Building Chatbots

Flow-Like makes it easy to build sophisticated chatbots—from simple Q&A bots to AI assistants with memory, knowledge bases, and tool-calling capabilities.

Chatbot Architecture

User Message
    │
    ▼
Chat Event (entry point)
    │
    ▼
┌─────────────────────────────────────┐
│  Conversation Context               │
│  ├── Chat history (memory)          │
│  ├── User profile                   │
│  └── Session state                  │
└─────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────┐
│  Processing Pipeline                │
│  ├── Intent detection (optional)    │
│  ├── RAG retrieval (optional)       │
│  ├── Tool execution (optional)      │
│  └── LLM response generation        │
└─────────────────────────────────────┘
    │
    ▼
Response to User

Simple Q&A Bot

The most basic chatbot—direct LLM responses:

Chat Event (user_message)
    │
    ▼
Invoke LLM
├── model: GPT-4
├── system: "You are a helpful assistant for our product."
└── prompt: user_message
    │
    ▼
Response ──▶ Return to user

Add personality:

System Prompt:
"You are Max, a friendly customer support agent for TechCorp.
- Be concise and helpful
- Use casual, friendly language
- If unsure, offer to connect with a human agent
- Never discuss competitor products"

Chat with Memory

Remember conversation history:

Board Variables:
├── chat_history: Array<Message> = []
└── user_name: String | null

Chat Event (user_message)
    │
    ▼
Get Variable: chat_history
    │
    ▼
Build Messages:
├── System: "You are a helpful assistant..."
├── History: [...chat_history]
└── User: user_message
    │
    ▼
Invoke LLM (messages)
    │
    ▼
Append to chat_history:
├── { role: "user", content: user_message }
└── { role: "assistant", content: response }
    │
    ▼
Set Variable: chat_history
    │
    ▼
Return response

Memory Strategies

Strategy	Best For	Implementation
Full History	Short conversations	Keep all messages
Sliding Window	Long conversations	Keep last N messages
Summarized	Very long sessions	Periodically summarize
Semantic	Topic-focused	Keep relevant messages

Sliding window example:

Get Variable: chat_history
    │
    ▼
Slice (last 20 messages)
    │
    ▼
Build Messages ──▶ Invoke LLM

Summarized memory:

If chat_history.length > 50:
    │
    ▼
Summarize older messages
    │
    ▼
Replace with summary + recent 10 messages

RAG-Powered Bot

Answer questions from your knowledge base:

Chat Event (user_message)
    │
    ▼
Embed Query (user_message)
    │
    ▼
Vector Search (knowledge_base, k=5)
    │
    ▼
Build Prompt:
"Answer based on this context:
{retrieved_documents}

User question: {user_message}

If the context doesn't contain the answer, say so."
    │
    ▼
Invoke LLM
    │
    ▼
Response (with source citations)

Building the Knowledge Base

Ingest pipeline:

For Each document in /docs
    │
    ▼
Extract Text (AI Extract Document)
    │
    ▼
Chunk (500 tokens, 50 overlap)
    │
    ▼
Embed Chunks
    │
    ▼
Insert to LanceDB (with metadata)

Hybrid Search

Combine semantic and keyword search:

Embed Query (user_message)
    │
    ▼
Hybrid Search
├── Vector search (semantic)
├── Full-text search (keywords)
└── Rerank results
    │
    ▼
Top 5 results ──▶ Build context

Agent Bot

Create a bot that can take actions:

Board: SearchTool
└── Quick Action Event (query)
        │
        ▼
    Web Search ──▶ Return results

Board: CalculatorTool
└── Quick Action Event (expression)
        │
        ▼
    Evaluate ──▶ Return result

Board: CreateTicketTool
└── Quick Action Event (title, description)
        │
        ▼
    Jira Create Issue ──▶ Return ticket_id

Board: MainChatbot
├── Variables:
│   └── chat_history: Array<Message>
│
└── Chat Event (user_message)
        │
        ▼
    Make Agent
    ├── model: GPT-4
    ├── tools:
    │   ├── SearchTool: "Search the web for information"
    │   ├── CalculatorTool: "Perform calculations"
    │   └── CreateTicketTool: "Create a support ticket"
    └── system: "You are a support assistant..."
        │
        ▼
    Run Agent (user_message, chat_history)
        │
        ▼
    Response ──▶ Update history ──▶ Return

The agent decides when to use tools based on the conversation.

