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# `BaseOpenAI` and `OpenAI` Documentation
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## Table of Contents
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1. [Overview](#overview)
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2. [Class Architecture](#class-architecture)
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3. [Purpose](#purpose)
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4. [Class Attributes](#class-attributes)
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5. [Methods](#methods)
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- [Construction](#construction)
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- [Configuration](#configuration)
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- [Tokenization](#tokenization)
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- [Generation](#generation)
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- [Asynchronous Generation](#asynchronous-generation)
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6. [Usage Examples](#usage-examples)
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- [Creating an OpenAI Object](#creating-an-openai-object)
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- [Generating Text](#generating-text)
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- [Advanced Configuration](#advanced-configuration)
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---
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## 1. Overview <a name="overview"></a>
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The `BaseOpenAI` and `OpenAI` classes are part of the LangChain library, designed to interact with OpenAI's large language models (LLMs). These classes provide a seamless interface for utilizing OpenAI's API to generate natural language text.
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## 2. Class Architecture <a name="class-architecture"></a>
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Both `BaseOpenAI` and `OpenAI` classes inherit from `BaseLLM`, demonstrating an inheritance-based architecture. This architecture allows for easy extensibility and customization while adhering to the principles of object-oriented programming.
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## 3. Purpose <a name="purpose"></a>
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The purpose of these classes is to simplify the interaction with OpenAI's LLMs. They encapsulate API calls, handle tokenization, and provide a high-level interface for generating text. By instantiating an object of the `OpenAI` class, developers can quickly leverage the power of OpenAI's models to generate text for various applications, such as chatbots, content generation, and more.
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## 4. Class Attributes <a name="class-attributes"></a>
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Here are the key attributes and their descriptions for the `BaseOpenAI` and `OpenAI` classes:
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| Attribute | Description |
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|---------------------------|-------------|
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| `lc_secrets` | A dictionary of secrets required for LangChain, including the OpenAI API key. |
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| `lc_attributes` | A dictionary of attributes relevant to LangChain. |
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| `is_lc_serializable()` | A method indicating if the class is serializable for LangChain. |
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| `model_name` | The name of the language model to use. |
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| `temperature` | The sampling temperature for text generation. |
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| `max_tokens` | The maximum number of tokens to generate in a completion. |
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| `top_p` | The total probability mass of tokens to consider at each step. |
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| `frequency_penalty` | Penalizes repeated tokens according to frequency. |
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| `presence_penalty` | Penalizes repeated tokens. |
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| `n` | How many completions to generate for each prompt. |
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| `best_of` | Generates `best_of` completions server-side and returns the "best." |
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| `model_kwargs` | Holds any model parameters valid for `create` calls not explicitly specified. |
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| `openai_api_key` | The OpenAI API key used for authentication. |
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| `openai_api_base` | The base URL for the OpenAI API. |
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| `openai_organization` | The OpenAI organization name, if applicable. |
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| `openai_proxy` | An explicit proxy URL for OpenAI requests. |
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| `batch_size` | The batch size to use when passing multiple documents for generation. |
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| `request_timeout` | The timeout for requests to the OpenAI completion API. |
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| `logit_bias` | Adjustment to the probability of specific tokens being generated. |
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| `max_retries` | The maximum number of retries to make when generating. |
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| `streaming` | Whether to stream the results or not. |
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| `allowed_special` | A set of special tokens that are allowed. |
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| `disallowed_special` | A collection of special tokens that are not allowed. |
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| `tiktoken_model_name` | The model name to pass to `tiktoken` for token counting. |
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## 5. Methods <a name="methods"></a>
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### 5.1 Construction <a name="construction"></a>
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#### 5.1.1 `__new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]`
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- Description: Initializes the OpenAI object.
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- Arguments:
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- `cls` (class): The class instance.
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- `data` (dict): Additional data for initialization.
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- Returns:
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- Union[OpenAIChat, BaseOpenAI]: An instance of the OpenAI class.
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### 5.2 Configuration <a name="configuration"></a>
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#### 5.2.1 `build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]`
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- Description: Builds extra kwargs from additional params passed in.
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- Arguments:
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- `cls` (class): The class instance.
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- `values` (dict): Values and parameters to build extra kwargs.
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- Returns:
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- Dict[str, Any]: A dictionary of built extra kwargs.
