botserver/docs/src/chapter-01/first-conversation.md

First Conversation

After BotServer starts, you can immediately start chatting with your bot. No programming required!

Just Start Talking

Open your browser to:

http://localhost:8080

And start chatting:

You: Hi!
Bot: Hello! How can I help you today?

You: I want to enroll in a course
Bot: I'll help you with enrollment. Let me collect your information...
     [Bot automatically calls enrollment.bas tool]

You: What documents do you have?
Bot: [Searches .gbkb/ folders and answers from your documents]

That's it! The LLM handles everything automatically.

How It Works

1. Drop Documents in .gbkb/

mybot.gbai/
  mybot.gbkb/
    policies/
      enrollment-policy.pdf
      course-catalog.pdf
    faqs/
      student-faq.docx
      payment-guide.txt

The bot automatically:

• ✅ Indexes all documents
• ✅ Creates vector embeddings
• ✅ Searches when users ask questions
• ✅ Provides accurate answers from your content

2. Create Tools as .bas Files

Create enrollment.bas:

PARAM name AS string          LIKE "John Smith"           DESCRIPTION "Student full name"
PARAM email AS string         LIKE "john@example.com"     DESCRIPTION "Contact email"
PARAM course AS string        LIKE "Computer Science"     DESCRIPTION "Course to enroll in"

DESCRIPTION "Enrollment tool - collects student information and processes enrollment"

SAVE "enrollments.csv", name, email, course, NOW()
TALK "Enrollment complete! Welcome to " + course + ", " + name

The LLM automatically:

• ✅ Discovers this tool
• ✅ Knows when to call it (when users want to enroll)
• ✅ Collects required parameters through conversation
• ✅ Executes the tool when ready

3. The LLM Does Everything Else

You DON'T need to write:

• ❌ IF/THEN logic
• ❌ Intent detection
• ❌ Dialog flows
• ❌ State machines
• ❌ Complex routing

The LLM automatically:

• Understands user intent
• Calls appropriate tools
• Searches knowledge bases
• Maintains conversation context
• Handles follow-up questions

Real Example Conversation

With enrollment.bas tool and course documents in .gbkb/:

User: Hello, I'd like to know about your computer science program

Bot: I'd be happy to help you learn about our Computer Science program! 
     Based on our course catalog, the program offers a comprehensive 
     4-year curriculum covering software development, algorithms, 
     databases, and AI. Would you like to know about specific courses 
     or the enrollment process?

User: Yes, I want to enroll

Bot: Great! I'll help you enroll in the Computer Science program. 
     Let me collect your information. What's your full name?

User: John Smith

Bot: Thank you, John. What email address should we use for contact?

User: john.smith@gmail.com

Bot: Perfect! I'm enrolling you in Computer Science now...
     [Executes enrollment.bas tool]
     Enrollment complete! Welcome to Computer Science, John Smith!
     You'll receive a confirmation at john.smith@gmail.com.

User: What are the payment options?

Bot: [Searches payment-guide.txt from .gbkb/]
     According to our payment guide, we offer three options:
     1. Full payment with 10% discount
     2. Monthly installments over 12 months
     3. Semester-based payments
     Which option would you prefer?

The Magic Formula

📚 Documents (.gbkb/) + 🔧 Tools (.bas) + 🤖 LLM = ✨ Intelligent Bot

No complex programming needed!

Creating Your First Bot

Step 1: Create Structure

mkdir -p mybot.gbai/mybot.gbdialog
mkdir -p mybot.gbai/mybot.gbkb/docs
mkdir -p mybot.gbai/mybot.gbot

Step 2: Add Documents

Drop any documents in .gbkb/:

• PDFs
• Word documents
• Text files
• Markdown files

Step 3: Create a Tool (Optional)

Create mybot.gbdialog/my-tool.bas:

PARAM user_name AS string
PARAM request AS string

DESCRIPTION "Handles user requests"

result = CALL "/api/process", user_name, request
TALK "Done! " + result

Step 4: Start Chatting

Restart BotServer and chat! The LLM will:

• Answer questions from your documents
• Call your tools when appropriate
• Handle the entire conversation naturally

No Programming Required

Traditional chatbots require complex logic:

' ❌ OLD WAY - DON'T DO THIS!
IF user_input CONTAINS "enroll" THEN
  TALK "What's your name?"
  ' ❌ OLD WAY - Complex multi-step dialog
  IF intent = "enrollment" THEN
    TALK "Let me help you enroll. What's your name?"
    HEAR name
    TALK "What's your email?"
    HEAR email
    ' ... lots more code ...
  ENDIF

With BotServer:

' ✅ NEW WAY - Just create the tool!
' In enrollment.bas - becomes a tool automatically
PARAM name AS string
PARAM email AS string
DESCRIPTION "Collects enrollment information"

' The tool is called by LLM when needed
SAVE "enrollments.csv", name, email
TALK "Successfully enrolled " + name

The LLM handles all the conversation logic!

What Can You Build?

Customer Support Bot

• Add product manuals to .gbkb/
• Create create-ticket.bas tool
• LLM answers questions and creates support tickets automatically

HR Assistant

• Add employee handbook to .gbkb/
• Create leave-request.bas tool
• LLM explains policies and processes leave requests

Education Platform

• Add course materials to .gbkb/
• Create enrollment.bas and submit-assignment.bas tools
• LLM teaches content and manages student tasks

Sales Assistant

• Add product catalogs to .gbkb/
• Create create-quote.bas tool
• LLM answers product questions and generates quotes

Advanced Features

Dynamic Tool Loading

The LLM can load tools based on context:

' In start.bas - minimal setup, no HEAR needed
USE KB "general"  ' Load general knowledge base
' Tools in .gbdialog/ are auto-discovered
' LLM handles the conversation naturally

Multi-Language Support

The LLM handles multiple languages automatically:

User: Olá, quero me inscrever no curso
Bot: Claro! Vou ajudá-lo com a inscrição...

User: 我想了解计算机科学课程
Bot: 我很乐意帮您了解计算机科学课程...

Context Awareness

The LLM maintains conversation context:

User: I want to enroll
Bot: I'll help you enroll. What's your name?
User: Actually, first tell me about the prerequisites
Bot: Of course! The Computer Science program requires...

Tips for Success

1. Organize Documents Clearly

mybot.gbkb/
  policies/      # Policy documents
  products/      # Product information
  faqs/          # Frequently asked questions
  tutorials/     # How-to guides

2. Name Tools Descriptively

Good tool names:

• enrollment.bas
• create-ticket.bas
• schedule-meeting.bas

The LLM understands what each tool does from its name and description.

3. Use Tool Descriptions

Always add descriptions to tools:

DESCRIPTION "This tool processes student enrollment for courses"

4. Let the LLM Work

Don't try to control every aspect of the conversation. Let the LLM:

• Rephrase responses naturally
• Handle unexpected questions
• Maintain conversation flow

Next Steps

• Quick Start - Build your first bot
• About Packages - Package structure
• Tool Definition - Creating tools
• Knowledge Base - Document management

Remember: Just add documents and tools - the LLM does the rest! 🚀