Knowledge Base

The Knowledge Base tab allows you to manage structured data sources used by your AI agent to generate accurate and context-aware responses.

Knowledge Categories

• Knowledge Categories: Organize information into separate categories (e.g., FAQ, Vehicles, Pricing).
• Each category represents a specific type of data the agent can use during conversations.
• Categories can contain structured or unstructured data.

Creating a Category

• Enter a name in Category Name.
• Click to create the category.
• Add files to the category using supported formats.

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Supported File Types

You can create or upload different types of files depending on your use case:

• CSV: Structured data (e.g., pricing tables, vehicles, services).
• TXT: Plain text content (e.g., FAQs, company info, policies).
• JSON: Structured data with flexible schema for advanced use cases.
• Web Page: Import content directly from a URL.
• PDF (upload only): Documents such as manuals or policies.

Importing Files

• Drag and drop files into the upload area or click to upload.
• Supported formats: PDF, JSON, TXT, CSV.
• Maximum file size: 50MB.

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Preview Data

• Preview Data allows you to view the uploaded content inside a category.
• For CSV files, data is displayed in a table format (rows and columns).
• For text-based files, content is shown as plain text.

Use this section to:

• Verify that data is uploaded correctly
• Check column structure and values
• Ensure formatting is clean and usable by the agent

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Instructions

• Instructions define how the agent should interpret and use data from this category.
• You can provide additional guidance to improve how the model retrieves and responds with this data.

Examples:

• Explain what the data represents (e.g., “This file contains vehicle types and capacity”)
• Add constraints (e.g., “Use only exact matches for vehicle type”)
• Guide response formatting (e.g., “Always include capacity and luggage in the answer”)

This helps improve accuracy and reduces incorrect interpretations.

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Category Configuration

• Category Name: Defines how the category is labeled and referenced inside the agent.
• Data Source: Upload and manage files associated with the category.
• CSV Structure:
  • Columns represent attributes (e.g., vehicle type, capacity, luggage)
  • Rows represent individual records
  • Used for precise lookups and filtering

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Advanced Category & Document Settings

The Knowledge Base provides additional configuration options to fine-tune how data is processed and retrieved.

Search Configuration

• Chapter Count:
  Defines how many relevant data chunks are returned per request.
  Higher values increase context but may introduce noise.

• Threshold:
  Controls how strictly results are filtered by relevance.
  Higher = stricter matching (more precise results), lower = broader results (less strict).

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CSV Splitter Configuration

Used to control how structured data (CSV) is interpreted and returned.

• Use Custom Config:
  Enables manual control over how CSV data is processed.

Chapter Search Template

• Defines which fields are used to search and match data.
• Example:
  • Capacity
  • Luggage

These fields are used to filter and find relevant records.

Chapter Output Template

• Defines how the data is formatted and returned to the model.
• Example:
  • Vehicle
  • Capacity
  • Luggage
  • Vehicle type code

This controls what the agent receives and uses in responses.

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How It Works

• User sends a request
• The system searches the Knowledge Base
• Data is filtered using Threshold
• Top results are selected using Chapter Count
• CSV data is processed using Search Template
• Final output is formatted using Output Template

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Best Practices

• Use CSV or JSON for structured, filterable data.
• Use TXT or PDF for descriptive content.
• Keep categories focused and well-organized.
• Use Chapter Count (1–3) for precise results.
• Use Threshold (0.7–1) for strict matching.
• Add Instructions to guide the model behavior.
• Always verify data using Preview Data before deploying.

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Notes

• The Knowledge Base is the agent’s source of truth.
• The agent should rely only on this data when strict instructions are used.
• Poorly structured or outdated data may lead to incorrect responses.
• Advanced settings significantly impact response accuracy and relevance.