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#### 5.2.2 `validate_environment(cls, values: Dict) -> Dict`
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- Description: Validates that the API key and python package exist in the environment.
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- Arguments:
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- `values` (dict): The class values and parameters.
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- Returns:
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- Dict: A dictionary of validated values.
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### 5.3 Tokenization <a name="tokenization"></a>
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#### 5.3.1 `get_sub_prompts(self, params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) -> List[List[str]]`
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- Description: Gets sub-prompts for LLM call.
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- Arguments:
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- `params` (dict): Parameters for LLM call.
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- `prompts` (list): List of prompts.
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- `stop` (list, optional): List of stop words.
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- Returns:
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- List[List[str]]: List of sub-prompts.
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#### 5.3.2 `get_token_ids(self, text: str) -> List[int]`
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- Description: Gets token IDs using the `tiktoken` package.
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- Arguments:
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- `text` (str): The text for which to calculate token IDs.
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- Returns:
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- List[int]: A list of token IDs.
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#### 5.3.3 `modelname_to_contextsize(modelname: str) -> int`
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- Description: Calculates the maximum number of tokens possible to generate for a model.
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- Arguments:
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- `modelname` (str): The model name to determine the context size for.
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- Returns:
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- int: The maximum context size.
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#### 5.3.4 `max_tokens_for_prompt(self, prompt: str) -> int`
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- Description: Calculates the maximum number of tokens possible to generate for a prompt.
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- Arguments:
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- `prompt` (str): The prompt for which to
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determine the maximum token limit.
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- Returns:
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- int: The maximum token limit.
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### 5.4 Generation <a name="generation"></a>
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#### 5.4.1 `generate(self, text: Union[str, List[str]], **kwargs) -> Union[str, List[str]]`
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- Description: Generates text using the OpenAI API.
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- Arguments:
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- `text` (str or list): The input text or list of inputs.
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- `**kwargs` (dict): Additional parameters for the generation process.
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- Returns:
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- Union[str, List[str]]: The generated text or list of generated texts.
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### 5.5 Asynchronous Generation <a name="asynchronous-generation"></a>
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#### 5.5.1 `generate_async(self, text: Union[str, List[str]], **kwargs) -> Union[str, List[str]]`
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- Description: Generates text asynchronously using the OpenAI API.
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- Arguments:
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- `text` (str or list): The input text or list of inputs.
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- `**kwargs` (dict): Additional parameters for the asynchronous generation process.
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- Returns:
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- Union[str, List[str]]: The generated text or list of generated texts.
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## 6. Usage Examples <a name="usage-examples"></a>
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### 6.1 Creating an OpenAI Object <a name="creating-an-openai-object"></a>
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```python
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# Import the OpenAI class
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from swarms.models import OpenAI
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# Set your OpenAI API key
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api_key = "YOUR_API_KEY"
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# Create an OpenAI object
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openai = OpenAI(api_key)
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```
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### 6.2 Generating Text <a name="generating-text"></a>
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```python
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# Generate text from a single prompt
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prompt = "Translate the following English text to French: 'Hello, how are you?'"
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generated_text = openai.generate(prompt, max_tokens=50)
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# Generate text from multiple prompts
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prompts = ["Translate this: 'Good morning' to Spanish.", "Summarize the following article:", article_text]
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generated_texts = openai.generate(prompts, max_tokens=100)
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# Generate text asynchronously
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async_prompt = "Translate 'Thank you' into German."
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async_result = openai.generate_async(async_prompt, max_tokens=30)
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# Access the result of an asynchronous generation
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async_result_text = async_result.get()
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```
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### 6.3 Advanced Configuration <a name="advanced-configuration"></a>
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```python
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# Configure generation with advanced options
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custom_options = {
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"temperature": 0.7,
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"max_tokens": 100,
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"top_p": 0.9,
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"frequency_penalty": 0.2,
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"presence_penalty": 0.4
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}
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generated_text = openai.generate(prompt, **custom_options)
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```
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This documentation provides a comprehensive understanding of the `BaseOpenAI` and `OpenAI` classes, their attributes, methods, and usage examples. Developers can utilize these classes to interact with OpenAI's language models efficiently, enabling various natural language generation tasks.
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"""An ultra fast speech to text model."""
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