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GPTs, Assistant APIs and Retrieval Augmented Generation - What has fundamentally changed

· 5 min read

OpenAI's recent announcements of GPTs and Assistant APIs on November 6th, 2023 mark a significant advancement in the AI landscape, especially for developers and businesses looking to use AI in a more accessible and customizable manner. GPTs are a customisable version of Chat GPT that enable users to create AI experiences without writing any code where as Assistant APIs are a more powerful set of APIs to allow agent-like workflows similar to LangChain.

Difference Between GPTs and Assistants:​

GPTs (Customizable ChatGPTs)​

Purpose : To enable developers to create personalized AI models.

Key Features:

  1. No Coding Requirement: It simplifies the process for non-programmers, allowing them to develop AI models through basic instructions.
  2. Customization: Developers can tailor the AI to specific needs or industries.
  3. Ease of Use: The process is as simple as starting a conversation, making AI more accessible to a wider range of users.

Assistant APIs​

Purpose: To provide a more agent-like, task-oriented AI experience.

Key Features:

  1. Specificity: Designed for defined purposes with clear instructions and additional knowledge.
  2. Advanced Capabilities: Includes features like Code Interpreter and Retrieval, allowing the AI to perform more complex tasks.
  3. Model and Tool Integration: The ability to call upon various models and tools for executing specific tasks.
  4. Simplified Development: The API streamlines the development process, making it easier to create sophisticated AI assistants.

LLM Limitations: RAG (Retrieval Augmented Generation) and Fine Tuning

LLMs are great but are limited in their knowledge base. Two primary challenges encapsulate the issues faced by Large Language Models (LLMs):

  1. Stale Training Data: The data used to train LLMs tends to become outdated, posing a hurdle in keeping up with recent trends and events.
  2. Extrapolation and Hallucination: In the absence of factual information, LLMs often resort to extrapolation, resulting in the generation of confidently stated yet false statements.

These are the two popular ways of bridging this gap.

  1. RAG:

    Integrates a retrieval component with a generative model, dynamically fetching external information to enhance the generation process. It's great for queries needing up-to-date or specific knowledge, but can struggle with data quality, relevance, and increased response latency.

  2. Fine-Tuning:

    Involves adjusting a pre-trained model on a specific dataset to tailor its responses. It offers more control over the model's output and consistency in performance, but lacks the ability to incorporate real-time information and can be limited by the scope of the training data.

RAG (Retrieval-Augmented Generation) can be seen as superior to fine-tuning in certain scenarios due to its dynamic nature and ability to incorporate a wide range of up-to-date information. RAG retrieves information in real-time, it can adapt to new topics or recent developments more effectively than a fine-tuned model, which might require retraining to stay current.

Langchain has been a leading solution in integrating knowledge using RAG, gaining wide popularity. However, OpenAI's recent advancements, particularly in assistant APIs, suggest a shift in the landscape. These APIs now enable direct integration of similar functionalities with potentially higher quality, potentially rendering approaches like vector stores and Langchain less critical in certain applications.

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling.

How Dozer Can Help You with RAG?

Dozer supercharges AI applications with lightning-fast API data retrieval, seamlessly fueling retrieval systems and tools for smarter, more informed responses.

  • Enhanced Data Access for RAG:

    RAG models rely on external data for generating informed and accurate responses. Dozer's ability to quickly fetch data via APIs means that these models can access a broader range of current and relevant information, improving the quality of their output.

  • Real-Time Information Retrieval:

    In scenarios where up-to-date information is crucial, Dozer's rapid API calls enable real-time data retrieval. This is particularly useful for applications that require the latest data, like news updates, stock prices, or weather reports.

  • Integration with Diverse Data Sources:

    The ability to tap into various APIs allows for the integration of diverse data sources. This is beneficial in creating more comprehensive and multifaceted AI applications that can pull in specialized information from different fields.

  • Streamlining Development with Assistant APIs:

    When building applications with Assistant APIs, the rapid integration of external data through Dozer can streamline the development process. It allows developers to focus on building the core functionality of their applications without worrying about the complexities of data retrieval.

  • Customization and Flexibility:

    Dozer's API-driven approach provides developers with the flexibility to customize their retrieval sources. This customization is crucial in building applications that cater to specific domains or user needs.

  • Scalability and Efficiency:

    Efficient data retrieval via APIs contributes to the scalability of AI applications. It ensures that as user demand increases, the system can maintain performance without significant increases in latency or resource consumption.

In our next article, we'll dive into a hands-on example illustrating how Dozer's effortless API integration can enhance RAG with Open AI APIs. Please reach out to us with your thoughts or if you want to have a discussion.