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Advanced Features

Advanced features of Hummerbot AI including file upload, reasoning enhancement, web search, and tool integration.

Hummerbot AI - Advanced Features

Overview

Hummerbot AI includes several advanced features that enhance the capabilities of the platform beyond basic conversational AI. These features provide additional functionality for complex tasks and specialized use cases.

File Upload & Analysis

Supported File Types

Hummerbot AI supports uploading and analyzing various text-based file types:

  • Text files (.txt)
  • Markdown files (.md)
  • Code files (.tsx, .jsx, .ts, .js, .html, .css, .json, .xml)
  • Programming files (.py, .java, .cpp, .c, .h, .hpp, .cs, .rb, .php, .sql)
  • Configuration files (.yaml, .yml, .toml)

File Analysis Process

  1. Upload: Click the paperclip icon to select files from your device
  2. Processing: Files are read and their content is made available to the AI
  3. Context Integration: File content is incorporated into the conversation context
  4. Analysis: The AI can reference and analyze the uploaded content

Best Practices

  • Keep file sizes under 5MB
  • Use descriptive file names
  • Ensure files contain relevant content for your task
  • Remove files when no longer needed to maintain context clarity

Reasoning Enhancement

How It Works

When reasoning enhancement is enabled:

  • The AI is prompted to think step-by-step
  • Reasoning processes are shown in the response
  • Complex problems are broken down into logical steps
  • Final answers are supported by clear reasoning

Use Cases

  • Complex problem solving
  • Mathematical calculations
  • Logical puzzles
  • Analytical tasks
  • Research and analysis

Web Search Integration

Functionality

  • Real-time web search capabilities
  • Integration of current information into responses
  • Source citation and verification
  • Enhanced accuracy through external data

How It Works

  1. Enable Web Search: Toggle the Web button in the interface
  2. Query Processing: The AI uses your request as a search query
  3. Result Integration: Search results are incorporated into the response
  4. Source Attribution: Sources are cited in the response

Benefits

  • Access to current information
  • Enhanced accuracy for time-sensitive queries
  • Verification of facts
  • Broader knowledge base

Tool Integration

Available Tools

Hummerbot AI integrates with various specialized tools:

Intelligence Tools

  • Web Search: Live information from the internet
  • Source Verifier: Check reliability of online sources
  • Document Researcher: Gather and summarize multiple documents

Development Tools

  • Code Runner: Execute and test code snippets
  • Repository Analyzer: Understand codebase structure
  • Automation Runner: Execute workflows or scripts
  • API Tester: Test REST or GraphQL endpoints

Data Tools

  • File Reader: Read and extract data from files
  • Data Visualizer: Create charts and graphs
  • Structured Parser: Parse JSON, XML, or CSV

Reasoning Tools

  • Context Memory: Store and recall contextual information
  • Knowledge Retriever: Access private knowledge bases
  • Decision Mapper: Analyze options and propose decisions

Media Tools

  • Image Interpreter: Understand visual content
  • Image Generator: Create images from text
  • Video Analyzer: Extract information from videos
  • Speech Processor: Convert speech to text

Communication Tools

  • Text Editor: Refine and rewrite text
  • Email Composer: Generate professional emails
  • Language Translator: Translate across multiple languages
  • Meeting Notes: Summarize calls or transcripts

Ollama Integration

Setup

  1. Install Ollama on your local machine
  2. Start the Ollama service (default: http://localhost:11434)
  3. Configure the Ollama URL in the model selector settings
  4. Available models will automatically populate in the offline models section

Benefits

  • Complete privacy control
  • No API costs
  • Custom model experimentation
  • Offline capability
  • Full model control

Configuration

  • Access Ollama settings via the gear icon in the model selector
  • Enter your Ollama URL (default: http://localhost:11434)
  • Save settings to load available models

Task Orchestration

Agentic Workflow

In agentic mode, complex tasks are broken down into smaller, manageable subtasks:

  • Task decomposition based on user request
  • Dependency management between tasks
  • Status tracking (pending, in progress, completed, failed)
  • Error handling and retry mechanisms

Process Flow

  1. Request Analysis: AI analyzes the user's complex request
  2. Task Breakdown: Request is decomposed into specific tasks
  3. Execution: Tasks are executed in optimal order
  4. Result Aggregation: Individual task results are combined
  5. Response: Comprehensive response is provided to the user

Memory Management

Context Preservation

  • Conversation history is maintained during sessions
  • Context is preserved across multiple interactions
  • Memory is trimmed automatically to maintain performance
  • Cross-context sharing between different AI agents

Memory Controls

  • Clear conversation history with the reset button
  • Export/import conversation history
  • Memory optimization settings
  • Context window management

Customization Options

Parameter Tuning

  • Temperature: Control creativity vs. predictability
  • Maximum Length: Set response length limits
  • Top-P: Adjust response diversity
  • Model-specific settings

Interface Customization

  • Theme selection
  • Layout preferences
  • Font size adjustment
  • Display options