Build Your Own Custom ChatGPT App with Low-Code

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Build Your Own Custom ChatGPT App with Low-Code

Table of Contents

  1. Introduction
  2. Low-Code Approach: Building Chat Applications with Custom Data
    • History and Evolution of Language Models
    • Use of Large Language Models (LLMs)
    • Multimodal Applications and Advanced Data Analysis
    • Limitations of LLMs and Solutions
    • A Low-Code Approach to Building Chat Applications
  3. Document Store and Loading Documents
    • Types of Data Sources
    • Creating a Chat Application with Custom Data
    • Splitting and Storing Documents
    • Using a Vector Store
  4. Retrieval: Finding Relevant Text
    • Introduction to Retrieval Step
    • Querying the Vector Store
    • Combining Relevant Text into a Prompt
    • Hybrid Search: Combining Vector and Full Text Search
  5. Vector Search and Embedding Models
    • Working with Vectors
    • Creating Numerical Representations of Texts
    • Cosine Similarity as a Distance Metric
    • Hybrid Search and Merging Results
  6. Azure Open AI
    • Azure Open AI Service
    • Azure Open AI Studio Chat Playground
    • Using Azure Open AI in the Low-Code Approach
  7. Building a Chatbot with Power Virtual Agents
    • Overview of Power Virtual Agents
    • Combining Azure and Power Platform
    • Conversational Boosting with AI Models
    • Integrating Azure Open AI with Power Virtual Agents
  8. Deployment and Integration
    • Deploying Bots to Power Virtual Agents
    • Integrating Chatbots within Teams
    • Evaluating the End-to-End Solution
  9. Conclusion
  10. Highlights
  11. FAQ

Introduction

Welcome to this session on building chat applications with custom data using a low-code or no-code approach. In this session, we will explore the history and evolution of language models, the use of large language models (LLMs) in chat applications, and the limitations and solutions associated with LLMs. We will then dive into the low-code approach to building chat applications, covering topics such as document storage, retrieval of relevant text, vector search, and embedding models. We will also explore the features of Azure Open AI and demonstrate how to build a chatbot using Power Virtual Agents and integrate it within Teams.

Low-Code Approach: Building Chat Applications with Custom Data

History and Evolution of Language Models

Language models have come a long way in recent years, with the introduction of large language models such as GPT. These models have been trained on massive amounts of data and have the ability to generate coherent text. However, they are limited by their knowledge cut-off, i.e., they can only provide information up to a certain date or time. To address this limitation, solutions like Bing browsing have been introduced, allowing models to retrieve up-to-date information from the internet.

Use of Large Language Models (LLMs)

Large language models like GPT have gained popularity due to their ability to generate text that appears coherent and Meaningful. These models can be used in chat applications to provide responses Based on user queries and contextual information. However, they have certain limitations, such as the lack of real-time updates and the inability to provide accurate information beyond their knowledge cut-off date.

Multimodal Applications and Advanced Data Analysis

Modern language models are becoming more than just text-based. They can now process multimodal inputs, such as images, and extract information from them. This opens up possibilities for more complex applications, such as generating text based on images or performing advanced data analysis.

Limitations of LLMs and Solutions

While language models like GPT are powerful tools, they do have limitations. For example, they can only provide information up to a certain date, and they may not always produce accurate responses. However, these limitations can be overcome by using solutions like Bing browsing, which retrieves Current information from the internet.

A Low-Code Approach to Building Chat Applications

In the low-code approach, we start by storing and loading documents into a document store. We then split and store the documents into a vector store, which allows us to retrieve relevant text based on user queries. The retrieval process involves querying the vector store, finding relevant text, and combining it into a prompt that is sent to the language model. This approach enables us to build chat applications with custom data using a combination of low-code tools and services.

Document Store and Loading Documents

In building chat applications with custom data, it is important to have a reliable document store that can handle various types of data sources. These sources can include structured, semi-structured, and unstructured data. The documents need to be loaded from a storage location, such as a SharePoint site, a file share, or an Azure storage account.

Once the documents are loaded, they need to be processed to extract the relevant text. This includes support for specific document types such as DOC, DOCX, PowerPoint, PDFs, text files, and HTML. Since these documents can be large, it is advisable to split them into smaller chunks or splits. This not only helps with processing but also allows for more efficient retrieval of relevant text.

To store and organize these splits, a vector store or database can be used. A vector store is designed to store the splits along with their corresponding numerical representations, which convey the semantic meaning of the text. This representation enables efficient retrieval of relevant text based on user queries.

Retrieval: Finding Relevant Text

The retrieval process is a crucial step in building chat applications with custom data. It involves finding relevant pieces of text based on user queries and combining them to form a coherent answer. This process typically consists of three steps: querying the vector store, retrieving relevant text, and bundling the text with the query in a larger prompt that is sent to the language model.

In the querying phase, the user's query is transformed into a numerical representation using an embedding model. This numerical representation is then compared with the vectors stored in the vector store to find relevant pieces of text. This can be done using techniques like cosine similarity, which measures the similarity between two vectors.

Once the relevant text is identified, it is bundled together with the query in a larger prompt. This prompt is then sent to the language model, which generates a response based on the combined information. The response can then be further processed and presented to the user in a coherent manner.

