Mastering In-context Question Answering

Table of Contents

1. Introduction
2. The Problem with OpenAI in Healthcare
3. Introducing In-Context Question and Answering
4. Using Embeddings with Orbita's Platform
5. Ingesting Content from Websites and Assets
6. The Role of Embdeddings in Extracting Meaning
7. Splitting Documents for OpenAI Processing
8. Using OpenAI 4.0 for Larger Content Sizes
9. Answering Questions Based on Context
10. Retrieving Nearest Documents for Answering
11. Adding PDF Section Information
12. Demonstration of In-Context Question and Answering
13. Using Orbiter's Scraping Tool
14. Providing Links to Source Articles
15. Benefits of Orbita's Conversational Platform
16. Ingesting Assets and Building Virtual Assistants
17. Integration with the Vector Database
18. Interacting with OpenAI APIs
19. Importance of Analytics in Question and Answering
20. Updating Questions and Answers with Orbiter
21. Security and Compliance Features
22. Multi-Language Support with Orbita

Using OpenAI in Healthcare with In-Context Question and Answering

In this article, we will explore the use of OpenAI in healthcare and specifically focus on the concept of in-context question and answering with Orbita's conversational AI platform. We'll discuss the challenges faced when using OpenAI models for healthcare-related questions and how in-context answering helps overcome these challenges. Additionally, we'll Delve into the use of embeddings, the ingestion of content from websites and assets, and the benefits of Orbita's platform in building virtual assistants. So, let's dive in and explore how OpenAI can be utilized effectively in the healthcare domain.

1. Introduction

The field of conversational AI has seen tremendous advancements in recent years, enabling interactive and personalized experiences for users. OpenAI, a leading AI research organization, has developed powerful language models that can generate human-like responses. However, when it comes to healthcare, there are inherent challenges in using these models effectively. In this article, we will explore how in-context question and answering with Orbita's platform addresses these challenges and provides a more accurate and contextually Relevant healthcare information retrieval system.

2. The Problem with OpenAI in Healthcare

OpenAI's language models are trained on a vast amount of data, but they lack domain-specific expertise, especially in healthcare. When a healthcare-related question is asked to the model, it generates answers based on the data it has consumed during training. However, this data may be outdated, leading to incorrect or irrelevant answers. For example, if a patient asks when they should get a colonoscopy, the model may not be aware of the updated guidelines that recommend it at the age of 45 instead of 50. Furthermore, the model's sources of information are often unknown, making it difficult to trust the accuracy and reliability of the generated answers.

3. Introducing In-Context Question and Answering

To overcome the limitations of OpenAI in healthcare, the concept of in-context question and answering is introduced. With in-context answering, known sources of information are used to answer questions accurately. Orbita's platform integrates with OpenAI's embeddings, which extract the meaning of content from websites, PDF files, Word documents, and PowerPoint presentations. Embeddings represent the content's meaning in vectors, enabling efficient retrieval and matching of relevant information.

4. Using Embeddings with Orbita's Platform

Orbita's platform provides the capability to ingest content from various sources and store them in the vector database. By ingesting content, embeddings are generated, allowing for context-based question and answering. This means that when a user asks a question, the platform transforms the question into an embedding and retrieves the nearest documents associated with that question. The answer is then generated based on the context provided by the retrieved documents.

5. Ingesting Content from Websites and Assets

With Orbita's platform, content can be ingested from websites as well as assets like PDF files, Word documents, and PowerPoint presentations. This flexibility allows healthcare organizations to utilize their existing content without the need for extensive restructuring. Web page content can be ingested as a single document, while longer documents may need to be split into multiple sections to fit within the token limits of OpenAI.

6. The Role of Embeddings in Extracting Meaning

Embeddings play a crucial role in extracting the meaning of content stored within documents. By representing the content as vectors, Orbita's platform enables efficient matching and retrieval of information relevant to user queries. These embeddings capture the nuances and semantics of the content, providing a holistic view for answering healthcare-related questions accurately.

7. Splitting Documents for OpenAI Processing

To account for the token limitations of OpenAI models, longer documents may need to be split into smaller sections. Orbita's platform handles this process automatically, ensuring that content is processed effectively. By breaking down documents into manageable chunks, the platform maximizes the potential for accurate information retrieval and contextual answering.

8. Using OpenAI 4.0 for Larger Content Sizes

With the introduction of OpenAI 4.0, the content size that can be processed has significantly increased. Previously, content exceeding a few thousand tokens had to be split into multiple documents. However, with OpenAI 4.0, the platform can handle content sizes of up to approximately 50 pages or 20,000 words. This advancement allows for more comprehensive information to be included without the need for extensive fragmentation.

9. Answering Questions Based on Context

In in-context question and answering, when a question is asked, Orbita's platform retrieves the relevant documents based on their proximity to the question's embedding. The platform then utilizes the context provided by these documents to generate the most appropriate answer. If the retrieved context is insufficient to answer the question accurately, the platform will acknowledge this limitation and indicate that it does not have a suitable answer.

