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ThinkingModels CLI Interface

A powerful command-line interface for the ThinkingModels project that allows you to solve problems using AI and thinking models.

Installation

Ensure you have the required dependencies installed:

pip install click colorama rich requests

Quick Start

  1. Check available commands:

    python thinking_models.py --help

  2. View available thinking models:

    python thinking_models.py models

  3. Test your setup:

    python thinking_models.py test

  4. Start interactive mode:

    python thinking_models.py interactive

Configuration

Environment Variables

Set these environment variables for API access:

# Required
LLM_API_URL=https://your-llm-api-endpoint.com

# Optional
LLM_API_KEY=your-api-key-here
LLM_MODEL_NAME=gpt-3.5-turbo
LLM_TEMPERATURE=0.7
LLM_MAX_TOKENS=2000
THINKING_MODELS_DIR=models

Command Line Options

You can also configure settings via command-line options:

python thinking_models.py --api-url https://api.example.com --model gpt-4 --temperature 0.5 query "How can I improve productivity?"

Commands

1. Interactive Mode

Start an interactive session where you can ask questions and get responses:

python thinking_models.py interactive

Interactive Commands:

  • help - Show help information
  • models - List available thinking models
  • config - Show current configuration
  • quit / exit / q - Exit interactive mode

2. Single Query Processing

Process a single query and get a response:

python thinking_models.py query "How can I improve my startup's marketing strategy?"

Options:

  • -o, --output-file FILE - Save results to a file
  • --output-format [rich|json|plain] - Output format

Examples:

# Basic query
python thinking_models.py query "What's the best approach to prioritize tasks?"

# Save to file
python thinking_models.py query "How to reduce costs?" -o results.txt

# JSON output
python thinking_models.py --output-format json query "Investment strategies?"

3. Batch Processing

Process multiple queries from a file:

python thinking_models.py query -f example_queries.txt

Batch File Format:

# Comments start with #
How can I improve my startup's marketing strategy?

What's the best approach to analyze large datasets?

Help me prioritize my daily tasks more effectively.

Options:

  • -f, --batch-file FILE - Input file with queries
  • -o, --output-file FILE - Save results to a file

4. Model Information

List all available thinking models:

python thinking_models.py models

5. Configuration Check

View current configuration:

python thinking_models.py config

6. System Test

Test your ThinkingModels setup:

python thinking_models.py test

Output Formats

Rich Format (Default)

Beautiful, formatted output with tables, panels, and colors:

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃              Query Information        ┃
┠───────────────────────────────────────┨
┃ Query:   How to improve productivity? ┃
┃ Models:  eisenhower_matrix, agile     ┃
┃ Time:    2.34s                        ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

JSON Format

Structured JSON output for programmatic use:

{
  "query": "How to improve productivity?",
  "selected_models": ["eisenhower_matrix", "agile"],
  "solution": "To improve productivity...",
  "processing_time": 2.34,
  "error": null
}

Plain Format

Simple text output:

Query: How to improve productivity?
Selected Models: eisenhower_matrix, agile
Processing Time: 2.34s

Solution:
To improve productivity...

Examples

Example 1: Interactive Session

$ python thinking_models.py interactive
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃        ThinkingModels Interactive      ┃
┃              Mode                      ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

✓ 140 thinking models loaded

Your query: How can I manage my time better?

[Processing...]

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃            Query Information           ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

Example 2: Batch Processing

python thinking_models.py query -f business_questions.txt -o business_analysis.json --output-format json

Example 3: Different API Configuration

python thinking_models.py --api-url https://api.openrouter.ai/api/v1 --model anthropic/claude-3-haiku query "Explain machine learning"

Tips

  1. Be Specific: More specific queries lead to better model selection and solutions.

  2. Use Verbose Mode: Add -v for detailed processing information:

    python thinking_models.py -v query "Your question here"

  3. Check Model Relevance: Use models command to see what thinking models are available.

  4. Batch Processing: Use batch files for processing multiple related queries efficiently.

  5. Output Formats: Use JSON format when integrating with other tools or scripts.

Troubleshooting

Common Issues

  1. API Configuration Error

    Error: LLM_API_URL environment variable must be set

    Solution: Set the LLM_API_URL environment variable or use --api-url option.

  2. Model Loading Error

    Error loading models: [Errno 2] No such file or directory: 'models'

    Solution: Ensure the models directory exists or specify correct path with --models-dir.

  3. API Connection Error

    LLM API request failed after 3 attempts

    Solution: Check your API URL, key, and internet connection.

Debug Mode

Use verbose mode for detailed information:

python thinking_models.py -v test

Test Setup

Always run the test command after configuration:

python thinking_models.py test

This will check:

  • Model loading (140 models expected)
  • API configuration
  • API connection
  • Full query processing pipeline

Advanced Usage

Custom Model Directory

python thinking_models.py --models-dir /path/to/custom/models query "Your question"

Multiple Output Formats

# Save both rich and JSON outputs
python thinking_models.py query "Question" -o results.txt
python thinking_models.py --output-format json query "Question" -o results.json

Environment-Specific Configuration

Create different environment files:

.env.development

LLM_API_URL=http://localhost:1234/v1
LLM_MODEL_NAME=local-model

.env.production

LLM_API_URL=https://api.openai.com/v1
LLM_API_KEY=your-production-key
LLM_MODEL_NAME=gpt-4

Integration

The CLI can be easily integrated into scripts and workflows:

Bash Script Example

#!/bin/bash
# Process business questions
python thinking_models.py query -f business_questions.txt -o business_results.json --output-format json

# Check if successful
if [ $? -eq 0 ]; then
    echo "Processing completed successfully"
    # Process results.json with other tools
else
    echo "Processing failed"
    exit 1
fi

Python Integration

import subprocess
import json

# Run CLI command
result = subprocess.run([
    'python', 'thinking_models.py', 
    '--output-format', 'json',
    'query', 'How to improve team productivity?'
], capture_output=True, text=True)

if result.returncode == 0:
    response = json.loads(result.stdout)
    print(f"Selected models: {response['selected_models']}")
    print(f"Solution: {response['solution']}")

Support

For issues and questions:

  1. Run python thinking_models.py test to diagnose problems
  2. Check that all 140 thinking models are loaded
  3. Verify API configuration with python thinking_models.py config
  4. Use verbose mode (-v) for detailed error information