In this article, we will explore how to harness the power of OpenAI's GPT-3.5-Turbo model for answering questions using the LangChain library. This step-by-step guide will walk you through setting up the environment, understanding the key components of the code, and utilizing this powerful tool for generating accurate and contextually relevant responses. Whether you're an AI enthusiast or a developer looking to integrate advanced language models into your projects, this guide will provide you with the knowledge and resources to get started.

What is GPT?

The Generative Pre-trained Transformer (GPT) is a language model developed by OpenAI. It uses a deep learning architecture called a Transformer to understand and generate human-like text. GPT models generate coherent and contextually relevant text based on input, leveraging pre-training on a vast amount of diverse text data. This allows the model to understand language patterns and nuances before being fine-tuned for specific tasks. The underlying Transformer architecture enables GPT to generate text that is contextually appropriate and coherent..

GPT models are used in various applications, including chatbots, content generation, translation, and more, offering powerful capabilities for natural language understanding and generation. Know more about their models here, we are utilizing GPT-3.5-turbo-instruct

What is Langchain?

LangChain is a framework that simplifies the development and deployment of applications using large language models (LLMs). It offers tools for integrating LLMs, managing prompts, chaining components, integrating with LLM provider APIs, and supporting various applications.

Read more here

Let's Begin

This article is not an introduction to Generative AI, If you are a complete newbie, check this article

Step 1 Setting Up Libraries

1. Install Libraries

1.  pip install langchain_openai langchain openai
   

2. Import-Package and setup client

    import os
    from langchain_openai import OpenAI
    from langchain_core.prompts import PromptTemplate
   
    openaikey = os.environ.get('OPENAI_API_KEY')
   

   Get Key from here

Step 2 Talking to Code

1. Function

    def ask_gpt_question(question:str):
        template = """
        Context: You are an AI bot responsible for answering questions.
        TASK: You are given a question "{question}". Please provide a short answer to the question."""
        prompt = PromptTemplate.from_template(template)
   
        llm = OpenAI(model_name="gpt-3.5-turbo-instruct", api_key=openaikey, temperature=0.9)
        llm_chain = prompt | llm
        response = llm_chain.invoke(question)
   
        return response
   

   1. Prompt Template: Defines the structured format and context for the question to guide the language model's response.

   2. Language Model Initialization: Sets up the GPT-3.5-Turbo model with specified parameters, including the API key and temperature.

   3. LLM Chain: Combines the prompt template and language model into a processing pipeline to generate responses.

   4. Invocation: Executes the LLM chain with the input question to obtain the generated answer.

   5. Response Handling: Returns the language model's generated response to the caller.

2. Calling Function

    if __name__ == "__main__":
        question = "What is the capital of France?"
        print(ask_gpt_question(question))
   

   Response

    The capital of France is Paris.
   

Other Important Terms

1. Temperature

   In the context of language models like GPT-3.5, temperature is a parameter that controls the randomness or creativity of the model's responses:

   • Low Temperature (e.g., 0.1 - 0.3): Makes the model's output more deterministic and focused, producing more predictable and repetitive responses. This is useful for tasks requiring consistency and accuracy.

   • Medium Temperature (e.g., 0.5 - 0.7): Balances creativity and coherence, generating responses that are reasonably varied but still contextually relevant.

   • High Temperature (e.g., 0.8 - 1.0): Increases randomness and creativity, leading to more diverse and novel responses, but potentially less coherent or relevant.

2. Top-p

   Top-p (also known as nucleus sampling) is a parameter used in language models to control the diversity of generated text by focusing on a subset of the most likely next words. Here’s how it works:

   • Top-p (e.g., 0.9): The model considers the smallest set of words whose cumulative probability exceeds the threshold p. Only words in this set are considered for generating the next token. This means that the model focuses on the most likely words but excludes less probable ones, allowing for diverse yet coherent responses.

   • Lower Top-p (e.g., 0.5): Narrows down the range of possible words, leading to more focused and conservative outputs.

   • Higher Top-p (e.g., 0.9): Includes a broader range of words, increasing the model's creativity and variability in responses.

Conclusion

The function showcases an efficient approach to using OpenAI's GPT-3.5-Turbo model with LangChain's prompt management. By structuring the prompt and setting up the model, it generates relevant and coherent responses to user queries. This demonstrates the ease and flexibility of integrating powerful language models into applications and managing AI interactions. Whether for chatbots, user experiences, or complex AI-driven solutions, it serves as a practical example of harnessing cutting-edge AI effectively.