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# `OpenAIChat`` Documentation
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## Table of Contents
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1. [Introduction](#introduction)
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2. [Class Overview](#class-overview)
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3. [Class Architecture](#class-architecture)
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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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- [Message Handling](#message-handling)
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- [Generation](#generation)
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- [Tokenization](#tokenization)
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6. [Usage Examples](#usage-examples)
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7. [Additional Information](#additional-information)
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---
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## 1. Introduction <a name="introduction"></a>
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The `OpenAIChat` class is part of the LangChain library and serves as an interface to interact with OpenAI's Chat large language models. This documentation provides an in-depth understanding of the class, its attributes, methods, and usage examples.
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## 2. Class Overview <a name="class-overview"></a>
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The `OpenAIChat` class is designed for conducting chat-like conversations with OpenAI's language models, such as GPT-3.5 Turbo. It allows you to create interactive conversations by sending messages and receiving model-generated responses. This class simplifies the process of integrating OpenAI's models into chatbot applications and other natural language processing tasks.
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## 3. Class Architecture <a name="class-architecture"></a>
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The `OpenAIChat` class is built on top of the `BaseLLM` class, which provides a foundation for working with large language models. This inheritance-based architecture allows for customization and extension while adhering to object-oriented programming principles.
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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 `OpenAIChat` class:
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| Attribute | Description |
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|-----------------------------|-------------------------------------------------------------------------------|
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| `client` | An internal client for making API calls to OpenAI. |
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| `model_name` | The name of the language model to use (default: "gpt-3.5-turbo"). |
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| `model_kwargs` | Additional 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_proxy` | An explicit proxy URL for OpenAI requests. |
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| `max_retries` | The maximum number of retries to make when generating (default: 6). |
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| `prefix_messages` | A list of messages to set the initial conversation state (default: []). |
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| `streaming` | Whether to stream the results or not (default: False). |
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| `allowed_special` | A set of special tokens that are allowed (default: an empty set). |
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| `disallowed_special` | A collection of special tokens that are not allowed (default: "all"). |
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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 `__init__(self, model_name: str = "gpt-3.5-turbo", openai_api_key: Optional[str] = None, openai_api_base: Optional[str] = None, openai_proxy: Optional[str] = None, max_retries: int = 6, prefix_messages: List = [])`
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- Description: Initializes an OpenAIChat object.
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- Arguments:
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- `model_name` (str): The name of the language model to use (default: "gpt-3.5-turbo").
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- `openai_api_key` (str, optional): The OpenAI API key used for authentication.
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- `openai_api_base` (str, optional): The base URL for the OpenAI API.
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- `openai_proxy` (str, optional): An explicit proxy URL for OpenAI requests.
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- `max_retries` (int): The maximum number of retries to make when generating (default: 6).
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- `prefix_messages` (List): A list of messages to set the initial conversation state (default: []).
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### 5.2 Configuration <a name="configuration"></a>
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#### 5.2.1 `build_extra(self, values: Dict[str, Any]) -> Dict[str, Any]`
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- Description: Builds extra kwargs from additional parameters passed in.
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- Arguments:
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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(self, 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 Message Handling <a name="message-handling"></a>
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#### 5.3.1 `_get_chat_params(self, prompts: List[str], stop: Optional[List[str]] = None) -> Tuple`
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- Description: Gets chat-related parameters for generating responses.
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- Arguments:
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- `prompts` (list): List of user messages.
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- `stop` (list, optional): List of stop words.
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- Returns:
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- Tuple: Messages and parameters.
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### 5.4 Generation <a name="generation"></a>
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#### 5.4.1 `_stream(self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> Iterator[GenerationChunk]`
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- Description: Generates text asynchronously using the OpenAI API.
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- Arguments:
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- `prompt` (str): The user's message.
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- `stop` (list, optional): List of stop words.
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- `run_manager` (optional): Callback manager for asynchronous generation.
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- `**kwargs` (dict): Additional parameters for asynchronous generation.
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- Returns:
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- Iterator[GenerationChunk]: An iterator of generated text chunks.
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#### 5.4.2 `_agenerate(self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any) -> LLMResult`
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- Description: Generates text asynchronously using the OpenAI API (async version).
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- Arguments:
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- `prompts` (list): List of user messages.
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- `stop` (list, optional): List of stop words.
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- `run_manager` (optional): Callback manager for asynchronous generation.
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- `**kwargs` (dict): Additional parameters for asynchronous generation.
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- Returns:
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- LLMResult: A result object containing the generated text.
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### 5.5 Tokenization <a name="tokenization"></a>
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#### 5.5.1 `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
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token IDs.
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## 6. Usage Examples <a name="usage-examples"></a>
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### Example 1: Initializing `OpenAIChat`
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```python
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from swarms.models import OpenAIChat
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# Initialize OpenAIChat with model name and API key
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openai_chat = OpenAIChat(model_name="gpt-3.5-turbo", openai_api_key="YOUR_API_KEY")
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```
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### Example 2: Sending Messages and Generating Responses
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```python
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# Define a conversation
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conversation = [
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"User: Tell me a joke.",
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"Assistant: Why did the chicken cross the road?",
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"User: I don't know. Why?",
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"Assistant: To get to the other side!",
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]
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# Set the conversation as the prefix messages
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openai_chat.prefix_messages = conversation
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# Generate a response
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user_message = "User: Tell me another joke."
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response = openai_chat.generate([user_message])
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# Print the generated response
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print(response[0][0].text) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!"
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```
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### Example 3: Asynchronous Generation
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```python
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import asyncio
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# Define an asynchronous function for generating responses
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async def generate_responses():
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user_message = "User: Tell me a fun fact."
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async for chunk in openai_chat.stream([user_message]):
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print(chunk.text)
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# Run the asynchronous generation function
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asyncio.run(generate_responses())
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```
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## 7. Additional Information <a name="additional-information"></a>
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- To use the `OpenAIChat` class, you should have the `openai` Python package installed, and the environment variable `OPENAI_API_KEY` set with your API key.
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- Any parameters that are valid to be passed to the `openai.create` call can be passed to the `OpenAIChat` constructor.
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- You can customize the behavior of the class by setting various attributes, such as `model_name`, `openai_api_key`, `prefix_messages`, and more.
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- For asynchronous generation, you can use the `_stream` and `_agenerate` methods to interactively receive model-generated text chunks.
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- To calculate token IDs, you can use the `get_token_ids` method, which utilizes the `tiktoken` package. Make sure to install the `tiktoken` package with `pip install tiktoken` if needed.
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---
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This documentation provides a comprehensive overview of the `OpenAIChat` class, its attributes, methods, and usage examples. You can use this class to create chatbot applications, conduct conversations with language models, and explore the capabilities of OpenAI's GPT-3.5 Turbo model.
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