Multi-Turn Conversations

Handle complex dialogues with context:

Chat Event
    │
    ▼
Detect Intent:
├── "greeting" ──▶ Greeting flow
├── "product_question" ──▶ RAG lookup
├── "support_request" ──▶ Ticket creation flow
├── "order_status" ──▶ Order lookup
└── "other" ──▶ General LLM

Slot Filling

Collect required information across turns:

Variables:
├── slots:
│   ├── name: null
│   ├── email: null
│   └── issue: null
└── current_slot: "name"

Chat Event
    │
    ▼
Extract slot value from message
    │
    ▼
Update slots
    │
    ▼
All slots filled?
├── Yes ──▶ Process request
└── No ──▶ Ask for next slot

Conversation flow:

Bot: "What's your name?"
User: "I'm Alice"
Bot: "Hi Alice! What's your email?"
User: "alice@example.com"
Bot: "Got it. What can I help you with today?"
User: "I can't log into my account"
Bot: "I've created a support ticket for your login issue.
      Reference: #12345"

Platform Integrations

Discord Bot

Discord Session (bot_token)
    │
    ▼
On Message Event
    │
    ▼
Process with chatbot logic
    │
    ▼
Discord Send Message (channel_id, response)

With commands:

Discord Command: /ask {question}
    │
    ▼
Process question ──▶ Reply

Telegram Bot

Telegram Session (bot_token)
    │
    ▼
On Message Event
    │
    ▼
Process with chatbot logic
    │
    ▼
Telegram Send Message (chat_id, response)

With keyboards:

Telegram Send Message
├── text: "How can I help?"
└── keyboard: [
    ["Product Info", "Support"],
    ["Order Status", "Talk to Human"]
]

Slack Bot

HTTP Event (Slack webhook)
    │
    ▼
Verify Slack signature
    │
    ▼
Process message
    │
    ▼
HTTP Request (Slack response URL)

Web Chat (A2UI)

Build a chat interface in your app:

Page: /chat
├── Column:
│   ├── ScrollArea (chat messages)
│   │   └── For Each message in chat_history:
│   │       └── MessageBubble (message)
│   │
│   └── Row:
│       ├── TextField (user input)
│       └── Button: Send
│           └── onClick: invoke ChatHandler

Advanced Patterns

Conversation Branching

Chat Event
    │
    ▼
Classify Intent (LLM)
    │
    ▼
Switch intent:
    │
    ├── "sales" ──▶ Sales Bot (different personality)
    │
    ├── "support" ──▶ Support Bot (access to tickets)
    │
    └── "general" ──▶ General Assistant

Human Handoff

Chat Event
    │
    ▼
Process with AI
    │
    ▼
Confidence below threshold?
├── No ──▶ Return AI response
│
└── Yes ──▶ Check agent availability
                │
                ├── Available ──▶ Transfer to human
                │                   │
                │                   ▼
                │               Notify agent
                │                   │
                │                   ▼
                │               "Connecting you with a human agent..."
                │
                └── Not available ──▶ "I'm not sure about that.
                                        Would you like me to create
                                        a support ticket?"

Proactive Messages

Scheduled Event (check_for_updates)
    │
    ▼
Query: Users with pending actions
    │
    ▼
For Each user
    │
    ▼
Send reminder message
├── "Hi! Your order shipped yesterday. Track it here: {link}"

Multi-Language Support

Chat Event (user_message)
    │
    ▼
Detect Language (LLM or library)
    │
    ▼
Set response language
    │
    ▼
Process with language-specific system prompt
    │
    ▼
Respond in detected language

Bot Personality & Safety

System Prompt Engineering

System Prompt:
"""
You are Luna, an AI assistant for SpaceTravel Inc.

