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?"
  HEAR name
  TALK "What's your email?"
  HEAR email
  ' ... lots more code ...
ENDIF

With BotServer:

' ✅ NEW WAY - Just create the tool!
PARAM name AS string
PARAM email AS string
DESCRIPTION "Enrollment tool"
SAVE "enrollments.csv", name, email

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

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
ADD_KB "general"  ' Load general knowledge base
' Tools are auto-discovered from .gbdialog/ folder

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

• Understanding Sessions - How conversations persist
• About Packages - Package structure
• Tool Definition - Creating tools
• Knowledge Base - Document management

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