate_response(
        message=request.message,
        conversation_id=request.conversation_id,
        user_id=request.user_id
    )
    return response

@app.get("/api/conversations/{user_id}")
async def get_conversations(user_id: str):
    return await chatbot.get_user_conversations(user_id)

Knowledge & Data Integration​

Vector Database Integration​

Click to enlarge

Data Source Integration​

Document Sources​

  • PDF, Word, PowerPoint files
  • Web scraping and crawling
  • Wiki and documentation sites
  • Email and messaging archives

Structured Data​

  • SQL databases and data warehouses
  • REST APIs and GraphQL endpoints
  • CSV, JSON, XML files
  • Real-time data streams

Enterprise Integration​

  • Salesforce, HubSpot, Zendesk
  • SharePoint and OneDrive
  • Slack, Teams, Discord
  • Cloud storage (S3, Blob, GCS)

Enterprise Security​

Authentication & Authorization​

User Authentication

  • OAuth 2.0 / OpenID Connect integration
  • SAML SSO for enterprise environments
  • API key management for programmatic access
  • Multi-factor authentication support

Data Protection

  • Conversation encryption at rest and in transit
  • PII detection and masking
  • GDPR compliance features
  • Data retention and deletion policies

Rate Limiting & Usage Control​

# Rate limiting configuration
rate_limits:
  per_user:
    requests_per_minute: 20
    requests_per_hour: 500
  per_api_key:
    requests_per_minute: 100
    requests_per_hour: 2000
  global:
    requests_per_minute: 1000

# Usage tracking and billing
usage_tracking:
  track_tokens: true
  track_requests: true
  billing_integration: true
  cost_alerts: true

Advanced Features​

Conversation Management​

# Advanced conversation features
class ConversationManager:
    async def create_conversation(
        self,
        user_id: str,
        context: Optional[dict] = None,
        persona: Optional[str] = None
    ) -> Conversation:
        """Create new conversation with optional context and persona"""
        
    async def add_message(
        self,
        conversation_id: str,
        message: str,
        role: MessageRole,
        metadata: Optional[dict] = None
    ) -> Message:
        """Add message to conversation with metadata"""
        
    async def get_context_window(
        self,
        conversation_id: str,
        max_tokens: int = 4000
    ) -> List[Message]:
        """Get conversation context within token limit"""
        
    async def summarize_conversation(
        self,
        conversation_id: str
    ) -> ConversationSummary:
        """Generate conversation summary for long contexts"""

Multi-Modal Capabilities​

Input Modalities​

  • Text conversations and commands
  • Image analysis with GPT-4 Vision
  • Document upload and analysis
  • Voice input with transcription
  • Screen sharing for troubleshooting

Output Modalities​

  • Rich text responses with formatting
  • Code generation with syntax highlighting
  • Image generation and editing
  • Chart and graph creation
  • File downloads and exports

Deployment & Scaling​

Container Deployment​

# Docker Compose configuration
version: '3.8'
services:
  chatbot-api:
    build: .
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - VECTOR_DB_URL=${PINECONE_URL}
      - KNOWLEDGE_GRAPH_URL=${NEO4J_URL}
    depends_on:
      - redis
      - postgres
      
  chatbot-frontend:
    build: ./frontend
    ports:
      - "8501:8501"
    depends_on:
      - chatbot-api
      
  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data
      
  postgres:
    image: postgres:15
    environment:
      - POSTGRES_DB=chatbot
    volumes:
      - postgres_data:/var/lib/postgresql/data

Kubernetes Configuration​

High Availability​

  • Multi-replica deployment
  • Load balancing across instances
  • Database connection pooling
  • Redis cluster for session management

Auto-Scaling​

  • CPU and memory-based scaling
  • Request queue depth monitoring
  • Custom metrics for LLM usage
  • Cost-aware scaling policies

AI Model Management​

Model Selection Strategy​

Dynamic Model Routing

Intelligent routing of queries to appropriate models based on complexity, cost, latency requirements, and content type.

Cost Optimization

Automatic cost optimization through model selection, caching strategies, and request batching for optimal performance per dollar.

Model Configuration​

# Model configuration example
model_config = {
    "primary_models": {
        "general": "gpt-4-turbo",
        "code": "codellama-70b",
        "analysis": "claude-3-opus"
    },
    "fallback_models": {
        "general": "gpt-3.5-turbo",
        "code": "codellama-7b",
        "analysis": "gpt-4"
    },
    "routing_rules": {
        "code_keywords": ["function", "class", "import", "def"],
        "analysis_keywords": ["analyze", "compare", "evaluate"],
        "simple_queries_threshold": 20  # token count
    }
}

Use Cases & Integration​

Enterprise Use Cases​

  • Customer Support: Intelligent helpdesk with knowledge base integration
  • Internal Knowledge Assistant: Employee onboarding and training support
  • Code Assistant: Development support with codebase understanding
  • Document Analysis: Contract review, compliance checking, research
  • Sales Assistant: Product information, pricing, and proposal generation
  • Data Analytics: Natural language queries over business data

Integration Patterns​

Enterprise Integration
  • Slack/Teams Bots: Direct integration with collaboration platforms
  • API Gateway: Expose chatbot capabilities as enterprise APIs
  • Webhook Integration: Real-time notifications and event processing
  • SSO Integration: Seamless authentication with enterprise identity providers
  • Monitoring Integration: Comprehensive logging and alerting systems

This archetype provides a comprehensive foundation for building enterprise-grade AI chatbot services that can scale from simple Q&A systems to sophisticated AI assistants with multi-modal capabilities and enterprise integration.