PERSONALITY:
- Enthusiastic about space exploration
- Uses space-related metaphors occasionally
- Professional but warm

BOUNDARIES:
- Never make up flight information
- Don't discuss competitors
- For medical questions, recommend consulting a doctor
- For pricing, always refer to official sources

RESPONSE STYLE:
- Keep responses under 150 words
- Use bullet points for lists
- Include relevant emojis sparingly 🚀
"""

Guardrails

Chat Event (user_message)
    │
    ▼
Content Moderation Check
├── Inappropriate ──▶ Polite refusal
│
▼
Process normally
    │
    ▼
Response Validation
├── Contains PII ──▶ Redact
├── Off-topic ──▶ Redirect
│
▼
Return safe response

Handling Edge Cases

Empty message ──▶ "I didn't catch that. Could you try again?"

Very long message ──▶ Truncate + "I noticed a long message.
                       Let me address the main points..."

Repeated questions ──▶ "I mentioned earlier that...
                        Would you like more details?"

Frustration detected ──▶ "I understand this is frustrating.
                          Let me connect you with someone
                          who can help directly."

Metrics & Improvement

Track Conversations

After each response:
    │
    ▼
Log to database:
├── conversation_id
├── user_message
├── bot_response
├── intent_detected
├── tools_used
├── response_time
└── timestamp

Feedback Loop

After response:
    │
    ▼
Show: "Was this helpful? 👍 👎"
    │
    ▼
On feedback:
├── 👍 ──▶ Log positive
└── 👎 ──▶ Log negative + offer escalation

Analyze & Improve

Weekly Analysis:
├── Most common questions ──▶ Add to FAQ/knowledge base
├── Low-rated responses ──▶ Review and improve prompts
├── Unanswered questions ──▶ Expand capabilities
└── Tool usage patterns ──▶ Optimize tool descriptions

Example: Full Customer Support Bot

Board: SupportBot
├── Variables:
│   ├── chat_history: Array<Message>
│   ├── customer: Customer | null
│   ├── current_ticket: Ticket | null
│   └── awaiting_rating: Boolean
│
├── Tools:
│   ├── LookupOrder (order_id)
│   ├── CreateTicket (title, description)
│   ├── SearchKnowledge (query)
│   └── TransferToHuman ()
│
└── Chat Event (user_message, customer_id)
        │
        ▼
    Load customer profile (if customer_id)
        │
        ▼
    awaiting_rating?
    ├── Yes ──▶ Process rating ──▶ Thank user
    │
    ▼
    Make Agent
    ├── model: GPT-4
    ├── tools: [LookupOrder, CreateTicket, SearchKnowledge, TransferToHuman]
    └── system: "You are a support agent for TechCorp.
                 Customer: {customer.name}
                 Previous purchases: {customer.orders}
                 Be helpful and empathetic."
        │
        ▼
    Run Agent (user_message, chat_history)
        │
        ▼
    Update chat_history
        │
        ▼
    Ticket created?
    ├── Yes ──▶ Set awaiting_rating = true after resolution
        │
        ▼
    Return response

Best Practices

1. Start Simple

Begin with a basic Q&A bot, then add features iteratively.

2. Test Extensively

Create test conversations covering edge cases before deployment.

3. Monitor in Production

Log all conversations, watch for failures, iterate on prompts.

4. Set Expectations

Be clear about what the bot can and cannot do.

5. Provide Escape Hatches

Always offer a way to reach a human.

6. Respect Privacy

Don’t store sensitive information longer than needed.

Next Steps

• GenAI Agents – Deep dive into agent capabilities
• RAG – Build knowledge bases
• API Integrations – Platform connections
• Building Internal Tools – Chat UIs