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Documentation Index

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Chunking is the process of splitting large documents or datasets into smaller, manageable pieces called chunks.

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Why Chunking Matters

AI models can only process a limited number of tokens at once.
Chunking ensures that long documents remain searchable, indexable, and retrievable.

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Best Practices

  • Smaller chunks → Higher precision (AI focuses better) but may lose broader context
  • Larger chunks → Preserve context but may retrieve less relevant information
  • The right balance depends on your data and use case

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Summary

Chunking makes your data searchable and structured, preparing it for retrieval systems like RAG.
RAG (Retrieval-Augmented Generation) improves chatbot accuracy by combining knowledge retrieval with LLM generation.
Instead of relying only on the model’s memory, responses are grounded in your uploaded data.

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How RAG Works in Chatzy

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1) Knowledge Base Upload & Processing

  • Users upload their Knowledge Base in the Data section
  • The system:
    • Splits content into chunks
    • Generates vector embeddings
    • Stores them in the database
Chunking can be configured in Advanced Bot Settings:
  • Number of chunks
  • Chunk length

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2) Query Understanding

When a user sends a message:
  • The system checks if KB(knowledge base) sources are available
  • A lightweight GPT model analyzes:
    • User message
    • Conversation history
  • It generates a search query based on user intent
👉 If no relevant intent is found, the search query may be null.

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3) Retrieval Step

If query is null
  • No KB context is attached
  • The LLM answers normally
If query exists
  • Vector search is performed on the KB
  • Top N relevant chunks are retrieved
Similarity and relevance thresholds can be configured in Advanced Settings.

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4) Hybrid Search (Optional)

Hybrid Search combines similarity search and text (keyword) search into a final relevancy score.How it works:
  • Extra chunks are fetched as a broad match
  • This step may include some noise
  • The system then reranks results
  • Only the top N most relevant chunks are kept
This ensures:
  • Better precision
  • Less irrelevant context
  • Higher response quality

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5) Generation Step

  • Retrieved chunks are combined
  • Added to the system prompt as context
  • Sent to the LLM with instructions
The LLM then generates a grounded, context-aware response.

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RAG Process Flow

User Query
   │
   ▼
Intent → Search Query Generation
   │
   ▼
Vector / Hybrid Search on KB
   │
   ▼
Reranking (Top N Chunks)
   │
   ▼
Context + Prompt → LLM
   │
   ▼
Final Answer

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Benefits of RAG

✅ Reduces hallucinations
✅ Grounds answers in your data
✅ Works well for large knowledge bases
✅ Configurable retrieval behavior
✅ More accurate and trustworthy responses
In short: Chunking makes your data searchable, and RAG ensures the model uses the right information at the right time.

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Location Request

If your chatbot needs the user’s live location during the conversation, you can instruct it to send a location request button. This is useful for scenarios such as booking a service visit, delivery verification, or assigning the nearest agent.Important:
You must clearly mention the use case inside your Base Prompt, explaining when the bot should send this JSON.Use this when:
  • You need the user’s address or live location for delivery, service visits, event check-ins, etc.
JSON format (use exactly as shown):
{
      "type": "location_request", 
      "content": "Replace with the message that you want to display"
}

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Call Permission Request

Use this when your AI agent needs to ask the user for permission to place a WhatsApp call (required for business-initiated calls).Why it’s needed:
  • WhatsApp requires explicit user consent before initiating an outbound call.
Best used for:
  • Sales follow-ups
  • Demo or consultation calls
  • Appointment confirmation calls
Important:
Specify when the bot should request permission inside your Base Prompt along with json so it triggers appropriately.(e.g., “If the user requests a callback, use ONLY the following for call permission request”).JSON format:
{
      "type": "call_permission_request", 
      "content": "Replace with the message that you want to display"
}

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WhatsApp Call Button

This option allows the agent to display a Call Now button directly inside the conversation.Use this when:
  • You want the user to start the call themselves (no approval template required).
  • Ideal for support lines, quick escalation, or urgent help.
When the user taps the button, the call is placed immediately from their device.Important:
In your Base Prompt, describe in which situations this button should appear (e.g., “If the user asks to talk to support, use ONLY the following for call button”).JSON format:
{
      "type": "voice_call", 
      "content": "Replace with the message that you want to display"
}