Token Estimator

Text: "Hello, world!"
Model: "gpt-3.5-turbo"

Result:
- tokenCount: 4
- tokenizerName: "tiktoken"
- model: "gpt-3.5-turbo"

Text: "This is a longer text that might exceed our safety limit..."
Model: "gpt-3.5-turbo"
SafetyLimit: 10

Result:
- tokenCount: 15
- tokenizerName: "tiktoken"
- model: "gpt-3.5-turbo"
- limitExceeded: true

  # Tiktoken Estimator Integration

Estimate token count for text using the tiktoken library, enabling accurate token counting for OpenAI models.

## Features

- **Accurate Token Counting**: Uses the official tiktoken library to provide precise token estimates
- **Multi-Model Support**: Supports various OpenAI models (gpt-3.5-turbo, gpt-4, etc.)
- **Safety Limits**: Optional safety limit checking to prevent token overages
- **Zero Configuration**: No setup required - works out of the box
- **Error Handling**: Graceful error handling with descriptive messages

## Usage

### Estimate Tokens Action

The integration provides a single action: `estimateTokens`

**Input Parameters:**
- `text` (required): The text to estimate tokens for
- `model` (optional): The OpenAI model to use for tokenization (defaults to "gpt-3.5-turbo")
- `safetyLimit` (optional): Safety limit for token count estimation. If left empty, no limit will be applied

**Output:**
- `tokenCount`: The estimated number of tokens in the text
- `tokenizerName`: The name of the tokenizer used
- `model`: The model the tokenization was based on
- `limitExceeded`: Indicates if the estimated token count exceeded the safety limit (only present when safetyLimit is provided)

### Example Usage

**Basic Usage:**
```
Text: "Hello, world!"
Model: "gpt-3.5-turbo"

Result:
- tokenCount: 4
- tokenizerName: "tiktoken"
- model: "gpt-3.5-turbo"
```

**With Safety Limit:**
```
Text: "This is a longer text that might exceed our safety limit..."
Model: "gpt-3.5-turbo"
SafetyLimit: 10

Result:
- tokenCount: 15
- tokenizerName: "tiktoken"
- model: "gpt-3.5-turbo"
- limitExceeded: true
```

## Supported Models

- `gpt-3.5-turbo`
- `gpt-4`
- `gpt-4-turbo`
- `text-davinci-003`
- `text-davinci-002`
- `code-davinci-002`
- And other OpenAI models supported by tiktoken

## Recommended Safety Limits

When setting safety limits, consider that your actual API calls will include additional tokens for system prompts, conversation history, and response generation. Here are conservative recommendations:

### GPT-3.5-Turbo (4,096 token limit)
- **Conservative**: 2,500 tokens (leaves ~1,600 for system prompts + response)
- **Moderate**: 3,000 tokens (leaves ~1,100 for system prompts + response)
- **Aggressive**: 3,500 tokens (leaves ~600 for system prompts + response)

### GPT-4 (8,192 token limit)
- **Conservative**: 5,000 tokens (leaves ~3,200 for system prompts + response)
- **Moderate**: 6,000 tokens (leaves ~2,200 for system prompts + response)
- **Aggressive**: 7,000 tokens (leaves ~1,200 for system prompts + response)

### GPT-4 Turbo (128,000 token limit)
- **Conservative**: 100,000 tokens (leaves ~28,000 for system prompts + response)
- **Moderate**: 110,000 tokens (leaves ~18,000 for system prompts + response)
- **Aggressive**: 120,000 tokens (leaves ~8,000 for system prompts + response)

**Note**: These recommendations assume typical system prompt sizes (200-800 tokens) and desired response lengths (500-2,000 tokens). Adjust based on your specific use case.

## Error Handling

The integration handles various error scenarios:

- **Invalid Input**: Returns clear error messages for missing or invalid text
- **Empty Text**: Returns 0 tokens for empty strings
- **Unsupported Model**: Returns error for models not supported by tiktoken
- **Tokenization Errors**: Handles tiktoken library errors gracefully
- **Safety Limit Warnings**: Logs warnings when token counts exceed safety limits

## Benefits

- **Cost Optimization**: Estimate token costs before making API calls
- **Rate Limiting**: Manage token budgets and prevent overages with safety limits
- **Workflow Logic**: Enable conditional logic based on token counts and safety thresholds
- **Transparency**: Provide visibility into token usage patterns
- **Proactive Monitoring**: Set safety limits to catch potential token overages early