Vector Search and Embedding Models

Vector search plays a crucial role in retrieving relevant text from the vector store. It involves comparing the numerical representation of the user's query with the vectors stored in the store to find the most similar ones. The similarity is often measured using techniques like cosine similarity.

To generate the numerical representations of text, embedding models are used. These models take the raw text as input and produce a vector representation as output. These vectors typically consist of floating-point numbers and can have a large number of Dimensions. In our case, we use an embedding model with 1,536 dimensions.

By using embedding models and vector search, we can efficiently find relevant text based on user queries. This enables us to provide accurate and coherent answers to user questions within a chat application.

Azure Open AI

Azure Open AI is an Azure service that provides access to the same open AI models available on the internet. It allows users to deploy and utilize these models within their own Azure environment, ensuring data residency and compliance. With Azure Open AI, users can leverage the power of AI models while having control over their data and infrastructure.

Azure Open AI offers a range of models, including GPT-4, which is a highly advanced language model capable of reasoning and generating detailed answers. These models can be deployed and used within Azure, making it easy to integrate them into existing applications and workflows.

Using Azure Open AI in a low-code approach allows developers to build chat applications with powerful language models without the need for extensive coding or infrastructure setup. This enables faster development and deployment of AI-powered applications.

Building a Chatbot with Power Virtual Agents

Power Virtual Agents is a low-code platform that allows users to build and deploy chatbots easily. It provides a visual interface for designing conversational flows and integrates with various AI models, including Azure Open AI. With Power Virtual Agents, users can Create chatbots that can understand and respond to user queries, all without writing code.

By combining Azure Open AI with Power Virtual Agents, users can build chatbots that leverage the advanced language models and capabilities provided by Azure. This enables the chatbot to understand and respond to a wide range of user queries, providing a more natural and engaging conversational experience.

The integration between Azure Open AI and Power Virtual Agents allows developers to harness the power of AI models while maintaining the simplicity and ease of use provided by the low-code platform. This combination enables the creation of chatbots that can handle complex scenarios and provide accurate and informative responses.

Deployment and Integration

Once the chatbot is built using Power Virtual Agents and leveraging Azure Open AI, it can be deployed and integrated within various interfaces and channels. One such integration is with Microsoft Teams, a popular collaboration platform used by many organizations.

By deploying the chatbot to Power Virtual Agents and configuring the Teams Channel, users can Interact with the chatbot directly within the Teams interface. This allows for seamless communication and access to the chatbot's capabilities within the Context of the Teams environment.

The integration of the chatbot with Teams provides users with a convenient and familiar interface for interacting with the chatbot. It enables organizations to leverage the power of AI and automation to enhance productivity and streamline workflows.

Conclusion

In conclusion, building chat applications with custom data can be achieved using a low-code approach combined with Azure Open AI and Power Virtual Agents. This approach allows developers to leverage powerful language models and AI capabilities without the need for extensive coding or infrastructure setup.

By utilizing Azure Open AI, users can deploy and utilize advanced language models within their own Azure environment, ensuring data residency and compliance. Power Virtual Agents provides a low-code platform for designing and deploying chatbots, integrating seamlessly with Azure Open AI to leverage its capabilities.

Overall, this combination of Azure Open AI, Power Virtual Agents, and low-code development enables the creation of chat applications that can understand and respond to user queries, providing a more engaging and efficient user experience.

Highlights

  • Building chat applications with custom data using a low-code approach
  • Document storage and loading from various sources
  • Retrieval of relevant text using vector search
  • Utilizing embedding models for generating numerical representations of text
  • Integration of Azure Open AI and Power Virtual Agents
  • Deployment and integration within Microsoft Teams
  • Enhancing productivity and automation through AI-powered chatbots

FAQ

Q: What are the limitations of large language models like GPT? A: Large language models have limitations such as knowledge cut-off, inability to provide real-time updates, and occasional inaccuracies in responses.

Q: How does vector search work? A: Vector search involves comparing the numerical representations of user queries with the stored vectors to find the most similar ones. This is often done using techniques like cosine similarity.

Q: Can vector search be combined with full-text search? A: Yes, a hybrid approach combining vector search and full-text search can be used for more accurate results. This involves merging the results from both types of searches to provide comprehensive answers.

Q: What is Azure Open AI? A: Azure Open AI is an Azure service that provides access to open AI models within the Azure environment. It allows for the deployment and utilization of these models while maintaining data residency and compliance.

Q: Can chatbots built with Power Virtual Agents integrate with Azure Open AI? A: Yes, chatbots built with Power Virtual Agents can integrate with Azure Open AI to leverage its language models and capabilities.

Q: How can chatbots be deployed and integrated within Microsoft Teams? A: Chatbots built with Power Virtual Agents can be deployed and integrated within Microsoft Teams by configuring the Teams channel in the Power Virtual Agents platform. This allows for seamless communication and interaction with the chatbot within the Teams interface.

Q: How does the low-code approach benefit the development of chat applications? A: The low-code approach allows for faster development and deployment of chat applications by utilizing pre-built tools and services. It reduces the need for extensive coding and infrastructure setup, enabling developers to focus on building the application logic.

Q: Can the low-code approach be customized to specific requirements? A: Yes, the low-code approach allows for customization to specific requirements through the use of configuration options and integration with additional services. This provides flexibility in tailoring the chat application to specific use cases.

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