10. Retrieving Nearest Documents for Answering

Orbita's platform utilizes the nearest document retrieval technique to identify the most relevant sources of information for answering user questions. By comparing the embeddings of questions with the embeddings of stored documents, the platform determines the closest matches. This retrieval process ensures that the answers are contextually aligned with the available information, providing users with accurate and reliable healthcare insights.

11. Adding PDF Section Information

When ingesting PDF documents, Orbita's platform not only stores the content but also captures the section and page details. This additional information allows users to navigate directly to the referenced section within the PDF. By associating the answer with the specific location within the document, users can access the relevant information quickly and conveniently.

12. Demonstration of In-Context Question and Answering

To showcase the effectiveness of in-context question and answering, we will demonstrate how Orbita's platform leverages OpenAI in real-world scenarios. By utilizing a scraping tool, we ingested several thousand pieces of health content from MedlinePlus, a government Website with extensive healthcare information. The platform can answer questions accurately and provide links to source articles, enabling users to further explore the topic.

13. Using Orbiter's Scraping Tool

Orbiter, a component of Orbita's platform, offers a scraping tool specifically designed for healthcare content. This tool enables the ingestion of vast amounts of health-related information from reliable sources. By scraping and storing this content in the vector database, the platform can provide accurate and up-to-date answers to user queries.

14. Providing Links to Source Articles

In addition to answering questions, Orbita's platform includes links to the source articles from which the answers were derived. This feature enhances transparency and allows users to verify the information independently. By accessing the referenced articles, users can delve deeper into the subject matter and gain a comprehensive understanding of the healthcare topic.

15. Benefits of Orbita's Conversational Platform

Orbita's conversational platform offers several advantages for healthcare organizations and virtual assistants leveraging OpenAI. It provides tools for efficient ingestion of assets such as PDFs, Word documents, and web pages. With a comprehensive framework for building virtual assistants, organizations can customize the platform to Align with their brand. Additionally, the integration with the vector database enhances context-based question and answering, ensuring accurate and reliable healthcare information retrieval.

16. Ingesting Assets and Building Virtual Assistants

By utilizing Orbita's platform, healthcare organizations can easily ingest assets like PDFs, Word documents, and web pages. This allows the platform to extract and process the content effectively, generating embeddings that capture the meaning of the information. Moreover, the platform provides a framework for building virtual assistants that are tailored to specific healthcare needs, enhancing user experience and satisfaction.

17. Integration with the Vector Database

Orbita's platform seamlessly integrates with the vector database, a crucial component in enabling in-context question and answering. By leveraging the vector database's capabilities, the platform can retrieve and match relevant information based on the embeddings of user queries and stored documents. This integration ensures accurate and contextually appropriate answers to healthcare-related questions.

18. Interacting with OpenAI APIs

Orbita's platform simplifies the interaction with OpenAI APIs, eliminating the need for complex setup procedures or key management. The platform's nodes and APIs are preconfigured to communicate effectively with OpenAI, ensuring a seamless integration. Moreover, by using Orbita's APIs, content confidentiality is maintained, as OpenAI guarantees not to utilize the content shared through these APIs in their models.

19. Importance of Analytics in Question and Answering

Orbita's platform recognizes the significance of analytics in understanding user interactions and improving the question and answering process. Analytics provide insights into the questions asked by users, allowing organizations to identify knowledge gaps and enhance their content accordingly. By leveraging these analytics, healthcare organizations can continuously refine the question and answering system and ensure accurate and comprehensive responses.

20. Updating Questions and Answers with Orbiter

With Orbiter's question and answer engine, organizations can easily update and append additional content to their virtual assistants. Analytics gleaned from user interactions can inform the need for new facts or knowledge to answer previously unaddressed questions. By continuously updating the question and answer index, organizations can ensure that their virtual assistants possess the most up-to-date and relevant information.

21. Security and Compliance Features

Orbita's platform prioritizes security and compliance, crucial considerations in healthcare-related applications. With HIPAA compliance and adherence to high trust standards, data privacy and protection are upheld. Additionally, the platform employs secure content delivery networks (CDNs) for assets, ensuring secure and reliable access to content.

22. Multi-Language Support with Orbita

Orbita's platform offers multi-language support, enabling the processing and response generation of healthcare-related questions in 180 languages. This feature enables organizations to cater to a diverse range of users and provide accurate and contextually relevant healthcare information in multiple languages.

In conclusion, OpenAI, when combined with Orbita's conversational AI platform, offers an innovative solution for healthcare question and answering. By leveraging in-context answering, embeddings, and content ingestion capabilities, organizations can provide accurate, reliable, and contextually relevant healthcare information to users worldwide. With the ability to build virtual assistants customized to their brand and access deep analytics, healthcare organizations can continually enhance their question and answering systems, providing even better user experiences and outcomes.